From 0e79794c80136b10f61b2ea85a8943ffd682ed29 Mon Sep 17 00:00:00 2001 From: Michael Klein Date: Tue, 11 Feb 2025 17:40:30 -0500 Subject: [PATCH 1/9] chore: update quicksort from iterative noir_sort version, wip debugging SSA panic --- noir_stdlib/src/array/mod.nr | 120 +++++++++++++++++++++++++---- noir_stdlib/src/array/quicksort.nr | 42 ++++++---- 2 files changed, 132 insertions(+), 30 deletions(-) diff --git a/noir_stdlib/src/array/mod.nr b/noir_stdlib/src/array/mod.nr index 85cc0580aae..3517d6cf2e9 100644 --- a/noir_stdlib/src/array/mod.nr +++ b/noir_stdlib/src/array/mod.nr @@ -183,25 +183,25 @@ where /// assert(sorted_descending == [32, 42]); // does not verify /// } /// ``` - pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self { - /// Safety: `sorted` array is checked to be: - /// a. a permutation of `input`'s elements - /// b. satisfying the predicate `ordering` - unsafe { - let sorted = quicksort::quicksort(self, ordering); - - if !is_unconstrained() { - for i in 0..N - 1 { - assert( - ordering(sorted[i], sorted[i + 1]), - "Array has not been sorted correctly according to `ordering`.", - ); - } - check_shuffle::check_shuffle(self, sorted); + pub fn sort_via(self, sortfn: fn(T, T) -> bool) -> Self + { + // Safety: `sorted` array is checked to be: + // a. a permutation of `input`'s elements + // b. satisfying the predicate `ordering` + let sorted = unsafe { quicksort::quicksort(self, sortfn) }; + + if !is_unconstrained() { + for i in 0..N - 1 { + assert( + sortfn(sorted[i], sorted[i + 1]), + "Array has not been sorted correctly according to `ordering`.", + ); } - sorted + check_shuffle::check_shuffle(self, sorted); } + sorted } + } impl [u8; N] { @@ -232,4 +232,92 @@ mod test { fn map_empty() { assert_eq([].map(|x| x + 1), []); } + + global arr_with_100_values: [u32; 100] = [ + 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54, + 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98, + 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35, + 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8, + 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32, + ]; + global expected_with_100_values: [u32; 100] = [ + 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32, + 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62, + 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86, + 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118, + 119, 120, 121, 123, 123, 123, 125, 126, 127, + ]; + fn sort_u32(a: u32, b: u32) -> bool { + a <= b + } + + #[test] + fn test_sort() { + let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1]; + + let sorted = arr.sort(); + + let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10]; + assert(sorted == expected); + } + + #[test] + fn test_sort_100_values() { + let mut arr: [u32; 100] = [ + 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, + 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, + 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, + 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, + 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32, + ]; + + let sorted = arr.sort(); + + let expected: [u32; 100] = [ + 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, + 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, + 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, + 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, + 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127, + ]; + assert(sorted == expected); + } + + #[test] + fn test_sort_100_values_comptime() { + let sorted = arr_with_100_values.sort(); + assert(sorted == expected_with_100_values); + } + + #[test] + fn test_sort_via() { + let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1]; + + let sorted = arr.sort_via(sort_u32); + + let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10]; + assert(sorted == expected); + } + + #[test] + fn test_sort_via_100_values() { + let mut arr: [u32; 100] = [ + 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, + 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, + 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, + 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, + 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32, + ]; + + let sorted = arr.sort_via(sort_u32); + + let expected: [u32; 100] = [ + 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, + 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, + 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, + 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, + 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127, + ]; + assert(sorted == expected); + } } diff --git a/noir_stdlib/src/array/quicksort.nr b/noir_stdlib/src/array/quicksort.nr index 5e9c575c5ce..e43b069ab27 100644 --- a/noir_stdlib/src/array/quicksort.nr +++ b/noir_stdlib/src/array/quicksort.nr @@ -1,8 +1,8 @@ -unconstrained fn partition( +unconstrained fn partition( arr: &mut [T; N], low: u32, high: u32, - sortfn: fn[Env](T, T) -> bool, + sortfn: unconstrained fn(T, T) -> bool, ) -> u32 { let pivot = high; let mut i = low; @@ -14,34 +14,48 @@ unconstrained fn partition( i += 1; } } + let temp = arr[i]; arr[i] = arr[pivot]; arr[pivot] = temp; i } -unconstrained fn quicksort_recursive( +unconstrained fn quicksort_loop( arr: &mut [T; N], low: u32, high: u32, - sortfn: fn[Env](T, T) -> bool, + sortfn: unconstrained fn(T, T) -> bool, ) { - if low < high { - let pivot_index = partition(arr, low, high, sortfn); - if pivot_index > 0 { - quicksort_recursive(arr, low, pivot_index - 1, sortfn); + let mut stack: [(u32, u32)] = &[(low, high)]; + // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized + for _ in 0..2 * N { + if stack.len() == 0 { + break; + } + + let (new_stack, (new_low, new_high)) = stack.pop_back(); + stack = new_stack; + + if new_high < new_low + 1 { + continue; + } + + let pivot_index = partition(arr, new_low, new_high, sortfn); + stack = stack.push_back((pivot_index + 1, new_high)); + if 0 < pivot_index { + stack = stack.push_back((new_low, pivot_index - 1)); } - quicksort_recursive(arr, pivot_index + 1, high, sortfn); } } -pub(crate) unconstrained fn quicksort( - _arr: [T; N], - sortfn: fn[Env](T, T) -> bool, +pub unconstrained fn quicksort( + arr: [T; N], + sortfn: unconstrained fn(T, T) -> bool, ) -> [T; N] { - let mut arr: [T; N] = _arr; + let mut arr: [T; N] = arr; if arr.len() <= 1 {} else { - quicksort_recursive(&mut arr, 0, arr.len() - 1, sortfn); + quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn); } arr } From eb0435888b4ef1ae4a164b51d5becdcb6a9120b4 Mon Sep 17 00:00:00 2001 From: Michael Klein Date: Thu, 13 Feb 2025 15:08:16 -0500 Subject: [PATCH 2/9] add regression test --- .../regression_noir_sort/Nargo.toml | 6 ++ .../regression_noir_sort/src/main.nr | 45 ++++++++++++++ .../regression_noir_sort/src/quicksort.nr | 61 +++++++++++++++++++ 3 files changed, 112 insertions(+) create mode 100644 test_programs/noir_test_success/regression_noir_sort/Nargo.toml create mode 100644 test_programs/noir_test_success/regression_noir_sort/src/main.nr create mode 100644 test_programs/noir_test_success/regression_noir_sort/src/quicksort.nr diff --git a/test_programs/noir_test_success/regression_noir_sort/Nargo.toml b/test_programs/noir_test_success/regression_noir_sort/Nargo.toml new file mode 100644 index 00000000000..5e536f653c1 --- /dev/null +++ b/test_programs/noir_test_success/regression_noir_sort/Nargo.toml @@ -0,0 +1,6 @@ +[package] +name = "regression_noir_sort" +type = "bin" +authors = [""] + +[dependencies] \ No newline at end of file diff --git a/test_programs/noir_test_success/regression_noir_sort/src/main.nr b/test_programs/noir_test_success/regression_noir_sort/src/main.nr new file mode 100644 index 00000000000..6a3d7506501 --- /dev/null +++ b/test_programs/noir_test_success/regression_noir_sort/src/main.nr @@ -0,0 +1,45 @@ +mod quicksort; + +pub fn sort(input: [T; N]) -> [T; N] where T: Ord { + sort_via(input, |a, b| a <= b) +} + +pub fn sort_via(input: [T; N], sortfn: fn(T, T) -> bool) -> [T; N] +{ + // Safety: test + let sorted = unsafe { quicksort::quicksort(input, sortfn) }; + + for i in 0..N - 1 { + assert( + sortfn(sorted[i], sorted[i + 1]), + "Array has not been sorted correctly according to `ordering`.", + ); + } + + sorted +} + +mod test { + use crate::sort; + + global arr_comptime: [u32; 7] = [3, 6, 8, 10, 1, 2, 1]; + global expected_comptime: [u32; 7] = [1, 1, 2, 3, 6, 8, 10]; + + #[test] + fn test_sort() { + let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1]; + + let sorted = sort(arr); + + let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10]; + assert(sorted == expected); + } + + #[test] + fn test_sort_comptime() { + + let sorted = sort(arr_comptime); + + assert(sorted == expected_comptime); + } +} diff --git a/test_programs/noir_test_success/regression_noir_sort/src/quicksort.nr b/test_programs/noir_test_success/regression_noir_sort/src/quicksort.nr new file mode 100644 index 00000000000..e43b069ab27 --- /dev/null +++ b/test_programs/noir_test_success/regression_noir_sort/src/quicksort.nr @@ -0,0 +1,61 @@ +unconstrained fn partition( + arr: &mut [T; N], + low: u32, + high: u32, + sortfn: unconstrained fn(T, T) -> bool, +) -> u32 { + let pivot = high; + let mut i = low; + for j in low..high { + if (sortfn(arr[j], arr[pivot])) { + let temp = arr[i]; + arr[i] = arr[j]; + arr[j] = temp; + i += 1; + } + } + + let temp = arr[i]; + arr[i] = arr[pivot]; + arr[pivot] = temp; + i +} + +unconstrained fn quicksort_loop( + arr: &mut [T; N], + low: u32, + high: u32, + sortfn: unconstrained fn(T, T) -> bool, +) { + let mut stack: [(u32, u32)] = &[(low, high)]; + // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized + for _ in 0..2 * N { + if stack.len() == 0 { + break; + } + + let (new_stack, (new_low, new_high)) = stack.pop_back(); + stack = new_stack; + + if new_high < new_low + 1 { + continue; + } + + let pivot_index = partition(arr, new_low, new_high, sortfn); + stack = stack.push_back((pivot_index + 1, new_high)); + if 0 < pivot_index { + stack = stack.push_back((new_low, pivot_index - 1)); + } + } +} + +pub unconstrained fn quicksort( + arr: [T; N], + sortfn: unconstrained fn(T, T) -> bool, +) -> [T; N] { + let mut arr: [T; N] = arr; + if arr.len() <= 1 {} else { + quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn); + } + arr +} From ff114ec5dac717195daf3348dd03473c0676e184 Mon Sep 17 00:00:00 2001 From: Tom French Date: Tue, 25 Feb 2025 14:43:06 +0000 Subject: [PATCH 3/9] . --- noir_stdlib/src/array/mod.nr | 1 - 1 file changed, 1 deletion(-) diff --git a/noir_stdlib/src/array/mod.nr b/noir_stdlib/src/array/mod.nr index c18e74639e0..527f3b50d49 100644 --- a/noir_stdlib/src/array/mod.nr +++ b/noir_stdlib/src/array/mod.nr @@ -183,7 +183,6 @@ where /// assert(sorted_descending == [32, 42]); // does not verify /// } /// ``` - pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self { // Safety: `sorted` array is checked to be: // a. a permutation of `input`'s elements From 71c04a4d21d0a4ec754c5f03079ed711e69deacd Mon Sep 17 00:00:00 2001 From: Tom French Date: Tue, 25 Feb 2025 14:44:22 +0000 Subject: [PATCH 4/9] . --- noir_stdlib/src/array/quicksort.nr | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/noir_stdlib/src/array/quicksort.nr b/noir_stdlib/src/array/quicksort.nr index e43b069ab27..94c38b6dbc7 100644 --- a/noir_stdlib/src/array/quicksort.nr +++ b/noir_stdlib/src/array/quicksort.nr @@ -54,7 +54,7 @@ pub unconstrained fn quicksort( sortfn: unconstrained fn(T, T) -> bool, ) -> [T; N] { let mut arr: [T; N] = arr; - if arr.len() <= 1 {} else { + if arr.len() > 1 { quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn); } arr From 1f213006c216b7aaff4a5b13377f7d44aaf250e5 Mon Sep 17 00:00:00 2001 From: Tom French Date: Tue, 25 Feb 2025 15:06:07 +0000 Subject: [PATCH 5/9] . --- .../noir_test_success/regression_noir_sort/src/main.nr | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/test_programs/noir_test_success/regression_noir_sort/src/main.nr b/test_programs/noir_test_success/regression_noir_sort/src/main.nr index 6a3d7506501..83aa894e328 100644 --- a/test_programs/noir_test_success/regression_noir_sort/src/main.nr +++ b/test_programs/noir_test_success/regression_noir_sort/src/main.nr @@ -1,11 +1,13 @@ mod quicksort; -pub fn sort(input: [T; N]) -> [T; N] where T: Ord { +pub fn sort(input: [T; N]) -> [T; N] +where + T: Ord, +{ sort_via(input, |a, b| a <= b) } -pub fn sort_via(input: [T; N], sortfn: fn(T, T) -> bool) -> [T; N] -{ +pub fn sort_via(input: [T; N], sortfn: fn(T, T) -> bool) -> [T; N] { // Safety: test let sorted = unsafe { quicksort::quicksort(input, sortfn) }; @@ -37,7 +39,6 @@ mod test { #[test] fn test_sort_comptime() { - let sorted = sort(arr_comptime); assert(sorted == expected_comptime); From 80b5d4668604dc7ed651f16635051d37518df143 Mon Sep 17 00:00:00 2001 From: Michael Klein Date: Wed, 9 Apr 2025 15:12:40 -0400 Subject: [PATCH 6/9] fix parser error from merging master --- noir_stdlib/src/array/mod.nr | 1 + 1 file changed, 1 insertion(+) diff --git a/noir_stdlib/src/array/mod.nr b/noir_stdlib/src/array/mod.nr index da5c3aad522..97a148f205a 100644 --- a/noir_stdlib/src/array/mod.nr +++ b/noir_stdlib/src/array/mod.nr @@ -399,6 +399,7 @@ mod test { 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127, ]; assert(sorted == expected); + } #[test] fn mapi_empty() { From ff2c2b45af2bf4ad3a64adb47040b18d16d39046 Mon Sep 17 00:00:00 2001 From: Michael Klein Date: Mon, 21 Apr 2025 11:18:12 -0400 Subject: [PATCH 7/9] cargo insta --- ..._force_brillig_false_inliner_-9223372036854775808.snap | 8 ++++---- .../execute__tests__force_brillig_false_inliner_0.snap | 8 ++++---- ...__force_brillig_false_inliner_9223372036854775807.snap | 8 ++++---- ...__force_brillig_true_inliner_-9223372036854775808.snap | 8 ++++---- .../execute__tests__force_brillig_true_inliner_0.snap | 8 ++++---- ...s__force_brillig_true_inliner_9223372036854775807.snap | 8 ++++---- ...__force_brillig_true_inliner_-9223372036854775808.snap | 8 ++++---- .../execute__tests__force_brillig_true_inliner_0.snap | 8 ++++---- ...s__force_brillig_true_inliner_9223372036854775807.snap | 8 ++++---- ...__force_brillig_true_inliner_-9223372036854775808.snap | 2 +- .../execute__tests__force_brillig_true_inliner_0.snap | 2 +- ...s__force_brillig_true_inliner_9223372036854775807.snap | 2 +- ..._force_brillig_false_inliner_-9223372036854775808.snap | 8 ++++---- .../execute__tests__force_brillig_false_inliner_0.snap | 8 ++++---- ...__force_brillig_false_inliner_9223372036854775807.snap | 8 ++++---- ...__force_brillig_true_inliner_-9223372036854775808.snap | 8 ++++---- .../execute__tests__force_brillig_true_inliner_0.snap | 8 ++++---- ...s__force_brillig_true_inliner_9223372036854775807.snap | 8 ++++---- 18 files changed, 63 insertions(+), 63 deletions(-) diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_false_inliner_-9223372036854775808.snap b/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_false_inliner_-9223372036854775808.snap index 9aede848a39..d48cac28258 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_false_inliner_-9223372036854775808.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_false_inliner_-9223372036854775808.snap @@ -53,19 +53,19 @@ expression: artifact } } }, - "bytecode": 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"file_map": { "2": { "source": "use crate::cmp::Eq;\n\nunconstrained fn __get_shuffle_indices(lhs: [T; N], rhs: [T; N]) -> [u32; N]\nwhere\n T: Eq,\n{\n let mut shuffle_indices: [u32; N] = [0; N];\n\n let mut shuffle_mask: [bool; N] = [false; N];\n for i in 0..N {\n let mut found = false;\n for j in 0..N {\n if ((shuffle_mask[j] == false) & (!found)) {\n if (lhs[i] == rhs[j]) {\n found = true;\n shuffle_indices[i] = j;\n shuffle_mask[j] = true;\n }\n }\n if (found) {\n continue;\n }\n }\n assert(found == true, \"check_shuffle, lhs and rhs arrays do not contain equivalent values\");\n }\n\n shuffle_indices\n}\n\nunconstrained fn __get_index(indices: [u32; N], idx: u32) -> u32 {\n let mut result = 0;\n for i in 0..N {\n if (indices[i] == idx) {\n result = i;\n break;\n }\n }\n result\n}\n\npub(crate) fn check_shuffle(lhs: [T; N], rhs: [T; N])\nwhere\n T: Eq,\n{\n // Safety: shuffle_indices is ensured to be a permutation of 0..N, and then\n // shuffle_indices is ensured to map lhs to rhs: assert(lhs[i] == rhs[shuffle_indices[i]]), for all i in 0..N\n unsafe {\n let shuffle_indices = __get_shuffle_indices(lhs, rhs);\n\n for i in 0..N {\n let idx = __get_index(shuffle_indices, i);\n assert_eq(shuffle_indices[idx], i);\n }\n for i in 0..N {\n let idx = shuffle_indices[i];\n let expected = rhs[idx];\n let result = lhs[i];\n assert_eq(expected, result);\n }\n }\n}\n\nmod test {\n use crate::cmp::Eq;\n use super::check_shuffle;\n\n struct CompoundStruct {\n a: bool,\n b: Field,\n c: u64,\n }\n impl Eq for CompoundStruct {\n fn eq(self, other: Self) -> bool {\n (self.a == other.a) & (self.b == other.b) & (self.c == other.c)\n }\n }\n\n #[test]\n fn test_shuffle() {\n let lhs: [Field; 5] = [0, 1, 2, 3, 4];\n let rhs: [Field; 5] = [2, 0, 3, 1, 4];\n check_shuffle(lhs, rhs);\n }\n\n #[test]\n fn test_shuffle_identity() {\n let lhs: [Field; 5] = [0, 1, 2, 3, 4];\n let rhs: [Field; 5] = [0, 1, 2, 3, 4];\n check_shuffle(lhs, rhs);\n }\n\n #[test(should_fail_with = \"check_shuffle, lhs and rhs arrays do not contain equivalent values\")]\n fn test_shuffle_fail() {\n let lhs: [Field; 5] = [0, 1, 2, 3, 4];\n let rhs: [Field; 5] = [0, 1, 2, 3, 5];\n check_shuffle(lhs, rhs);\n }\n\n #[test(should_fail_with = \"check_shuffle, lhs and rhs arrays do not contain equivalent values\")]\n fn test_shuffle_duplicates() {\n let lhs: [Field; 5] = [0, 1, 2, 3, 4];\n let rhs: [Field; 5] = [0, 1, 2, 3, 3];\n check_shuffle(lhs, rhs);\n }\n\n #[test]\n fn test_shuffle_compound_struct() {\n let lhs: [CompoundStruct; 5] = [\n CompoundStruct { a: false, b: 0, c: 12345 },\n CompoundStruct { a: false, b: -100, c: 54321 },\n CompoundStruct { a: true, b: 5, c: 0xffffffffffffffff },\n CompoundStruct { a: true, b: 9814, c: 0xeeffee0011001133 },\n CompoundStruct { a: false, b: 0x155, c: 0 },\n ];\n let rhs: [CompoundStruct; 5] = [\n CompoundStruct { a: false, b: 0x155, c: 0 },\n CompoundStruct { a: false, b: 0, c: 12345 },\n CompoundStruct { a: false, b: -100, c: 54321 },\n CompoundStruct { a: true, b: 9814, c: 0xeeffee0011001133 },\n CompoundStruct { a: true, b: 5, c: 0xffffffffffffffff },\n ];\n check_shuffle(lhs, rhs);\n }\n}\n", "path": "std/array/check_shuffle.nr" }, "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n unsafe {\n let sorted = quicksort::quicksort(self, ordering);\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "4": { - "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: fn[Env](T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_recursive(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: fn[Env](T, T) -> bool,\n) {\n if low < high {\n let pivot_index = partition(arr, low, high, sortfn);\n if pivot_index > 0 {\n quicksort_recursive(arr, low, pivot_index - 1, sortfn);\n }\n quicksort_recursive(arr, pivot_index + 1, high, sortfn);\n }\n}\n\npub(crate) unconstrained fn quicksort(\n _arr: [T; N],\n sortfn: fn[Env](T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = _arr;\n if arr.len() <= 1 {} else {\n quicksort_recursive(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", + "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn(T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", "path": "std/array/quicksort.nr" }, "5": { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_false_inliner_0.snap b/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_false_inliner_0.snap index 9dd184059f3..efae7af661a 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_false_inliner_0.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_false_inliner_0.snap @@ -53,19 +53,19 @@ expression: artifact } } }, - "bytecode": 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"file_map": { "2": { "source": "use crate::cmp::Eq;\n\nunconstrained fn __get_shuffle_indices(lhs: [T; N], rhs: [T; N]) -> [u32; N]\nwhere\n T: Eq,\n{\n let mut shuffle_indices: [u32; N] = [0; N];\n\n let mut shuffle_mask: [bool; N] = [false; N];\n for i in 0..N {\n let mut found = false;\n for j in 0..N {\n if ((shuffle_mask[j] == false) & (!found)) {\n if (lhs[i] == rhs[j]) {\n found = true;\n shuffle_indices[i] = j;\n shuffle_mask[j] = true;\n }\n }\n if (found) {\n continue;\n }\n }\n assert(found == true, \"check_shuffle, lhs and rhs arrays do not contain equivalent values\");\n }\n\n shuffle_indices\n}\n\nunconstrained fn __get_index(indices: [u32; N], idx: u32) -> u32 {\n let mut result = 0;\n for i in 0..N {\n if (indices[i] == idx) {\n result = i;\n break;\n }\n }\n result\n}\n\npub(crate) fn check_shuffle(lhs: [T; N], rhs: [T; N])\nwhere\n T: Eq,\n{\n // Safety: shuffle_indices is ensured to be a permutation of 0..N, and then\n // shuffle_indices is ensured to map lhs to rhs: assert(lhs[i] == rhs[shuffle_indices[i]]), for all i in 0..N\n unsafe {\n let shuffle_indices = __get_shuffle_indices(lhs, rhs);\n\n for i in 0..N {\n let idx = __get_index(shuffle_indices, i);\n assert_eq(shuffle_indices[idx], i);\n }\n for i in 0..N {\n let idx = shuffle_indices[i];\n let expected = rhs[idx];\n let result = lhs[i];\n assert_eq(expected, result);\n }\n }\n}\n\nmod test {\n use crate::cmp::Eq;\n use super::check_shuffle;\n\n struct CompoundStruct {\n a: bool,\n b: Field,\n c: u64,\n }\n impl Eq for CompoundStruct {\n fn eq(self, other: Self) -> bool {\n (self.a == other.a) & (self.b == other.b) & (self.c == other.c)\n }\n }\n\n #[test]\n fn test_shuffle() {\n let lhs: [Field; 5] = [0, 1, 2, 3, 4];\n let rhs: [Field; 5] = [2, 0, 3, 1, 4];\n check_shuffle(lhs, rhs);\n }\n\n #[test]\n fn test_shuffle_identity() {\n let lhs: [Field; 5] = [0, 1, 2, 3, 4];\n let rhs: [Field; 5] = [0, 1, 2, 3, 4];\n check_shuffle(lhs, rhs);\n }\n\n #[test(should_fail_with = \"check_shuffle, lhs and rhs arrays do not contain equivalent values\")]\n fn test_shuffle_fail() {\n let lhs: [Field; 5] = [0, 1, 2, 3, 4];\n let rhs: [Field; 5] = [0, 1, 2, 3, 5];\n check_shuffle(lhs, rhs);\n }\n\n #[test(should_fail_with = \"check_shuffle, lhs and rhs arrays do not contain equivalent values\")]\n fn test_shuffle_duplicates() {\n let lhs: [Field; 5] = [0, 1, 2, 3, 4];\n let rhs: [Field; 5] = [0, 1, 2, 3, 3];\n check_shuffle(lhs, rhs);\n }\n\n #[test]\n fn test_shuffle_compound_struct() {\n let lhs: [CompoundStruct; 5] = [\n CompoundStruct { a: false, b: 0, c: 12345 },\n CompoundStruct { a: false, b: -100, c: 54321 },\n CompoundStruct { a: true, b: 5, c: 0xffffffffffffffff },\n CompoundStruct { a: true, b: 9814, c: 0xeeffee0011001133 },\n CompoundStruct { a: false, b: 0x155, c: 0 },\n ];\n let rhs: [CompoundStruct; 5] = [\n CompoundStruct { a: false, b: 0x155, c: 0 },\n CompoundStruct { a: false, b: 0, c: 12345 },\n CompoundStruct { a: false, b: -100, c: 54321 },\n CompoundStruct { a: true, b: 9814, c: 0xeeffee0011001133 },\n CompoundStruct { a: true, b: 5, c: 0xffffffffffffffff },\n ];\n check_shuffle(lhs, rhs);\n }\n}\n", "path": "std/array/check_shuffle.nr" }, "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n unsafe {\n let sorted = quicksort::quicksort(self, ordering);\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "4": { - "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: fn[Env](T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_recursive(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: fn[Env](T, T) -> bool,\n) {\n if low < high {\n let pivot_index = partition(arr, low, high, sortfn);\n if pivot_index > 0 {\n quicksort_recursive(arr, low, pivot_index - 1, sortfn);\n }\n quicksort_recursive(arr, pivot_index + 1, high, sortfn);\n }\n}\n\npub(crate) unconstrained fn quicksort(\n _arr: [T; N],\n sortfn: fn[Env](T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = _arr;\n if arr.len() <= 1 {} else {\n quicksort_recursive(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", + "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn(T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", "path": "std/array/quicksort.nr" }, "5": { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_false_inliner_9223372036854775807.snap b/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_false_inliner_9223372036854775807.snap index 9dd184059f3..efae7af661a 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_false_inliner_9223372036854775807.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_false_inliner_9223372036854775807.snap @@ -53,19 +53,19 @@ expression: artifact } } }, - "bytecode": 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"file_map": { "2": { "source": "use crate::cmp::Eq;\n\nunconstrained fn __get_shuffle_indices(lhs: [T; N], rhs: [T; N]) -> [u32; N]\nwhere\n T: Eq,\n{\n let mut shuffle_indices: [u32; N] = [0; N];\n\n let mut shuffle_mask: [bool; N] = [false; N];\n for i in 0..N {\n let mut found = false;\n for j in 0..N {\n if ((shuffle_mask[j] == false) & (!found)) {\n if (lhs[i] == rhs[j]) {\n found = true;\n shuffle_indices[i] = j;\n shuffle_mask[j] = true;\n }\n }\n if (found) {\n continue;\n }\n }\n assert(found == true, \"check_shuffle, lhs and rhs arrays do not contain equivalent values\");\n }\n\n shuffle_indices\n}\n\nunconstrained fn __get_index(indices: [u32; N], idx: u32) -> u32 {\n let mut result = 0;\n for i in 0..N {\n if (indices[i] == idx) {\n result = i;\n break;\n }\n }\n result\n}\n\npub(crate) fn check_shuffle(lhs: [T; N], rhs: [T; N])\nwhere\n T: Eq,\n{\n // Safety: shuffle_indices is ensured to be a permutation of 0..N, and then\n // shuffle_indices is ensured to map lhs to rhs: assert(lhs[i] == rhs[shuffle_indices[i]]), for all i in 0..N\n unsafe {\n let shuffle_indices = __get_shuffle_indices(lhs, rhs);\n\n for i in 0..N {\n let idx = __get_index(shuffle_indices, i);\n assert_eq(shuffle_indices[idx], i);\n }\n for i in 0..N {\n let idx = shuffle_indices[i];\n let expected = rhs[idx];\n let result = lhs[i];\n assert_eq(expected, result);\n }\n }\n}\n\nmod test {\n use crate::cmp::Eq;\n use super::check_shuffle;\n\n struct CompoundStruct {\n a: bool,\n b: Field,\n c: u64,\n }\n impl Eq for CompoundStruct {\n fn eq(self, other: Self) -> bool {\n (self.a == other.a) & (self.b == other.b) & (self.c == other.c)\n }\n }\n\n #[test]\n fn test_shuffle() {\n let lhs: [Field; 5] = [0, 1, 2, 3, 4];\n let rhs: [Field; 5] = [2, 0, 3, 1, 4];\n check_shuffle(lhs, rhs);\n }\n\n #[test]\n fn test_shuffle_identity() {\n let lhs: [Field; 5] = [0, 1, 2, 3, 4];\n let rhs: [Field; 5] = [0, 1, 2, 3, 4];\n check_shuffle(lhs, rhs);\n }\n\n #[test(should_fail_with = \"check_shuffle, lhs and rhs arrays do not contain equivalent values\")]\n fn test_shuffle_fail() {\n let lhs: [Field; 5] = [0, 1, 2, 3, 4];\n let rhs: [Field; 5] = [0, 1, 2, 3, 5];\n check_shuffle(lhs, rhs);\n }\n\n #[test(should_fail_with = \"check_shuffle, lhs and rhs arrays do not contain equivalent values\")]\n fn test_shuffle_duplicates() {\n let lhs: [Field; 5] = [0, 1, 2, 3, 4];\n let rhs: [Field; 5] = [0, 1, 2, 3, 3];\n check_shuffle(lhs, rhs);\n }\n\n #[test]\n fn test_shuffle_compound_struct() {\n let lhs: [CompoundStruct; 5] = [\n CompoundStruct { a: false, b: 0, c: 12345 },\n CompoundStruct { a: false, b: -100, c: 54321 },\n CompoundStruct { a: true, b: 5, c: 0xffffffffffffffff },\n CompoundStruct { a: true, b: 9814, c: 0xeeffee0011001133 },\n CompoundStruct { a: false, b: 0x155, c: 0 },\n ];\n let rhs: [CompoundStruct; 5] = [\n CompoundStruct { a: false, b: 0x155, c: 0 },\n CompoundStruct { a: false, b: 0, c: 12345 },\n CompoundStruct { a: false, b: -100, c: 54321 },\n CompoundStruct { a: true, b: 9814, c: 0xeeffee0011001133 },\n CompoundStruct { a: true, b: 5, c: 0xffffffffffffffff },\n ];\n check_shuffle(lhs, rhs);\n }\n}\n", "path": "std/array/check_shuffle.nr" }, "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n unsafe {\n let sorted = quicksort::quicksort(self, ordering);\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "4": { - "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: fn[Env](T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_recursive(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: fn[Env](T, T) -> bool,\n) {\n if low < high {\n let pivot_index = partition(arr, low, high, sortfn);\n if pivot_index > 0 {\n quicksort_recursive(arr, low, pivot_index - 1, sortfn);\n }\n quicksort_recursive(arr, pivot_index + 1, high, sortfn);\n }\n}\n\npub(crate) unconstrained fn quicksort(\n _arr: [T; N],\n sortfn: fn[Env](T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = _arr;\n if arr.len() <= 1 {} else {\n quicksort_recursive(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", + "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn(T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", "path": "std/array/quicksort.nr" }, "5": { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_true_inliner_-9223372036854775808.snap b/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_true_inliner_-9223372036854775808.snap index cd229096551..ad62538cde0 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_true_inliner_-9223372036854775808.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_true_inliner_-9223372036854775808.snap @@ -45,15 +45,15 @@ expression: artifact } } }, - "bytecode": 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"file_map": { "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n unsafe {\n let sorted = quicksort::quicksort(self, ordering);\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "4": { - "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: fn[Env](T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_recursive(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: fn[Env](T, T) -> bool,\n) {\n if low < high {\n let pivot_index = partition(arr, low, high, sortfn);\n if pivot_index > 0 {\n quicksort_recursive(arr, low, pivot_index - 1, sortfn);\n }\n quicksort_recursive(arr, pivot_index + 1, high, sortfn);\n }\n}\n\npub(crate) unconstrained fn quicksort(\n _arr: [T; N],\n sortfn: fn[Env](T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = _arr;\n if arr.len() <= 1 {} else {\n quicksort_recursive(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", + "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn(T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", "path": "std/array/quicksort.nr" }, "5": { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_true_inliner_0.snap b/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_true_inliner_0.snap index 42401a14f8d..d616ffb6771 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_true_inliner_0.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_true_inliner_0.snap @@ -45,15 +45,15 @@ expression: artifact } } }, - "bytecode": 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"file_map": { "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n unsafe {\n let sorted = quicksort::quicksort(self, ordering);\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "4": { - "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: fn[Env](T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_recursive(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: fn[Env](T, T) -> bool,\n) {\n if low < high {\n let pivot_index = partition(arr, low, high, sortfn);\n if pivot_index > 0 {\n quicksort_recursive(arr, low, pivot_index - 1, sortfn);\n }\n quicksort_recursive(arr, pivot_index + 1, high, sortfn);\n }\n}\n\npub(crate) unconstrained fn quicksort(\n _arr: [T; N],\n sortfn: fn[Env](T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = _arr;\n if arr.len() <= 1 {} else {\n quicksort_recursive(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", + "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn(T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", "path": "std/array/quicksort.nr" }, "5": { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_true_inliner_9223372036854775807.snap b/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_true_inliner_9223372036854775807.snap index 42401a14f8d..d616ffb6771 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_true_inliner_9223372036854775807.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_true_inliner_9223372036854775807.snap @@ -45,15 +45,15 @@ expression: artifact } } }, - "bytecode": 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"file_map": { "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n unsafe {\n let sorted = quicksort::quicksort(self, ordering);\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "4": { - "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: fn[Env](T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_recursive(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: fn[Env](T, T) -> bool,\n) {\n if low < high {\n let pivot_index = partition(arr, low, high, sortfn);\n if pivot_index > 0 {\n quicksort_recursive(arr, low, pivot_index - 1, sortfn);\n }\n quicksort_recursive(arr, pivot_index + 1, high, sortfn);\n }\n}\n\npub(crate) unconstrained fn quicksort(\n _arr: [T; N],\n sortfn: fn[Env](T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = _arr;\n if arr.len() <= 1 {} else {\n quicksort_recursive(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", + "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn(T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", "path": "std/array/quicksort.nr" }, "5": { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_true_inliner_-9223372036854775808.snap b/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_true_inliner_-9223372036854775808.snap index 6517b664361..96bc4154c6b 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_true_inliner_-9223372036854775808.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_true_inliner_-9223372036854775808.snap @@ -243,15 +243,15 @@ expression: artifact } } }, - "bytecode": 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", 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", "file_map": { "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n unsafe {\n let sorted = quicksort::quicksort(self, ordering);\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "4": { - "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: fn[Env](T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_recursive(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: fn[Env](T, T) -> bool,\n) {\n if low < high {\n let pivot_index = partition(arr, low, high, sortfn);\n if pivot_index > 0 {\n quicksort_recursive(arr, low, pivot_index - 1, sortfn);\n }\n quicksort_recursive(arr, pivot_index + 1, high, sortfn);\n }\n}\n\npub(crate) unconstrained fn quicksort(\n _arr: [T; N],\n sortfn: fn[Env](T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = _arr;\n if arr.len() <= 1 {} else {\n quicksort_recursive(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", + "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn(T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", "path": "std/array/quicksort.nr" }, "5": { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_true_inliner_0.snap b/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_true_inliner_0.snap index cf005934a69..1b5665b3174 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_true_inliner_0.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_true_inliner_0.snap @@ -231,15 +231,15 @@ expression: artifact } } }, - "bytecode": 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", 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", "file_map": { "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n unsafe {\n let sorted = quicksort::quicksort(self, ordering);\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "4": { - "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: fn[Env](T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_recursive(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: fn[Env](T, T) -> bool,\n) {\n if low < high {\n let pivot_index = partition(arr, low, high, sortfn);\n if pivot_index > 0 {\n quicksort_recursive(arr, low, pivot_index - 1, sortfn);\n }\n quicksort_recursive(arr, pivot_index + 1, high, sortfn);\n }\n}\n\npub(crate) unconstrained fn quicksort(\n _arr: [T; N],\n sortfn: fn[Env](T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = _arr;\n if arr.len() <= 1 {} else {\n quicksort_recursive(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", + "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn(T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", "path": "std/array/quicksort.nr" }, "5": { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_true_inliner_9223372036854775807.snap b/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_true_inliner_9223372036854775807.snap index e238db39ab1..f98424b14fe 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_true_inliner_9223372036854775807.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_true_inliner_9223372036854775807.snap @@ -231,15 +231,15 @@ expression: artifact } } }, - "bytecode": 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", 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", "file_map": { "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n unsafe {\n let sorted = quicksort::quicksort(self, ordering);\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "4": { - "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: fn[Env](T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_recursive(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: fn[Env](T, T) -> bool,\n) {\n if low < high {\n let pivot_index = partition(arr, low, high, sortfn);\n if pivot_index > 0 {\n quicksort_recursive(arr, low, pivot_index - 1, sortfn);\n }\n quicksort_recursive(arr, pivot_index + 1, high, sortfn);\n }\n}\n\npub(crate) unconstrained fn quicksort(\n _arr: [T; N],\n sortfn: fn[Env](T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = _arr;\n if arr.len() <= 1 {} else {\n quicksort_recursive(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", + "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn(T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", "path": "std/array/quicksort.nr" }, "5": { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/higher_order_functions/execute__tests__force_brillig_true_inliner_-9223372036854775808.snap b/tooling/nargo_cli/tests/snapshots/execution_success/higher_order_functions/execute__tests__force_brillig_true_inliner_-9223372036854775808.snap index b81d9f606d1..1f92fcdc759 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/higher_order_functions/execute__tests__force_brillig_true_inliner_-9223372036854775808.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/higher_order_functions/execute__tests__force_brillig_true_inliner_-9223372036854775808.snap @@ -44,7 +44,7 @@ expression: artifact "debug_symbols": "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", "file_map": { "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n unsafe {\n let sorted = quicksort::quicksort(self, ordering);\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "5": { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/higher_order_functions/execute__tests__force_brillig_true_inliner_0.snap b/tooling/nargo_cli/tests/snapshots/execution_success/higher_order_functions/execute__tests__force_brillig_true_inliner_0.snap index 50a8497ce70..e8673169977 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/higher_order_functions/execute__tests__force_brillig_true_inliner_0.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/higher_order_functions/execute__tests__force_brillig_true_inliner_0.snap @@ -44,7 +44,7 @@ expression: artifact "debug_symbols": "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", "file_map": { "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n unsafe {\n let sorted = quicksort::quicksort(self, ordering);\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "5": { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/higher_order_functions/execute__tests__force_brillig_true_inliner_9223372036854775807.snap b/tooling/nargo_cli/tests/snapshots/execution_success/higher_order_functions/execute__tests__force_brillig_true_inliner_9223372036854775807.snap index bcaa50fbb86..2b01da0352e 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/higher_order_functions/execute__tests__force_brillig_true_inliner_9223372036854775807.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/higher_order_functions/execute__tests__force_brillig_true_inliner_9223372036854775807.snap @@ -44,7 +44,7 @@ expression: artifact "debug_symbols": "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", "file_map": { "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n unsafe {\n let sorted = quicksort::quicksort(self, ordering);\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "5": { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_false_inliner_-9223372036854775808.snap b/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_false_inliner_-9223372036854775808.snap index f6956b13a64..2c6f3cad9cb 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_false_inliner_-9223372036854775808.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_false_inliner_-9223372036854775808.snap @@ -231,15 +231,15 @@ expression: artifact } } }, - "bytecode": 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", 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", 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"file_map": { "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n unsafe {\n let sorted = quicksort::quicksort(self, ordering);\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "4": { - "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: fn[Env](T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_recursive(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: fn[Env](T, T) -> bool,\n) {\n if low < high {\n let pivot_index = partition(arr, low, high, sortfn);\n if pivot_index > 0 {\n quicksort_recursive(arr, low, pivot_index - 1, sortfn);\n }\n quicksort_recursive(arr, pivot_index + 1, high, sortfn);\n }\n}\n\npub(crate) unconstrained fn quicksort(\n _arr: [T; N],\n sortfn: fn[Env](T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = _arr;\n if arr.len() <= 1 {} else {\n quicksort_recursive(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", + "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn(T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", "path": "std/array/quicksort.nr" }, "5": { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_false_inliner_0.snap b/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_false_inliner_0.snap index 1b8e401fab0..19cf24af2cb 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_false_inliner_0.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_false_inliner_0.snap @@ -223,15 +223,15 @@ expression: artifact } } }, - "bytecode": 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rJLUpV0lqU66S1KZcJalNuUpSm3KVpDblKkltylWS2pSrJLU5V0mac5WkOVdJmnOVpDlXyedPO/g5ac+5StKcqyTNuUrSnKskzblK8pyrJM+5SvKcqyTPuUo+f9rBz0l7zlWS51wlec5VkudcJXnOVVLmXCVl1FXS1b//qUfbpj3qKnmSduEq+SGg1QK9WsCqBbxaIKoFslhAW7XA011HyVeB7fPU9PzJCGcCUi3wdCV3Wj+DTr4V6NUCVi3g1QJRLZDFAs+fjHAmQNUCT1dy93YTsEZbAakW0GqBXi1g1QJeLRDVAlks8PyT5mcCVC1QXclWXclWXclWXclWXclWXclWXclWXcleXcleXcleXcleXcleXcleXcleXcleXcleXcleXclRXclRXclRXclRXclRXclRXclRXclRXclRXclRXclZXclZXclZXclZXclZXclZXclZXclZXclZXclZXMncWrUAVQtwtYBUC2i1QK8WsGoBrxaIaoHqSqbqSqbqSqbqSqbqSqbqSqbqSqbqSqbqSqbqSqbqSubqSubqSubqSubqSubqSubqSubqSubiu7H8/K4l6319BRZbAakW0GqBXi1g1QJeLRDVAlks8Pz+ojOBp9cDC7kJ+E6reH5/0ZmAVAvsVvK335L/Tn37bab7Etrt1k+1+0k+3m6fmPPJdktacr/97g2Jf/m9ntz568Wutxe6GCu+/O3H6+xv8jrtTV6nv8nrjDd5nfker3N/J94/4eukN3md/CavU97kdeqbvM43uR7qb3I91N/keqi/yfVQf5PrIXuT6yF7k+she5PrIXuT6yErvR76kOj1ElYv4fUSUS+R5RLe6iWoXoLrJV7QYmz9Y7VvD6qP2GJc3+R19jd5nfYmr9Pf5HXGm7zOfI/XGe1NXie9yevkN3mdb3I9FG9yPRRvcj0Ub3I9FG9yPRRvcj0Ub3I9lG9yPZRvcj2Ub3I9lKXXQx8SWi/R6yWsXsLrJaJeIqslpLV6CaqXeEWLUV4l1LcSUi+h9RK9XsLqJbxeIuolslyC6uuCXuAob7f98/rDkv0hYfUSXi8R9RJZLsGtXoLqJbheQuoltF6ivrq5vrq5vrq5vrq5vrqlvrqlvrqlvrqlvrqlvrqlvrqlvrrlFdVtnxLZthJRL5HlEtrqJaheguslpF5C6yV6vYTVS9RXt9ZXt9ZXd6+v7lc8RBacN4nY/r6CvOL5rTMJqZfQeoleL2H1El4vEfUSWS7xiucyziTqq9vqq/sVDwKEyyqROxJaL9HrJaxewuslol4iyyVesQf/TILqJbheor66vb66vb66vb66vb66j7Y6S64SfXs38Gjj8H3qaBvuCUUQxRB1YP4eK+Xb8cjR9sUTqkOUQZRD1IE34vYQDy1W3FKJUEdbkk4ogiiGqH1vLO/cSlFsKYWoDlEGUQ5R5bdW9SUbJ04kqF6C6yWkXkLrJXq9hNVLvGLjxNqW1bZtWV+yceJEIsslqNVLUL0E10tIvYTWS/R6CauXKL/NrfWbWLR+E4vWb2LR+k0sWr+JRes3sWj9Jhat38Si9ZtYtH4Ti0rxzxKrULUAVwtItYBWC/RqAasW8GqBqBYoPtJetbqStbqStbqStbqStbqS9ZU/7dB/e79/54+Xyfjtj5cB5vrH+T0ZGykZHymZGCmZHCiZ3kZKhkZKhkdKRkZKRkdKZqQO3It/iEe7VwtEtUDxT2qptWoBqhbgagGpFtBqgV4tUF3JVl3JVl3JVl3JXl3JXl3JXl3JXl3JXl3JXl3JXl3JXl3J8XQdsN+O2BCOHQGqFuBqAakW0GqBXi1g1QJeLRDVAlkskNWVnNWVnNWVnNWVnNWVnNWVnM9Xcq5TTWG6/8eRdIvLnze3vp0A84//6UfaPmfaMWfaOWPavbU506Y50+Y505Y509ZB03a9beX1aNu0+5xpj7pKnqT99CopQmva26fzeotqgSwWoFYtQNUCXC0g1QJaLdCrBaxaoLqS9/dQhtzOXl160n0Bpn67m8W0/bH4vr+D8pUCXC0g1QJaLdCrBaxawKsFologiwWkupIPdlrpeppy9t+cprxVIKFbM/p28sWXv/0Q0GqBXi1g1QJeLRDVAlkscLAb6YUCVC3A1QLVldyfr+Smn8+GNv3Nke4fEr1ewuolvF4i6iWyXMJavQTVS3C9hNRL1Fe31Ve3vaK6uX9KqG4kXl3dWwlv9RJUL8H1ElIvofUSvV7C6iUuqIuol8hyiXhFdfvnk4ULuJGgegmul5B6Ca2XeEF19y9LUo88+Wvr63E0zWzrPxstIR8toRgtoRwsoWyjJUSjJcSjJSSjJaSjJTRap87ROnWO1qlztE6dg3Vqa4N1amuDdWprg3Vqaxf3Iba85bP8MzbpXFxj7Ovvfi7/9E06OVQ6VFpfHxJUL8H1ElIvofUSvV7C6iW8XiLqJbJcguurm+urm+urm+urm+urm+urm+urm+urm+urm+urW15Q3Usv/byW460E1UtwvYTUS2i9RK+XsHoJr5eIeoksl9BX14XQRuIVjrr/ZVCtXsLrJaJeIsslequXqDdtl3oJrZfo9RJWL+H1ElEvkeUS1uolqF6ivrqtvrpfsVXspA1a/cJq9Qur1S+sVr+wWv3C6vUL6ys2vJ1JcL2E1EvUV7dffBvx5AaHx1jp5FDpRBsrHRorHR4rHRkrHR0rnbFurF69Ee8snau7svPtGAV22aYTY6WTQ6Vz9Qa8s3RorHR4rHRkrHR0rHT6WOnYWOmM1ZVzrK6cQ3Vlb0N1ZW9DdWVvQ3Vlf3Sb3QdkCOQIFAiUAPToFrYPiBCIEUgQSBEIccTBdq0uvEJKG8gRKBAoAehg29MJRAjECCQIpAjUEejAEd5XKHkDOQIFAiUAHWyVOYEIgRiBBIGQz0mQd0+Rd0+Rd0+Rd0+Rd+/gYJ8TqCMQ8jkpUk+K1NPRz3ZR3iCT+xcqxLz+evDyb//x6BU/+jGuV0pQvQTXS0i9hNZL9HoJq5fwF0jofYmol8hyCWv1Eq+o7vV42uXfXw6ovUm8orq13ZWQegmtl+j1Eq+obslVQtrmIte8XiLqJR6s7l8hbwhECHRQU+t9nPTtF9Kjn366DykCdQQyBHIECgRKADr6Iab7ECEQ4ohAHBGIIwJxRCCOCMQRgTgiEEck4ohEHJGIIxJxRCKOSMQRiTgiEUck4ogEHBGtIRAhECOQIJAiUEcgQ6B9RwTfHujOULl/ASGyXmhJP/vbe8enxsE8/6Fk7grsT+SpNVq/GrQmdldiuXj4/rdLV17/sn8X0EcFPjB77IV/QI5AgUAJQAcD8sh1UJQtNxAhECOQIJAiUEcgQ6CA3CcHVUHxeSOR2+YDlgOv0+eX4EbbGtx/xPAcMwxzDAsMSwjbn32fY4RhjGGCYZhLFHOJYi5RzCWKuUQxl3TMJR1zScdc0jGXdMwlvUOdqxuGOYYFhiWEWcMwbA0wxjDBMMUwzCWGucQwlxjmEsNc4phLHHOJYy5xzCWOucQxlzjmEsdc4phLHHNJYC4JzCWBuSQwlwTmksBcEphLAnPJ/hRqaZ+3xXTpbbSBOgIZAjkCBQLl41DuT6HOIEIgRiBBIEWgjkCGQI5AB44IWaHNTpRsCUDUEIgQiBFIEEgRqCOQIZAj0L4jfJ1TLdcGsoESgA5+PvIEIgRiBBIEUgTqCIR8ToK8e4K8e4K8e4K8e4K8e/uTsDPIEQj6nA7qydsK5dfdGI9vm0pt9RJUL8H1ElIvofUSvV7C6iX8BRJ6XyLqJbJcord6iVdU9929ftlfUd13t7Bll3oJrZfo9RKvqO67u+Sye71E1Es8WN2/QtYQiBBov6aWm3w3KDb3CHJ/1nwGKQJ1BDIEcgQKBEoA2p8un0GEQIgjHHGEI45wxBGOOMIRRzjiCEccEYgjAnHE/hQ5km+Nb5mO8v0uqeuOKeq8GTLsz5sfEmBafw6Pe24E9rd+tPVlL4O5flfg3halPNh22WINT+3HLSJ5sBnyBFIEMgTyhyFu+4NU6Xb79KW7biBFoI5AhkCOQIFACUD7g9QziBCIEQhxxMGWP2qyfgX59gOYG6xjmGGYY1hgWELY/lD1HCMMYwwTDDtySdAn9uOBYwvWMcwwzDEsMCwhTBqGEYZhn5tg76Rg76Rg76Ri76Ri76QyhgmGYZ+bYvWmWL0dbDsk+jzBh0j735+YOy4icYVIXiBysAHyxSJ0hQhfISJXiOgVIv0lInpfxK4Q8StE4gqR11T8vSE6N3tNxd+bDi8idIUIXyEiV4i8puLvDaIXkX6FiF0h8nDFf2CBYQlhflRnaSu2DK02GGEYY5hgmGJYxzDDMMewwLCEsMBcEphLAnNJYC4JzCWBuSQwlwTmksBcEphLEnNJYi5JzCWJuSQxlyTmksRckphLEnNJQi6h1jCMMOzAJRyfQ7vfrt0P3hZaJOQFEvduDC0SB36Q5FVC6f4WgDu3hpjo4EPp/DnG6PLj1QPRwUtXkc+8ettgimEdwwzDHMMCwxLCjgbnZxhhGGMY5hLGXMKYSxhzCWMuYcwljLlEMJcI5hI5conrJ5abNiqCYYphHcMMwxzDAsMSwo5uCpxhhGGYSxRziWIuUcwlirlEMZco5hLFXNIxl3TMJR1zScdc0jGXdMwlHXNJx1zSMZd0zCWGucQwlxjmEsNcYphLDHPJ0ajy5Mr8aPh4hgWGJYQ59q3jaPh4hjGE7c9QROw2VxVJ30D7e6RaW/cTNdoo7c9PziBCIEYgQSBFoI5AhkCOQIFAgCO4NQQiBGIEEgRSBOoIZAjkCBQIhDiCEEcQ4oiDfZSfu92Wf+YGEgRSANqffGisUy6N/HxNy7fUD4ogiiFKIEohqkOUQZRDVEBUIpRA3jgYd7C3z9u8Tj8u9Xww7jjFBMMUwzqGGYY5hgWG5T+AyY93BlgZw44+t/jcz+KbDX+simEdwwzDHMMCwxLCDgYQpxj2uR182xZp680dEd5i2Gsz7LUZYRhjGPZOGlYBhlWAYRVgWAUYVgGGucQxlzjmEsdc4phLHHOJYy5xzCWOucQxlzjmksBcEphLDvbsLPfAP/eS5lP32flgf89jEnfvs/PBXqBFon2RkA124IfU/onZVi0wLCHsYHfOKUYYxhgmGKYYtt81pK1fFJZ/x8aIB7tzTjHHsMCwRDA52J1zihGGMYYJhimGdQw7+NyWIfOK0eb7hRzsxLlzwfnYVh852LNzckUrB5tvzrD9wYSE3r5kSdjnN7pvmwm2r6bd3jBnvf+ny/35WLeDifP9xWD55nh7n1jbNnWbN3WfN/WYN/WcNvX9idkcqdO8qfO8qcu8qeu8qc+7msq8q6nMu5rKvKupzLua6ryrqc67muq8q6nOu5rqvKupPrqaflAGUQ5RAVGJUL1BFEEUQ5RAlEIU5I0OeaND3uiQNzrkDYO8YZA39u9KSvptl6Zk9C0lEKUQ1SHKIMohKiAqEWr/buQpRRC1vxGryW0B0aa5pQSiFKI6RBlEOUQFRCVC7d+DPKUIog68kbduo0SypQSiFKI6RBlEOUQFRCVCZYMogqh9b3C7PT6tTLalBKIUojpEGUQ5RAVEJUDp/l3IU4ogiiFKIEohqkOUQZRDVEAU5A2CvEGQN+jAG0or1XNLCUQpRO17Y7kDeKOWGeCWMohyiAqISoQ6eCjgjCKIYogSiFKIgrzB597otKUcogKh9u/49baedNab/uYaYJiRk+7f8ZsjdZk3dZ039T5v6jZv6j5v6jFv6jlt6vt3/OZIfd7VVOddTXXe1VTnXU113tVU511Ndd7VVOddTXXe1bTPu5r2R1fTD4ohSiBKIapDlEGUQ1RAVCKUHRi0t5Uy3VIEUQxRAlEKUR2iDKIcogKiEqH8wBtuK/Xl921XiiCKIUogSiGqQ5RBlENUQFQi1P798E7rXYhOO3ch9u+Hn1IMUQJRClEdogyiHKICohKhEvJGQt5IyBsJeSMhbyTkjYS8kZA3EvJGIt7orUHUgTc+1y8y3lIMUQJRClEdogyiHKICohKhqEHUuTdcthRDlECUQlSHKIMoh6iAqHNvxOZatHODKIIohiiBKIWoDlEGUQ5RAVGQNwTyhkDeEMgbAnlDIG8c3Gdlup1usDh120UPbnGeUQ5RAVGJUAe3s84ogiiGKIEohahzb2xnRF0NohyiAqISoXqDKIIohqhzb/RtB+gKUR2iDKIcogKiEqGsQdSBN/J2VNeyVuWWYogSiFKI6hBlEOUQFRCVCHUwFz2jzr3BbUsxRAlEKUR1iDKIcogKiDr3hmx2dPZoEEUQxRAlEKUQ1SHKIGrfG8st0ZXy7ad8MBc9oxKhDuaiZxRBFEOUQJRCVIcogyjIGwdzUenySW19eDAXvU/ZwVz0jCKIYogSiFKI6hBlEOUQte8NXX/pYfmG1rZUItTBXPSMIohiiBKIUojqEGUQ5RAFeeNgLqrrbzot1NaHB3PRM4ogiiFKIEohqkOUQZRDVEDUvjeWie6N6rx5/skO5qJnFEEUQ5RAlEJUhyiDKIeogCjIGwp5QyFvKOQNhbyhkDcU8sbBXFTXbZXL/G7b5w/momdUQFQi1MFc9IwiiGKIEohSiOoQtf952ed3WKffzOeH2fhrBwPTGVI/mNpOkTrNmzrPm7rMm7rOm3qfN3WbN3WfN/V5V1ObdzX1eVdTn3c19XlXU593NfV5V1OfdzX1eVdTn3c19UdX0w8qESoaRBFEMUQJRClEdYgyiNo3qFOulMiWCohKhDq4H35GEUQxRAlEKUR1iDKIOvDG+iOD3bcn9NnB/fAzKgHKD+6Hn1EEUQxRAlEKUR2iDKIOvLGeZ919e561H9wPP6MSoQ7uh59RBFEMUQJRClEdogyi9r0R63nWPbptqYCoRKiD++FnFEEUQ5RAlEJUh6h9b2RbvZGsW8ohKiAqEergfvgZRRDFECUQpRDVIQryhkDeEMgbAnlDIW8o5A2FvKGQNxTyhkLeOLgfnp876dK2a8rB/fAzKiAqEergfvgZRRDFECUQpRDVIercG75dibpDVEBUIpQ1iCKIYogSiDr3RviW6hBlEOUQFRCVCOUNogiiGKIEoiBvOOQNh7zhkDcc8oZD3tifi1pb94Fak20X3Z+LnlIMUQJRClEdogyiHKICohKh8twbuu3YSRDFECUQpRDVIcogyiHq3Bt92wEyASpagyiCKIYogSiFqA5RBlEOUQFRkDcI8gZB3iDIGwR5gw68sd7/Wv5pW6pDlEGUQ1RAVCIUN4giiGKIEoiCvMGQNxjyBh94w+OT2nqeA6ISoaRBFEEUQ5RAlEJUhyiDKMgbAnlDIG8o5A2FvKGQNxTyhkLeUMgbCnlDIW8o5A2FvNEhb3TIGx3yRoe80SFvdMgbHfJGh7zRIW90yBsGecMgbxjkDYO8YZA3DPKGQd4wyBsGecMgbzjkDYe84ZA3HPKGQ95wyBsOecMhbzjkDYe8EZA3AvJGQN4IyBsBeSMgbwTkjYC8EZA3AvJGQt5IyBsJeSMhbyTkjYS8kZA3EvJGQt5IxBvZGkQRRDFECUQpRHWIMohyiAqIgrxBkDcI8gZB3iDIGwR5gyBvEOQNgrxBkDcI8gZD3mDIGwx5gyFvMOQNhrzBkDeguWhCc9GE5qIJzUUTmosmNBdNaC6a0Fw0obloQnPRhOaiCc1FE5qLJjQXTWgumtBcNKG5aEJz0YTmonkwFyVf76fQdmdaHsxFz6iAqESog7noGUUQxRAlEKUQ1SEK8kaHvNEhbxzMRan3T2rr+YO56BlFEMUQJRClENUhyiDKISogat8b/NkPl96yoQ7momcUQRRDlECUQlSHKIMoh6iAKMgbAXnjYC4qsu63ke1+tjyYi55RAlEKUR2iDKIcogKiEqEO5qJn1Lk3tnuxMhmiBKIUojpEGUQ5RAVEnXvDfjyjVFprEEUQxRAlEKUQ1SHKIGrfG8p+o3RzbuhCBUQlQh3MRc8ogiiGKIEohagOUQZRkDcI8gZB3mDIGwx5gyFvMOQNhrzBkDcY8sbBXFSyfVK8pQKiEqEO5qJnFEEUQ5RAlEJUhyiDqN3Py5ebpt8pl/ablaj0JDFp+1PQn5gPDZYPD5aPDJaPDpZPHywfGywfHyyfGCyfwfpzH6w/98H6cx+sP/fB+nMfrD/3wfpzH6w/98H6cx+sP/dH+/OvlDWIIohiiBKIUojqEGUQ5RC17zohXanN6VULlQi1f8/ulCKIYogSiFKI6hBlEOUQBXnDIW8E5I2AvBGQNwLyRkDeCMgbAXkjIG8E5I2AvJEH3ljP5nXZnM27UARRDFECUQpRHaIMohyiAqISoKg1iCKIOvBG3mbUriT3r9Cy2+2aK/vmZ7QXCamX0HqJXi9h9RJeLxH1ElkuQa1eguol6qubXlAXRrcelcYnf+xrOv5lU6by92xsqGx8qGzitdkIbc2Q5RLc6iWoXmJ/TdT10EhXky2lENUhyiDKISogKhFq/+b1KUUQxRAFeUMgbwjkDYG8IZA3BPKGQN5QyBsKeePg1rBGW6nNht6FEohSiOoQte+NbrchrvfNwxQL5RAVEJUIdXAr7YwiiGKIEohSiOoQBXmjQ97okDc65A2DvGGQNwzyhkHeMMgbBzcI+npnxq35ljKIcogKiEqEOrhBcEYRRDFECUQpREHecMgbBzcIvK03j5y2A7GDGwRnVCLUwQ2CM4ogiiFKIEohqkOUQRTkjYC8EZA3EvJGQt5IyBsJeSMhbyTkjYS8cXCDYPn+v1L9N9/ot1//pdvtIlm6f14k53eFAx9JfCpsvzEc3Ey4T/HBzYQziiDqwEdGK+W0pQSiFKI6RBlEOUQFRCVCHQy5zyiCKMgbBHmDIG8Q5A2CvEGQNwjyBkHeYMgbB6NTj3V0Gm2HYogSiFKI6hBlEOUQFRCVCHUwQz2jIG8I5I2DGernjzJ5bmdyfDBDPaM6RBlEOUQFRCVCHcxQzyiCKIaoA2/4ulEiNz99vFAKUR2iDKIcogKiEqEOZqhnFEEUQxTkjYMZaq43DKNt5xN8MEM9owyiHKICohKhDmaoZxRBFEOUQBTkDYO8YZA3DPKGQd4wyBsOecMhbzjkDYe8sT9Djca2Utq2VIcogyiHqF1vxHLv/UbRHpUItT9DPaUIohiiBKIUojpEGUQ5REHeCMgbCXkjIW8k5I2EvJGQNxLyRkLeSMgbeeCNnivl2+v5TICS1iCKIOrAG0ErlbSlBKIUojpEGUQ5RAVEJUJRgyiCKMgbBHmDIG8Q5A2CvEGQNwjyBkHeYMgb+3PRWO4R3CjmHYohSiBKIWrfGyq378uh9ps9Ytt7ZZG3dy6/7G9mfuRPP3KxgXLxgXKJgXLJcXLZn2L/pFxooFx4oFxkoFx0oFwG6rsyUN+VgfquDNR3ZaC+qwP1XR2o7+pAfVcH6rs6UN/VgfquDtR3daC+qwP1XR2o7/aB+m4fqO/2gfpuH6jv9oH6bh+o7/aB+m4fqO/2gfpuH6jv2kB91wbquzZQ37WB+q4N1HdtoL5rA/VdG6jv2kB91wbquz5Q3/WB+q4P1Hd9oL7rA/VdH6jv+kB91wfquz5Q3/WB+m4M1HdjoL4bA/XdGKjvxkB9NwbquzFQ342B+m4M1HdjoL6bA/XdHKjv5kB9NwfquzlQ382B+m4O1HdzoL6bA/XdHKfvahun72obp+9qG6fvahun72q7tO+6fnlQfptLHyiXR/vuB+UQFRCVCEUNogiiGKIEovZ93O32wxfRt8/C6MHTBGeUQZRDVEBUItTB0wRnFEEUQ5RAFOQNhrzBkDcY8gZD3mDIGwJ5QyBvCOSNg93ZPW6nhIU13lIKUR2iDKIcogKiEqEO9tCeUQRRDFGQNxTyxsGuTPt8JsvS71/FfLv98/2Pv01q1z/O7wpWruDlClGukNUKB3sYX6lA5QpcriDlClquUF7TB/vVvK/PqLttzvrRg51lJ9TBHrAziiCKIUogSiGqQ5RBlEMU5A2DvOGQNxzyhkPecMgbDnnDIW845A2HvOGQNxzyRkDeCMgbAXkjIG8E5I2AvBGQNwLyRkDeCMgbCXkjIW8k5I2EvJGQNxLyRkLeSMgbCXkjEW/01iCKIIoh6sAb6097R/z2bPa6iXw/mMj/nFz6QLnYQLn4QLnEQLnkOLkc3On4ObnQQLnwQLkM1HdpoL5LA/VdGqjv0kB9lwbquzRQ3+WB+i4P1Hd5oL7LA/VdHqjv8kB9lwfquzxQ3+WB+i4P1HdloL4rA/VdGajvykB9VwbquzJQ35WB+q4M1HdloL4rA/VdHajv6kB9VwfquzpQ39WB+q4O1Hd1oL6rA/VdHajv6kB9tw/Ud/tAfbcP1Hf7QH23D9R3+0B9tw/Ud/tAfbcP1Hf7QH3XBuq7NlDftYH6rg3Ud22gvmsD9V0bqO/apX337pOX3WKgXHKcXPzRZwE+KIYogSiFqA5RBlEOUQFRiVDRIAryxtEeW779YnW4tC0lEKUQ1SHKIMohKiAqEepoj+0JRRAFeQPaY9uhPbYd2mPb89GV7oMKiEqAsoPdsksxr1ScrDkm60+Em3z54/yuQOUKXK4g5QpartDLFaxcwcsV4mmFjPWHAzMotiWX5RLU6iWoXoLrJaReQusler2E1Ut4vcQLqjv77aIk03wrkeUS3OolqF6C6yWkXkLrJXq9xPPVTa0Jf//r5d/KWxG/QiSuEMkLRKRdIUJXiPAVInKFiF4h0q8QuaLi5YqKlysqXq6oeL2i4vWKitcrKl6vqHi9ouL1iorXKyper6h4vaLi9YqK71dUfL+i4vsVFd+vqPh+RcX3Kyq+X1Hx/YqK71dUfL+i4u2KircrKt6uqHi7ouLtioq3l1R8t0+RuOjUbjObN3WfN/WYN/WcNnVv86ZO86bO86Yu86au86Y+72rq866mPu9q6vOupj7vahrzrqYx72oa866mMe9qGvOupjHvahrzrqYx72oa866mMe9qmvOupjnvaprzrqY572qa866mOe9qmvOupjnvaprzrqY57WrqbdrV1Nu0q6m3aVdTb9Oupt6mXU29Tbuaept2NfU27WrqbdrV1Nu8qymVP83hVNwJPkTsChG/QiSuEMkLRLhdIUJXiPAVIq+4TqFoqwhFbEX0CpF+hYhdIeJXiMQVInmByEuefToVoStE+AqRV1Q8+6eIUNuK6BUi/QoRu0LErxCJK0TyApGXPPt0KvKKihf+3OgrvV10ff2SJ6p+Uuoyb+o6b+p93tRt3tR93tRj2tRf8oQbt1hTZ9o8wOEveS5MxVcR1e2M4yXPhZ2K2BUifoVIXCGSF4i85LmwUxG6QoSvEJErRF5S8ZafIt63Iv0KEbtCxK8QiStE8gKRlzxpdCpCV4jwFSJyhcgVFe9XVLxfUfF+RcX7FRXvV1R8XFHxcUXFxxUVH1dUfFxR8XFFxccVFR9XVHxcUfFxRcXnFRWfV1R8XlHxeUXF5xUVn1dUfF5R8XlFxecVFZ8XVHy0doUIXSHCV4jIFSJ6hUi/QsSuEPErRF5S8fG5E6aT//2SaXO8ZJfez0md2ryp07yp87ypy7yp67yp93lTv+BKLh7eq/lBMUQJRClEdYgyiHKICohKhHp4E+AHBXnjaJte3n4rZnG9nNiWSW+u5f4pkd8VpFxByxV6uYKVK3i5QpQrZLXC0Xa8FyrQ8wptGUutC8XyNW9T1spXiMgVInqFSL9CxK4Q8StE4gqRvECkv6DcW8bt8S2i1tpWhK4Q4StE5AoRvUKkXyFiV4j4FSJxhcgLKp6W1XcVId2KWLtChK4Q4StE5AoRvUKkXyFiV4j4FSL7Ff/5qHKkyZZKhDrYEXZGEUQxRAlEKUR1iDKI2rXSYp911EW9nVjp21mENyt9e5z4yxee7V+z5C0h1hY/fjna3/P0E/PJsfLZ30n1E/OhwfLhwfKRwfLRwfLpg+Vjg+UzWH+OwfpzDNafc7D+nIP15xysP+dg/TkH6885WH/OwfpzPtqfP6iAqASobA2iCKIYogSiFKI6RO27juV26kKy5nWuy/2Naj8xnxgsnxwrn/3dZz8xHxosHx4sHxksHx0snz5YPoP1ZxqsP9Ng/ZkG6888WH/mwfozD9afebD+zIP1Zx6sP/Oj/fmDcogKiEqEkgZRBFEMUQJRClEHrltPbV184FvKIMohKiAqEUobRBFEMUQJRClEQd7QA294rlRuv62rQ1RAVCJUbxBFEMUQJRClENUhCvLG/mappZGvB7AL6ZYKiEqE2t+fdEoRRDFECUQpRHWIMog68AavPUpk26MsICoRyhtEEUQxRAlEKUR1iDKIgrzhkDcc8kZA3gjIGwF542AvhHRfKTv5VnLnjz8ktF6i10tYvYTXS0S9RJZLHNzQf6kE1UtwvUR9defz1U3sbX3Elv3L08HfRxXZL9CwCzT8Ao24QCOrNbS1doEGXaDBr9YQ3Wj0CzReUh/hnxoZGw2/QCMu0Mh6DWoXaNAFGnyBhlygcUF98As+c5HPk+VFeKvx6Gf+QTFECUT1C97pKz7NC7odX9Dt+IJuJxd0O7mg28kF3U4u6HaiF2hcUOdyQZ3LBXUuF9S5XFDnekGd6wV1rhfUuV5Q53pBnesFda4X1LleUOf6ijqPWK8Al9tx9zeRkOb6K3adeZNPXp3PvXN8tPX2knzal3xko/GKultuvn9q2PZ16AUa/QINu0DDL9CICzSyXsPaBRovWF+l+XosyfKFle/2hDC9hQ4z2uTDg+Ujg+Wjg+XTB8vHBsvHB8snBssnx8rH22D5DNaffbD+7IP1Zx+sP/v1/Xk9oywsN99B3AbLxwfLJwbLJ8fKJ9pg+dBg+fBg+chg+ehg+Vzen33dYxium3lJ2GD5+GD5xGD55Fj5ZBssHxosHx4sn8H6T15fX97WfL4877I/z+b+Of3mLz8vcss+J86eWps6e5o6e546e5k6e506+z519jZ29no/e586+8HX2pPsB19r72dPg6+1+rmzgaNtsh98rdV2N/vB19qT7Adfa0+yH3ytPcl+8LVWPp8KkUab7Adfa0+yH3ytPcl+nLX2I59xVs9f8+Fx1sOPfC5f4ULW+3jRN9cLzIPlI4Plo4Pl0wfLxwbLxwfLJwbLJ8fKR9pg+QzWn2Ww/iyD9WcZrD/LYP1ZBuvPMlh/lsH6swzWn3Ww/qyX9+fk2xewSL3/t/efkyHlkXO/+0wN6dV9Ldvqg2y/8cH2b32N62GfSXzPPC/PfH18K5f7ED++k50Hy0cGy6cPlo8Nls8r/LxMHNd8aHPOC73k6Z3jMyZ2XvN6VyO/7DX+XsEveTbl/okX9JLnO840Dn5F2WJd8OJMo6mt88CmX+7/fD8Ygw5+sfXFInSFCF8hIleI6BUi/QoRu0LErxCJK0SuqPi4ouLjioqPKyo+XlLxnz/B0Dr5/T+PXH/DjD83Jn77fvCP/+lH6jpv6n3e1G3e1H3e1GPe1HPa1LPNmzoNnLqv+578y+6PNXWeN/WRV9OT1ItX0w+RV3Rg/vxe2Zh4KxJXiGS9CLd2hQhdIcJXiMgVInqFSL9CxK4Q8StE4gqRKyqerqh4eknF++cXS/56n+EmwleIyBUieoVIv0LErhDxK0TiCpG8QITbFSJXVDy/puLXe8ZNqG1F5AoRvUKkXyFiV4j4FSJxhUheICKvqHhh/hTp7ZqpAgvNmzrPm7rMm7rOm3qfN3WbN3WfN/UYOPW7wz+WnDZ1HXk1PUl95NX0JPXi1fRD5PkOnBnrK84vZ8evElEvkeUS/RU1sHwc66fRdDs16nSFCF8hIleI6BUi/QoRu0LErxCJK0TyAhG7ouLtioq3Kyrerqh4u6Li7YqKtysq3q6oeHtJxffPu9ot9KKvEy/ZH/1zUn/JruuflDrNmzrPm7rMm7rOm3qfN3UbOPX7X51fsp//J6U+8mp6kvrIq+n91KN4SfpVZH+/KK9zh6+HLen3xPY3ap4wAjAKMB1gDGAcYAJg8mFG9rcPnjAEMAwwAjAKMB1gDGAcYAJgdn0gEbfvQRL543Hfsr+r7AwiBGIEEgRSBOoIZAiEfE77O2i+HZP2Hfp2QscGEgRSBOoIZAjkCBQIlAC0v4PjDCIE2nfEcof3BrH6BhIEUgTqCGQI5AgUCJQAtH8X8gwiBEIcoYgjFHGEIo5QxBGKOEIRRyjiiI44oiOO6IgjOuKIjjiiI47oiCM64oiOOKIjjjDEEYY4whBHGOIIQxxhiCMMcYQhjjDEEYY4whFHOOIIRxzhiCMccYQjjnDEEY44whFHOOKIQBwRiCMCcUQgjgjEEYE4IhBHBOKIQBwRiCMScUQijkjEEYk4IhFHJOKIRByRiCMScUQCjtDWEIgQiBFIEEgRqCOQIZAjUCAQ4ghCHEGIIwhxBCGOIMQRhDiCEEcQ4ghCHEGIIxhxBCOOYMQRjDiCEUcw4ghGHMGIIxhxBCOOEMQRgjgCmVkqMrNUZGapyMxSkZmlIjNLRWaWiswsFZlZKjKzVGRmqcjMUpGZpSIzS0VmlorMLBWZWSoys1RkZqnIzFKRmaUiM0tFZpaKzCwVmVkqMrNUZGapyMxSkZmlIjNLRWaWiswsFZlZKjKzVGRmqcjMUpGZpSIzS0VmlorMLBWZWSoys1RkZqnIzFKRmaUiM0tFZpaKzCwVmVkqMrNUZGapyMxSkZmlIjNLRWaWiswsFZlZKjKzVGRmqcjMUpGZpSIzS0VmlorMLBWZWSoys1RkZqnIzLIjM8uOzCw7MrPsyMyyIzPLjswsOzKz7MjMsiMzy34ws1zuFt2gZaz5I3QwszyBCIEYgQSBFIE6AhkCOQIFAiGOYMQRjDiCEUcw4ghGHMGIIxhxBCOOYMQRjDhCEEcI4ghBHCHAbuIuhkCOQIFAwL7lrg2BCIEYgQSBFIEQRyjiCEUcoYgjFHFERxzREUd0xBEdcURHHNERR3TEEfuTRElaHydJ/vrTZjtPid39wca+P3V8SODuryr2g7lh67cH3ZYeTBuIEUgQSBGoI5AhkCNQIFAC0MHc8AQCnpnozggkCKQI1BHIEMgRKBAIeIqmR0MgxBEBfLi2vywTBa8/FknR7W7D658/iLGso58S9OAffyTkoyUUoyWUgyW0fxn0MxOi0RLi0RKS0RLS0RLqoyU0Wqfuo3Xq/nCn/sASwqxhGGEYY5hg2FE1rqdxLP/2k4/W++171nKNRptPy/oFGnaBhl+gERdoZL2Gtws06AINflTjAxMMO6rGXH+qhJJ4g3UMMwxzDAsMSwiLhmGEYfwPYEwbTDBMMWz3A3BdFwhXyQ20//ZbWydrxhsb7++XOIMIgRiBBIEUgToCGQI5AgUCAY7w/Xtsnnabn3oGb6AEoP17bGcQIRAjkCCQAtD+TSy3dQVyi76BCIEYgQSBFIE6AhkCOQIFAiUA7U9vPPptbOhhuoEIgRiBBIEUgZDPyZDSMKQ0DCkNQ0rDkNLY/6q1XDyv1xFLl9tAjkCBQAlA+19XziBCIEYgQSBFoI5AiCMccYQjjnDEEYE4IhBHBOKIQBwRiCMCcUQgjgjEEYE4IhBHJOKIRByRiCMScUQijkjEEYk4IhFH5IEj1kHP0q++Qo9uHPHMpwXubhyJ9uBFzVZAbN3EYp8L+P6PMUncckltn5uhKfbiStxmDCL55Y995485vxz92vj+HxM1/5xUt69D//h4Vwi4PgxSBOoIZAjkz37SSn47bFspNr+lFfv7nWGF3By1HQdf0x9QiPTbTrDln9vXIK1cgZ5XWPfWLv/MrQKXK8gL3iVaFbJtFQ68FOtx79k2FpcEoIPhxQlECMQIJAikCNQRyBDIEQhxhCKOOBheJK9fVVN5AxECMQIJAikCdQQyBHIECgRKADLEEYY4whBHGOIIQxxxMMW5Ox6OgynOCeQIFAgEzP7DGwIRAjECCQIpAiGOcMQRjjjCEUc44ohAHBGIIwJxRCCOCMQRgTgiEEcE4ohAHBGIIxJxRCKOSMQRiTgiEUck4ohEHJGIIxL4cHP/ye2+XK9/h/qyGv0I7T+5fQYRAjECCQIpAnUEMgRyBAoEQhzBiCMYcQQjjmDEEYw4ghFHMOKIgye3XdZHWnxz4ZsHT26fQAlAB09un0CEQIxAgkCKQMjndHAy4/IN7gYtl7kbiBCIEUgQSBGoI5AhkCNQIFAC0MHJjGnr+QTptIEIgRiBBIEUgToCGQI5AgUCJQAZ4ghDHGGIIwxxhCGOMMQRhjjCEEcY4ghDHOGIIxxxhCOOcMQRjjjCEUc44ghHHOGIIxxxRCCOCMQRgTgiEEcE4ohAHBGIIwJxRCCOCMQRiTgiEUck4ohEHJGIIxJxRCKOSMQRiTgiH3dEb60hECEQI5AgkCJQRyBDIEegQCDEEYQ4ghBHEOIIQhxBiCMIcQQhjiDEEYQ4ghBHMOIIRhzBiCMYcQQjjmDEEYw4ghFHMOIIRhwhiCMEcYQgjhDEEYI4QhBHCOIIQRwhiCMEcYQijlDEEYo4QhFHKOIIRRyhiCMUcYQijlDEER1xREcc0RFHdMQRHXFERxzREUd0xBEdcURHHGGIIwxxhCGOMMQRhjjCEEcY4ghDHGGIIwxxhCOOcMQRjjjCEUc44ghHHOGIIxxxhCOOcMQRgTgiEEcE4ohAHBGIIwJxRCCOCMQRgTgiEEck4ohEHJGIIxJxRCKOSMQRiTgiEUck4ghkZknIzJKQmSUhM0tCZpaEzCwJmVkSMrMkZGZJyMySkJklITNLQmaWhMwsCZlZEjKzJGRmScjMkpCZJSEzS0JmloTMLOlgT+JSnN+h5U98AzECCQIpAnUEMgRyBAoESgDan1meQYgjBHGEII4QxBGCOEIQRwjiCEEcIYgjFHGEIo5QxBGKOEIRR+jju1QXKBAoAag3BCIEYgQSBFIE6ghkCIQ4oiOO6IgjDHGEIY4wxBGGOOJgkuifG4PdnziHZRHoTwvcO4dlETjYD91uZ6B8O3RhAyUAHcwNTyBCIEYgQSBFoI5AhkCOQI/vkF+gBKBoCEQIxAgkCKQI1BHIEAj5cPdnbIfnL30wAjAKMB1gDGAcYAJg8mGG92drS8u4fW9eGsHn02hq3ymCKIYogahdPyweXlfMCN9SHaIMohyiAqISofanbEvzWM9RD9+hCKIYogSiFKI6RBlEOUQFRCVCMeQNht55ht55ht55ht55ht55ht55gd55gary4FS2vp5450afF4Gy8xViadO8/m1vX/72Q0CqBbRaoD8rsNwCuf2a0/LPLx/CdwGrFvBqgagWyGKBg/P2XihA1QJcLSDPC9gqQF9+n+UmoNUCvVrAqgW8WiCqBbJYoLdqAaoW4GqB/TrQNX58LoLL1PGDMYBxgAmAyceZg8MT7zMEMAwwAjAKMIAPDPCBAT4wwAcG+MABHzjgAwd8sD917to/fxvUfrz1x/tT5zOoI5AhkCNQIFAC0P7U+QxCPqf9CW3v6/ny3VrfQI5AgUAJQPs7O88gQiBGIEEgRaCOQPuOsLydfdu9xQZyBAoEysch2Z8+n0GEQIxAgkCKQB2BDIEcgQKBEEcQ4ghCHEGIIwhxBCGOIMQRhDiCEEcQ4ghCHMGIIxhxBCOOYMQRjDiCEUcw4ghGHMGIIxhxhCCOEMQRgjhCEEcI4ghBHCGIIwRxhCCOEMQRijhCEUco4ghFHKGIIxRxhCKOUMQRijhCEUd0xBEdcURHHNERR3TEER1xREcc0RFHdMQRHXGEIY4wxBGGOMIQRxjiCEMcYYgjDHGEIY4wxBGOOMIRRzjiCEcc4YgjHHGEI45wxBGOOMIRRwTiiEAcEYgjAnFEII4IxBGBOCIQRwTiiEAckYgjEnFEIo5IxBGJOCIRRyAzS0FmloLMLAWZWSoys1RkZqnIzFKRmaUiM0tFZpaKzCwVmVkqMrNUZGapyMxSkZmlIjNLRWaWiswsFZlZKjKzVGRmqcjMUpGZpSIzS0VmlorMLBWZWSoys1RkZqnIzFKRmaUiM0tFZpaKzCwVmVkqMrNUZGapyMxSkZmlIjNLRWaWiswsFZlZKjKzVGRmqcjMUpGZpSIzS0VmlorMLBWZWSoys1RkZqnIzFKRmaUiM0tFZpaKzCwVmVkqMrNUZGapyMxSkZmlIjNLRWaWiswsFZlZKjKzVGRmqcjMUpGZpSIzS0VmlorMLNUf/+3FBWIEEgRSBOoIZAjkCBQIlAAUDYEQRwTiiEAcEYgjAnFEII4IxBGBOCIQRyTiiEQckYgjEnFEIo5IYDexZiAQsJu4t4ZAhECMQIJAikAdgQyBHIECgRBHEOIIQhxBiCMIccTBJHFp1yuUXyv30XOO+sHU8RGBu+cc9YO54XLX+SbQrW2gBKCDueEJRAjECCQIpAjUEcgQyBEIeGaiM/DMRJeGQIRAjECCQIpAHYEMgZAPd3/Gxhnr4TutfXYu8u+UQJRCVIcogyiHqICoRKiOnHvUO0EUQ5RA1K43KNrtSUIK9i3VIcogyiEqICoRyhpEEUQxRAlEQd4wyBsGecMgbxjkDYO84ZA3HPKGQ95wyBt+8Hn1tdtE1y2FnGDUgxCtOHg37p5h1kMgSiGqQ5RBlENUQBRyelzPBlEEUcjpcT0FohSiOkQZRDlEBUQhHcBagyiCKIYogSiFqA5RBlGINww609GgMx0NOtPRoDMdDTrT0aAzHY2wdz4g6uljlO6femfcqgWoWqD4QDRjqRbQaoFeLWDVAl4tENUCxUcbmhQfiGZC1QJcLSDVAlot0KsFrFrAqwWiWqD4aEPbH0EvTfZ2b2v55+e9re8HwNv+CPqUUojav9xKv41qaZnaflLfIUMgR6BAoASgg+nzCUQIxAiEfE4HE9pcVx9ubD9ChrwR+5NWbu1T6Usl3SBGIEEg3YfIP9+IvoE6AhkC+flr+vo57VxZ+21Q57H9RCEbJAAdjGLvQ/u75ZbmfJsvLQ2RN41rf2J5SjFECVASBwPLEwgp80DacSDtOJB2HEg7TqQLJfI5HQz07ns2kTdifzB3v3X5/lzuDCIEYgQSBFIE6ghkCOSP93Df35F2BiUAUUMgQiBGoH/AEfcXM7Z22zrPy4wOXvic9PlUONdUJJ5I5R9w7wPvivpGAGhjfjAAPYGAywk/+MEYaq2v358atR+XXj/4yZhzzkEuQC4x7uCnY845AjkGOQE5BTnQL/tTD3O9lbEt3+lXKr9DjkCBQAlA+4+lnkGEQIxAgkCKQB2BDhyRt0u15fL+6wbmnc4reevsrJtHuVy9WiCqBbJYoLdqAaoW4GoBqRbQaoFeLVBdyb26knt1JffqSrbqSrbqSrbqSrbqSrbqSrbqSrYHK/kDcgQKBEoA8oZAhECMQIJAikD77on1efhluLdxxP5m1zPIAehg3BzrE3PLPFU20P67l+t+Fku1DaQI1BHIEMgRKAAokbd8f9vpGcQIJAikCNQRyBDIESgQKB+HojUEIgSKx3tEtAQgaghECMSPQdv1uUesp5NEfpmAt+8K/XkF5lXhy9MNq0I8q0BE+jkMor4ZBgXT8xonA6dgoMkHdwQyBHIECgRKANoff55BhECMQIJAiCMO9jpxu422nHkLJQAd/HDyCUQIxAgkCKQI1BHIEMgRaN8RsjavZS68hRKADn6A9wRCPqeOvBEdKY2OlIYhpWFIaRz8yCyvlzLLDay2gQSBFIE6AhkCOQIFAiUAHfzY7AlECIQ4whFHOOIIRxzhiCMccYQjjnDEEYE4IhBHBOKIQBwRiCMCcUQgjgjEEYE4IhBHJOKIRByRiCMScUQeOCLWnfHS2v1vTndPiIrsTwvcPSEqmz5+UZP7e8zOIEMgR6AHLwkf/t6f++OIxxTufu9P0mcVTr/3J/nzGiff+/PgcUsJXQ2cP7o+Dx6hPIEYgQSBFIE6AhkCOQIFAiUACeIIQRwhiCMEcYQgjhDEEYI4QhBHCOIIQRyhiCMORi3KfW1eslntDkYtJ5AgkCJQRyBDIEegQKAEoINRywmEOKIjjuiIIzriiI44oiOO6IgjOuKIjjjCEEcY4ghDHGGIIwxxhCGOMMQRhjjCEEcY4ghHHOGIIxxxhCOOcOTDPTjKrAWv3yyXf/fNpfn+iOYf4AjkGOQE5BTkOsgZyDnIBciBfjnYnkL5+eT89rv4wfaUE4gRSBBIEagjkCGQI1AgUD4MWTvYnsLrCdTG/vVU9Qc3QC4CVC3A1QJSLaDVAr1awKoFvFogqgWyWICqK5mqK5mqK5mqK5mqK5mqK5mqK5mqK5mqK5mqK5kfrOQPiBCIEUgQSBGoI5AhkCNQINC+e0TXPXPy45Od1g629Elbn06QrY32h9Mm3lcocgMZAjkCBQIlAB08VXwCIW/5wQPCJ5AiUEcgQyBHoECgBKCDB25PIEIgxBEdccTB45/3e8TBI50nUCAQ0sIOHqc8hH7Zaazd18ZqP55dtSjQ8wr3jhBcFPqzCstYhuRzREOx1bAXaNwdA1lzpCEePMB3AjECCQIpAiFLpCNLpCNLpCNLpCNLZCCO2J/OLrcLbw5fbgKePHQitB4jJr1vBHq1gFULeLVAVAtkscD+TPqVAlQtwNUCUi1QXcn5fCUvl/HrjwgtV+ebC4y0egmvl4h6iayWoNbqJaheguslpF5C6yV6vYTVS7yiutfb/Mu/VTcSr67urQRRvQTXS0i9hNZL9HoJq5fweokL6iLLJbjVS7yiul0/JVI2ElwvIfUSWi/R6yVeUN39y5LUf/MlfvvXKnGLrNpyk46PlU6MlU4OlY60sdKhsdLhsdKRsdLRsdLpY6UzVleWsbqyjNWVZayurGN1ZR2rK+tYXVnH6sp6cd9ZboHe7iMu/4xNOhdXFnusv7vg8ePGGuptrHRKK+tDguslpF5C6yV6vYTVS3i9RNRLZLmEtXqJ+uq2+uq2+uq2+uq2+uq2+uq2+uq2+uq2+ur2+ur2F1S30ecg1zjP/rp/7tgy24x9nUdLSEZLSEdLqI+WkI2WkI+WUIyWUA6WULTREhqtU8donTpG69QxWqeO0Tp1jNapY7ROHaN16ri4U0vE7XkAie1942xjpUNjpcNjpSNjpaNjpdPHSsfGSmeovsPt4spS4tuucyWJTToyVjo6Vjp9rHRsrHR8rHRirHRyqHSojZUOjZXO1V358wQEZfVNOjJWOjpWOn2sdGysdHysdGKsdHKodLiNlQ6Nlc5YXZnH6so8Vlfmsboyj9WVeayuzGN1ZR6rK8tYXVnG6soyVleWsbqyjNWVZayuLGN1ZRmrK8tYXVnG6so6VlfWsbqyjtWVdayurGN1ZR2rK+tYXVnH6so6VlfWsbpyH6sr97G6ch+rK/exunIfqyv3sbpyH6sr97G6ch+rK/exurKN1ZVtrK5sY3VlG6sr21hd2cbqyjZWV7axurKN1ZVtrK7sY3VlH6sr+1hd2cfqyj5WV/axurKP1ZV9rK7sY3VlH6srx1hdOcbqyjFWV46xunKM1ZVjrK4cY3XlGKsrx1hdOcbqyjlWV86xunKO1ZVzrK6cY3XlHKsr51hdOcfqyjlWV86hurK0obqytKG6srShurK0obqytKG6srShurK0obqytKG6srShurK0sboyjdWVaayuPNazfTLWs30y1rN9MtazfTLWs30y1rN9MtazfTLWs30y1rN9MtazfTLWs30y1rN9MtazfTLWs30y1rN9MtazfTLWs30y1rN9MtazfTLWs30y1rN9MtazfTLWs30y1rN9MtazfTLWs30y1rN9MtazfTLWs30y1rN9MtazfTLWs30y1rN9MtazfTLWs30y1rN9MtazfTLWs30y1rN9MtazfTLWs30y1rN9MtazfTLWs30y1rN9MtazfTLWs30y1rN9MtazfTLWs30y1rN9MtazfTLWs30y1rN9MtazfTLWs30y1rN9MtazfTLWs30y1rN9MtazfTLWs30y1rN9MtazfTLWs30y1rN9MtazfTLWs30y1rN9MtazfTLWs30y1rN9MtazfTLWs30y1rN9MtazfTLWs31y+bN9Sy3f0tGWP6Zz+bN9J+nQWOnwWOnIWOnoWOn0sdKxsdLxsdKJsdIZqitrG6oraxuqK2sbqitrG6oraxuqK2sbqitrG6oraxuqK2sbqitrG6sr01hdmcbqyjRWV375s31CGwmtl+j1ElYv4fUSUS+R5RIvf05tR6K+Ll7yTNDdX7DXlzzncyIR9RJZLvGSZ2xOJKheot60Ut/Mpb6ZS30zl/pmLvXNXOqbudY385c/HLEjwfUS9dWt9dX9kocN7rdBrV9YtX5h1fqFVesX1l6/sPb6hfUlG+dPJKReQusl6qv76q3Wy51R/v7Hy11J2aSTQ6Vz9Vbrs3RorHR4rHRkrHR0rHT6WOnYWOn4WOmM1ZVtrK7sY3VlH6sr+1hd2cfqyj5WV/axurKP1ZV9rK7sY3VlH6srX73VWpLolk6y3o+suX4L6syb1Gng1Jn0Fpj75o7j5ZuUW4/vf7zc/9zMoy7fpHySjo2Vjo+VToyVTg6VzuWblE/SobHS4bHSkbHSuborL6vELR2S2KTTx0rHxkrHx0onxkonR0qnX75J+SQdGisdHisdGSudobpyb0O1wf6Kfa+d+5pOj/vpsK3fyZZ/btJ5xb7XV6ZDY6XDY6UjY6WjY6XTx0rHxkrHx0onxkrn6q7sfBvysW8XCW5jpUNjpcNjpSNjpaNjpdPHSsfGSsfHSifGSmesrixjdWUZqyvLWF1ZxurKMlZXfnSj/wcUCJQA9Oj2+g+IEIgRSBBIEagjkCEQ4oiD7d5LL1ghpQ2UAHSwJfsEIgRiBBIEUgTqCGQI5Ah04AjvK5S8gRKADjYNn0CEQIxAgkCKQB2BkM/JkXfPkXfPkXfPkXfPkXfvYAPXCeQIBH1OSD0FUk8HW3qM8gaZ3L9QIeb1Hu/yb+8bCa6XkHoJrZfo9RJWL+H1ElEvkS+Q0LsSBxtnXipB9RJcL/GK6lb/lIi2kXhFdWu7K9HrJaxewuslXlHdkquEtM1Fbma1hLVWL/FgdX9AjECCQAc1td7HSd98IbWDE8lOIEMgR6BAoASgg/v6JxAhECOQIBDiCEIcQYgjCHEEIY4gxBGMOIIRRxzcnwu+nZ2coXK/d4msPV762d+uu/yXv+2bZOT5ZO4L7HqFWqP1qqQ1sbsS7rcpm8fnX/bvAvmowK/Ywe2Bwxf+ATECCQIpAu0Xc+T6HTU3p8XZwbk5J5AjUCBQAtDBgPwEIgQSyH16UBUUn/cwuG0+YD3wOn1efzfa1uD+dPgcIwxjDBMMUwzr0AfQsc9tf1Z8jgWGJYRZwzDCMMYwwTDFMMwlhrnEMJcY5hLDXOKYSxxziWMuccwljrnEMZc45hLHXOKYSxxzSWAuCcwlgbkkMJcE5pLAXBKYSwJzyf5Ikyg+91YsEe5frS+3vW8XqcvN7M+vP/TgH38kxKMlJKMlpKMl1EdLyEZLyEdLKEZLKMdKyPcHzj8zocE6tbfBOrW3hzv1B6YY1jHMMMwxLDDsqBotPjE/+Wh9PQKyu9Pm06J2gQZdoMEXaMgFGnqBRr9Awy7Q8Ec1PrDAsKNqzPV+JiX9uBfJuWEYYRhjmGCYYljHMMMw/wcwpg0WGJYQtn+z4qwA9u9WnFIdogyiHKICohKh9u9ZnFIEUQxRu94QltvRYMJfBu3cvlMKUR2iDKIcogKiEqH2b+CcUgRRCjWO3jHMMAzrpR3rpR3rpdYwjDBst6Y56fYliVN582nv309ZqL5SZltKIWq/phvf7tRJ09/4ce87n69f+WIrYNUCXi0Q1QJZLLB/f+iVAlQtwNUCUi2g1QLVlezPV7KtzchEtgJeLRDVAlksEK1agKoFuFpAqgW0WqBXC1RXclRXclRXcjxayb9S2SCKIIohSiBKIapDlEGUQ1RAFOKNaA2iDrxhn1TolmKIEohSiOoQZRDlEBUIRdDntT/YF1qfEhMyvt/O7vzxh0R/WoIs1gPnl1uLWwmrl/B6iaiXyOcllo53k1je+I3E/p2F10pQvQTXS0i9hNZL9HoJq5fweomol6ivbqmvbqmvbqmvbqmvbqmvbnlBdafcntei1B0Jq5eALqgkEUoPCoRkfTnsW4ogiiFKIOrgYnu9DbEEOPuY2ufD6O3LkWyrRK+XsOclqK+XWOTbS6yD+4cvlYh6iSyXOLiN+ZjEuumPuG0/7i7PS+h6Qv3yz51XofUSvV7C6iW8XiLqJbJcwl5QF13X0utf9m6sElQvwfUSUi+h9RK9XsLqJV5Q3X09PpOMZCsR9RJZLuGtXoLqJbheQuoltF7iBaUnn99upG/XC/d6iaiXyHKJaPUSVC/B9RJSL6H1Er1eor66o7664xXV/fmUgsT28iCyXCJbvQTVS3C9hNRLaL1E/ZfirP9SnPVfirP8S3G2R0vvg9o1ia1jSOPPyyn9juy+XdZvh6wv38+/INsXEu32l8s4YhM8KoNnYfD9pyX/8eC8nlLPX3794hacngy+Pk3KnTbBuTK4VAbXyuC9MviTPue2PkNA7cfg/KRbZJ0Zi22Dc2VwqQyulcF7ZXB7Mvj6/VgyN8G9MnhUBs/C4NIqgz9ZoUo3tyjrJjhXBpfK4E9WqLZby1XatFzplcGtMrhXBo/K4FkYXFtl8GcrdD0PWHXTz5Urg0tlcK0M3iuDW2VwrwwelcH3KzRv3/udNl8A90+T9fX3Rdy3iDyO6ONIfxzZdYXzigRvEH8ciceRfBjZv2d9H6HHEX4ckccRfRzZ/ShjfcAidIv440g8juTDyP4Nyrvzh/0bjvcRfhyRxxF9HOmPI/Y44o8j8TASz45u7n3TD64MLpXBtTL4s6Obe7O4sMrgXhk8KoNnYfB8dri67kjlvnFLUmVwrgxeNKLcmWYYreOpz5h970bF8o3y9hV/+Yr22Q8p9uJKrPcnJL/8se+luzTT2x+3Lw9W7f4xUfPPIxTb19Mo49u7561pzbv3EbxXBrfK4F4ZPK5yrJL7+vU+4qtnPzLJn5JJ6iYTbldlstzqXK9UlmuZbSY0TCZ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", "file_map": { "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n unsafe {\n let sorted = quicksort::quicksort(self, ordering);\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "4": { - "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: fn[Env](T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_recursive(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: fn[Env](T, T) -> bool,\n) {\n if low < high {\n let pivot_index = partition(arr, low, high, sortfn);\n if pivot_index > 0 {\n quicksort_recursive(arr, low, pivot_index - 1, sortfn);\n }\n quicksort_recursive(arr, pivot_index + 1, high, sortfn);\n }\n}\n\npub(crate) unconstrained fn quicksort(\n _arr: [T; N],\n sortfn: fn[Env](T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = _arr;\n if arr.len() <= 1 {} else {\n quicksort_recursive(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", + "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn(T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", "path": "std/array/quicksort.nr" }, "5": { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_false_inliner_9223372036854775807.snap b/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_false_inliner_9223372036854775807.snap index 0a55d596903..68a828fb80c 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_false_inliner_9223372036854775807.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_false_inliner_9223372036854775807.snap @@ -219,15 +219,15 @@ expression: artifact } } }, - "bytecode": 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", 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", "file_map": { "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n unsafe {\n let sorted = quicksort::quicksort(self, ordering);\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "4": { - "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: fn[Env](T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_recursive(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: fn[Env](T, T) -> bool,\n) {\n if low < high {\n let pivot_index = partition(arr, low, high, sortfn);\n if pivot_index > 0 {\n quicksort_recursive(arr, low, pivot_index - 1, sortfn);\n }\n quicksort_recursive(arr, pivot_index + 1, high, sortfn);\n }\n}\n\npub(crate) unconstrained fn quicksort(\n _arr: [T; N],\n sortfn: fn[Env](T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = _arr;\n if arr.len() <= 1 {} else {\n quicksort_recursive(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", + "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn(T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", "path": "std/array/quicksort.nr" }, "5": { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_true_inliner_-9223372036854775808.snap b/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_true_inliner_-9223372036854775808.snap index f6956b13a64..2c6f3cad9cb 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_true_inliner_-9223372036854775808.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_true_inliner_-9223372036854775808.snap @@ -231,15 +231,15 @@ expression: artifact } } }, - "bytecode": 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", 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", 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"file_map": { "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n unsafe {\n let sorted = quicksort::quicksort(self, ordering);\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "4": { - "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: fn[Env](T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_recursive(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: fn[Env](T, T) -> bool,\n) {\n if low < high {\n let pivot_index = partition(arr, low, high, sortfn);\n if pivot_index > 0 {\n quicksort_recursive(arr, low, pivot_index - 1, sortfn);\n }\n quicksort_recursive(arr, pivot_index + 1, high, sortfn);\n }\n}\n\npub(crate) unconstrained fn quicksort(\n _arr: [T; N],\n sortfn: fn[Env](T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = _arr;\n if arr.len() <= 1 {} else {\n quicksort_recursive(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", + "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn(T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", "path": "std/array/quicksort.nr" }, "5": { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_true_inliner_0.snap b/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_true_inliner_0.snap index 1b8e401fab0..19cf24af2cb 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_true_inliner_0.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_true_inliner_0.snap @@ -223,15 +223,15 @@ expression: artifact } } }, - "bytecode": 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HWJ5jaFTlxPOOsDx579m/POl61hGWp/y6b0TpSh1heY4hz7HtOYYec4Q1qvP2dzjCut8RFq97y+rFGQxbpz3xoQ8+9uGE0hT9vzvt/q4l/QZpQ/x6eu9a2g+vRr8nAU5CcF8v3iuzmIwdYNAaoSjAIC8mcQGCDVinZ09FXkzWKA/prwOsIqPAR4G+T+anBpVRwNpKObLHaI/pQH2QtgnBD+QhB7J4MdAe2KcnxTNuj5qgpyneY97FWUyvlb6wwfA3kv52jrG4ny7JV7W4nyae41g7TLvlTit4n3GE5TkZ3+sI6xFHWJ78GtWFr6eC5tmOo7qQ8zQUjOqCaVQXq6PaVz0NnZ78eskRlie/PA3WnvwaVYOP59j27BPHjrBuhTH0iiMsC3g3aGP112hj1dYXZTdWrfxfgI3Vv5fDVEHAHXXrfasbGggsKT2aL/hTQbosDy/qwKBQnCbpP9Y3w/c9s124XM6SWmtyADLjM64Fcf1gbcrt8juw1vtrFDw/0vp1I/LmdyfwklpTYZ048L5aj/K3weK8UV0vtqg886AI1nRFWHHX7d02rQfqjfibAVq5Hlm6lvrwJEt3jSgsq6PxBnlZpb8rPAiLg2mdNEidwqPsn6cdvwqPkp23apDB01ymN6qXULSoPPOgCNZ5CjKIvB4HGUwG1vFmCTLIes1Jx6/Cg7BMLqrLrwzGqOxN8eVXZfem7swnNo/Lr2qCnkHrr9fWNc6y6y8r/xFYf70u/70o6LJ5fJFg49yrnF6nKA91MoMRWUfsu2AZ+4LSGyaS/n6L7cc8fGOgL1TdF5soybtZylOXoFoe9ne+HPiCqONZBbG0tXJREMsLwCMsj7+zVKdn24H2qGobaQh6Il8EeszB8TFZHjvTYx4G7+L9QQw0Vqc8dNZnHRovmGMZhZc0Yl/jpGweeOH6T5aweSj7jPGCZfRboB8s5IMism3piO1AiaBdBZ9X7cq6GbYrz+UcQM5+cxpkd/rNEhcALgo8LI9ChyLUpe6R9eHK7cJjCtuFx5QKuhpql6rjrerFp8bDqmMK1wXMCyuP7Yo637X0xnedyu/DGFyiMYj6xVm1dVkeWrmM7os53SEenrc6vXgT1mmvcfPV6R/MD67TPLyH6xm1DuCLU5Q+hLAniC4VQF3pkEr3bCaDcaOefU86GHeI1gVRXumXSoe2d+eS/jo69pdDdWmTJbWnxPMLymm2ceOcxfMLzll8Ibe6yLts3zU+Vb3UfFT4y/oY8pf1MeQv62Ojwl+WCwaX5QLboCYEjcruYP2xrLyZEXSExrBy4kY9he0SuJ7nS9jRLsH6qrKJKhnG8g37Leuy53X9/KOO6+cZQU/cC0aqX8Ie0tmLLmFX4xp1duyHnNS4Nl5UvYQd+x9fwo7tzZew23u/mFc+7t7+2lbI5jxqB7ebgh6juyHyTnM54c7O4fHx0eFme/NgfW2/vRuSe/iM5aS6aE7ZGSNf7reuLifkCwinIG+e8uqQZzSqywkjXWS5Xob/iL8lyrPeWrYtWwIPn1UpC8suFFRzJc/dCKusTmDvKl8wtKMPQydguznWez4ZjBvHTZm2DNGq5suQPR7tK7w+UQE6RmUfbQbqz/NSaB/tiwFdQ7VNaM9+WtCjeLdEvIu09ujwriP3Er1mZt6hDtcUvLNn/8CRd8q2HIKlfIu4vlheXe5xIRlw8HKG/tvBSyZsicrZoLWy7NA0VUDw+wvwFW2U1+g5V9Q6FKeid99Pz7IU1ymyvcZKLyal9PICGZVeXiDjxhBvGl2GPFbAb4M8bCtOSpE2PlVdIKuN2rKTob2LEacit1ubFzKY1EJmgfKwTflWbmzTFuVdEvVWbVp1AXTSdlPCqWy72bsZuj+fA7c+PJV2y3m12/bOaudGJlOQzWGD0xTkY/mJnDdsOLL2uHJCOo+399rH63vHe5t7h4cbB3vLBP867vx7PgL+ve31nYO1jYPt/c31vfWtgfizfvIVclhhZRnzcBxOUB47bmKecqCK7JAZjNKmHD0mqDz+zhJHaWvljFKKQ1WHOWU4C8FSjm9c32lRHuFxfS5DfT6TC6wym83DVpqNtsrOZ4H2Uw6f+CzUfuzoOgNwlOLHC7lpoJf7DsLjwAJ/Pf+RfX1V/luNWx7TSC+P6ZDT7Xkdt29wHLdqXHEfwgU8zrPc7vOAV/U5Lt+ZY5PwYrJO5dtQ/8/m4zyy48hq5EVi0OCEdTL8Hs73HKEytvN9yGCTpZABjPvmaYxpBiuy/A8eqEBes8E5huN3lsxxmA3bHnjUuFUHfNB5+VqBsQLnDdTlZiEfy/9PMG/cFZg3Qg5oZ9EGijcxnO+xbrEPLZRx8lcbqDxPs1zFPDSEGYzIzredOdzWxDhHIc4m8AjL4+8s1enZtwfmcGU4w2ehsdck/sTZ4Klud2C7T9kNVOwXnJT9ADeLPtfqwuVyTCv2sWXKwzZdobwi2fZQwWGQItnWhHws/2GQbY+SbMP3WbYpmaNkIh/uQpnAgaMmBD7LmxT4svdNp+d6PQP9/8tL3XexDOqdar0RWQZ05m+1eYN9AvHjfyxf1jmOYZV1jos73ru8WBjACz6Q0BLlsY4h5zt7N67uXd1RhO3bZR1FQjZzloHKZl5WBno6h+G4Kzq4je+i7Cujg2Hd2FHrZjyU9P2Bed/jUFIIllprKUcN5HWWpiDPkdcbGQ9+f6lLRx/v0t481Kem015eYN61tJcXyilmUfBCwbprRGFZHdUa4wK9F5pfBm3qhsY8zvPXItjreB2A/Z1teef1APHfCMiDqgeIVZCGUXOs5vnR4xBc1b1hnB9fv9yFy+W4HupgQBk787AdS9jOXNax5Bci25kHraF+uWBPo2gNxfZiK/8rsIbaywspWcRyCtuU5VTInjjs9jbaqrb3FwLtXdXeOifoqQn6VDuyU1tsG4/i54Kgh/n1u8SjFvBHzRG8L61sS60A3kXCi/sfXJck0bzltrB6Tifl7BJW/v8Xe63cvsgHpIv3gRTNjYo0N0rQ/KUAzQsBmpWcsLbAPrwQKM/6CcPng7ZYh9CeMffJfxmwq6j9PBzD70xvfMd1rg7bEuYCPFH6suK5shszLIVb+SQYL5Yi86I1gBdLRP+SKI+yg/WTxQCs1gBesL1tJTIvlgfwYoXoVzdIok2mRbxYCsAaxIuraS8vLkbmxcoAXlwk+i+K8isBXiwLWLH3wJqEp+WIB8cA+zOuOOJBnvKBqyVHPNhXeV/ioiMehHUt7ZbJ8tAW2SSc+GxW0Irv81x1e66LZ1PWykwv3Av0DsqvKcqzspcB3n8zU1w/8+cuW07ZI42Xaq7htQLPowhDyTqc59g3zng5nWj7Ox/ws/KvzeuJuo/a82HbEe75NClvUuBFniq+XST6LO8N0Hb/7UxvnZUvW1kb1SzQZevJuIHG2htqfFpS9n1eD6P/NPtdo08870XeDnksk+6APPbXfhXk4SEpTsqWYjzM+PzOEraU0MVI6qAWjyVuM8wL7TPg/MNzIK4dVihPHaxluVeko7OcsvLfCGPxy2D/zhLa4Y0PWbn7gbeIJ0tT+Ttnpber9Zo6FFlL+uVZaG3VEu+jvZD5Mx/gTz3An0gBejv8UTZ1FXQgJN+wLy8Qf0I+mwo3rnVQVl4vk3bfD/U3pHEqf2eY/nPKd6uW9I+NkP+x2h8pCpyPsJDHIX4ajYqfCGMqfyeyX3Jpf0TmgdqHQH5y4HS1L6l0jSnKU2dylK5hcwDac2OcRVnfOd5bW93cOz5o7+6srW1WOYuibNu1gu8kKaejh4K1x/AjzJLtWYb27mL7XnM/RHup2neZSPr7YShw64dgPj7tOYoJQU/kcV35IpMa5am1i9q7m6Q8tSarCRqU3mi8qLoHh/2PfYcmRT1G5ayH0Vb1rMfHA30T+TopnoX6JrclznO4B/fJGY0TdV4OKIH1sPLfA+u7X6X1Yajdhi1TuN3KypTvjdxuNUGfag++gCW2n4Ti57ygh/n1g9TPOvqU4JHqc8o/oxnAe4Hwqr00pfdwYCQsr84tYvkG0WDlf1jYZrh9kQ9IF++lKZpnK9I8W4LmvxKgeT5As5XB3+hLp/oMl5+k+jB8nuuU7/WkgM998ifEOp1hTiR6DPNe2jDOjU0EeDIoIA37KE8GYCncIR/l2P7azQG8KOOvrc6cKJvQQkVe8F7aMPZYkRdV91iZF6E91kG8uJreXLxoCFhK30MeKHmSiHdQ78LynwO96pdoD4XfwX7Ltkkr+3mA94sCNsvgJOmf67LENnk1b6J+qda4tv+izrc1AQbrkzhfGDzLUzaGKHEsjjeOttd299YPj48P1nZ3x3Es4p2H/52Aru0RxyKy7nzAa3RMao0+TXke/rVV1+9oe6+yfld+ADdzjI5/5WhbCp3vLVq//xuS2+qMNcpm9jm08v8O5oIXc+Sn9fW/Gf35vxJobw9/fqUvlFn3xz4vddL198xsb70WgT9qjuB1vzqntRjAu0B41bpfrY3UOr6W9Osiqjz7slj5eaDhMwW2B+QD0sXrfkXzXEWa50rQ3ArQ3AzQrOSEtQX24WagPOsnDL/o7CzKuoaA3+dHBHXkdf+MoBnHMK/7h7HWnQ3wZNBal8+Kh/ircKu90bM6m7w4gBdlziaj7GD9ZCEAa3EAL3jdvxyZF0sDeLFM9Cs/NPR3WSRetAKwBvHiatrLi2H4EyMvqvoTMy+UP7Hym6gVfBsefhbSBzgO1KIjHhXbgM+6e+BRPtqxfYKXoAy3PfvtcvlEvMPzpJX/U3ljZfPrN8z2wp2nd1CusF3Gyn4TwPvTAjbPT1mqapfB87y2bpgjunzHaHtd+WZbYj9KHAPKj5L9vdGPkn2n0Y8S+wcntfbFixI/MmSfR+X/oNZeLLNwrbNMeepSTdYfinQq7r9W/t1Cp6rqg4Z61lT+zjBtx8reG5oDlZ1GyVz0B2b+NAL8Cfk8RtpHL+3zWCYmrLLvqvMj7D+pcON4jOHzOAwfvTL2ACwf8nkMna8c+zyenc8j6i+8V1F2vuC9bJ4vuE35TAmekZgUZdiGYuWfA5meLpev80n8PM3mGGOvZnt1bW/t8PBwY3/9cH178F6N+15R+2h99+C43V77k+2i/e2Ns8a/f3iwtXG0ub61t9He2jvcPGv87bXDg7399sbGwcbh2v76wUn2ylg/zVLccz6rG8M8T6DOOCk9oFbwbbA4z3A1Ei1nrvjUrV1Gb8hSaA+E90cVrJmKsOZEXow2nQ7UG/E3A7Qqf+1rqQ9PsnTXiMKyOvJck6V7KA/7OF8IpubXqvMX6wG4XrR9uChz1vb6/urG5v7awebxcXv/8CRnE2KdGbD2uZnPDPwM6D4xzgwo3vGe3rD9NZh3Zf01fs6Rd2XO1SH9Sr9kvfZzYF/7jdlefNjP+Xz4zdjPPx+5nzeTYrmZofu1WY2zrC+vlf8CtOnfE+2rfMfQP4zh/VZgjxB5zTRmifcIY5+fVGvZIj9DHruhca9skAwr5I+eJd4jjOSnEIzlrGIc1Og/li8by7lZkRe8R3je/KRDl6IvVOTF1fTm4oXak1d+UOhfwDB5L5FlTSLeKbK7TghaWb5i+X8NMnN+rrhurKOc13nv30XWUSLLuyP2DcUUOr+pfEPrlIf73eyLiusQlIWc1L4axhGqcvm92ufiPSOsE8/l03mHyeoylf8OrWtwDCSJHjdqjmC884B3riLeUF9E3vJ+76SgGfUs73Xq5sFOe629d7B9sLV7fHi0fda2xYPNvfXNnePt1d2do/3D/Uq2RRujfP7ypLRQ2oi7t96d27A/qLmNxz7yplbwnSTafmjPGgTLuW7tUN1U2yp7EPKgCNZkRVhzIi9Gm04kxfVG/M0ArcpmZbLmtDzJ0l0jCsvqaLxBXlbp7woPwiqSvR541JrX8Ew54kFYti5Xe5AGI/KeS0fvs/kR9T7EWYf6YHn8nST95xx28vlV6X1qXzy0V1ET9MwlUeXitrLtWlL60gTlqdgHSpfiOQNtBu9Ke/Nwzf3utAuDk9IJrVxGc5m7bG4F/r497c1D/n5r2oXBaczfbl6Iv9ZHFX+tb4/5e3L+fnfaLcdpEA9fqeBvWRP1UOeAJymv7J4h63yR9sqDc15d0Fp1znsoMOfh/D8pnoVs/Ly/OiXox/Y2+2edyj8O9H117igceZ9+R/kAWlJjgXUwHAs8huZFnZWsMTlfdpwYL6raT7C/cyxabEP2kTNYyvcmS9fSG991Kv8CtOfX5u05Xnv38ihJeuVXQnnjtXc/rPHau5vGa+9ePGe19j7p+FV4MM/W3pHl1qbyIUyojrjO5rmN52PMY/8MzEP/bF7f4D7NO9IuDE5qTsSzOlX075uZv7w+R/5+S9qFwWnM325eiL+8fkT+Vl2fV+VvaJ1Sde1To7yagHlWax9rs6K1j9E2kfS3Mfra1unZzwfWPiyv+Vlo7VMj/ozKOoX1zTLrFO4XnAatRd640oXL5ZhW7GO8FsE25ftNI/ng76rxzTTzugjziu7/46R4iGep3nKxC5fLMT3qXFNku3zlfsj+7SgjP552y3FSfMI7SB6swCfV19SZW5Z1kcZyR9Ypv1Xl/z+R9Pd95D/7G/yTgKxT5xHwWUjW8dmJSD4/e6qvWFLjjWUd+mSwrGM/HPvNSfU/q+/1c3CXunC5HNOq7uVUuobRPWpx5Nn3R/mqleUhnmuuIutUzKLIc0LlfsjzGNr4PpF2y3EaNCdUkXWqr41af+Lxiv2Jx6u6l+Akfe1zrS5cLse0qjGp+qHBimyPqtwP2ede3RFelof2LIP5w5e7cLkc06P8ziOvCSvrJtzXkL/flXbLcRq0fvvHFfgUsskrm1lkHm7UBK3KBm34G0l/e8ewQav9KeQPt+WsoJXjK2UJ9144b1I8mwjAepcjLLMtnKd1D/YBToPmuOXbu3C5HNOj9qp4L3G10YW9dnvv+zUBP/Y+VNlxZfjPam8ntMeRJP3jStmblA8Z2m+Lxm9ofxnzvsURltnlRm0uYh276B4NToPmoirjCvu+0VanvGswrjaHP662b7VxxfPVacaC5xh9lyMsz/H+bkdYoyo7Qv5Zp5EdO86y4wGQHfs57LE/SLHsLCvbxv4g/Xljf5DRgmV1HPuDDMaDeWN/kBtp7A/SSxfmjf1Bxv4gtwJ/P5R2y3EaxMOjErrsrcDDD6fdcpwG8fCLJXhYxi9JxTStehcan7OLZB/t7NUbD3GvHnFyPA9cnyH/Od7G34I1Ee/VV42dp+IKnqe9P44/gXt/VX1qcO+vil8S9rEqe3+jcmcc+9sgD6vu/eHdb1X26rGvMZ8i+YxU7ocNysN6fSTtluM0yC/kwRP6b/Ed98inUYsHiLGFuO+H4gH+fwFZhzJyUjwLybo54k+kO5gPVV+xpMYbyzp1X5nlYRyeqrLO6lvVLwn7GN//gm1qdEf2Aa4s63i/XN3ZW5aHeD9AFVmHeg7Lukg+mG5zbpY+mnbLcfL0wVT35CldkfWW2PEllF6HOPmsbVm97o8Csq7qHsSMoGdOvHfFhzfHPFf7wV5rq75bozpFkt+l9+ANfyOJqtt17OkqViXyJ+TDyndQ4Fj8M2m3HOdNimcTBbB4jJ4W1qjO16E7lavOJzgnV9mDV3OyWmNknyv5/9VTpY1tNe/ymMT282ujtZ2yY9LwN5KofaYd6t/In5Cfqr3bSsJ6CeYhngWBpyXynkn9YD3vCOteR1iPO8J62hHWBx1hebbjsyMK62FHWJ68vxX6vWcdPdvRs3896AjLk1+e7egpJzxloWe/96zjI46wPOv4hCMsz/H4kiOs5xxhPeAIy7MdX3aENe4T1WC9ksNiu8vLuYIdd19m7UCtLWtJL+6FSLhrhM/4h88Qf2iN0hB5p4nDvdZeW91cPdrfOzpaX9/a2qva1lZe3aur1l/G61YcXm8bn/DuU4w9n6UpyFugvDrkGY2ZTeA2oj/OvcZr22X4j/jVWLsn7Zbzaku1jx23LVfXz3dbhu+oVm25nPSOdZSHHOMgcaNzbT2e/XhjbD/upqj2Y5sH7DfPdVVtvjOOsNh+HClOR+X9yJD/SlX7McbVOenZyI7corxPzXdh2xmuyL5vlfcsQ32Wfd+QzzXKw7nnY2m3HKdBvm8Hd3ThcjlLMWJyRe7jIxu7Aft/ldgNSg6rPWjeq58kWq/k/1dPlyrzl/uvki9leWjPThO7gWOtjAqfiuRDlr4z7ZbjNIhPVWI3qL7G59eydCX/Xj1V2tjiNnCEvRbPn2Bnjccf8jOyfCt9PnhU70NX+8L2bovysvSetFuO88r41WHee0cQFsun08KadITFvjk3+5xy0vPBRhufD/5d0EvH54N7+VcT/C0r2+zZ+HxwlHqPzwc7wLI6srww+Oo7SfS6gvEgrHsIz6QjHrV+O+34VXgw783pje94+tvaXmS/7y3jEcYhTYh/qB+ybsRnvzAP52duEzyfaXoTz608F3BScyT6kleJ5Y38ZbsB+0FjHtvWMA95c0bxW6Pf2dzKFQiPO5unBT0MS8XnzZLJEo6/ezmnL/Z5Hb7jFnGpvsHtH/vMpGr/0JnJsu1/Z6D9q56ZnBb0jNqZvjLnIpW9k+e6FuThOpGTkml4FrDKnVPY/5qUh+1tdeI7y74uZ1JWz/ULusx/R+Mt1h0FaryxTaNO9VB35dWp7BrRH+vMi6LfcCF/b4Vz8FX7P+4F/GaJ/q/2AnjeVvdaqPme7QrnNS791YDcrhqXfkrQU6M8dYdIlkxn5n2xe0ZgHJ6XGMlq7PHZBszDue48j71hx5ngsVf2POK3ndHYC93fkyUee1Z+j8ZerLWJGnuGS/UNbv9hn0fl+87Ltv+Dgfavet/5lKAnstyqHKea5Za646iKrTxLb01789APy2KX1ZL+NEimnfS+ryoyLbRPcV7jSXws0Ker7uNMCXqq6hM8nhAm2uWupb31sfIvj4AMvBX8gGwcn2SsnoX+ERqrkWwSnbFqvMe+ru64mUj62wp1vDo9+74zGqtq/gzpH1b+UzT2IvmQyrHH8TBC+kdsm61qfxWLqGr7//D51j8q2+zK+MKxbFJyC212rH8sQV5s/SPe/lJ3z31U4grFaDuLX8ttx32Wk8f+0q0wNji2LY4NjIPCaRR0c+VfGzfuRlfeG+9R3qvzARNJf1vh+qdOz345IO/VeQV1tkvJe16rDdLNr6a99Fn5vzsC833kcXmk1qmW1PjiccnnsDAP5RePy2XAw3JvBfJix5yOOS7jnO3sjktrF+zXiJPHJbYjrnXq9Oz3RnxcfunmH5fHak1qSY09HpdLRBfmYb14XOLYG/U1cGjsRTpf2Rl7xnvsu4iTxx62FeokdXr2789o7Kk1HI69t6W99Fn5r9DYi3QOV449w1XmrEAcmbC2VSN8SdLvj4f4z+qsgOobobMCLUFrK+nvnxy/oiXwtAQeBet9jrCecIT1uCOspx1hPeQIy5P3D4woXS84wvJsx+ccYXn21UcdYXny62FHWJ5jaFTlxPOOsDx579m/POl61hGWp/y6zxGWJ12pIyzPMeQ5tj3H0GOOsEZ13v4OR1j3O8J6hWCV1YsvwPMnPvTBxz6cUJqi/3envf/vKUD8enrvWtoPr0a/JwEOPq8RPMsvsykYy0jCjYYLSsTJm4ItyMMG5AXxu3MLwvncFGyvqo0IS8pYwwu1ZaIL81aSXn4kSW+fQd5gwvpmfP2e2S5cLmfptJd2tQStlof153bh+l/J/6+eMhkdxkPst4jTaJtI+nmOxrI6PXsw0G9x42lSPON+Wxf0MCykH/saOzNY+Udz+uZEvWIbcgyX6hvc/kuR6Aq1P+I02qq2/1OB9q86wdUFPcafi1H40+4cwLiU9CfLuwy4+TD6bZDHh5NuB1jWPy3vDsiruvljvKh6AAn732XKw/a2OvEc9W9zJFkzv0zjCuVl9rmS/189VVrfVg4RfvC7l7BMC97FPWTSLh1EjwOyRHK8DgZkUU7w6qARB2TBwxWHabcc502KZxMBWM84wrrXEdaLjrAedIT1pCOsFxxhPewIy7MdH3GE5dlXn3WE5cmvJxxhefaJlxxhefFLzZengRVa4znOxbvDC17WPjrpXDlqwcvKzpW8IXmafvs+R1ie4/xxR1hPO8J6yBGWJ+8fGFG6nnOE9bwjLM+5clT76qOOsDz7xH0jSpenHuypp4xqn/Acj485whpVufrtTrCUHncaunjDaErAUmv8qnrOiTeYmLCVAgJeQ+9fS/vh1ujdhP6vDMh/jYCVMaeZux2fdpNgWuBXUapYsZ8lmFfy/6unTEbHoIgLHKWq7In7PwoYiatGqaoLeiKf3naNDH/SCFZVjcTGi4znJ41SxVHjsb0NFkegupBXKLLnuNxwMVxqrI4jCnbzQhHlLud8U2O16gSkIgreDBHluF9wUuMRo8ZVucUB+1iZqHGLST9/uW/GdpIwOoucJIy2iaS/PZAndXr2NYG+ibJ5UjwL9U2+CSrO7eJrh+okox/8dXnKrUZ1i3NipN2uET5rB3yG+NVtezEMdOrGNXVDm9ok5ZvgcPPa88SAp5e/pwfpS46wXnGEda8TLDXWTwOr6QTLs45Z8uyrLzrCetAR1pOOsDxPWHiORxtDaj3Ccj7Oafnyct7wn9XNf03B19DNf4uC1hblZYnHjppPFgUeBet9jrCecIT1uCOspx1hPeQIy5P3D4woXY84wvLsEw87wnrSEdao9i+T87aWQ1nOa5VhnwBvQn1YH8K5i50Jnw+s5ZQsbwZ4Ny/oMTmPdmFzwszQpQu9OOeAzkl6t5b0RzO38t/f6sJ8hWxON6N96M+ekX0oko2icsQJtg+h0zTbh9AmWdU+ZPXN+PDGlS5cLse0Yh9rUR62qdGtokrYevisIi2piC6hSEtlI7r8YKBvVo0qoSLMKN6Nmjw+aUSOH3Xk3bygZ5A8/vGK8niW6mHlj0Ae/wTtRUTaHxpZmzPuAaWXunC5HNMasjnjHMJyI46T/sbW2Em/S1CN8Bk9zB9e7zYErS2Rx23YEHgaAk9L5LHj82lgveAI62lHWA85wrrXEdaLjrAedIT1pCMszz7xsCMsz3Z82RHWuE8Mr0/wmh7lLOuQsW81UmtD5XdUdW34pYAOWfUQ+7ygZ5AO+eUFjbOsDmnl7wYd8vfHPl/XU1Wfrz8M9AUPn6+meK9W8G14+BnjwXZgH64pRzzKrhQ6XHNSPMg3tj9F0k8r307D7alum1U+dsw3XNfsp91ynNQ6yXiR9dWDO7pwuZylW4GHB2m3HKdBPDy6vQuXy1m6FXh4lHbLcRrEwy9W4OHNfNPUe9NuOU6Khxjw5ZXlLlwuZ2mOaHfm4UbkNlqvJb38Nb5wnbgP1wQ/yswziKtBsJzr1g7VLTTvIc3IgyJYkxVhzYm8GG06kRTXW40pRSvXI0vXUh+eZOkuR1hvcYRldTTeIC+r9HeFB2HxzdKTjniwDMvIk45fhQfzbD0V+Saajchr3c4YUjd6htYTJ9XzEVcjiSrz26G6If2h/Va281a9+VTBiqzTddq0Hqg34m8GaOV6ZInl4kl5kqW7HGG9xREWy8UY680ssVycdsSjbiWOuU7PEstFthVcyf+vni6tRfYP2WO9HZPS21mnZ78MzGP/Z8xDn3u7ccNjj9P4lOn7v1ph7c43S17J/6+eLm2M2llEbj/27cC8sreG8k1G6GOAfOU0aI+6SvuNCn+L1v9qHkL+stwaNf6Oivxh/iLvmb/Ie+bvqMgfZfPnfQS2RWCeWhMs0n/km9qbmKa8YcynpocYDdjfeJ19Xm/l/oG8Y6k9kKo3/U4IehTvWBc/r7ck/4gj7+qCHoaF9GNfnib6rPyP5fRl+X9zsRcf9vO70xvfyieY+/l59Qn+qUBbVfUJnhD0KNsK7vv+b4saJ+77YpvOUD2s/KehTf930b5Kzk4RLMv7W/k7ke3LMg6C4ZoXdfw/oa2+vKT5NpFoe9s70946RZIt69xfk0TbtPnspjoDjn3Y6qfkJsNSuLHf3kO8iOQzvc5ytogXvPZSZyhQ9jaIF/MBWIN48ba0lxeRznSvh+qGvOB1zIIo3wzw4kIA1iBeXE1vLl7MCliLSf/YMjk4IWAif5SsScQ7RfP0hKCVZT6W/y2Q429u9dIf0pti65xW17I6Z1PwCetrz347st6k9oRrBd+Gh58xHoR1Le3F03TEg7DMpmp9AccJ94Vhn+lYgPpgefydJP1nOv55oC8oObAQ4F1d0BP3dtR2m89cYVJnrmqUh3He2I6AF0qwHeES5OG8yEnZA4wXGc+/7s4uXC7H9cD+Z3VaTPr5e9YXhxgtZS8OWRb1SJJu37Rn/ynQN6vGSFAXh/BZlizhmuGPac3QADp5Xse+xWufCfAV/fqWhjlRAJPPPMYZR+Fzdy1BD5+ra1C9rA0nk7CebeXV5T3LAbxLhDfL/yzRoM5XI94G0WD1nC4ov0g0WPkFoOEzBe2LfEC6TC8O0bxQkeaFEjSvBGhuBWjGfs5th324FSjPehPD54udsA6qL/G4s/J3QB15HbsoaEa9ndexkWToekielbnsCssjz61+Sg9nWAo39kNex0a6IGudZUcRLzg264ooj7KjRbxYCsAaxAtex8a5dKrLi5UBvLhI9F8U5VcCvFgOwBrEi6vpzcWLRQFL6dPIAyVPEvEO6g5Yvg26wWarFy6/g/12nvKs7A7A2xCwWQZnKWS7Yh0J30UdSa3TzK7Nayvsd6gTKXtCaB+jSTyw974J6v2NrV4a1P4SrvtrBGsq/2TPLgi6GPebAffVU+LOkrKxFvkT4rto8+azS28V82Jkm0ZnDKtzPdimPK+rfqrO4Kg9TLZT4Xt81iW2LUftgamzBVX3wN4DbXnaM1TTgh7FOz6LOOxYscy7snaw+x15Ny/oCcFSvnhcXyyv4tQNDKQ/Tf9NIDNhK1TOBIyVrRcQ2CqAX+TIV6PnPKmzoCt6pxWAH3L8zBILT3XQDg+KsrH4wYDwHMYGDwqRqhs8LDzV5Ko2PFh4Rqp39GBATwYEQNWANtOCnlEzfrKBE5VhNnCiMs0O8Gj8rOpshsbPKpcOYP9bpjxs79CNymyUPq+G0Y8H+m1Vw+iCoEfxjif9Yd9Gzrwrexv1n3Pk3bygJwRLbZ6oeTA06Vv5iyXLD1QSZuk/KwlWEZ7szRJiZXm3jy2GKwSHK1KkTPDOML6bJJpWe8YCqMy7ZWFjnto1Lat02LueEc5+AFahn27pMn8xYB3FnWVVH7aCRdr9DHowoOLDfU1Z0ZXngFJuGJbCje3N1tFIOzTrobohL8oIIhWdVSmBPCEgD3kyHfYOuNFWdQf8JwITgmp7fBaaTJsB3vFkGntXT+2utQK8QyVlSfDOnv0NR96FvMsUrNBkqnZHQpPpcsnyp55Mi1bOPJniSoMHJVawVVCR8ziZ4mTlPbFaKOcG4bR3ruT/Vyumjc297YO97XZ7d6N9tNHeZFfbLKGrrTf+rY2t9s7O3s7B1sHx7sbB/iD8Wft/Je/BkY/irkYOTxU0Kauw0ac9zoq4GgTLuW6V7w5vUXnmQRGsmYqwRvVIfoyjw1kyd6Bm0s+v0+JRId3UNgYfARl2uDcO/Vc23NtvB5SFqqH/JgQ96jgaunP9w5bGWbS4qlE9rPzPLnVh/iPausS+wEfokGbeXlEhAyOH11jlsYbtjTjZbXta1BH5ZM++HGhvdbdwKCzFhKAndPww7ty2uV52vhjVcB8sz/Bdlk9Z2ku75U4zl2TpKUdYzzjCesER1tOOsJ53hHWvIyxP3j/iCMuzji86wnrQEdaTjrAed4T1kCMsz3Z82BGWJ+896fKUq550jaosfM4Rlmdf9aTrMUdYozrXeo7HUZVfnu3oOQ95zo+eMseT9x9whOVZx1GV0Z68f9kRlqdcHVV9wlOPPnaENao6k2e/f8URlucY8tSZPNcKo6qvesqJ+xxhjeqc5qnLjaqt41FHWJ569Kjyy3PevhXWos86wvKU0aMqV8e6yfB0k2/LYanQkLx3NQN5Mfau8JjepMA5DfXB8vg7S3yM7wfzPT61d1U1tPaEoIdhIf014hnSZ+V/hk6exNkT3twKhfKJe3Ruc6tG+IzfCfHI8DcFPUZ3Q+Sdxu9mZ+Noa+vw6HjzePWovbvbFz7RaOVn2BeyjwpHp/br4h6121wzv7TJtAt/HviapSnIa1BeHfLwmvLbiP444QA318rwH/G3RHkOxXHSqxxx3HrAqp0Q1nLSOwZQTjSTYlmDISHND+DTIA/ZWRz9QVQf5lAasY8YK59BHHdljhirsG/qeDrDUrjVlQeRj86uh+QL8oKv2FKn3lSoXhVW5EJFXvAhgvMWHpN5EXJgHsSLq+nNxYu6gKX8xdgXk+VJkhTLMJZRvwF+Wf90qZge9stSV5kuBvCOyrH3k4Z//PsB3bZq+EcVqqNsn0dfpOtl0u77yOssTUGeI6/XMjy/v9Slo493aW8eHjqYTnt5EQrTVTWUIsJinQXXEHOUh2sfqweOBx5jSM9JTsmbT6W3X/vB5tb+wcbm3p9o19fbaZBfux0EmkmhXmnvO/OYR2UQnsGahbz5ErDmA7DmArAaJWGFcCOtUwT/Qv5/ugD+HJVv5v+noPyMoKdO8P8DyN+55d4yBvM/Qpn/DOPuetm0i4PbbgHzRHkcP1ze6prhnM3p6sxXgMd7ni2iG/uC4Vc8qBV8Iyx8ZrgaBMu5bm3PNgnBWigJy/iJ7Zv9vnQ2bb3GdbB1ENYT8Xdkdhx6On1vMQ3z1PB79D3DdVZ9T9Ut1PewPPc9BWuxJCzjp+pvzTg8WGe5nhTwAPHjf5TrOP/ZuyyzX7fcC0fxC+cwm4s6Zx0hr0F5S5B3gehdTvvpRViLRO8y0Wtzr+rzLYF/gfAjLoWf5+UlUX5JlM/6yp3E02nxLrav8XR8hk+vG9W3weI8wzU+wxel3rf8Gb7I57Z2VNhyS7xfg7xQ+yPcBmjDwjUfp0n6j/XN6HpPibD26uwb22ZUf4ncfyvzl8+jNQRPyvLQ6pThfqjC1QDKJqDGHM8TvPZn2aRg8Bk2tZdg5Xm+UuEkDV6L4GSJQ8hOCBwtoIXr2RI0s42PoxtintrbVnWYTHQdFJ0TATonBZ1chyxdS298o+0DdT9eP5t+gbqU0pfrVH4P1lMH+W/eD0R8WblnSA+eBVpirLt6bD5ED+JXNiL7HZnW9RCtM4JWxeOqc9VM2vtsTuBGPs0Q7h67FOXhunaK8Jjein0NYRkdbD/6MOntaNOy91sCP7YZ41L4y9jTlP0t69uP5jR2rkkE3J66I46jIhl6HuWWyfMYsVDWdna2dtf2Vze2Dw+ODzfWq8RCUXyrBfjGPFV7SOf9ys4/lzPQ48rOGUEP88vwY395b9pbdjZQ9iNUthEo+1EqOx8o+3EqeyFQ9hN5WbVOtXVIxsrvh/U/thfGolB6G8fU+BTMzz8A8kLtX3J/xv1C9ktaGEAPh7+38n8Z+kyZoILIH/YTaQG+GOvTQUECW8STlii/KHiirotjWAp3KKhgpGirHV4sDeAFX6GmAqZh0Di+fqYVgLU0gBejcOUK8qLqNSNLxIvQlSuDeHE17eXFpci8uDiAF5eI/kui/MUAL1YELLUPXyv4Njz8jOcfFbhSRc8+LR4V3ZkjanvgQZ5yNOxLjniwfVegTPb/ssiz/1w+Ee/gHIPlPwdz2utWesuYrvC7UOYXlntxG5zppFj2sL/3bwO8v7PcW8b0iL8PZT5PZUx/+H+gzK9QGdMbfgvK/FoB7Uq3+O60N8/K/gast5nHnvqmukb8cqLptz6ieJ9QHtcnS+9Pb3yz3w2+l/HvHy8Xl1sZoXKcx0Fos3RXeuNb8Y5v6FCwlgOwlgtghcZqC36zjBlUX9YLbRxnZb5E/TXO9dftztkF7KMJ1eM2wN2kvNshb4ny7oC8i5T3KshD+clJ2X+NF5lc+gjZ2xIBS+maNcrDduR1K+uVCIOv+eM+ZjqqlTc+o58RlmHZa+X/ENYMaf67lfTWBevYEvTyrQ8hfqg6K30kFMCZ9Ric9y9SHs7VRfpS0TqL58jO+zmhuM5SfpTW9lm5+0lmoZ4wlb9jYxLHRQy9EsfkpODNbcSn20R5nOfYDxLftzZV/LkU4M9cgD+xr8BsifrOiTrxWMXyOO4XiT+qn9foP8JS14Eqfnbs6Ek/PxHGVP7OMPmpbo0py0+M483v8/Wqyg8aeRziZ8cWnvTzE2FM5e9EvtkmyM9mgAeDrqvl8avkaZk5QZ1hUPMFXj9pukmovdQZHrbx4bto4/O2cx/t7x/u7R9sbe0e7B+sHRyfdczxw52NjdW9w82DjbWdo82jrXHM8f46jf2VimGN/ZVGy1/pZo45fg305GHEHL97ReOsGnP81Re7MN+WwxzHHO+Pi/DuQHuf85jjq2Xni3HM8f68cczxarDGMceHV8dxzPFqsMYxx4dH1zjmeDVY45jjN8dcO445Prx5aBxzfHh1HMccH55cHcccH14dxzHHh6czjWOOV4M1jjk+vL46jjl+c8zbTzjC8pQTLznCGsfjvjnm7VshHvcfB/Z1RiEe9x35/lzkeNzryjfN6Iscj7v0nbfDiMe9eXC0uXW0e7y/vrZ1uLp5WCP4Ris/w76QfUYjHvfG8fmOx71xXIb/iL8lyo/jcd9IKh73a3J5M47HPY7Hjbw4TTzuq+nNxYu6gKX8hzxjUK+Cn87Vi8X0eMWgjiO/u/qc8Q31IcXziaS/zbC9+AzcDsivWzwGdVvFoO7hXdqbh37f02kvL9AHu0F8Khs32nBjH+ZxcZ1XSf/cUjZu9N2gz3MMDpyrWd+K5Mu0yvWbEvVD2iaS/jGNvGG58I5Af5+E9ybFs9D6hfWaSUF/DWBdTXvps/LvpvVLLP8/dYa0XlAH5MeEqIPyf2R+1AfAeifBmgzQNT0A1jcTLCXvm+I9oz+yb/xGjfBZPfAZ4m8kur9d8aGnXZavE8Q7ZU9QcqxGv09qY1H96jSw2HcY6bT3iuITKV/h8+6z+1hANlb12a0JekI+u2XGhMKj2iEUQ+q829+eDbRRVfubkjWRZd9a5Fhc68x/xQvEz2M/Sar1ScTVSPQ8eMWnbu1Q3UJjBPsWj4mq8cMUrLM66zUdqDfibwZo5Xpk6c2pD0+yZLay2GeTWL/nWG7quywe5JuVMzyoExrfMrq+j9Yn9h6er8F335R287H8X4B1+4v5ofAy52tw/pulPDXnj0pMQKNtgsrjb+STPfvLgXmgqk6kztbVKK9oHWN9gNdZL+Rtl5H2o2R/Gbdj99lPnlE7sr5TNGco+wvyOktTaS+sK/nz1dOl63eAJa/p0tHHu7Q3T9lfytwBqOIqNAUvDNYFAUv15QbwidsjRp9U99LxWtnqw3wI3Uv3mUCf5LmkqB+F9muUHWyG8kJ7CYoGtGFzPByDPZ1o2ynOVVj+F4APn2lpmDi/XRAwQzRPCpqZho+BHP3lnJ7Y55GZN3OBumbpatpLB8d7RRsCx5FtBspyHNmFQFmOI7sYKPtxKtsKlP1EXlbFmDA+ZW3zBdJ/lgCmGpscu9bKfw209xcD+xa27lX7TUWyH2lBuXo17aXFyv92YG9TyVBlz7fy6t5FZSNXcz23u/XH3ztD+i4QfXOCviHOnRsZD15Pc2ePjE/L11fxR8XJVfdstqg88krxpzlE/tQD/Cm7XxXiJ/aRC8Qf5B3PfWqvpszeJY4Z1p2Vrmp6CvbzGHqK1RX1FMTJe5fIG+QrxzL8j457lzOCnhAslJ+sbxh906I8z6c9dbzUrQ/rG6p/ld0/xfbO0hTkeevvvH+KcsPGmOW1II/3Ty1PtUdL8KIleKFg8ThEHZRjWKEOukg0cPyQLKEdiXUgK78A8/tXk32jLvCdlb1axX1R623ltxGK+3IR+vRp474sCnpq4n+ZMYrxwSZFmUZBfV4F9UkL7g7A+qi7pUPx5YYxRkNrbNTzeYzieofXDMpH6ILghYJVp7yQv4RaJ+LYYrul0Wflvo90bNwX570ljPMb8tviOMpvhD7DeqqK646xXVnPGBTXnfUFFded+3Q7QN+livQNiifKejTSfpnos/Z8J8jMLSAIaZkW9b1I8GzcvR3g7V7qLWPj9a1Q5uupjI3Vu6HMn6YyNue8Bcp8YwHtat353WlvnpV9Uw4j8r0D0j+E49lbGZsTFO+TpH/scB/iGONL4r2Mf996qbjc8giV4zyWB1niuOAr4r0QrIsBWBcLYNWS/nZLkuK2K1tftmHcC33+o6TjICy2YWCfYxuGkr1Yt6tpLy1W/v6Kshfjo1aVvctUHyV7Q7BCuAfJVZb7Sq4qvcNwDmP9jTyop+Xrq/gzKC61tU2LynN/Z/5cGiJ/lgL8qdp3FT+V3tBK+nm3QnmoF5WJLxyKVY/yncsjPB7Xz4i1slrLLwF8k0UhWtWZBGX7RbhfILv8IF1z0N628Xccs7e/TWoF3waL8wzXOGZvlHqPY/Ym/XVlX/nzGrP3U462mwlBz6CYvT90SeNEHbBMzN6nLndh/kgFnyKkmc8C3Ywxe/9aoL3PeczetbLzxThmb3/eOGZvNVjjmL3Dq+M4Zm81WOOYvcOjaxyztxqscczem2OuHcfsHd48NI7ZO7w6jmP2Dk+ujmP2Dq+O45i9w9OZxjF7q8Eax+wdXl8dx+y9OebtW8EG4zmGRlUWjvWJ4ekTt0Is4ffm+3KjGkv4yZy+yLGEN4cYS3izRviM3wnxaBixhHf2No422quHx3vH+xubh5s1gm+08jPsC9lnNGIJb7bPdyzhzXYZ/iP+lig/jiV8I6lYwk+DPBzHEi6GNehs5tuIF+NYwuNYwjyGkqR43PK4/CT4D/2ly8X03OqxhP/ngD43jiU8OrGErQ/zuLjOq6R/bikbS/iHwH8sFC/zZvGX+6uB/u7hL8ewysYStvI/ReuXWH7j6qwYx9lFeu135FjS2zXClyR6bWP4zyp+o+obSOME8W5a0MrjP0t2Dqwm8sqsocewTg/rrGLcDorH+DmS8fYe+k7ju1fTbj6W/zug+/wi6T5qDM0PyOc5Z6KAHtbhh3EmBWUyx8RUMUJxvuX1zHQAlsKtziGd17Ud675qbad0NZwnsjSVRqn3tjp713MuIi1f3yQpZ39CHvLZO9TFcR5l/swNkT+1AH+UPqnkW0juqrNDSuedpTwlK2uCBiVzWM+dFLBQtt4qceH/haOeq+aEcVz4Ls6T7qX8oeNeSk3QM44LX61PIq5xXPgo9R7HhRewRi0ufOu23nfsvaJ1yJvSbj6WX7kNYN5+4/c4nnh/rKRX53wa1bjwi3nbZaS9Nqd13I797fg1Z9SOrO8UzRnjuPBdXtyqceF3A32S55KifhTae1frO177jUJc+G8CPgwrLnwT5Oi1nJ5RjQvPsnKioLzpSPZOKDb8d+ZlB8VlfzvpHwuCZrWnzPEAPw36xzfTvKXW2ItJ/9gqkr1IC46/q2kvLVb+PdD/2E8E5xsFk/1vVFxvtZdq5Vui/KKoo9UfYzm3ksG4se/yeB0Ur4lpXRblMV7VItGK9HG8Pevr7w/w3ps+jok/J+gL4Q7Fv12pSKuKN4bx7ZaIVqQvFF/tjHQIacNEHtTT8vVV/BkUG5DnMYzdhzYH5s/KEPlTD/Bn0J4F80ftKaA8XCT+qL4+av1nJsCfUH0Vf5QsRB6E7lcI3T8xM0T+1CvyJ9R/Bu0xLRF/UDdsUd5Z368Qab8ueL+C8s+eSPrn5ND9Ct8T0Pdj36+AOkuZ+xXUfRJcn+8N6O3j+xXCOl5L8ELBOqv7FXhtYOV/GPT0nw3o6TfL/Qo/FhijVWP0Vb1fITRGB92v0Cyoz09Cfcb3K3RpHLX7FX6WbA5Fe+UtGoPYx3iP1uSIWptn6WrazcfyPxdYk7WAHwomr4fVmgxlLOslah2Fax/Dr+Kfc0x84/3PB+qzUpG+Qes2XmOqdVtCeKZFXXi9bGPw10Eef/62Xngc2xntO9+V9uZZ2V/LYcwJemP7tOI6BGUP8+Y3bqtWjvWULwDPGrTXhbDY1qTsMLWkn9eq31xNe2mx8v9vCXvHRAFMHluq7yr5XbbvlrE5hHCjzYTnr0sVaR0Un3+FaFXx+UOwQrgHxb5nGYR03VYCN+p+zKfbK9J6hyh/O5S5TLQiffaumt8N5zDWuciDelq+voo/rxLl74AyvM59FeRdCvDn9iHyZyHAH2W3DPWf0L0ZOM6Une22AH+G2X9C9zwMkltl+IMysxXgT8jOdkb3hFTuP2qtGuo/Sr/D9etl4o+aU5QdaYHwKHsCzo33FNRjOtH7KnwHrpWfzQf2oHsw0GZiOkWIVuV/rvbaEO7bK64JBvnyFN2DEdNfNEt3p700qb3z2GeqDJe1IdoNECfbDbDNe+zT9OxO6DentRvUBD2D/MJee7vGWfZ8ipV/PeyDv65kXwrt2zLfkyTsz2DPB50HsbqUOQ+iYE0EcCu7yGwAt7LFMi11QSevKaYFPcpPS8lL9jdVfU75QSjZgmdQrV+h/TsB/Ffy/6sV09Fae2d1a22zvXF41D7eWOf1IfJhPgL+tb397b21/b3dw9319s7mQPxjeekrL/+HIcvLNzvJy7eCvLzbUV6iLFD0sC2grK96WRkX2qvj/Y3QGYAssW7WqEjroPNeob3D+RKwQnPBoHgazKdQbBFv3FzvaYGby/I45PbAPLajqnlGzVkqTgfLk6I5js8PTAVgec1xb440x+1uHx/sHe4eHm+vH+8d7+1VmWNCZ6vUmGRf2pC+Msy9deu/ZffWsb/j+KnTs8cD84nH3nrkeD+HytfakrJ7czwIZavmfWvuF5wm6T/WN3vvc60uXC7HtGIfW6A8bNMz2jfeOKt4TYPWLReIFyc9t4S4Gkm/rHCsW7vsPB8638Sy+zTn4VhmDSOWgVpvNgO0qjNs11IfnmTprhGFZXWM6UufpbM6p8dyN9a5Q1ur2DvsR4/rxQMq2wyUPaKyC4GyH8rLKv9DXEv9EK2lGgCT1284N/AZrR+HtdSP0FqKYxphnjqPFbILhdaNofi3LcKPdM2L+vx10El4r1ed3VB+FJH35TsyTu0b4TzN/vFVfe1bAVhlfe2NFyuRebE8gBcrRP8gnxXeV28IWNZ3bJz/EoyFn7m9t4yN75+HMj9LZWxc/20o82nYKEW61Nj/cNqbZ2V/LocxTH8Rpt/WCrh39a70xrfSj/Gu8c/fXh7mO1J/mJ9M/WG+Pa0Gcy7pH6N+bdluq31ZS5aH7c3rFxxvrGuh3wfPzbgXu0h56M/B635Mak3U2SvMcE534XI5/M0yreq+IPoK/hDMKzHsBaxfD/vMt9FW9cz3PwzYBKrqtOqsAvOrjG42GyjLulkjULasbvYl0s3UGVNlf2Pb35dhHvnn+W+0ZbNNOEn0GfEiG2dZXdHK/+uAboU2JDXOOLY32lVi6BPqHDzypIyvgzqPoc7NMyyFG21frFvFmQO6vFgcwAs+0zrofCzHCah6PhZ5cd517jLnb8vy4mray4vzpnMzL5RPG9sjLV99Gx5+xvME0sy2vkVHPEXnFZmXp8WjfO/Ved/T4lF+yDbfXxR59p/LJ+Idtp1Y+ZXcETSb0x64o7eMzelfDWUuURmby18HZW6jMjaHvxbK3HFHbx1Osg57TQ5jTtQ39jqMzzFUWYddEO9lPPnaO8rDfEcEmJ+MAPPtFWFGXodt8JoJk1ozzVMerpl4PYV+5XyuA/3RWfaiLzbLMfTFRt5xGrRGe+dyFy6Xs6R0qRrloW7B6yduM8zDuWee8tRemDp/skx5OC6L5sayMVms/DfkjYE6dci3Oyt3P/CW54Sp/B3r19i3YugQg863XCI+DYoxwWf38H1r01CMCcWfuQB/liLzR60r1Jl/nlexPI6BJvFH9fMa/UdY6rxrKC6z4ifCmMrfGSY/cZ1WlZ98z5fS7WtJfz9ifTlLIX7aM8VPhDGVvzMnaDgrfs4HeKDWMMhPHr9Knqq9ID5zj3oltxHqtnjmwPTJUHuVtf3h2e8vRbb93Qq+Qo/CPDf2FerNG/sKjX2FTpHGvkKBeiP+sa9QcR3HvkKD8ZzUV2g/7aWpaN/oU6S/nNSn50fB7vWX8t/n2afnx8QamXlU1qcn9vpE7Z3gfFpmfYL6APv0LAZgKdwhn57zttfCMSAaAhbvWf4fMBZ+muyILXoHx+zH0t48K/szZDeM1J+kDZjt8BgXCPtGlswOmlAe7+djWdZxEcegODCfLeAtl83SW9Ph0BZ5DXWs9pwSohHHNq8DsC+xzoL9gec4tLc1KQ9ta8gfTmptYXwals/Np0Duj9fdN1LVdfcXI6+7Q7BQx+PziBjfnMsr3cbK/w7Ux2JFlPGnGvZ91OxPVfY+6n8UaD8Pf6oa5U0meu3IZ1/RZ0nZtdk/y8r/L6++8Z39/2ekm/IaFPOQXh7Tym/tvI/bP3Act2pccR9C/RnXbdzu84BX9Tkur2KGIg+Kzr3+e6j/Z/NxHts38qxsUGpfBuvENqiTrskRVyPpH2MxbFCqbqF7fbAvcN9UsOYrwoos/4P3zap7R9SeZJU2VfVGWGZPiO1nxbqrso1kz+Zf1X2Hx3vIVsc+Qc/AvLGQwwzFSVDzxlm0geJNDJsa1i2mTS1L1p5q3c3ztPI7Vn4erHvjus5gRPYV7szhtl7DOUrtufM6G39nqU7Pvirvp2oOVz7C+Cw09prEn0h7xKvDtJkpO5fag6s6HxquRtLf72LMh6puoXZG+wDvvStYrYqwhulzHrLdxPDTzRLPh2yXUt9l8SAP2Sc+JM9i2QyN92YrQnm2JGidoPL4O0t1evbfR5Zng3SJK6/SOIt0CT7LY+XfBLrEm0iXwLmNfQPVmYmbef566xm1t5Lncee2zfWy84XhP6v5IiQ3ka/KnmzvsnzK0l7aLXeauSRLTznCesYR1guOsJ52hPW8I6x7HWF58v4RR1iedXzREdaDjrCedIT1uCOshxxhebbjw46wPHnvSZenXPWka1Rl4XOOsDz7qiddjznCGtW51nM8jqr88mxHz3nIc370lDmevP+AIyzPOo6qjPbk/cuOsDzl6qjqE5569LEjrFHVmTz7/SuOsDzHkKfO5LlWGFV91VNO3OcIa1TnNE9dblRtHY86wvLUo0eVX57z9q2wFn3WEZanjB5VuTrWTYanm3xbDkudKeG9qxbkxdi7sn3JKVEPpG2CyuPvLHGsnj8K7F2pPZbFAO/KnIcv8v/kc2FW/nK+Bxl3T3hzKxSfKm6csc2tGuEzfuMzxK/OMxjdDZF3mntCdjaOtrYOj443j1eP2ru7nf6oYpzhswnAn31U/BO1Xxc3Hsvmmoo5cRH4mqUpyFuhvDrkGY3Z2LmN6I8T82pzrQz/EX9LlGff47Jt2RJ4OCbRaWA1TghrOekdAygnlE8dy+5Ie9VBv4Om4GFVv4M7c9moZLe6n+mC4J2S3aGz/ehn8tpXa5xl/Uys/BvAz+R1+W91fol9VpUvqfJ1DZ3D5bO2EwKf5U0KfNn7dlaD6/VGaB8+j4ttzrzKEp/HPW/xbax+yu+HYSnc2FfPexzYBeLFBQErsg9U5TOXc5SHehf7baPcZV/rsmcuq8bQwzhvv3pHFy6Xw99FclHdB8iyD99F2cc+c9cCYx79yxRujv183s6dh+Idl7lXQ8Vci6tPdXmxMoAXF4n+QfH8WP9YDsAaxAueC4YRqxB5UTVWIfMiFPdwEC+upjcXL5oCllrDs+8ny5NEvMP3J1n5+0HfeurVxfSwX6+KX74YwBs7/i/zHPVrxfOJpL/NsL3q9OwooF+rforPWL9eEPSU7fPoy3q9TNp9H3mdpSnIc+T1WsaD34e4q328S3vzLkPedNrLC4zVy/PCZcGLy4IXCtYK5amzMWznw3rgeOAxhvSU1ReaAPdKhbN4uA7mmLyoJ1m5s1rHWl8ru47FPo86Pt8D9aLjOjZ0x7GCpc7cqnGGvM7SFOQ58npDjbMe3qW9eajP8zjDvGtpLy+q2nYx7y5HWG9xhGV1tDx1VlzZO/heklCcvCyFxjzaBeYDY76W9OaVjdswSXnqrvFRieWBMcd4PIZiefxgQB6omDn4jPvHrKBn1GKg8noa5y1eT5ddo2P8Ok5qPY3xUb/uzi5cLsf1wP7Hdo5QvJFIvF/leRv7JuLkeCM4j2G9WXf+XwN90yPeyCCb60+TfoJxAJTNleOGWPm/CWuAN+TtrWQRyylsU5ZTobgSw25vo61qe38m0N5V427MCXpqgj7Vjvb7rM5KKn6quM3Mr1+i/tkC/qg5guOTqb2SVgDvIuHFODhclyTRvOW2sHpOF5RvEg1W/v8GGj5DNKh4QMrGE6K5UZHmRgmafz1A80KAZiUn0AdB9Rkuz/oJw+d9AnWOWcUO4z75WwGbrIrrhGOYbbLD2IeZC/Bk0D6M1e+k+zAqNt0w411UvRMEZQfrJ4sBWK0BvLia9vJiGHfQIS+q3kEXst+xn0msGEzX0m4ZbquQvRPliIp7y+P/D0DX+Rev7oVb5HeU/Z6iPCv7rwDeH726uH53p9XKKRuP8VKNX9a/WDYhjNAeZZY47iTeAab2NJnXVv4/iPlE7buH7jFpUt6kwIs8VXzjuJgd29Sd3ff/uECXTpLq6/5ZoOunAz4M3J6hGKxq3W8wVMxzpVdxjPDZvP6D7tEyutQ9PLjensrfGWZMwKo2P6UztER5G5che6Diz0yAP7Hj6w26t6PM3VcoXxeIP/j+FMGaErBQpwjd+xTqb1inqfyds+JnYwB/yvAT+6fxSPGzTrDqAhbyOMRPo1HxE2FM5e9EjgMbvIME+xH7ACr+Y3/mexSwvOkXVf2+uI1Q9uJ9V2Y3ydoQY2/PJf1y33ONPqr3xWB/rRV8GyzOM1yNpH8sONatHaob0s96K/Y5ns8VrJmKsCLfG9Vp0+lAvRG/skNWaVNVb2XTVHPmafEo+TCXRO1XO2qussTrKOSFsqNzGxTNuZyUXd/qm9H1nhJ2/UWBZ5ryVH+J3H8r83e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", "file_map": { "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n unsafe {\n let sorted = quicksort::quicksort(self, ordering);\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "4": { - "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: fn[Env](T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_recursive(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: fn[Env](T, T) -> bool,\n) {\n if low < high {\n let pivot_index = partition(arr, low, high, sortfn);\n if pivot_index > 0 {\n quicksort_recursive(arr, low, pivot_index - 1, sortfn);\n }\n quicksort_recursive(arr, pivot_index + 1, high, sortfn);\n }\n}\n\npub(crate) unconstrained fn quicksort(\n _arr: [T; N],\n sortfn: fn[Env](T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = _arr;\n if arr.len() <= 1 {} else {\n quicksort_recursive(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", + "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn(T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", "path": "std/array/quicksort.nr" }, "5": { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_true_inliner_9223372036854775807.snap b/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_true_inliner_9223372036854775807.snap index 0a55d596903..68a828fb80c 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_true_inliner_9223372036854775807.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_true_inliner_9223372036854775807.snap @@ -219,15 +219,15 @@ expression: artifact } } }, - "bytecode": 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", 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", "file_map": { "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n unsafe {\n let sorted = quicksort::quicksort(self, ordering);\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "4": { - "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: fn[Env](T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_recursive(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: fn[Env](T, T) -> bool,\n) {\n if low < high {\n let pivot_index = partition(arr, low, high, sortfn);\n if pivot_index > 0 {\n quicksort_recursive(arr, low, pivot_index - 1, sortfn);\n }\n quicksort_recursive(arr, pivot_index + 1, high, sortfn);\n }\n}\n\npub(crate) unconstrained fn quicksort(\n _arr: [T; N],\n sortfn: fn[Env](T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = _arr;\n if arr.len() <= 1 {} else {\n quicksort_recursive(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", + "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn(T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", "path": "std/array/quicksort.nr" }, "5": { From d161d975f8896b197cd80e678ff35dfdcafc2a85 Mon Sep 17 00:00:00 2001 From: "Michael J. Klein" Date: Tue, 29 Apr 2025 14:10:06 -0400 Subject: [PATCH 8/9] restore missing 'fn[Env]' from debugging, try to fix snap files --- noir_stdlib/src/array/mod.nr | 2 +- noir_stdlib/src/array/quicksort.nr | 12 ++++++------ ...e_brillig_false_inliner_-9223372036854775808.snap | 8 ++++---- ...xecute__tests__force_brillig_false_inliner_0.snap | 8 ++++---- ...ce_brillig_false_inliner_9223372036854775807.snap | 8 ++++---- ...ce_brillig_true_inliner_-9223372036854775808.snap | 8 ++++---- ...execute__tests__force_brillig_true_inliner_0.snap | 8 ++++---- ...rce_brillig_true_inliner_9223372036854775807.snap | 8 ++++---- ...e_brillig_false_inliner_-9223372036854775808.snap | 2 +- ...xecute__tests__force_brillig_false_inliner_0.snap | 2 +- ...ce_brillig_false_inliner_9223372036854775807.snap | 2 +- ...ce_brillig_true_inliner_-9223372036854775808.snap | 8 ++++---- ...execute__tests__force_brillig_true_inliner_0.snap | 8 ++++---- ...rce_brillig_true_inliner_9223372036854775807.snap | 8 ++++---- ...ce_brillig_true_inliner_-9223372036854775808.snap | 2 +- ...execute__tests__force_brillig_true_inliner_0.snap | 2 +- ...rce_brillig_true_inliner_9223372036854775807.snap | 2 +- ...e_brillig_false_inliner_-9223372036854775808.snap | 8 ++++---- ...xecute__tests__force_brillig_false_inliner_0.snap | 8 ++++---- ...ce_brillig_false_inliner_9223372036854775807.snap | 8 ++++---- ...ce_brillig_true_inliner_-9223372036854775808.snap | 8 ++++---- ...execute__tests__force_brillig_true_inliner_0.snap | 8 ++++---- ...rce_brillig_true_inliner_9223372036854775807.snap | 8 ++++---- 23 files changed, 73 insertions(+), 73 deletions(-) diff --git a/noir_stdlib/src/array/mod.nr b/noir_stdlib/src/array/mod.nr index 97a148f205a..9808cdeb7af 100644 --- a/noir_stdlib/src/array/mod.nr +++ b/noir_stdlib/src/array/mod.nr @@ -265,7 +265,7 @@ where /// assert(sorted_descending == [32, 42]); // does not verify /// } /// ``` - pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self { + pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self { // Safety: `sorted` array is checked to be: // a. a permutation of `input`'s elements // b. satisfying the predicate `ordering` diff --git a/noir_stdlib/src/array/quicksort.nr b/noir_stdlib/src/array/quicksort.nr index 94c38b6dbc7..0d072506521 100644 --- a/noir_stdlib/src/array/quicksort.nr +++ b/noir_stdlib/src/array/quicksort.nr @@ -1,8 +1,8 @@ -unconstrained fn partition( +unconstrained fn partition( arr: &mut [T; N], low: u32, high: u32, - sortfn: unconstrained fn(T, T) -> bool, + sortfn: unconstrained fn[Env](T, T) -> bool, ) -> u32 { let pivot = high; let mut i = low; @@ -21,11 +21,11 @@ unconstrained fn partition( i } -unconstrained fn quicksort_loop( +unconstrained fn quicksort_loop( arr: &mut [T; N], low: u32, high: u32, - sortfn: unconstrained fn(T, T) -> bool, + sortfn: unconstrained fn[Env](T, T) -> bool, ) { let mut stack: [(u32, u32)] = &[(low, high)]; // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized @@ -49,9 +49,9 @@ unconstrained fn quicksort_loop( } } -pub unconstrained fn quicksort( +pub unconstrained fn quicksort( arr: [T; N], - sortfn: unconstrained fn(T, T) -> bool, + sortfn: unconstrained fn[Env](T, T) -> bool, ) -> [T; N] { let mut arr: [T; N] = arr; if arr.len() > 1 { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_false_inliner_-9223372036854775808.snap b/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_false_inliner_-9223372036854775808.snap index f7f25fb0222..b3557496601 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_false_inliner_-9223372036854775808.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_false_inliner_-9223372036854775808.snap @@ -53,19 +53,19 @@ expression: artifact } } }, - "bytecode": 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- "debug_symbols": 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"file_map": { "2": { "source": "use crate::cmp::Eq;\n\nunconstrained fn __get_shuffle_indices(lhs: [T; N], rhs: [T; N]) -> [u32; N]\nwhere\n T: Eq,\n{\n let mut shuffle_indices: [u32; N] = [0; N];\n\n let mut shuffle_mask: [bool; N] = [false; N];\n for i in 0..N {\n let mut found = false;\n for j in 0..N {\n if ((shuffle_mask[j] == false) & (!found)) {\n if (lhs[i] == rhs[j]) {\n found = true;\n shuffle_indices[i] = j;\n shuffle_mask[j] = true;\n }\n }\n if (found) {\n continue;\n }\n }\n assert(found == true, \"check_shuffle, lhs and rhs arrays do not contain equivalent values\");\n }\n\n shuffle_indices\n}\n\nunconstrained fn __get_index(indices: [u32; N], idx: u32) -> u32 {\n let mut result = 0;\n for i in 0..N {\n if (indices[i] == idx) {\n result = i;\n break;\n }\n }\n result\n}\n\npub(crate) fn check_shuffle(lhs: [T; N], rhs: [T; N])\nwhere\n T: Eq,\n{\n // Safety: shuffle_indices is ensured to be a permutation of 0..N, and then\n // shuffle_indices is ensured to map lhs to rhs: assert(lhs[i] == rhs[shuffle_indices[i]]), for all i in 0..N\n unsafe {\n let shuffle_indices = __get_shuffle_indices(lhs, rhs);\n\n for i in 0..N {\n let idx = __get_index(shuffle_indices, i);\n assert_eq(shuffle_indices[idx], i);\n }\n for i in 0..N {\n let idx = shuffle_indices[i];\n let expected = rhs[idx];\n let result = lhs[i];\n assert_eq(expected, result);\n }\n }\n}\n\nmod test {\n use crate::cmp::Eq;\n use super::check_shuffle;\n\n struct CompoundStruct {\n a: bool,\n b: Field,\n c: u64,\n }\n impl Eq for CompoundStruct {\n fn eq(self, other: Self) -> bool {\n (self.a == other.a) & (self.b == other.b) & (self.c == other.c)\n }\n }\n\n #[test]\n fn test_shuffle() {\n let lhs: [Field; 5] = [0, 1, 2, 3, 4];\n let rhs: [Field; 5] = [2, 0, 3, 1, 4];\n check_shuffle(lhs, rhs);\n }\n\n #[test]\n fn test_shuffle_identity() {\n let lhs: [Field; 5] = [0, 1, 2, 3, 4];\n let rhs: [Field; 5] = [0, 1, 2, 3, 4];\n check_shuffle(lhs, rhs);\n }\n\n #[test(should_fail_with = \"check_shuffle, lhs and rhs arrays do not contain equivalent values\")]\n fn test_shuffle_fail() {\n let lhs: [Field; 5] = [0, 1, 2, 3, 4];\n let rhs: [Field; 5] = [0, 1, 2, 3, 5];\n check_shuffle(lhs, rhs);\n }\n\n #[test(should_fail_with = \"check_shuffle, lhs and rhs arrays do not contain equivalent values\")]\n fn test_shuffle_duplicates() {\n let lhs: [Field; 5] = [0, 1, 2, 3, 4];\n let rhs: [Field; 5] = [0, 1, 2, 3, 3];\n check_shuffle(lhs, rhs);\n }\n\n #[test]\n fn test_shuffle_compound_struct() {\n let lhs: [CompoundStruct; 5] = [\n CompoundStruct { a: false, b: 0, c: 12345 },\n CompoundStruct { a: false, b: -100, c: 54321 },\n CompoundStruct { a: true, b: 5, c: 0xffffffffffffffff },\n CompoundStruct { a: true, b: 9814, c: 0xeeffee0011001133 },\n CompoundStruct { a: false, b: 0x155, c: 0 },\n ];\n let rhs: [CompoundStruct; 5] = [\n CompoundStruct { a: false, b: 0x155, c: 0 },\n CompoundStruct { a: false, b: 0, c: 12345 },\n CompoundStruct { a: false, b: -100, c: 54321 },\n CompoundStruct { a: true, b: 9814, c: 0xeeffee0011001133 },\n CompoundStruct { a: true, b: 5, c: 0xffffffffffffffff },\n ];\n check_shuffle(lhs, rhs);\n }\n}\n", "path": "std/array/check_shuffle.nr" }, "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "4": { - "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn(T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", + "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", "path": "std/array/quicksort.nr" }, "5": { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_false_inliner_0.snap b/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_false_inliner_0.snap index 9c4643b7302..281514c3e42 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_false_inliner_0.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_false_inliner_0.snap @@ -53,19 +53,19 @@ expression: artifact } } }, - "bytecode": 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"file_map": { "2": { "source": "use crate::cmp::Eq;\n\nunconstrained fn __get_shuffle_indices(lhs: [T; N], rhs: [T; N]) -> [u32; N]\nwhere\n T: Eq,\n{\n let mut shuffle_indices: [u32; N] = [0; N];\n\n let mut shuffle_mask: [bool; N] = [false; N];\n for i in 0..N {\n let mut found = false;\n for j in 0..N {\n if ((shuffle_mask[j] == false) & (!found)) {\n if (lhs[i] == rhs[j]) {\n found = true;\n shuffle_indices[i] = j;\n shuffle_mask[j] = true;\n }\n }\n if (found) {\n continue;\n }\n }\n assert(found == true, \"check_shuffle, lhs and rhs arrays do not contain equivalent values\");\n }\n\n shuffle_indices\n}\n\nunconstrained fn __get_index(indices: [u32; N], idx: u32) -> u32 {\n let mut result = 0;\n for i in 0..N {\n if (indices[i] == idx) {\n result = i;\n break;\n }\n }\n result\n}\n\npub(crate) fn check_shuffle(lhs: [T; N], rhs: [T; N])\nwhere\n T: Eq,\n{\n // Safety: shuffle_indices is ensured to be a permutation of 0..N, and then\n // shuffle_indices is ensured to map lhs to rhs: assert(lhs[i] == rhs[shuffle_indices[i]]), for all i in 0..N\n unsafe {\n let shuffle_indices = __get_shuffle_indices(lhs, rhs);\n\n for i in 0..N {\n let idx = __get_index(shuffle_indices, i);\n assert_eq(shuffle_indices[idx], i);\n }\n for i in 0..N {\n let idx = shuffle_indices[i];\n let expected = rhs[idx];\n let result = lhs[i];\n assert_eq(expected, result);\n }\n }\n}\n\nmod test {\n use crate::cmp::Eq;\n use super::check_shuffle;\n\n struct CompoundStruct {\n a: bool,\n b: Field,\n c: u64,\n }\n impl Eq for CompoundStruct {\n fn eq(self, other: Self) -> bool {\n (self.a == other.a) & (self.b == other.b) & (self.c == other.c)\n }\n }\n\n #[test]\n fn test_shuffle() {\n let lhs: [Field; 5] = [0, 1, 2, 3, 4];\n let rhs: [Field; 5] = [2, 0, 3, 1, 4];\n check_shuffle(lhs, rhs);\n }\n\n #[test]\n fn test_shuffle_identity() {\n let lhs: [Field; 5] = [0, 1, 2, 3, 4];\n let rhs: [Field; 5] = [0, 1, 2, 3, 4];\n check_shuffle(lhs, rhs);\n }\n\n #[test(should_fail_with = \"check_shuffle, lhs and rhs arrays do not contain equivalent values\")]\n fn test_shuffle_fail() {\n let lhs: [Field; 5] = [0, 1, 2, 3, 4];\n let rhs: [Field; 5] = [0, 1, 2, 3, 5];\n check_shuffle(lhs, rhs);\n }\n\n #[test(should_fail_with = \"check_shuffle, lhs and rhs arrays do not contain equivalent values\")]\n fn test_shuffle_duplicates() {\n let lhs: [Field; 5] = [0, 1, 2, 3, 4];\n let rhs: [Field; 5] = [0, 1, 2, 3, 3];\n check_shuffle(lhs, rhs);\n }\n\n #[test]\n fn test_shuffle_compound_struct() {\n let lhs: [CompoundStruct; 5] = [\n CompoundStruct { a: false, b: 0, c: 12345 },\n CompoundStruct { a: false, b: -100, c: 54321 },\n CompoundStruct { a: true, b: 5, c: 0xffffffffffffffff },\n CompoundStruct { a: true, b: 9814, c: 0xeeffee0011001133 },\n CompoundStruct { a: false, b: 0x155, c: 0 },\n ];\n let rhs: [CompoundStruct; 5] = [\n CompoundStruct { a: false, b: 0x155, c: 0 },\n CompoundStruct { a: false, b: 0, c: 12345 },\n CompoundStruct { a: false, b: -100, c: 54321 },\n CompoundStruct { a: true, b: 9814, c: 0xeeffee0011001133 },\n CompoundStruct { a: true, b: 5, c: 0xffffffffffffffff },\n ];\n check_shuffle(lhs, rhs);\n }\n}\n", "path": "std/array/check_shuffle.nr" }, "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "4": { - "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn(T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", + "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", "path": "std/array/quicksort.nr" }, "5": { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_false_inliner_9223372036854775807.snap b/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_false_inliner_9223372036854775807.snap index 9c4643b7302..281514c3e42 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_false_inliner_9223372036854775807.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_false_inliner_9223372036854775807.snap @@ -53,19 +53,19 @@ expression: artifact } } }, - "bytecode": 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"file_map": { "2": { "source": "use crate::cmp::Eq;\n\nunconstrained fn __get_shuffle_indices(lhs: [T; N], rhs: [T; N]) -> [u32; N]\nwhere\n T: Eq,\n{\n let mut shuffle_indices: [u32; N] = [0; N];\n\n let mut shuffle_mask: [bool; N] = [false; N];\n for i in 0..N {\n let mut found = false;\n for j in 0..N {\n if ((shuffle_mask[j] == false) & (!found)) {\n if (lhs[i] == rhs[j]) {\n found = true;\n shuffle_indices[i] = j;\n shuffle_mask[j] = true;\n }\n }\n if (found) {\n continue;\n }\n }\n assert(found == true, \"check_shuffle, lhs and rhs arrays do not contain equivalent values\");\n }\n\n shuffle_indices\n}\n\nunconstrained fn __get_index(indices: [u32; N], idx: u32) -> u32 {\n let mut result = 0;\n for i in 0..N {\n if (indices[i] == idx) {\n result = i;\n break;\n }\n }\n result\n}\n\npub(crate) fn check_shuffle(lhs: [T; N], rhs: [T; N])\nwhere\n T: Eq,\n{\n // Safety: shuffle_indices is ensured to be a permutation of 0..N, and then\n // shuffle_indices is ensured to map lhs to rhs: assert(lhs[i] == rhs[shuffle_indices[i]]), for all i in 0..N\n unsafe {\n let shuffle_indices = __get_shuffle_indices(lhs, rhs);\n\n for i in 0..N {\n let idx = __get_index(shuffle_indices, i);\n assert_eq(shuffle_indices[idx], i);\n }\n for i in 0..N {\n let idx = shuffle_indices[i];\n let expected = rhs[idx];\n let result = lhs[i];\n assert_eq(expected, result);\n }\n }\n}\n\nmod test {\n use crate::cmp::Eq;\n use super::check_shuffle;\n\n struct CompoundStruct {\n a: bool,\n b: Field,\n c: u64,\n }\n impl Eq for CompoundStruct {\n fn eq(self, other: Self) -> bool {\n (self.a == other.a) & (self.b == other.b) & (self.c == other.c)\n }\n }\n\n #[test]\n fn test_shuffle() {\n let lhs: [Field; 5] = [0, 1, 2, 3, 4];\n let rhs: [Field; 5] = [2, 0, 3, 1, 4];\n check_shuffle(lhs, rhs);\n }\n\n #[test]\n fn test_shuffle_identity() {\n let lhs: [Field; 5] = [0, 1, 2, 3, 4];\n let rhs: [Field; 5] = [0, 1, 2, 3, 4];\n check_shuffle(lhs, rhs);\n }\n\n #[test(should_fail_with = \"check_shuffle, lhs and rhs arrays do not contain equivalent values\")]\n fn test_shuffle_fail() {\n let lhs: [Field; 5] = [0, 1, 2, 3, 4];\n let rhs: [Field; 5] = [0, 1, 2, 3, 5];\n check_shuffle(lhs, rhs);\n }\n\n #[test(should_fail_with = \"check_shuffle, lhs and rhs arrays do not contain equivalent values\")]\n fn test_shuffle_duplicates() {\n let lhs: [Field; 5] = [0, 1, 2, 3, 4];\n let rhs: [Field; 5] = [0, 1, 2, 3, 3];\n check_shuffle(lhs, rhs);\n }\n\n #[test]\n fn test_shuffle_compound_struct() {\n let lhs: [CompoundStruct; 5] = [\n CompoundStruct { a: false, b: 0, c: 12345 },\n CompoundStruct { a: false, b: -100, c: 54321 },\n CompoundStruct { a: true, b: 5, c: 0xffffffffffffffff },\n CompoundStruct { a: true, b: 9814, c: 0xeeffee0011001133 },\n CompoundStruct { a: false, b: 0x155, c: 0 },\n ];\n let rhs: [CompoundStruct; 5] = [\n CompoundStruct { a: false, b: 0x155, c: 0 },\n CompoundStruct { a: false, b: 0, c: 12345 },\n CompoundStruct { a: false, b: -100, c: 54321 },\n CompoundStruct { a: true, b: 9814, c: 0xeeffee0011001133 },\n CompoundStruct { a: true, b: 5, c: 0xffffffffffffffff },\n ];\n check_shuffle(lhs, rhs);\n }\n}\n", "path": "std/array/check_shuffle.nr" }, "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "4": { - "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn(T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", + "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", "path": "std/array/quicksort.nr" }, "5": { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_true_inliner_-9223372036854775808.snap b/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_true_inliner_-9223372036854775808.snap index 686136409c2..303f501544f 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_true_inliner_-9223372036854775808.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_true_inliner_-9223372036854775808.snap @@ -45,15 +45,15 @@ expression: artifact } } }, - "bytecode": 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"file_map": { "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "4": { - "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn(T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", + "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", "path": "std/array/quicksort.nr" }, "5": { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_true_inliner_0.snap b/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_true_inliner_0.snap index 48964aa6ca6..d68c3d95d69 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_true_inliner_0.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_true_inliner_0.snap @@ -45,15 +45,15 @@ expression: artifact } } }, - "bytecode": 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"file_map": { "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "4": { - "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn(T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", + "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", "path": "std/array/quicksort.nr" }, "5": { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_true_inliner_9223372036854775807.snap b/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_true_inliner_9223372036854775807.snap index 48964aa6ca6..d68c3d95d69 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_true_inliner_9223372036854775807.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/array_sort/execute__tests__force_brillig_true_inliner_9223372036854775807.snap @@ -45,15 +45,15 @@ expression: artifact } } }, - "bytecode": 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"file_map": { "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "4": { - "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn(T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", + "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", "path": "std/array/quicksort.nr" }, "5": { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_false_inliner_-9223372036854775808.snap b/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_false_inliner_-9223372036854775808.snap index 4b5a4b3e995..7acdd2270e3 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_false_inliner_-9223372036854775808.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_false_inliner_-9223372036854775808.snap @@ -204,7 +204,7 @@ expression: artifact } }, "bytecode": 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", 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"file_map": { "5": { "source": "use crate::meta::derive_via;\n\n#[derive_via(derive_eq)]\n// docs:start:eq-trait\npub trait Eq {\n fn eq(self, other: Self) -> bool;\n}\n// docs:end:eq-trait\n\n// docs:start:derive_eq\ncomptime fn derive_eq(s: TypeDefinition) -> Quoted {\n let signature = quote { fn eq(_self: Self, _other: Self) -> bool };\n let for_each_field = |name| quote { (_self.$name == _other.$name) };\n let body = |fields| {\n if s.fields_as_written().len() == 0 {\n quote { true }\n } else {\n fields\n }\n };\n crate::meta::make_trait_impl(\n s,\n quote { Eq },\n signature,\n for_each_field,\n quote { & },\n body,\n )\n}\n// docs:end:derive_eq\n\nimpl Eq for Field {\n fn eq(self, other: Field) -> bool {\n self == other\n }\n}\n\nimpl Eq for u128 {\n fn eq(self, other: u128) -> bool {\n self == other\n }\n}\nimpl Eq for u64 {\n fn eq(self, other: u64) -> bool {\n self == other\n }\n}\nimpl Eq for u32 {\n fn eq(self, other: u32) -> bool {\n self == other\n }\n}\nimpl Eq for u16 {\n fn eq(self, other: u16) -> bool {\n self == other\n }\n}\nimpl Eq for u8 {\n fn eq(self, other: u8) -> bool {\n self == other\n }\n}\nimpl Eq for u1 {\n fn eq(self, other: u1) -> bool {\n self == other\n }\n}\n\nimpl Eq for i8 {\n fn eq(self, other: i8) -> bool {\n self == other\n }\n}\nimpl Eq for i16 {\n fn eq(self, other: i16) -> bool {\n self == other\n }\n}\nimpl Eq for i32 {\n fn eq(self, other: i32) -> bool {\n self == other\n }\n}\nimpl Eq for i64 {\n fn eq(self, other: i64) -> bool {\n self == other\n }\n}\n\nimpl Eq for () {\n fn eq(_self: Self, _other: ()) -> bool {\n true\n }\n}\nimpl Eq for bool {\n fn eq(self, other: bool) -> bool {\n self == other\n }\n}\n\nimpl Eq for [T; N]\nwhere\n T: Eq,\n{\n fn eq(self, other: [T; N]) -> bool {\n let mut result = true;\n for i in 0..self.len() {\n result &= self[i].eq(other[i]);\n }\n result\n }\n}\n\nimpl Eq for [T]\nwhere\n T: Eq,\n{\n fn eq(self, other: [T]) -> bool {\n let mut result = self.len() == other.len();\n for i in 0..self.len() {\n result &= self[i].eq(other[i]);\n }\n result\n }\n}\n\nimpl Eq for str {\n fn eq(self, other: str) -> bool {\n let self_bytes = self.as_bytes();\n let other_bytes = other.as_bytes();\n self_bytes == other_bytes\n }\n}\n\nimpl Eq for (A, B)\nwhere\n A: Eq,\n B: Eq,\n{\n fn eq(self, other: (A, B)) -> bool {\n self.0.eq(other.0) & self.1.eq(other.1)\n }\n}\n\nimpl Eq for (A, B, C)\nwhere\n A: Eq,\n B: Eq,\n C: Eq,\n{\n fn eq(self, other: (A, B, C)) -> bool {\n self.0.eq(other.0) & self.1.eq(other.1) & self.2.eq(other.2)\n }\n}\n\nimpl Eq for (A, B, C, D)\nwhere\n A: Eq,\n B: Eq,\n C: Eq,\n D: Eq,\n{\n fn eq(self, other: (A, B, C, D)) -> bool {\n self.0.eq(other.0) & self.1.eq(other.1) & self.2.eq(other.2) & self.3.eq(other.3)\n }\n}\n\nimpl Eq for (A, B, C, D, E)\nwhere\n A: Eq,\n B: Eq,\n C: Eq,\n D: Eq,\n E: Eq,\n{\n fn eq(self, other: (A, B, C, D, E)) -> bool {\n self.0.eq(other.0)\n & self.1.eq(other.1)\n & self.2.eq(other.2)\n & self.3.eq(other.3)\n & self.4.eq(other.4)\n }\n}\n\nimpl Eq for Ordering {\n fn eq(self, other: Ordering) -> bool {\n self.result == other.result\n }\n}\n\n// Noir doesn't have enums yet so we emulate (Lt | Eq | Gt) with a struct\n// that has 3 public functions for constructing the struct.\npub struct Ordering {\n result: Field,\n}\n\nimpl Ordering {\n // Implementation note: 0, 1, and 2 for Lt, Eq, and Gt are built\n // into the compiler, do not change these without also updating\n // the compiler itself!\n pub fn less() -> Ordering {\n Ordering { result: 0 }\n }\n\n pub fn equal() -> Ordering {\n Ordering { result: 1 }\n }\n\n pub fn greater() -> Ordering {\n Ordering { result: 2 }\n }\n}\n\n#[derive_via(derive_ord)]\n// docs:start:ord-trait\npub trait Ord {\n fn cmp(self, other: Self) -> Ordering;\n}\n// docs:end:ord-trait\n\n// docs:start:derive_ord\ncomptime fn derive_ord(s: TypeDefinition) -> Quoted {\n let signature = quote { fn cmp(_self: Self, _other: Self) -> std::cmp::Ordering };\n let for_each_field = |name| quote {\n if result == std::cmp::Ordering::equal() {\n result = _self.$name.cmp(_other.$name);\n }\n };\n let body = |fields| quote {\n let mut result = std::cmp::Ordering::equal();\n $fields\n result\n };\n crate::meta::make_trait_impl(s, quote { Ord }, signature, for_each_field, quote {}, body)\n}\n// docs:end:derive_ord\n\n// Note: Field deliberately does not implement Ord\n\nimpl Ord for u128 {\n fn cmp(self, other: u128) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\nimpl Ord for u64 {\n fn cmp(self, other: u64) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for u32 {\n fn cmp(self, other: u32) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for u16 {\n fn cmp(self, other: u16) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for u8 {\n fn cmp(self, other: u8) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for i8 {\n fn cmp(self, other: i8) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for i16 {\n fn cmp(self, other: i16) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for i32 {\n fn cmp(self, other: i32) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for i64 {\n fn cmp(self, other: i64) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for () {\n fn cmp(_self: Self, _other: ()) -> Ordering {\n Ordering::equal()\n }\n}\n\nimpl Ord for bool {\n fn cmp(self, other: bool) -> Ordering {\n if self {\n if other {\n Ordering::equal()\n } else {\n Ordering::greater()\n }\n } else if other {\n Ordering::less()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for [T; N]\nwhere\n T: Ord,\n{\n // The first non-equal element of both arrays determines\n // the ordering for the whole array.\n fn cmp(self, other: [T; N]) -> Ordering {\n let mut result = Ordering::equal();\n for i in 0..self.len() {\n if result == Ordering::equal() {\n result = self[i].cmp(other[i]);\n }\n }\n result\n }\n}\n\nimpl Ord for [T]\nwhere\n T: Ord,\n{\n // The first non-equal element of both arrays determines\n // the ordering for the whole array.\n fn cmp(self, other: [T]) -> Ordering {\n let mut result = self.len().cmp(other.len());\n for i in 0..self.len() {\n if result == Ordering::equal() {\n result = self[i].cmp(other[i]);\n }\n }\n result\n }\n}\n\nimpl Ord for (A, B)\nwhere\n A: Ord,\n B: Ord,\n{\n fn cmp(self, other: (A, B)) -> Ordering {\n let result = self.0.cmp(other.0);\n\n if result != Ordering::equal() {\n result\n } else {\n self.1.cmp(other.1)\n }\n }\n}\n\nimpl Ord for (A, B, C)\nwhere\n A: Ord,\n B: Ord,\n C: Ord,\n{\n fn cmp(self, other: (A, B, C)) -> Ordering {\n let mut result = self.0.cmp(other.0);\n\n if result == Ordering::equal() {\n result = self.1.cmp(other.1);\n }\n\n if result == Ordering::equal() {\n result = self.2.cmp(other.2);\n }\n\n result\n }\n}\n\nimpl Ord for (A, B, C, D)\nwhere\n A: Ord,\n B: Ord,\n C: Ord,\n D: Ord,\n{\n fn cmp(self, other: (A, B, C, D)) -> Ordering {\n let mut result = self.0.cmp(other.0);\n\n if result == Ordering::equal() {\n result = self.1.cmp(other.1);\n }\n\n if result == Ordering::equal() {\n result = self.2.cmp(other.2);\n }\n\n if result == Ordering::equal() {\n result = self.3.cmp(other.3);\n }\n\n result\n }\n}\n\nimpl Ord for (A, B, C, D, E)\nwhere\n A: Ord,\n B: Ord,\n C: Ord,\n D: Ord,\n E: Ord,\n{\n fn cmp(self, other: (A, B, C, D, E)) -> Ordering {\n let mut result = self.0.cmp(other.0);\n\n if result == Ordering::equal() {\n result = self.1.cmp(other.1);\n }\n\n if result == Ordering::equal() {\n result = self.2.cmp(other.2);\n }\n\n if result == Ordering::equal() {\n result = self.3.cmp(other.3);\n }\n\n if result == Ordering::equal() {\n result = self.4.cmp(other.4);\n }\n\n result\n }\n}\n\n// Compares and returns the maximum of two values.\n//\n// Returns the second argument if the comparison determines them to be equal.\n//\n// # Examples\n//\n// ```\n// use std::cmp;\n//\n// assert_eq(cmp::max(1, 2), 2);\n// assert_eq(cmp::max(2, 2), 2);\n// ```\npub fn max(v1: T, v2: T) -> T\nwhere\n T: Ord,\n{\n if v1 > v2 {\n v1\n } else {\n v2\n }\n}\n\n// Compares and returns the minimum of two values.\n//\n// Returns the first argument if the comparison determines them to be equal.\n//\n// # Examples\n//\n// ```\n// use std::cmp;\n//\n// assert_eq(cmp::min(1, 2), 1);\n// assert_eq(cmp::min(2, 2), 2);\n// ```\npub fn min(v1: T, v2: T) -> T\nwhere\n T: Ord,\n{\n if v1 > v2 {\n v2\n } else {\n v1\n }\n}\n\nmod cmp_tests {\n use crate::cmp::{max, min};\n\n #[test]\n fn sanity_check_min() {\n assert_eq(min(0 as u64, 1 as u64), 0);\n assert_eq(min(0 as u64, 0 as u64), 0);\n assert_eq(min(1 as u64, 1 as u64), 1);\n assert_eq(min(255 as u8, 0 as u8), 0);\n }\n\n #[test]\n fn sanity_check_max() {\n assert_eq(max(0 as u64, 1 as u64), 1);\n assert_eq(max(0 as u64, 0 as u64), 0);\n assert_eq(max(1 as u64, 1 as u64), 1);\n assert_eq(max(255 as u8, 0 as u8), 255);\n }\n}\n", diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_false_inliner_0.snap b/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_false_inliner_0.snap index 0c845cc46e3..2351c31c405 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_false_inliner_0.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_false_inliner_0.snap @@ -204,7 +204,7 @@ expression: artifact } }, "bytecode": 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", 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", "file_map": { "5": { "source": "use crate::meta::derive_via;\n\n#[derive_via(derive_eq)]\n// docs:start:eq-trait\npub trait Eq {\n fn eq(self, other: Self) -> bool;\n}\n// docs:end:eq-trait\n\n// docs:start:derive_eq\ncomptime fn derive_eq(s: TypeDefinition) -> Quoted {\n let signature = quote { fn eq(_self: Self, _other: Self) -> bool };\n let for_each_field = |name| quote { (_self.$name == _other.$name) };\n let body = |fields| {\n if s.fields_as_written().len() == 0 {\n quote { true }\n } else {\n fields\n }\n };\n crate::meta::make_trait_impl(\n s,\n quote { Eq },\n signature,\n for_each_field,\n quote { & },\n body,\n )\n}\n// docs:end:derive_eq\n\nimpl Eq for Field {\n fn eq(self, other: Field) -> bool {\n self == other\n }\n}\n\nimpl Eq for u128 {\n fn eq(self, other: u128) -> bool {\n self == other\n }\n}\nimpl Eq for u64 {\n fn eq(self, other: u64) -> bool {\n self == other\n }\n}\nimpl Eq for u32 {\n fn eq(self, other: u32) -> bool {\n self == other\n }\n}\nimpl Eq for u16 {\n fn eq(self, other: u16) -> bool {\n self == other\n }\n}\nimpl Eq for u8 {\n fn eq(self, other: u8) -> bool {\n self == other\n }\n}\nimpl Eq for u1 {\n fn eq(self, other: u1) -> bool {\n self == other\n }\n}\n\nimpl Eq for i8 {\n fn eq(self, other: i8) -> bool {\n self == other\n }\n}\nimpl Eq for i16 {\n fn eq(self, other: i16) -> bool {\n self == other\n }\n}\nimpl Eq for i32 {\n fn eq(self, other: i32) -> bool {\n self == other\n }\n}\nimpl Eq for i64 {\n fn eq(self, other: i64) -> bool {\n self == other\n }\n}\n\nimpl Eq for () {\n fn eq(_self: Self, _other: ()) -> bool {\n true\n }\n}\nimpl Eq for bool {\n fn eq(self, other: bool) -> bool {\n self == other\n }\n}\n\nimpl Eq for [T; N]\nwhere\n T: Eq,\n{\n fn eq(self, other: [T; N]) -> bool {\n let mut result = true;\n for i in 0..self.len() {\n result &= self[i].eq(other[i]);\n }\n result\n }\n}\n\nimpl Eq for [T]\nwhere\n T: Eq,\n{\n fn eq(self, other: [T]) -> bool {\n let mut result = self.len() == other.len();\n for i in 0..self.len() {\n result &= self[i].eq(other[i]);\n }\n result\n }\n}\n\nimpl Eq for str {\n fn eq(self, other: str) -> bool {\n let self_bytes = self.as_bytes();\n let other_bytes = other.as_bytes();\n self_bytes == other_bytes\n }\n}\n\nimpl Eq for (A, B)\nwhere\n A: Eq,\n B: Eq,\n{\n fn eq(self, other: (A, B)) -> bool {\n self.0.eq(other.0) & self.1.eq(other.1)\n }\n}\n\nimpl Eq for (A, B, C)\nwhere\n A: Eq,\n B: Eq,\n C: Eq,\n{\n fn eq(self, other: (A, B, C)) -> bool {\n self.0.eq(other.0) & self.1.eq(other.1) & self.2.eq(other.2)\n }\n}\n\nimpl Eq for (A, B, C, D)\nwhere\n A: Eq,\n B: Eq,\n C: Eq,\n D: Eq,\n{\n fn eq(self, other: (A, B, C, D)) -> bool {\n self.0.eq(other.0) & self.1.eq(other.1) & self.2.eq(other.2) & self.3.eq(other.3)\n }\n}\n\nimpl Eq for (A, B, C, D, E)\nwhere\n A: Eq,\n B: Eq,\n C: Eq,\n D: Eq,\n E: Eq,\n{\n fn eq(self, other: (A, B, C, D, E)) -> bool {\n self.0.eq(other.0)\n & self.1.eq(other.1)\n & self.2.eq(other.2)\n & self.3.eq(other.3)\n & self.4.eq(other.4)\n }\n}\n\nimpl Eq for Ordering {\n fn eq(self, other: Ordering) -> bool {\n self.result == other.result\n }\n}\n\n// Noir doesn't have enums yet so we emulate (Lt | Eq | Gt) with a struct\n// that has 3 public functions for constructing the struct.\npub struct Ordering {\n result: Field,\n}\n\nimpl Ordering {\n // Implementation note: 0, 1, and 2 for Lt, Eq, and Gt are built\n // into the compiler, do not change these without also updating\n // the compiler itself!\n pub fn less() -> Ordering {\n Ordering { result: 0 }\n }\n\n pub fn equal() -> Ordering {\n Ordering { result: 1 }\n }\n\n pub fn greater() -> Ordering {\n Ordering { result: 2 }\n }\n}\n\n#[derive_via(derive_ord)]\n// docs:start:ord-trait\npub trait Ord {\n fn cmp(self, other: Self) -> Ordering;\n}\n// docs:end:ord-trait\n\n// docs:start:derive_ord\ncomptime fn derive_ord(s: TypeDefinition) -> Quoted {\n let signature = quote { fn cmp(_self: Self, _other: Self) -> std::cmp::Ordering };\n let for_each_field = |name| quote {\n if result == std::cmp::Ordering::equal() {\n result = _self.$name.cmp(_other.$name);\n }\n };\n let body = |fields| quote {\n let mut result = std::cmp::Ordering::equal();\n $fields\n result\n };\n crate::meta::make_trait_impl(s, quote { Ord }, signature, for_each_field, quote {}, body)\n}\n// docs:end:derive_ord\n\n// Note: Field deliberately does not implement Ord\n\nimpl Ord for u128 {\n fn cmp(self, other: u128) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\nimpl Ord for u64 {\n fn cmp(self, other: u64) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for u32 {\n fn cmp(self, other: u32) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for u16 {\n fn cmp(self, other: u16) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for u8 {\n fn cmp(self, other: u8) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for i8 {\n fn cmp(self, other: i8) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for i16 {\n fn cmp(self, other: i16) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for i32 {\n fn cmp(self, other: i32) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for i64 {\n fn cmp(self, other: i64) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for () {\n fn cmp(_self: Self, _other: ()) -> Ordering {\n Ordering::equal()\n }\n}\n\nimpl Ord for bool {\n fn cmp(self, other: bool) -> Ordering {\n if self {\n if other {\n Ordering::equal()\n } else {\n Ordering::greater()\n }\n } else if other {\n Ordering::less()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for [T; N]\nwhere\n T: Ord,\n{\n // The first non-equal element of both arrays determines\n // the ordering for the whole array.\n fn cmp(self, other: [T; N]) -> Ordering {\n let mut result = Ordering::equal();\n for i in 0..self.len() {\n if result == Ordering::equal() {\n result = self[i].cmp(other[i]);\n }\n }\n result\n }\n}\n\nimpl Ord for [T]\nwhere\n T: Ord,\n{\n // The first non-equal element of both arrays determines\n // the ordering for the whole array.\n fn cmp(self, other: [T]) -> Ordering {\n let mut result = self.len().cmp(other.len());\n for i in 0..self.len() {\n if result == Ordering::equal() {\n result = self[i].cmp(other[i]);\n }\n }\n result\n }\n}\n\nimpl Ord for (A, B)\nwhere\n A: Ord,\n B: Ord,\n{\n fn cmp(self, other: (A, B)) -> Ordering {\n let result = self.0.cmp(other.0);\n\n if result != Ordering::equal() {\n result\n } else {\n self.1.cmp(other.1)\n }\n }\n}\n\nimpl Ord for (A, B, C)\nwhere\n A: Ord,\n B: Ord,\n C: Ord,\n{\n fn cmp(self, other: (A, B, C)) -> Ordering {\n let mut result = self.0.cmp(other.0);\n\n if result == Ordering::equal() {\n result = self.1.cmp(other.1);\n }\n\n if result == Ordering::equal() {\n result = self.2.cmp(other.2);\n }\n\n result\n }\n}\n\nimpl Ord for (A, B, C, D)\nwhere\n A: Ord,\n B: Ord,\n C: Ord,\n D: Ord,\n{\n fn cmp(self, other: (A, B, C, D)) -> Ordering {\n let mut result = self.0.cmp(other.0);\n\n if result == Ordering::equal() {\n result = self.1.cmp(other.1);\n }\n\n if result == Ordering::equal() {\n result = self.2.cmp(other.2);\n }\n\n if result == Ordering::equal() {\n result = self.3.cmp(other.3);\n }\n\n result\n }\n}\n\nimpl Ord for (A, B, C, D, E)\nwhere\n A: Ord,\n B: Ord,\n C: Ord,\n D: Ord,\n E: Ord,\n{\n fn cmp(self, other: (A, B, C, D, E)) -> Ordering {\n let mut result = self.0.cmp(other.0);\n\n if result == Ordering::equal() {\n result = self.1.cmp(other.1);\n }\n\n if result == Ordering::equal() {\n result = self.2.cmp(other.2);\n }\n\n if result == Ordering::equal() {\n result = self.3.cmp(other.3);\n }\n\n if result == Ordering::equal() {\n result = self.4.cmp(other.4);\n }\n\n result\n }\n}\n\n// Compares and returns the maximum of two values.\n//\n// Returns the second argument if the comparison determines them to be equal.\n//\n// # Examples\n//\n// ```\n// use std::cmp;\n//\n// assert_eq(cmp::max(1, 2), 2);\n// assert_eq(cmp::max(2, 2), 2);\n// ```\npub fn max(v1: T, v2: T) -> T\nwhere\n T: Ord,\n{\n if v1 > v2 {\n v1\n } else {\n v2\n }\n}\n\n// Compares and returns the minimum of two values.\n//\n// Returns the first argument if the comparison determines them to be equal.\n//\n// # Examples\n//\n// ```\n// use std::cmp;\n//\n// assert_eq(cmp::min(1, 2), 1);\n// assert_eq(cmp::min(2, 2), 2);\n// ```\npub fn min(v1: T, v2: T) -> T\nwhere\n T: Ord,\n{\n if v1 > v2 {\n v2\n } else {\n v1\n }\n}\n\nmod cmp_tests {\n use crate::cmp::{max, min};\n\n #[test]\n fn sanity_check_min() {\n assert_eq(min(0 as u64, 1 as u64), 0);\n assert_eq(min(0 as u64, 0 as u64), 0);\n assert_eq(min(1 as u64, 1 as u64), 1);\n assert_eq(min(255 as u8, 0 as u8), 0);\n }\n\n #[test]\n fn sanity_check_max() {\n assert_eq(max(0 as u64, 1 as u64), 1);\n assert_eq(max(0 as u64, 0 as u64), 0);\n assert_eq(max(1 as u64, 1 as u64), 1);\n assert_eq(max(255 as u8, 0 as u8), 255);\n }\n}\n", diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_false_inliner_9223372036854775807.snap b/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_false_inliner_9223372036854775807.snap index 0c845cc46e3..2351c31c405 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_false_inliner_9223372036854775807.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_false_inliner_9223372036854775807.snap @@ -204,7 +204,7 @@ expression: artifact } }, "bytecode": 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", 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", "file_map": { "5": { "source": "use crate::meta::derive_via;\n\n#[derive_via(derive_eq)]\n// docs:start:eq-trait\npub trait Eq {\n fn eq(self, other: Self) -> bool;\n}\n// docs:end:eq-trait\n\n// docs:start:derive_eq\ncomptime fn derive_eq(s: TypeDefinition) -> Quoted {\n let signature = quote { fn eq(_self: Self, _other: Self) -> bool };\n let for_each_field = |name| quote { (_self.$name == _other.$name) };\n let body = |fields| {\n if s.fields_as_written().len() == 0 {\n quote { true }\n } else {\n fields\n }\n };\n crate::meta::make_trait_impl(\n s,\n quote { Eq },\n signature,\n for_each_field,\n quote { & },\n body,\n )\n}\n// docs:end:derive_eq\n\nimpl Eq for Field {\n fn eq(self, other: Field) -> bool {\n self == other\n }\n}\n\nimpl Eq for u128 {\n fn eq(self, other: u128) -> bool {\n self == other\n }\n}\nimpl Eq for u64 {\n fn eq(self, other: u64) -> bool {\n self == other\n }\n}\nimpl Eq for u32 {\n fn eq(self, other: u32) -> bool {\n self == other\n }\n}\nimpl Eq for u16 {\n fn eq(self, other: u16) -> bool {\n self == other\n }\n}\nimpl Eq for u8 {\n fn eq(self, other: u8) -> bool {\n self == other\n }\n}\nimpl Eq for u1 {\n fn eq(self, other: u1) -> bool {\n self == other\n }\n}\n\nimpl Eq for i8 {\n fn eq(self, other: i8) -> bool {\n self == other\n }\n}\nimpl Eq for i16 {\n fn eq(self, other: i16) -> bool {\n self == other\n }\n}\nimpl Eq for i32 {\n fn eq(self, other: i32) -> bool {\n self == other\n }\n}\nimpl Eq for i64 {\n fn eq(self, other: i64) -> bool {\n self == other\n }\n}\n\nimpl Eq for () {\n fn eq(_self: Self, _other: ()) -> bool {\n true\n }\n}\nimpl Eq for bool {\n fn eq(self, other: bool) -> bool {\n self == other\n }\n}\n\nimpl Eq for [T; N]\nwhere\n T: Eq,\n{\n fn eq(self, other: [T; N]) -> bool {\n let mut result = true;\n for i in 0..self.len() {\n result &= self[i].eq(other[i]);\n }\n result\n }\n}\n\nimpl Eq for [T]\nwhere\n T: Eq,\n{\n fn eq(self, other: [T]) -> bool {\n let mut result = self.len() == other.len();\n for i in 0..self.len() {\n result &= self[i].eq(other[i]);\n }\n result\n }\n}\n\nimpl Eq for str {\n fn eq(self, other: str) -> bool {\n let self_bytes = self.as_bytes();\n let other_bytes = other.as_bytes();\n self_bytes == other_bytes\n }\n}\n\nimpl Eq for (A, B)\nwhere\n A: Eq,\n B: Eq,\n{\n fn eq(self, other: (A, B)) -> bool {\n self.0.eq(other.0) & self.1.eq(other.1)\n }\n}\n\nimpl Eq for (A, B, C)\nwhere\n A: Eq,\n B: Eq,\n C: Eq,\n{\n fn eq(self, other: (A, B, C)) -> bool {\n self.0.eq(other.0) & self.1.eq(other.1) & self.2.eq(other.2)\n }\n}\n\nimpl Eq for (A, B, C, D)\nwhere\n A: Eq,\n B: Eq,\n C: Eq,\n D: Eq,\n{\n fn eq(self, other: (A, B, C, D)) -> bool {\n self.0.eq(other.0) & self.1.eq(other.1) & self.2.eq(other.2) & self.3.eq(other.3)\n }\n}\n\nimpl Eq for (A, B, C, D, E)\nwhere\n A: Eq,\n B: Eq,\n C: Eq,\n D: Eq,\n E: Eq,\n{\n fn eq(self, other: (A, B, C, D, E)) -> bool {\n self.0.eq(other.0)\n & self.1.eq(other.1)\n & self.2.eq(other.2)\n & self.3.eq(other.3)\n & self.4.eq(other.4)\n }\n}\n\nimpl Eq for Ordering {\n fn eq(self, other: Ordering) -> bool {\n self.result == other.result\n }\n}\n\n// Noir doesn't have enums yet so we emulate (Lt | Eq | Gt) with a struct\n// that has 3 public functions for constructing the struct.\npub struct Ordering {\n result: Field,\n}\n\nimpl Ordering {\n // Implementation note: 0, 1, and 2 for Lt, Eq, and Gt are built\n // into the compiler, do not change these without also updating\n // the compiler itself!\n pub fn less() -> Ordering {\n Ordering { result: 0 }\n }\n\n pub fn equal() -> Ordering {\n Ordering { result: 1 }\n }\n\n pub fn greater() -> Ordering {\n Ordering { result: 2 }\n }\n}\n\n#[derive_via(derive_ord)]\n// docs:start:ord-trait\npub trait Ord {\n fn cmp(self, other: Self) -> Ordering;\n}\n// docs:end:ord-trait\n\n// docs:start:derive_ord\ncomptime fn derive_ord(s: TypeDefinition) -> Quoted {\n let signature = quote { fn cmp(_self: Self, _other: Self) -> std::cmp::Ordering };\n let for_each_field = |name| quote {\n if result == std::cmp::Ordering::equal() {\n result = _self.$name.cmp(_other.$name);\n }\n };\n let body = |fields| quote {\n let mut result = std::cmp::Ordering::equal();\n $fields\n result\n };\n crate::meta::make_trait_impl(s, quote { Ord }, signature, for_each_field, quote {}, body)\n}\n// docs:end:derive_ord\n\n// Note: Field deliberately does not implement Ord\n\nimpl Ord for u128 {\n fn cmp(self, other: u128) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\nimpl Ord for u64 {\n fn cmp(self, other: u64) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for u32 {\n fn cmp(self, other: u32) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for u16 {\n fn cmp(self, other: u16) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for u8 {\n fn cmp(self, other: u8) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for i8 {\n fn cmp(self, other: i8) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for i16 {\n fn cmp(self, other: i16) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for i32 {\n fn cmp(self, other: i32) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for i64 {\n fn cmp(self, other: i64) -> Ordering {\n if self < other {\n Ordering::less()\n } else if self > other {\n Ordering::greater()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for () {\n fn cmp(_self: Self, _other: ()) -> Ordering {\n Ordering::equal()\n }\n}\n\nimpl Ord for bool {\n fn cmp(self, other: bool) -> Ordering {\n if self {\n if other {\n Ordering::equal()\n } else {\n Ordering::greater()\n }\n } else if other {\n Ordering::less()\n } else {\n Ordering::equal()\n }\n }\n}\n\nimpl Ord for [T; N]\nwhere\n T: Ord,\n{\n // The first non-equal element of both arrays determines\n // the ordering for the whole array.\n fn cmp(self, other: [T; N]) -> Ordering {\n let mut result = Ordering::equal();\n for i in 0..self.len() {\n if result == Ordering::equal() {\n result = self[i].cmp(other[i]);\n }\n }\n result\n }\n}\n\nimpl Ord for [T]\nwhere\n T: Ord,\n{\n // The first non-equal element of both arrays determines\n // the ordering for the whole array.\n fn cmp(self, other: [T]) -> Ordering {\n let mut result = self.len().cmp(other.len());\n for i in 0..self.len() {\n if result == Ordering::equal() {\n result = self[i].cmp(other[i]);\n }\n }\n result\n }\n}\n\nimpl Ord for (A, B)\nwhere\n A: Ord,\n B: Ord,\n{\n fn cmp(self, other: (A, B)) -> Ordering {\n let result = self.0.cmp(other.0);\n\n if result != Ordering::equal() {\n result\n } else {\n self.1.cmp(other.1)\n }\n }\n}\n\nimpl Ord for (A, B, C)\nwhere\n A: Ord,\n B: Ord,\n C: Ord,\n{\n fn cmp(self, other: (A, B, C)) -> Ordering {\n let mut result = self.0.cmp(other.0);\n\n if result == Ordering::equal() {\n result = self.1.cmp(other.1);\n }\n\n if result == Ordering::equal() {\n result = self.2.cmp(other.2);\n }\n\n result\n }\n}\n\nimpl Ord for (A, B, C, D)\nwhere\n A: Ord,\n B: Ord,\n C: Ord,\n D: Ord,\n{\n fn cmp(self, other: (A, B, C, D)) -> Ordering {\n let mut result = self.0.cmp(other.0);\n\n if result == Ordering::equal() {\n result = self.1.cmp(other.1);\n }\n\n if result == Ordering::equal() {\n result = self.2.cmp(other.2);\n }\n\n if result == Ordering::equal() {\n result = self.3.cmp(other.3);\n }\n\n result\n }\n}\n\nimpl Ord for (A, B, C, D, E)\nwhere\n A: Ord,\n B: Ord,\n C: Ord,\n D: Ord,\n E: Ord,\n{\n fn cmp(self, other: (A, B, C, D, E)) -> Ordering {\n let mut result = self.0.cmp(other.0);\n\n if result == Ordering::equal() {\n result = self.1.cmp(other.1);\n }\n\n if result == Ordering::equal() {\n result = self.2.cmp(other.2);\n }\n\n if result == Ordering::equal() {\n result = self.3.cmp(other.3);\n }\n\n if result == Ordering::equal() {\n result = self.4.cmp(other.4);\n }\n\n result\n }\n}\n\n// Compares and returns the maximum of two values.\n//\n// Returns the second argument if the comparison determines them to be equal.\n//\n// # Examples\n//\n// ```\n// use std::cmp;\n//\n// assert_eq(cmp::max(1, 2), 2);\n// assert_eq(cmp::max(2, 2), 2);\n// ```\npub fn max(v1: T, v2: T) -> T\nwhere\n T: Ord,\n{\n if v1 > v2 {\n v1\n } else {\n v2\n }\n}\n\n// Compares and returns the minimum of two values.\n//\n// Returns the first argument if the comparison determines them to be equal.\n//\n// # Examples\n//\n// ```\n// use std::cmp;\n//\n// assert_eq(cmp::min(1, 2), 1);\n// assert_eq(cmp::min(2, 2), 2);\n// ```\npub fn min(v1: T, v2: T) -> T\nwhere\n T: Ord,\n{\n if v1 > v2 {\n v2\n } else {\n v1\n }\n}\n\nmod cmp_tests {\n use crate::cmp::{max, min};\n\n #[test]\n fn sanity_check_min() {\n assert_eq(min(0 as u64, 1 as u64), 0);\n assert_eq(min(0 as u64, 0 as u64), 0);\n assert_eq(min(1 as u64, 1 as u64), 1);\n assert_eq(min(255 as u8, 0 as u8), 0);\n }\n\n #[test]\n fn sanity_check_max() {\n assert_eq(max(0 as u64, 1 as u64), 1);\n assert_eq(max(0 as u64, 0 as u64), 0);\n assert_eq(max(1 as u64, 1 as u64), 1);\n assert_eq(max(255 as u8, 0 as u8), 255);\n }\n}\n", diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_true_inliner_-9223372036854775808.snap b/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_true_inliner_-9223372036854775808.snap index ea43238d515..654ebd37f9b 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_true_inliner_-9223372036854775808.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_true_inliner_-9223372036854775808.snap @@ -243,15 +243,15 @@ expression: artifact } } }, - "bytecode": 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", 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", "file_map": { "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "4": { - "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn(T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", + "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", "path": "std/array/quicksort.nr" }, "5": { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_true_inliner_0.snap b/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_true_inliner_0.snap index 8313db95d65..707df43c36c 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_true_inliner_0.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_true_inliner_0.snap @@ -231,15 +231,15 @@ expression: artifact } } }, - "bytecode": 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", 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"file_map": { "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "4": { - "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn(T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", + "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", "path": "std/array/quicksort.nr" }, "5": { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_true_inliner_9223372036854775807.snap b/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_true_inliner_9223372036854775807.snap index 5ec7849a048..e598eec8e31 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_true_inliner_9223372036854775807.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/hashmap/execute__tests__force_brillig_true_inliner_9223372036854775807.snap @@ -231,15 +231,15 @@ expression: artifact } } }, - "bytecode": 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tYUvYEraELWFL2BK2gq1gK9gKtoKtYCvYCrbpD70Dpj8czOkP/YvJ0x8GBahAAzowgBuYwPpwwbZgW7At2BZsC7YF24JtwbZgE9gENoFNYBPYBDaBTWAT2AQ2hU1hU9gUNoVNYVPYFDaFTWEz2Aw2g81gM9gMNoPNYDPYDDaHzWFz2Bw2h81hc9gcNofNYQvYAraALWAL2AK2gC1gC9iml5wfNXJ6yeACClCBBjy2/qSCHlF8uIEJrA+7l1xcQAEq0ICwJWwJW8KWsBVsBVvBVrAVbAVbwVawFWz12Xqk8eECClCBBnRgADcwgbAt2BZsC7YF24JtwbZgW7At2BZsApvAJrAJbAKbwCawCWwCm8CmsClsCpvCprApbAqbwqawKWwGm8FmsBls00v6UyamlwwGcAMTWB92LzlDf6uHJx8KUIEGdGAAN7BfWzbWh91LLi6gABVoQAcGcANhC9g2bN1LegKoJyofKtCADgzgBuaHifds+kO/qdMfBgO4gQmsD6c/DC6gABUIW8FWsBVsBVs9m/x+P+ACClCB/e54owMDuIEJrA+7P1xcQAEqELbuD/3ZGj1r+XADE1gfdn+4uIACbNtuPLb+eIqetXwYwA1MYH3Y/eHiAgpQgbApbApbd4KprDtBfyhGT1U+FKACDejAAG5gv4psrA+7E1xcQAEq0IAODOAGwuawBWyBV9FbWvuU6y3dn+nS45EXe0ufISPp8ciHAlSgAR0YwA1MYBfZn53TlwcXF1CACjSgAwPYtj7Gvf0v1oe9/S8uoAAVaEAHBhC23v7W729v/8Yej3y4gL1uNPYKuzGB9WFv6YsLKEAFGtCBAYStt/SZhJIeebzYW/riAgpQgQZ0YNu8cQMTWB/OBxP1pyHNRxMNHtsZaZIeeXxowG5X898GcH/YW/oMOkmPPIp0Zb15LzowgGcF7yPUm/difdib9+ICClCBBnRgAGHrzXumsaRHHi/25r24gAJUoAEd2LZ+q3vPR78lvecv1oe95y8uoAAVaEAHBhC2DduGrXf3VNa7+0wASQ9CPnRgADcwgfVh7+6L/Sr6XO/dfVGBBnRgADcwH/bI4yjm87/OcI3MJ4D5/H83MIGnyN3YW/riAgpQgQZ0YAA3MIGwCWwCm8AmsAlsApvAJrAJbAKbwqawKWwKm8KmsClsCpvCprAZbAabwWawGWwGm8FmsBlsBpvD5rA5bA6bw+awOWwOm8PmsAVsAVvAFrAFbAFbwBawBWwB24Ztw7Zh27Bt2DZsG7YN22z/aqwP+5/8iwsoQAW2TRsdGMANTGB92E3h4gK27deoQAM6MIAbmMB62JOScubEpCclHwpQgQZ0YAA3MIH14YJtwTa9JBoVaEAH9rrnre7pR8l+Fd0fLirQgA4M4AYmsD7s/nARtu4P5wMfpKcfHxrQgQHcwATWh9MfsnEBBajAtkmjA9umjRuYwP4Hu1eYy4PBBezFrLG/rd/13uiDvdEvLqAAFWhABwZwA9vWNfRGH+yNfnEBBdi2Ph96o1cfod7oF9u2GzcwgfVhb/SLCyhABbat36je6BcDuIEJrA97o19cQAEqELaCrWDrjV59jHujX6yHPR75cAEFqEADtm0+PTOAG5jA+rA3+sUFFKACDQjbgq0vGs6vrUqPRz6sD/ui4aKej8D8NcbB1biBCawP+xN5Ly6gABVoQAfCpm2TxgTWh/YDLqAAFWjAfneqMYAbmMC29SH0H7Bt1ihABfax6FfRneBifDifR+qN/W39rs8nkA46MIAbmMD6cP+ACyjAtvVr608lvejAAG7gsa0+H/rzSVe/iv6E0otty0YBKtCADgzgBiawbf1G1Q+4gAJUoAEdGMANTOBn69nFhwvYNmtUoAEdGMANTGB92J+A2p8z3LOLDwWoQAM6MIAbmMD6UGCTtmWjABVowLNuf/RxzyNqf9RxzyM+FKACDejAAG5gAutDg603en+Ecs8jPlSgAR0YwA1MYNvOCd7ziA8XUIBt6+PWn0Z8sW19lvQnEl/cwD4W/Sr6c4kHuxNc7HX70457d/cNip4b1L5r2XODDxVoQAcGcAMTWB/OJw0Ptq0aBajAY+vbbj6fOTwYwA1MYH3Y+/jiAgpQgbD1PtZ+J3sfX9zABNbDnht8uIACVKABHRjADWybNdaHvY8vLqAAFWhAB7ZtN25gAuvD3scXF1CACjSgA2Hr3d23jXvG8GF92Hv+4ln3/IKn9Nyg9t3Qnht8mMD6sPfxxQUUoAIN6EDYeh/3rdWeG3xYH/Y+vriAAlSgAdv2awzgBiawbX3ceh9fbFufJf0p4xcV2MeiX0X/O38xgL3u6QQ9C6h997ZnAbVvuPYs4EMHnhX67mLPAj5MYH3Ye/7iAgpQgQZ0IGwJW8KWsBVsBVvBVrAVbAVbwVawFWz12XoW8OECClCBBnRgADcwgbAt2BZsC7YF24JtwbZgW7At2BZsApvAJrAJbAKbwCawCWwCm8CmsClsCpvCprApbAqbwqawKWwGm8FmsBlsBlv3h35K0HODDzcwgfVh94eLbfNGASrQgA4M4AYmsG39JwC6P1xcQAEq0IAODOAGJhC2DduGrXtJ36DoucGHBnRgADcwgfVh95K+x99zgw8FqEADOjCAG5jA+rBgm79oII0CVKABe91zWHoWUM8v/ErPAj4UoAIN6MAAbmAC68MFW/eHfqLQs4APFWhABwZwAxPYtvOPT88CPlxAAbYtGw3YtmoM4Ab2ke9XMf2hcfrD4FnsfGCu9FCf9q3gHup7WB/2Rr+4gAJUoAEdGMC29WvrjX6xPuyNfnEB29bnQ2/0vjHaQ30P26aNAdzABNaHvdEvLqAAj61vgfZQ30MHBnADE1gf9ka/uIAChG3DtmHrjd63gnuo72EC68Pe6BcXUIAKbFsfod7oFwO4gQmsD3ujX1xAASoQtr5o6Pu0PdT3cAPzYY/vad857UE97ZuSNX+8ZDCAG5jA+nD+jMngAgpQgbD1Ru/bsD2o93ADE1gf9ka/uIACbJs3GtCBAWxbNSbw2Pr+bw/qPVzAPvL9KvpC4KIBz2J9y6sn7rTvhvbE3UMFGtCBAdzABNaHvdEvtq1fW2/0iwo0oAPb1udDb/S+G9oTdw/bdhpIT9w9XEABKtCADgzgn836vmdP3D2sD/uz5i8uoAAVaEAHBhC2DduGrf92Q9//7Tm8hwJUoAEdGMANbFsfof5rDoP99xwuLqAAFWhABwZwA2Hrv7Jybs5qT+c9XEAB9rq7sVfIxvqw/5bDxQUUoAIN6MAAbiBs8xeMqv/I0g+4gAJUoAEdGMC2RWMC68P+uysXj+3c9NWeuHt4bOemr/bE3UMH9lndr6IvBC7mh/13V84tL/3Nnh90YAA3MIH14ez5wQUUoAK7Xm90YAA3MIH1Yf+diIsLKEAFwhawBWwBW8AWsG3YNmwbtg3bhq33/OpTuff8xQ1MYH3Ye/5i2/ok6D1/UYEGdGAANzA/LKzb+3j1Jut9fDGAG5jAethjdg8XUIAKbFs2OjCAG5jA+rD3/MUFFKACYVuwLdgWbAu2BZvAJrAJbAKbwCawCWwCm8AmsClsCpvCprApbAqbwqawKWwKm8FmsBlsBpvBZrAZbAabwWawOWwOm8PmsDlsDpvD5rA5bA5bwBawBWwBW8AWsAVsAVvAFrBt2DZsG7YN24Ztw7Zh27Bt2DZsCVvClrAlbAlbwpawJWwJW8JWsBVsBVvBVrAVbAVbwVaw1WeT3w+4gAJUoAEdGMANTCBs6CWCXiLoJYJeIuglgl4i6CWCXiLoJYJeIuglgl4i6CWCXiLoJYJeIuglgl4i6CWCXiLoJYJeIuglgl4i6CWCXiLoJYJeIuglgl4i6CWCXiLoJYJeIuglgl4i6CWCXiLoJYJeIuglgl4i6CWCXiLoJYJeIuglgl4i6CWCXiLoJYJeIuglgl4i6CWCXiLoJYJeIuglgl4i6CWCXiLoJYJeIuglgl4i00uqcQMTWB9OLxlcQAEq0IAOhC1hS9gStoKtYCvYCraCrWAr2Aq2gq0+W88C2nlarT0L+FCACjSgAwO4gQmsDxds3UvO83ntWcCHCjSgAwPYNmtMYH3YXeM8ztaeBbTztFp7FtDO02qdv6F6MYH14fxt1cEFFKACT73nA411/srqxQAem3bp3R8u1ofdHy4uoAAVaMC29Zva/eHiBiawPuz+cHEBBahAA/Y7uRsDuIEJrA+7P1xcQAEq0ID92voQdn+4uIEJrA+7P1xcQAEq0ICwbdg2bBu2DVvClrAlbAlbwpawdX/QPmm7P1xMYH3Y/eHiAratT/DuDxcN6MAAbmAC6+H8VdeLvcJuDOAGJrA+7D1/cQEFqEADti0bA7iBCawP+/rh4gIKUIEGhE1gE9gENoFNYVPYFDaFTWFT2BQ2hU1hU9gMNoPNYDPYDDaDzWAz2Aw2g81hc9gcNofNYXPYHDaHzWFz2AK2gC1gC9gCtoAtYAvYAraAbcO2Yduwbdg2bBu2DduGbcO2YUvYEraELWFL2BK2hC1hS9gStoKtYCvYCraCrWAr2Aq2gq0+W08ePlxAASrQgA4M4AYmELYF24JtwbZgW7Chlzh6iaOXOHqJo5c4eomjlzh6iaOXOHqJo5c4eomjlzh6iaOXOHqJo5c4eomjlzh6iaOXOHqJo5c4eomjlzh6iaOXOHqJo5c4eomjlzh6iaOXOHqJo5c4eomjlzh6iaOXOHqJo5c4eomjlzh6iaOXOHqJo5c4eomjlzh6iaOXOHqJo5c4eomjlzh6iaOXOHqJo5c4eomjlzh6iaOXOHqJo5c4eomjlzh6iaOXOHqJo5c4eomjlzh6iaOX+PSSalxAASrQgA4M4AYmsB7G7wdcQAEq0IAODOAGJhC2BduCbcG2YJu/Ny+NDgzgBiawPpy/PT+4gAJUIGzzV+hXYwA3MIH14fw9+sG2WaMAFdjremOvcC72Yv7ufDYuoAAVaEAHBnADT71njkl7+vFi94eLx+ZdeveHiwo0oAMDuIEJbFu/qd0fLi6gABVoQAcGcAMT2O/kuZCN+Zv1g23rt3r+9vagAg3owABuYALrw+4PF2FL2BK2hC1hS9gStoQtYSvYCraCrWAr2Aq2gq1gK9jqs/Wk5MMFFKACDejAAG5g26yxPuz+cHEBBajAtkWjAwO4gQmsD7s/XFxArNt7/vzxD+3px4f1Ye/5iwsoQAUa0IEBbFs2JrA+7P5wcQEFqEADOjCAsBlsBpvD5rA5bA6bw+awOWwOm8PmsAVsAVvAFrAFbAFbwBawBWwB24Ztw7Zh27Bt2DZsG7YN24Ztw5awJWwJW8KWsCVsCVvClrAlbAVbwVawFWwFW8FWsBVsBVt9tvz9gAsoQAUa0IEB3MAEwrZgW7At2BZsC7YF24JtwbZgW7AJbAKbwCawCWwCm8AmsAlsApvCprApbAqbwqawKWzoJYlekugliV6S6CWJXpLoJYlekugliV6S6CWJXpLoJYlekugliV6S6CWJXpLoJYlekugliV6S6CWJXpLoJYlekugliV6S6CWJXpLoJYlekugliV6S6CWJXpLoJYlekugliV6S6CWJXpLoJYlekugliV6S6CWJXpLoJYlekugliV6S6CWJXpLoJYlekugliV6S6CWJXpLoJYlekuglhV5S6CWFXlLoJYVeUuglhV5S6CWFXlLoJYVeUuglhV5S6CU1vaQaHRjADUxgfTi9ZHABBahA2AQ2gU1gE9gENoVNYVPYFDaFTWFT2BS27iVnWl77UxMvdi+5uIACVKABHRjADYTNYHPYHDaHzWFz2LqXnF8K0J7hfLiBCawPu5dcbJs1ClCBva439grnInLmMqNPo+4PFwWoQAM6MIAbeOo94/Q6c5mD3R8uHtvu0rs/XFSgAR0YwA1MYNv6Te3+cHEBBahAAzowgBuYwLbpf/iHzVzmxQUUoAIN6MAAbmACYev+cMb/bWY4LwqwbdFoQAcGcAMTWB92f7iIdXvPn0Fpm7nM84sJNnOZF3uFPNh7/uICClCBBnRgADcwgbD1nj+/H2AzrXlRgMd2Pm/FeobzoQMDuIEJrA97z1/Eur2Pz0i/zVzm+bUCm7nMi71CH83exxcXUIAKNKADA7iBCYRtw7Zh27Bt2DZsG7be89nnWe/5iwlsW58lvecvLqAAFWhABwYQ6/Y+zj77eh9nn3K9jy+eFaoPQO/jixuYwHo4c5kXF1CACjSgAwO4gQmEbcG2YFuwLdgWbAu2BduCbcG2YBPYBDaBTWAT2AQ2gU1gE9gENoVNYVPYFDaFTWFT2BQ2hU1hM9gMNoPNYDPYDDaDzWAz2Aw2h81hc9gcNofNYXPYHDaHzWEL2AK2gC1gm/5QjQ4M4AYmsD7s/nB+r8NmLvOiABVoQAcGcAP7tWVjfdj94eICClCBBnRgADcQtoStYOtrgvNrMTZzmRcVaEAHBnADE/hn8/P7ItZzmQ8XUIAKNKADA7iBCYRttW01LqAAFdjrWmOvcNp2z1o+XEABKtCADgzgBiYQNm1bNC6gABVoQAcGcAPbpo31of2AC9i23ajAtmWjAwPYR75fxfSHwfrQe91qPCusftfPnn+4gQmsD8+ef7iAAlSgAdvWry0CuIEJrA932/p82G3rV7EF2DZpNKADA7iBCawP8wdsW79RKUAFGtCBAdzABNaH9QPCVrAVbNW2PsblwABuYALrYc9PPlzAYztTitbzkw8N6MAAbmAC68Pe8xcXELbe82fA0np+8qEDA9jrnsPdk5J+xiOtP0vxoQEdGMANTGB92Hv+4gLC1nv+jPpZz08+dGAANzCB9WHv+Ytts0YBKtCAbevj1nv+YtuqMYH1ofeR71fhCyjAs+4Z/bSeiXTtd733/GDv+YsLKEAFGtCBAdzAtvVr6z0/2Hv+4gIKsG19PvSe134Vvecvtk0bNzCB9WHv+YsLKEAFHpv1G9V7/mIANzCB9WHv+YsLKEAFwlawFWy957WPce/5i/WwP1bx4QIKUIEGbJs0BnADE1gf9p6/uIACVKABYes9fz4vyHp+8mEC68PuBGcEwHom0s+Da+uZyIcbmMD6sPf8xQUUoAINCFvv+TMuYD0T+TCB9WHv+YsLKEAFts0bHRjADWxbH7fe84O958+cgvVM5EMB9pHvV+EGdOBZ90wkWM85uve73nv+ogIN6MAAbmAC68Pe8xfb1ge29/xFBRrQgQHcwATWh73nL8KWsCVsCVvClrAlbAlbwlawFWwFW8FWsBVsBVvBVrDVZ+s5x4cLKEAFGtCBAdzAtnljfdh7/uICClCBbduNDgzgBiawPuyfAy4uINbtPX+ePljPLj7sFc6+6NnFhwsoQAUa0IEBPLbzSdXWs4sP68Pe8xcXUIAKNKADAwibwWawOWwOm8PmsDlsDpvD5rA5bA5bwDb9wRoFqMC2SaMDA7iBCawPuz9cXEABKhC2DduGbcO2YduwJWwJW8KWsCVsCVvClrAlbAlbwVawFWwFW8FWsBVsBVvBVp+tZxcfLqAAFWhABwZwAxMI24JtwbZgW7At2BZsC7YF24JtwSawCWwCm8AmsAlsApvAJrAJbAqbwqawdS85j8Gs/zr0QwcGcAMT2LbT2mJ6yeACClCBBnRgADcwgbA5bA7bdI1s7BW0MYH14fSHwVPZebxmMXu+GjcwgfXh7PnBBRSgAg3owGPrB1M9j/gwgfVh7/mLCyhABRrQgbAlbAlbwlawFWwFW8FWsBVsBVvv+d2nXO/5i/Ww5xEfLqAA22aNBnRgADcwgfVh7/mLWLf3cT8D7BnDh71CNNaHvY8vLqAAFWhAB7ZtN25gAuvD3scXF1CACjSgA2FT2BQ2hc1gM9gMNoPNYDPYDDaDzWAz2Hof97PFnkd8KEAFGtCBAdzABNaHAVtfE/QDxZ5HfKhAAzowgG3r07P7w8X6sDtBP/jrGUPvx2s9Y+j9KK5nDB8msD7sPX9xAQWowH53stGBAWxbl957/mJ92Hv+4gIKUIEGPLbqN7X3/MUNTGA97BnDhwsoQAUasF+bNwZwAxNYH/aev7iAAlSgAfu1aWMANzCB9WH3h4sLKEAFGhA2gU1gE9gENoVNYVPYFDaFTWHr/tCPwXrG8GEC68PuDxcXsG3WqEADOjCAG5jA+tCxbu/5fkrQc4MPe4VoTGB92Hv+4gIKUIEGbNtuDOAGJrA+7GuCiwsoQAUaELYN24Ztw7ZhS9gStoQtYUvYEraELWFL2E5/iH5O1nODDxdQgAo0oAMDuIEJ/Gw1/aEaF1CACjSgA9smjRuYH65+FdrY9Vpj1xuNAdzABNaH8gMuoAD73clGAzqwbV26bGAC60P9ARdQgAo8ttVv6tnzDwO4gQmsD8+ef7iAAlRgvzZvdGAANzCB9aH/gAvYttWoQAM6MIAbmMD6MH7ABYQtYAvYArZoW59GsYEJbFufXPsHXEABKtCADgwg1s1eod+o7BX6BE8DOjCAG5jA+rD3/MUFFCBsBVvBVrAVbAVbPZv3LODDBRSgAg3owABuYAJhW7At2BZsC7YF24JtwbZgW7At2AQ2gU1gE9gENoFNYBPYBDaBTWFT2BQ2hU1hU9gUNoVNYVPYDDaDzWAz2Aw2g81gM9gMNoPNYXPYHDaHzWFz2Bw2h81hc9gCtoAtYAvYAraALWAL2AK2gG3DtmHbsG3YNmwbtg3bhm3DtmFL2BK2hC1hS9gStoQtYUvYEraCrWAr2Aq2gm16STQGcAMTWA/X9JLBBRSgAg3owABuYNuysT6cXjLYtmoUoAIN6MAAbmB+KFi3+8P5q0S+pj/sxgCeFc5wgveM4cP6sPvDxQUUoAIN6MAAts0bE1gfdn+4uIACVKABHRhA2Aw2g81hc9gcNofNYXPYHDaHzWFz2AK2gC1gC9gCtoAtYAvYAraAbcO2Yduwbdi6P5zJDO8Zw4cB3MAE1ofdH6RP++4PFwWoQAM6MID7w8K6veelT9re8xd7hd5OvecvbmAC62HPDT5cQAG2rRoN6MAAbmAC68Pe8xcXUICwLdgWbAu2BduCbcEmsAlsApvAJrAJbALb9AdtTGB92P3hfCqa94zhQwEq0IAODOAGJrA+NNgMNoPNYDPYDDaDzWAz2Aw2h81hc9gcNofNYXPYHDaHzWEL2AK2gC1gC9gCtoAtYAvYArYN24Ztw7Zh27Bt2DZsG7YN24YtYUvYEraELWFL2BK2hC1hS9gKtoKtYCvYCraCrWAr2Aq27iVn0sx7HvHhAgpQgQZsWzQGcAMTWB92L7m4gAJUoAFhW7At2LprnIEZ73nEOHNt3vOIDx0YwLOC9Qvq/nCxPuz+cHEBBahAAzowgLApbAqbwWawGWwGm8FmsBlsBpvBZrA5bA6bw+awOWwOm8PmsDlsDlvAFrAFbAHb9IdqdGAANzCB9WH3h/N5Nt6ziw8FqEADOjCA+8PEur3nrU+j3vMXewVtDOAGJrA+7D1/cQEF2DZrNKADA7iBCayHPY/4cAEFqEADOjCAG5hA2BZsC7YF24JtwbZgW7D1nj9Tf97ziA/rw+4EFxdQgAo0oAMDCJvAJrApbAqbwqawTX+IRgcGcAMTWB9Of6jGBRTgWfcMFnrPI8aZG/SeR4wz1Oc9j/hwAQWoQAM6MICn3jNT5j2l+LA+7D3vXXrv+YsCVKABHRjADWxbv6m95wd7z19cQAEq0IAODOAGti0b68PuBBcXUIAKNKADA7iBsCVsBVvBVrAVbAVbwVawFWwFW322nl2MM1PmPbv4UIDHdkZjvGcXHzowgBuYwPqw+8NFrNt7/szveM8jxhn1855HfNgrnPOh5xEfLqAAFWhABwZwAxMIm8KmsClsCpvCprApbAqbwqawGWwGm8FmsBlsBpvBZrAZbAabw+awOWwOm8PmsDlsDpvD5rAFbAFbwBawdX84H1XgPbv4MIAbmMD6sPtD9Ane/eGiABVoQAcGcAOPbfeZ2v1hsPvDxQUUoAIN6MAAbiBsCVvB1v1h9x7q/nBRgQZ0YAA3MIFtO+9kzy4+XEABKtCADgzgBiYQtu4PZ0zJe3bxoQAV2OtmY69wWkXPIz5cQAEq0IAODOAGJhC27g9n+sZ7HvGhABVoQAcGcAPbthvrw+4PFxewbatRgW2TRgcGsM/qfhXTHwbrw97+Z9bHYzZ622ajDyawPpyNPriAAlSgAR3YRfb50Bv9YgLrw97oFxdQgAo0oANh27D1Rs8upzf6YG/0iwsoQAUa0IEB3EDYEraCrWDrjV59jHujXzSgAwO4gQmshz2w+HABBahAA7bt1xjADUxgfdgb/eICCrBt2mhABwZwAxNYH3ZTuLiAAoStm8IZz/EebnwYwP1hb/8zVOI9sBjnt8m9BxYfOjCAG5jA+rA3+sUFFCBsvdHPdIj3wOLDAG5gAuvDvhC4uIBti0YFGtCBf7b96+N2+sPDPNhnyekPF09/eNhndb+K7g8XFdjrSmOv0Edo/4B60BsN6MBeoYvcG5jA+vBs6f1r8dnSDxVoQAcGcAMTWB+eLb37GWDPIz4UoAIN6MAAbmAC62HPIz5cQAG2bTUa0IEB3MAE1ofrB2ybNQpQgQZ0YAA3MIH1ofyAsEnbvFGBBnRgr3sOS88Y7n4O2TOGDxVoQAcGcAMTWB/aDwjb2dK7n3r2jOFDAzowgBuYwPrQ27YbF1CACmxbHzd3YNv6LOktfTGBfVb3q5gtPbiAva429r5oWySwPpwtPbiAAlSgAR0YwK63z4fe6Bfrw/PP+MNj68d2PWP4UIEGdGAANzCBx9a3z3vG8OECClCBBnRgADcwgZ+tZwwfLmC/tmpUoAEdGMANTGB92Hu+76/35OFDASrQgA4M4AYmsD4U2HrPn98Q955HfKhAA/a657D0jOHuZwc9Y/hQgAo0oAMDuIEJrA8Ntt7z53PzvWcMHyrQgA4M4AYmsG2nn/WM4cMFFGDb+rj1nr94bH0vvmcMH25gH/l+Fb3nB3vPXzzr9o3ynhvc/ai+5wYfJrA+7D1/cQEFqEADOrDr7fOh9/zFBNaHvef7vnJ/3uBDASrQgA4M4Aa2rY9F7/nB3vMXF1CACjSgAwO4gbDVs0XPGD5s224UoAIN6MAAbmACj+3cbY6eMXy4gAJUoAEdGMANTCBsAlvv+XObO3rG8KECDdjr2sHe8+cmdfTc4EMBKtCADgzgBiawPjTYes+fu9jRc4MPFWhABwZwAxPYNj3Ye/7iAgqwbX0Ie89fbFufML3nL25gH/l+Fb3nB3vPX+x1q7HPnbb1nr+YwPqw9/zFBRSgAg3owGOLPsa9589t4+hZwIf1Ye/5i8cWfT70nr+oQAM6MIAbmMC29XvWe/7iAgpQgQZ0YAA3MIGfrWcBHy5g23ajAg3owABuYALrw97z59dJo2cBHwpQgQZ0YAA3MIH1ocDWe/7clIz+bMKHCjRgr3sOS88C7nObMHoW8KEAFWhABwZwAxNYHxpsvefP70NGzwI+VKABHRjADUxg284O6FnAhwsowLb1ces9f7FtfZb0nr+4gX3k+1X0nh/sPX+x163GPnfa1nv+YgLrw97zFxdQgAo0oAOPLft86D1/MYH1Ye/5c4czer7voQAVaEAHBnAD29bHovf8YO/5iwsoQAUa0IEB3EDY6rP1LODDtnmjABVoQAcGcAMT2LZzPvQs4MMFFKACDejAAG5gAmHrPX9++zJ6FvChABV41j2/pBc937fPjbvo+b6HCyhABRrQgQHcwATC1nv+3AWMnu97KEAFGtCBAdzAtq3G+rD3/MUFbFsft97zF9vWZ0nv+YsB7GPRr6L3/MX6MHrd3dgr9Lvee/7iBiawPuw9f3EBBahAA/7Z8tev7ez5hxuYwPrw7Pn89flw9vxDOdgv6Oz5h3bw1+jAAG5gAuvD+gEXsG39npUCDejAAG5gAuthz+w9XEABKtCAbYvGAG5gAuvD9QMuoADbVo0GdGAANzCB9aH8gAsoQNjOv/N5fh0ver7vYQD3h6cT5Pk9teiZvTz3BqNn9h46MIAbmMD60H7ABRQgbNY2b3RgADcwgfWh/4AL2DZpVKABHdi2Pm6+gW3rs8Trw/gB+1j0qwgBKrAXO227h+9S+l0/G/2hABVoQAcGcAMTWB/2Rpd+bb3RLwpQgQZsW58PvdGlX0Vv9IttW431YW/0iwsoQAUa0IFt6zeqN/rFBNbDHtR7uIACVKABHRjADUxg284x7kG9hwsoQAUa0IEBPLZzozF6UO9hfdgb/eICClCBBnRgAGGTtq3G+rC3/8UF7HWtsVfwxgTWh73RLy6gABVoQAcGELbe6OdOZPSg3sXe6BcXUIAKNKAD26aNG5jA+rA3uvZx641+sW19lvRGv2jAPvL9KiKA+8PuBOeWYvTwXa5BAzowgBuYwPqw9/zFBRTgsVmfD73nLzowgMd2bghGD989rA97z19cQAEq0IBt62PRe/7iBiawHvbw3cMFFKACDejAAG5g27yxPuw9f3EBBahAAzqwbdm4gQmsD3vPX1xAASrQgA6Erff8mXuNHtR7WB/2nr941j1/aTN6+C77RlgP3z1MYH3Ye/7iAgpQgQZ0IGy95/v2Yw/fPawPe89fXEABKtCAbVuNAdzABLatj1vv+Ytt67Ok9/xFBfax6FfRe/5iAHvd0zV6oC6tbb3nLxrQgQHcwATWh73nLy5g19vnQ+/5iwZ04LGdAdbogbqHCawPe89fXEABKvDY+tZfD9Q9DOAGJrAe9kDdwwUUoAIN6MAAtk0bE1gf9p6/uIACVKAB2xaNAdzABNaHvecvLqAAFWhA2HrP9z3HHr57mMD68Pxon32bsAfqsu8C9kDdww1MYH3Ye/7iAgpQgQaErff8GcaMHqh7mMD6sPf8xQUUoAL73alGBwZwA9vWx633/GDv+b4b2nN4DwXYx6JfRe/5iw7sdU/X6Nm67Nv9PVv3UIEGdGAANzCB9WHv+Ytdb58PvecvKtCAfSz6fOg9f3EDE1gf9p6/uIACbFuX03v+ogMDuIEJrIc9W/dwAQWoQAM6MIAbmEDYFmwLtgXbgm3BtmBbsC3YFmwLNoFNYBPYBDaBTWAT2AQ2gU1gU9gUNoVNYVPYFDaFTWHr/tB3kHsO72L3hzNPGz2H91CACjSgAwO4gW2zxvqw+8PFBRSgAg3owABuIGzdH/qGa8/hPVxAASrQgA4M4AYmELYN24ate8kZa42e5HtoQAcGcAMTWB92L6k+hN1LLgpQgQZ0YAA3MIH1YcHWvaRvzvZQ30MFGrDXPYelB/Wy7972oN5DASrQgA4M4AYmsD5csHV/6LvCPaj3UIEGdGAANzCBbTv/DvWg3sMFFGDbdqMB25aNAdzAPvL9Kro/DHZ/uNjrVuPfCtX3aXsOr/q2cc/hPRSgAg3owABuYALrQ4fNYXPYHDaHzWFz2Bw2h81hC9gCtoAtYAvYAraALWAL2AK2DduGbcO2Yduwbdg2bBu2DduGLWFL2BK2hC1hy7b1JssAbmAC68P6ARdQgAo0IGwFW8FWsNVn65m9hwsoQAUa0IEB3MAEwrZgW7At2BZsC7YF24JtwbZgW7AJbAKbwCawCWwCm8AmsAlsApvCprApbAqbwqawKWwKm8KmsBlsBpvBZrAZbAabwWawGWzTS04/q+klgwvY/25KowEdGMANTGB9ONcEgwvYL2g3KtCADgzgBiawPpwGMriAsHUDORPl0QOADx0YwA1MYH3YDeTiAgoQtoQtYesG0k+YegDwYQLrw24gFxdQgApsmzY6MIAbmMC6uHsA8OECClCBBmybNQZwA/PDbhXn+dDuob46j392D/U9DOAGJrA+7KZwcQEFqEDYuimcm767Pzjw4QYmsD7spnBxAQXYtmg0oAMDeGznadTuAcCHx3ae+eweAHy4gH1W96uYpjBowF5XDvZGlz5C30XD/n0XDfv3XTTs31wpzLcFsBfzxgTWh32lcHEBBahAAx6bdL290S9uYALrw97oFxdQgAo0IGwbtg3bhm3DlrAlbAlbwpawJWwJW8KWsCVsvdGl90Vv9IsCVKABHdi2PhF7o19MYD3sqb+HCyhABQbwrHAeme2e5Ht4VjgPx3ZP8j1UoAEdGMANTGDbzonYk3wPF1CACjSgAwO4gQmETWFT2BQ2hU1hU9gUNoVNYVPYDDaDzWDrjX4euu2e+nvowABuYALrw24KF9tmjQJUoAEdGMANTGB92P3hImwBW8AWsAVsAVvAFrAFbBu2DduGbcO2Yduwbdg2bBu2DVvClrAlbAlbwpawJWwJW8KWsHV/OM9Yd08IPhSgAg3owABuYALrYU8IPlxAASrQgA4M4AYmELYF24JtwbZgW7At2BZsC7YF24JNYBPYBDaBTWAT2AQ2gU1gE9gUNoVNYVPYFDb99rFMf/DGBRSgAg3owABu4Kn3PM7ePSF4sfvDxQUUoAIN6MAAbiBsDlvAFrAFbNMfqtGADgzgBiawbecflJ4mfLiAAlSgAR0YQKzbe/48wN89IfiwV+hD2Hv+YgA3MIH1Ye/5iwvYNmtUoAEdGMANTGA97AnBhwsoQAUa0IEB3MAEwrZgW7At2BZsC7YFW+/5M0+we0LwYQLrw97zFxdQgAo8tvObQrsnBB8GcAMTWB/2nr+4gAJUIGwKm8LWneA8id89TfiwPuz+cHEBBahAA7at38nuDxc3MIH1YfeHiwsoQP/eyd7z5wn/7gnBi73nLy6gABVoQAcGcANhC9g2bBu2DVvv+TOysHuw8KEDA7iBCWzbuYrvwcKHCyhABRrQgQHEur3nz0PN3cOCD88K0WdJ7/mLAdzABNbDHhZ8uIDHdp7d7h4WfGhABwZwAxNYH/aev7iAsC3YFmwLtgXbgm3BtmAT2AQ2gU1gE9gEtt7z5/e+dg8LPkxgfdh7/uICClCBbdNGBwZwAxNYH/aev7iAAlQgbAabwWawGWwGm8PmsDlsDpvD5rA5bA6bw+awBWwBW8AWsAVsAVvAFrAFbAHbhm3DtmHbsG3YNmwbtg1b94czi7J7CPFi94eLCyhABRrQgTj75j6BNQpQgQZ0YAA3MIH10Oc+wWDXm40CVKABHRjAYztzNrsHCx/Wh90fLi6gABVowGM7wyq7BwsfbmAC68PuDxcXUIAKNCBsApvAJrAJbAqbwqawKWwKm8KmsClsCpvC1v3hTADtHkJ8KEAFGtCBbesj3/3hYgLrw+4PFxdQgArEur3nz1zQ7sHCh71CNApQgQZ0YAA3MIFtO//k96f6PVxAASrQgA4M4AYmELaELWFL2BK2hC1hS9gStoQtYSvYCraCrfvDGTrbPYT40IFtq8YNTGA97CHEhwsoQAUa0IEB3MAEwrZgW7At2BZsC7YF24JtwbZgW7AJbAKbwCawCWwCm8AmsAlsApvCprApbAqbwqawKWwKm8KmsBls0x+yUYAKNKADA3hsZy5o98Diw/qw+8PFBRSgAg3owADC5rA5bN01zujG7iHEOiNYuz8M8OEGJrBXOFukPwzw4QIKUIEGdGAAj61a0f3hYn3Y/eHiAgpQgQZ0YABhS9gStoKtYCvYCraCrWAr2Aq2gq0+Ww8sPlzAfierUYEGdGAAN7Btq7E+7P5wcQEFqEADOhDr9p4/vxy8ewjxYa+gjQZ0YAA3MIH1Ye/5i22zRgEq0IAODOAGJrA+7D1/ETaDzWAz2Aw2g81gM9gMNofNYXPYHDaHbfZ8NgZwA9sWjfVhXz9cXEABKtCADgzgBsIWsG3YNmwbtg3bhm3DtmHbsG3YNmwJW8KWsCVsCVvClrAlbAlbwlawFWwFW8FWsBVsBVvBVrDVZ8vfD7iAAmybNxrQgQHcwAT+2f6ed5493ROLHy9iIVZiI3biIN7ESUxeIa+QV2Z9ae7qd2MC68PpFoPzfdY8nn63bF5HNSuxETtxEG/iJC6w/4gXMXmdvE5eJ6+T18nr5HXyBnmDvEHeIG+QN8gb5A3yBnmDvJu8m7ybvJu8m7ybvJu8m7ybvJu8Od4+1rmIhViJjdiJ27v6/MpNnMQFrh/xIhZiJTZiJyZvkbfIW/D24OLHi1iIldiInTiIN3ESk3eRd5F3kXeRd5F3kXeRd5F3kXeRV8gr5BXyynh/zUbsxEG8iZN4vKdX9Fjjx4tYiJXYiJ04iMebzUlcYPsRL2IhVmIjduIgJq+R18g7/aqnzWr61WUhVmIjduIg3sTt7dGtmn41PP3q8iIWYiU2YicO4k1M3ulXPR1W068uL2IhnvX72E3/6Zmsmv4zPP3n8iIWYiU2YicO4k1M3uk/59NOd03/ubyIhViJjdiJg3i82pzE9Th/038uj3c3C/F4s9mInXjOE2/exAme3nLmqfI3PeRMO+VvesjlTZzEBZ4ecnkRC7ESG/F4+/VOD7m8iZO4wNNDzlBK/qaHXB5vv8bpIZfHK81OHMSbOIkLPD3k8iIeb7+f00MuG7ETB/EmTuICTw+5vIjJ6+R18k4P0T43podc3sRJXODpIZcXsRC31/qYTg+57MRBvImTuMDTQy4vYiEm71zzWB+L6S2Xg3iDp7dYnxvTQ85nDeRveshlJw7iTZzEBZ4ecnkRCzF5p4dY7/HpIZeDeBMncX28podcXsTjtWYlNmInHm82b+LxVnOB5xrm8pwn0SzEStxrntmCXNNDzmcc5JoeclmIldiInTiIN3ESF3h6iPfrnR5yWYiV2IjH683j7dc1PeTyeLW5wNNDLi9iIVZiI3bi9ka/h9NDLidxgaeHXF7EQqzERuzE5HXyOnmnh3ifD9NDLi9iIVZiI3biIB5vH8fpIZcLPD3k8iIWYiU2YicOYvLOdUv0sZjrluHpLZcX8azf58b0kOi9OT3kcoGnh1xexEKsxEbsxEFM3ukh0ft6ekizTA+5vIiFWImN2InH682bOIkLPD3kfApAyvSQy+09T1lTpodcNuI5T3ZzEG/w9JnzeCll+sl5TJky/eSyEwfxJk7iAk8/ubyIhXi8/Xqnn1x24iDexOON5vH265p+cnm81izESmzEThzEmziJ25v9Hk4/ubyIhViJjdiJg3gTJzF5g7xB3ukn2efD9JPLRuzEQbyJk7jA00+yj+P0k8tCrMRG7MRBvImTuMBJ3ukn2cdifg66rMRGPOv3uTH9JHtvTj+5LMRKbMROHMSbOInrY/39iMdbzUKsxEbsxEG8iZN4vGeP6PSTy4tYiNt7HgilTj+53N7zDCZ1+snlTTz7ol/X/Ew0fPvM8KwvzbO/hjdxEhd4+sl5IpE6/eSyECuxETtxEG/iJC6wkXf6ybmHnTr95LISG7ETB/EmTuLx9rGYvnF51t/NRuzEQbyJk7jA0zcun9e1fv0+d994rMRG7MRBvImTuMDdNx6Td5N3k3fT68r5Xm2eYzEsxEpM70/yOvT+JL0/Se9P0fsz/eGyENNxKTouRd4ib5G3yFvw2u9HvIjn/fHmeX+ieY7L/Dfz/mdzEhd4/YgXsRArsRHPca/mIN7ESVxg+REvYiFWYiMmr5BXyCvkFZxvPQT68SIWYiU2YicO4vGu5iQusP2IF7EQK7ERt3cNB/EmTuIC93XI40UsxEpsxOT18fbr9U2cxAWevtH302z6w+rzc/rD5U2cxAWe/nB5EQuxEhsxefd4e1/sTZzEBZ7ecnkRC7ESj9eanTiIN/F4ex9lgWu8fd7WIhbi6TPDRuzEvf65/5w9F/rHq1mIu87zm8jp0yv6/qov9FW/fWDYseai9WdfD8++vryIhViJjZjWn3197j+nz76+nMQFnn19eRELsRKPN5qdOIg38Xh383jP8fLZ15cXsRCPt5qN2ImDeLzWnMQFnn2t/V7Nvr4sxEpsxE4cxJs4iQsc5A3yBnmDvEHeIO/0gb4/7NMH+t61z37v+9I++3rO1dnXl514g++e7e+dPdv303z25mUnDuJNjF7kiV7k9SOe9ft8mL15WYnH2+dAOX1vEG9i8ha8PcL58SIWYiU2YieGK+b6v6+9YymxETtxEG/iJC6w0Ppz3/Jy13x+czBj+sBlI3biIN7ESVzg6QN93zumD1wWYiUeb9c2feD8Cl/G9IHLmziJx3v2Y0wfuLyIhXjOjWo2Yicerzdv4iQu8PSBy4tYiJXYiJ2YvE5eJ6+TN8g7faDvgcf0gb7XHbPfvd/n+fe972nH7PG+tx/zb/plIe6a+150zN6/7MRBvIkTPD1hvPPvdd8fjukJl2fNPkbTEy5v4iQu8PSEy4tYiJXYiMlb5C3yFnkL3v37ES9iIVZiI3biIN7ESUzeRd5F3kXeRd5F3kXeRd5F3kWuecbR92f29Ip+prCnJ1zexElcYMU9lq2LWIhn/Wg2Yiceb9ejm743iXFvZxt5jbxGXlNiI3biICavkWvuD/T92z33AS4H8SZO4gLHj3gR0/pz//DyvFfV7MRBvImTuMDTEy4v4j6v+rnJnp5w2YiduL3RtU1P6OcRe3rC5QJPf7jc3n4esec6/7ISG/G83n7/b98Y3sTj7fN5+sbw9I3Li1iIldiInTiINzF5C978/YgXsRCPdzePN5t7/X6mMHOaq59BzGzm6ucUM5v52Ii75j5nZjbz8SZO4gLPzxSXBd65Tuj7gTk94fKsqc1JXOC5Tri8iIVYiY3YiYOYvEpeJa+R18hr5DXyGnmNvEZeI6+R18jr5HXyOnmdvE5eJ6+T18nr5HXyBrkCz/hm5nP1c5OZ7Xxc4OkJlxcxnvHd2c7LRjzr97k3PeHyJh5v17PxbPHOdl5exORN8iZ504mDeBMnMXnpOead2+zrljufeTmJ6+M7n3l5EQuxEhuxE897lc2bOIkLPH3g8iIWYiWe86qanTiIN3F7+xnTzGeufp4185mPF7EQt/d8smHOfOZjJw7i8e7mJC7w9I1+3jTzmY+FWImN2ImDeBMncYGNvEZeI6+R18g7faOf6cx85uprqpnDXP1saOYtV19LzIzl6mdkM2P5OIi75n4eNDOWjws8PyNcXsRCbPDOdUI/P5r5ycezZh+j6QmXF7EQK7ERO3EQb+IkJm+SN8mb5E3yJnmTvEneJG+SN8lb5C3yFnmLvEXeIm+Rt8hb5K3PWzNv+XgRK/HMQXnzrKmHpydcXsRCrMTfXFndecvLQTzrW3MSF3h6QnU9svC9IsRKTF4hr5BXNnESF1h/xORVcs1zxnN/r+7M5PD8jHB5EQuxEhuxE9P68zPC5XmvdnOB53rg8iIWYiU2Yiee8yqbN3ESF/j2hK6te4KcD+uumZl8rMRG7M2rOYg3cRKPt9//2zeGF/F4+3zeSmzEThzEmziJC9x94/EiJm+SN8mb5E3y5nj7PM/x9mupWb/f55p1+hjVfG/vtdrESTw1n+Myc5KPF7EQK7ERx+edGUg5v+NQMwP5uNc8z5VqZiAfK7ERO3EQb+IkLrD8iMkr5BXyCnmFvEJeIa+QV8ir5FXyKnmVvEpeJa+SV8mr5FXyGnmNvEZeI6+Ra2YPzvOFmtlIOc/yamYgHyuxETvx9zsFdWcgLyfxrH/OvZmBfLyIx9v1hOJ7w4idmLxB3iBvFHj/iBexEJN3k+v+Xlg0z2vp///s98uLWIiV2IidOIg3cRKTt8hb5C3yFnmLvDXe3rPTK86zyJq5RznP42rmHh/3+ueZS83c4+Ne/zxPrJlvlPM8sWa+8XHX3+fYzDdenv5weRHP+t6sxEbsxEG8iZO4wNMfLi9i8gp5hbxCXiGvkFfIK+RV8ip5lbxKXiWvklfJq+RV8ip5jbxGXiOvkdfIdX/ntI/L9IfzbLRm1vHxIhZiJTZiJw7iTZzE5A3yBnmDvEHeIO9cS5znszWzjtLXSzPTKNrnc/cH0T6fuz887vW1z+e5ZrhsxE4cxJs4iQs8PeTyIiZvkjfJm+RN8iZ5k7xJ3iJvkbfIW+Qt8hZ5i7xF3iJvwTszkI8XsRArsRPPOSnNs+Y5P2eO8bESG7ETB/EmTmJaf/rG5ak5moVYiY3YiYN4EyfxeM+5OnOPcp4V1sw9PhZiJTZiJw7iTZzEBZ6+cZ6Z1sw9PhZiJTbi9lofo/45RayPXf+c8ri95xluzdzj5ektlxexECuxETtxXxv0zwV3HvJyEhf4/j778CIWYiXua5L+OUXv77MPB/EmTuIC399nH17E83q9WYmN2ImDeBMncYGn51xexOSdntM/z+r0nMtOHMSzfp8/00O8X8v0kMtG7MRBvImTuD6eOcnHi1iIx7uajdiJg3gTJ3GB57rl8rxv2SzESmzE45XmIB6vNidxgfujr/pH6vnozIsCnMWtue7nUtTMOd7/t9J/Mv3g8iZO4gJPP7i8iGn96QeX5/2OZicO4k2cxAWefnB5EY+3X+P0g8tG7MTj7bdqrjXO88aaecbHBZ5rjcvtjT6n5lrjshKP15udOIjbG33uzH2LywWea5DLi1iIldiInTiIybvJu8mb5E3yTj+IPqbTD6Jfy+z76Pd29n30cZnriOjzenrAZSWemvtYTA+4HMSbOInr45lzHK/P/j7PCWvmHB/3mucZZs3M4+MkLvDs78uLWIiV2IidmLyLvIu8i7xCXiGvkFfIK+QV8gp5hbxCXiGvklfJq+RV8ip5lbxKXiWvktfIda8d+hhNrzjPe2tmIR8ncYGnJ1xG73cXYiWe9aXZiYN4vNqc9L34N8fjR0zeIG+QN4zYiYN4E5N3k2s+u6pf7nx21aADA7iBCawP57OrBhdQgLAlbAlbwpawJWzTD84z15oZStm9R6cHnOdqNbOSj+eY9J6eHnB5E8+50OfL9IDmmZV8vIiFWImN2ImDeBMnMXkXeRd5F3kXeadPnOeaNXOWj4N4Eydxgb/PwKv5hMvzqYo1E5RynoLWTEo+LvDs9MuLWIiV2Ihp/dnpl6dibU7iAs+uv7yIhViJjXi8/RrnyuE81ayZlJTzVLNmUvJxgadLXF7EQqzERuzEQTzePqLTJS4XeLrE5UXc3uqjOF2id0tMl7g83mwO4k2cxAWejnF5EQvx2dvngW7dT8kcdGAANzCB9WF3kovnrJxD1p3kogIN6MAAbmAC+/X1U8OZvHy8iIVYiY3YiYN4EycxvDN5Kf3EcSYvHwuxEs/6u3nWOe/9TFI+XsRCrMRG7MRBvImTmLxzZdFP4PZcWVwWYiU2YicO4k083mgu8PSby4v4eLV/yp0pzMfWvJqdOIj7LBlMYH1os7g099nVa3dfuP9vp//EldiInTiIN3ES0/q9/x+v5j4vQoiV2IidOIg3cRKP9/THmah8vIiFeLx9HPZ4+zzdThzEm3i8fU7tAuePeLy9b1KIlXi8fe6kEwfxJk7iAtePeBELsRKTt8hb5C3yFnm7H2g/7ZqJSu0nVjM5qf1UpT/28o+tub+3n37Op11eXj/irvn8dkbNROVjJTZiJw7ihFdmzd0sxLNmNhuxEwfxJk7iAuuPeBELMXmVvEpeJa+SV8mr5DXyGnmNvEZeI6+R18hr5DXyGnmdvE5eJ6+T18nr5HVyzbVC/1sxk5N6fuOvZnLysRMH8SZG75+Jysv7R9zr9xO9mah8rMTt7ad7M1H5vjeINzF5N3mTvLmIhViJjZi8Sa75GP0+zedj9AcFqEADOjCAG5jAejh/uPviAgpQgQZ0YB+rfgo6k5XaTzhnglL7CdtMUD6eYxLNSmzEfS6c30qsmaB8vImTuMDyI17EQqzERkxeIa+QV8gr5FXyTp/op3AzQflYiY3YiYO4j1m/bfPHdqRRgAo0oAMDuIEJnJdwttcMSz5WYiN24iDexElM688lw/lFyZrBycdC3K+oz6bvT+xUfX9ip+r7EztV35/Yqfr+xE7V9yd2qr4/sVPz97n7Htr8fe6LCjSgAwO4gQns19cPVmeY8vEibkvviu+PbNX8Be6LCawPuxNcXEABKtCADoStYJt/8fs548xG6nned57IzHHwCcqhz4Dz2O4E5xAc+iQ4vyV6QnIoCtMaXlgchINymApygnMIDptDcigKfXvh3PU+fN46m9fZH6f/OIg3cRIXuD8l+/Eintc0stnpLwSHzSE5FIW5LHhhcWDPXBmc55QnGAfn0BWcR4knbA7JoSjMBcILi4NwUA5TgUxwDsFhc0gORWH6yAuLg3BQDlzB/EW/KW3+pN/lTZzEBZ4/63d5EQvx2Zg2R2L+nOdlJw7iTZzEBe7bEI/nNc+unR9AXlAOxsE5BIfNITkUhflB5AWuYH4Uuaf7/CzygnFwDuPp7TjTmDovbsYxv6AcjINzCA6bQ3IoCtOFXuAKpgvNsZzpzS8YB+cQHDaH5FAU5lLF9oTFQTgoh6lgTXAOU4FM2BySQ59Ts9i0rMuLeCQ6YZayCb0rRvj9JdDzgdw/4kUsxEpsxE7cu3Fc318EPZzEBf7+KOjhRSzESjyvzCc4h+CwObRv3r1pMPedmR9ZXggOm0NyKApzL+OFxYE986OLz7GZn11ecA7BYXNIDkVhLllemApmT05beUE5GIepYDbOtJWYs3vaygvJoShMW4k5/NNWXhAOU0FMMA7OYSqYgzW3OV5IDoUwM6BfWByEg3IwDs4hOGwOyYErWFzB4gqmFYVNmAp8wnj2hMIendnPLwgHR9Od0U6NG4rC/CjzwuIgHKg3zyDnF5zDeG7YHJJDV7D7pJhxzreALQ7CgSswrsC4AgsOm0NyoH8dxLkCZ+n8meE1HMSbOIkLPH91/PIiFmIlNmLyBnmn1+w5X6bX7O4bM9ype479dJQX+o3bcyZNR3nBOfQbt2PC5pAcisJ0lBcWB+EwFczpOx3lBecQHDaHpDB/r3zegv6pSOcd6B+LHjtxEG/iJK6P5y+UP57XlBOMg3MIDptDcigK0wdeYM/0gV0TlENXkGuCcwgOm0NyKArTO15YHM4pOj8ozt8sf2zEThzEmziJC9zXIvPD3vzt8sdCrMRG7MRBvInnNcuEojDN5oXFoX0+fNaVec/7yuRxEhe4r0weL2IhVmIjdmLyOnmdvE7eIG+Qd65cct7s+dEo55XNlUvGhM1h3rN5A+fK5Ya5cnlh3rM5U6fPvKAcjINzCA6bQ3IoCtNnXuAKkitIriC5guQKkiuYPpPzvk2feaEozJXLC4uDcOgT9TfcB7BPrvkL6I8XsRArsRE7cRDz+l14rQlFYdrMC4uDcFAOxsE59FtXI50280JyKArTZkonTAU2QTgoB+MwFfiE4LA5TAW/CUVhrnFemApignBQDsbBOQSHzSE5FIVpOy9wBcYVzDVO5YSpoCYcj/3mKEy7mTd02s1lJT5nytrD881zOPqGyhcWB+GgHM7puKasvjR5HMQjGXs3ky8UhT36ObJ9T+V+f99TeazE5N7k3uTueyqPk7jAfU/lMXmTXKcz5H3Hz/Z/fHb/x4tYiJXYiJ2Y1q95v+bAV3IohP7b6AiLg3BQDsbBO+wJwWFzSA5TQb8pMz5qv5qwOAgH5dAVrN8E5xAcpgKfkByKQncQW2vC4iAclINxcA7BYXNIDkVBuQLlCnQq0AlTgU0YzxyFvn/ym1fd908eC3GfKfPNNt88h8OKgv84LA7CoU/Hef/ciJ14JHOYp0m8kBxGP0c2fvj+WMRCTO4gd5A7gngTJ3GBN3k3uU5LyH05iQt8tv7Hi1iIldiIaf1uD4/77ZI5Cfq64QtFoa8bvrA4CAflYBz61J1/5meS9AubQ3KYCvqcnGlSE5uwOAgH5TAV+ATnEBymgt+E5FAUpoFITFgchINyMA7OIThsDsmhKAhXIFyBcAXCFQhXIFzBNBDJCVNBb46ZRzVdE3o1lQnGoVebHzFn9vQLm0NyKAr247A4CAflYBy4AuMKjCswrsC4AucKnCtwrsC5AucKnCtwrsC5AucKnCsIriC4guAKgisIriCmgjnjIzhsDsmhKMylzAuLg3LorjFbYc/Ks33yx2FxEA7KoVvTnGDTmi4H8UjmZJ3W9EJRmNY0PzH39On7/r6qeazE5C5yF7lPU/o4ievjnj79eBEr8VlzblX3xOnj00s+XsRCrMRG7MS0/mkiH8/bVROKwvSQFxYH4aAcjINz6PNtnpjO6OkXkkNRmO4ydwJn/NTmNtjMn35BORiHrsB0QnDYHOY9yAlF4fadG6YCmyAclINxcA7BYXNIDkVh+s4LXIFzBc4VOFfgXMH0nXkQNoOwNjfMZ+LV5tndjLba3BKeeVabJ5sz0PqFzaFfwjzpmpnWF6ZtvLA4CAfl4FTBnqXnmO6iMB1lniTN4OoXhINyMA7OIThsDsmhKBRXUFxBcQXFFRRXUFxBcQXFFRRXUFTBzLJ+YXEQDsrBODiH4LA5JAeuYHEFiytYXMFi6fQgGZ6V+xSd8dcvCAflYBy60c3CEsSbeCQ+oShMm3lh9DFB8P2qxEZMbiW3kvs0mI8LbD/iRUxeI9fpGTkNtUdZP17EQqzERuzEQczrJ/G8Xd0p8naKGxYH4aAcjINzCA5zvo30NpQbisJtKDd0BfO8dWZibZ6qzlDsF4yDc+gK5pHkDMZ+ITlMBd068/adGxaHqWCO1/SdF4yDcwgOm0NyKArTd15YHLiC4gqKKyiuoLiC6TsxW2D6zjyJnQ8ptXmmOZ9GavO4dAZlbR7lzqTsF5JDv4S5Npwh2i8sDsJBORiHoArm2mWupmZO9guztEwQDsrBODiH4LA5JIeiME3lBa5AuQLlCpQrUK5AuQLlCpQrUK7AuALjCowrMK7AuALjCowrMK7AuALjCpwrcK7AuQLnCpyl04Pm1JkeNA9/Z6j2C8rBODiHbnQ+vImTeCRjnDbzwuIw+vmerfj+bcROTO5N7k3uvoFzuW/gPF7EQkzeJNfpGTl3snq09mMhVmIjduIg3sS8fj1e8+mj1s+s13z86BeEg3IwDs4hOGwOc75daVG4DeWGxWEqqAldwfmlshOMg3MIDl1BPy1eM3r7haJw+05MWByEw1QgE4yDcwgOm0NyKArTd15YHIQDV6BcgXIFyhUoVzB9px+yrhnitZwXN90l542fHpJz5KZtnN8KPSE5FIVpGzkHa9rGC8JBORgH57Cpgrl2qTmmc+3yQi9dcxino7xgHJxDcNgckkNRmKbywuLAFWyuYHMFmyvYXMHmCjZXsLmC5AqSK0iuILmC5AqSK0iuILmC5AqSKyiuoLiC4gqKKyiuoFg6PahPnfkUVOvnsGvGa79gHJxDcOhGZ8NJXOBpMzXGaTMvCIfR3+8x+n4nDmJyL3Ivcp8G8/EiFmIlJq+Q6/SMXb0p+uNNP1ZiI3biIN7ESUzrnwuRj+ftignCQTkYB+cQHDaH5DDn20inobywOAiHqSAnTAU1wTkEh83hVOD9cGvNh6W+0H3nC1OBTxAOysH6e9YE5xAcNofkUBT2j8PiIByUA1ewuYLNFWyuYHMFeyqYUy6ngnlxOZ5543NWmyOXs8BsxywK9eMwL2EOVgkH5WAcnENwSFQwU7LeD3bXTMl+oZfuZ7lrpmS/4ByCw+aQHIpCN5UvLA7CgStYXMHiChZXsLiCxRUsrkC4AuEKhCsQrkC4AuEKhCsQrkC4AuEKlCtQrkC5AuUKlCtQrkBZOj1IhmflNcE4OIfgsDl0o9PhAvuPeCRjdOGgHEZ/v8fp+4N4E5PbyR3kPg3mYyFWYiMmb5Dr9Iw9F809VPuxETtxEG/iJC5w0vrnQuTjebt8gnIwDs4hOGwOyaEoTENZs+unobwgHJTDVLAnTAU5IThsDslhKuhXOp+i+oXFYSqwCcrBOHQF/dx5zVTuFzaH5FAUpu+8sDgIB+VgHLiCxRUsrmBxBYsrEK5g+k4/1l/zuazej/XXfACr94P4NZ+06v14es1Hrb4wPUT2hMVBOCgH4+AcgsPmkByKgnEFxhUYV2BcgXEFxhUYV2BcgXEFxhU4V+BcgXMFzhU4V+BcgXMFzhU4V+BcwVzv9MTAmo9i/YJwUA7GwTkEh6Swu2vMVpjrGJntM1crLwSHzSE5dGuaE2xa0+VF3BKdk3Va0wvGofU9F7F6/Pb7/k2cxOQuche5T1P6WImN2InJW3D1PO7uu+er524/duIg3sRJXODTRj6m9U8T+XjeLp1gHJxDcNgckkNRmB7yQp9vfdtwzcjtF5SDcZgK5g2aa5eeGlgzcvuF5FAUpu/0cMGakdsvCIepQCYYB+cwFeSEzSE5FIXpOy8sDsJBORgH58AVGFdgXIFxBc4VTN/ph/drPunVbV7cdBebN356iM2Rm7ahc7pM23hBOPRLsDlY0zZecA7BYXNICvOT0a1gfv6xOabz888Ls/QcxukoL2wOyaEozM8/LywOwkE5GAeuILmC5AqSK0iuoLiC4gqKKyiuoLiC4gqKKyiuoLiCogpmuPcLi4NwUA7GwTkEh82BpdODbHhWzgnBYXNIDkVButH58CIW4pHUBOPgHFrfIxer53K/70/iAiu5ldxK7r6B89iInTiIyavkOj1jz+7tOd2Pg3gTJ3GB+2eix4uY1j8XIh/329WDAWs+6PULwWFzSA5FYRrKC4tDn2/9XH3NB75+wTg4h6lgzqRpKPfdmobyQlGY7vLCVBAThINymArmDZm+80JwmArmJJ++80JRmL7zwuIgHJSDcXAOwYErSK4guYLiCoorKK5g+s5cLMxMsMdsjuku/SB+zXyv97PqNfO9X+jV+lHzmvneLxgH5xAcNofkUBTmZ6YXFgeuYHEFiytYXMHiChZXsLiCxRUIVyBcgXAFwhUIVyBcgXAFwhUIVyBcgXIFyhXM9U7/IveameAvGAfnEBw2h6QwlzgvnK4xV18z6+txw+aQHIrCXK28cFrTXKP1QO/HSjySG5xDcBh9Tkj6/gL37ZrH5A5yB7n7ds1jJw7iTUzeTa6+jTM/Ufec7sebOIkL3D8TPV7EQkzrnybycb9dPbexYnrIC5tDcigK00NeWByEQ59vPfix5tNjv+AcgsNUMN1lrl32VD3XLhPmM2S/sDhMBTZBORiHOWVqQnDYHKYCn1AUpu+8sDgIB+VgHJxDcNgcuILFFQhXIFyBcAXCFUzfmYf3MxPs81R+Jn99nrDPfK/Pc/SZ7/1CrzbPnWe+9wvOIThsDsmhKExDeWFxEA5cgXEFxhUYV2BcgXEFxhU4V+BcgXMFzhU4V+BcgXMFzhU4V+BcQXAFwRUEVzDXSPOIfj7v9gvOIThsDsmhKMxl0Quna8y90pkC9pztM1c4LxSFucJ5YXE4rWnuqPYQ8MdGPJI5Wac1vbA5jD4mFL6/b9c8XsTkLnIXuft2zeMg3sRJDG+P+n581pwnYz20+3ESF7hv1zxexEKsxLT+NJGZ75gPsf3C5pAcisI0kRcWB+EwJ9y8wGkiLziH4NAVzEjHTPz6TAnMxO8Lc/HywuLQFcyDovnk2y8Yh6lgTwgOm8NUoBOKwjSeFxYH4aAcjINzCA6bA1dgXIFzBc4VOFfgXME0nnk+PZ+H6/O8Pae9zLPzHgrev/mWvqB5rMRO/PfNMQ/hZ9DX54H6jPN+QTkYB+cQZ7Ep5FzVfJzERxLzOH5meb+wOEiHOTNO+3jff9rHx05M7iR3kvu0jsendXy8iIWYvEWu0xZiembP5X4sxEpsxE4cxJuY1y/wmrfrhsVBOCgH4+AcgsPmkB1sQlGQH4fFYSrwCVNBTDAOziE4TAV7QnIoCjoVyITFQThMBTnBODiH4LA5JIeiYD8Oi4Nw4AqMKzCuwLgC4wq6f8Q8957h3pgn1zPCG/MYcqZ2Yx5jz6flxoxMzMflfqEo9HVHzKPY+cTcLwgH5WAcnMOmCvYsPcd0Lw6z9BzGrRyMg3MIDptDcigK01ReWBy4guQKkitIriC5guQKkitIrqC4guIKiisorqC4guIKiisorqC4gkIFMgPBX1gchINyMA7B4fSg/sFBZtA31g3KwTg4h+BwGl1PG0hP835c4Gkz64bFQTi0vh9fS8/yft/vxEFMbiG3kPs0mI8XsRArMXmVXKdnRP+ei8yH64bcsDgIB+VgHJxDcNgc+oToR/gyg70v+I/D4iAclMNU4BOmgjkhfCqYA3p6TsT9liQu8Gkr4XOgTlf52IidOIg3cRIXeLqO3rA4CAflYBycQ3DYHPoN1nm3p+vcMF1H5+VO13lBOEwFOsE4OIfgsDkkh6IwXeeFxUE4cAXFFRRXUFxBcQXFFUzX6Se5MiPAX1gchINyMA59Cqzhc6jtcoFPA/p4EQuxEhuxEwfxJibvIq+QV8gr5BXyzmVP3ySV+UDd6BuBMh+bG/2sVtZc3NwwFzf9qFXWXNy8IBz6Dezf1Jb5TN0vOIfgsDkkh6IwneqFxUE4cAXGFRhXYFyBcQXGFUynuu/bdKoXFgfhoByMQx9YGT4H8L7tc9Fz/4e5tHnBODiH4LA5JIeisNkzveiFeQFzrk8vesE4OIfgsDkkh6Iwvcjm1JteZPMeTC+yOdumF71gHJxDcNgckkNRmF70wuLQFUzbnnnhLxgH5xAcuoLbJKYX+by904smzIfvRj/ulRkr/oJwUA7GwTkEh83htIvZ5T1V/Ljb1ONFLMRKbMROfM7mHo6Rnib+OIkL3G3q8SIWYiWed90mOIfgsDkkh6IwDeyFxUE4KAeuYBqYT23TwF7YHJLCtKn+PWaZoeLwOTjTjF4IDptDcigK04xeWByEg3LgCuayqZ+lisxl0wubQ3IoCtO1XlgchMNUsCcYB+cQHKaCNSE5TAWzk6afvbA49Dmlw0psxCOZkH1CzjdPw3n/w7/7r5JDUZi28sLiIByUA3umrbwwh2NOomkrLySHQpip4S8sDsJBOUwFMcE5BIfNYSrYE6aCPsFnavgLi4NwmApqgnFwDsFhKrAJyaEozE9n/dRPZmr4C8JBORgH5xAcNofkUBSUK1CuQLkC5QqUK5g2088QZSaNY8+Lm6uhPUdhrnn2HMbpLP3gUmZQ+AvBYV7CHLnpLC8UheksLywOwsGogmkZ84P4TAB/YZaewzgt44XFQTgoB+PgHILD5pAcuILNFWyuYHMFmyvYXMHmCjZXsLmCzRVsriC5guQKkitIriC5guQKkitIriC5guQKiisorqBYeq9t5jBOE+qnsmLTal5YHISDcqB/e+znHILDeNaE5FAUptX0Q0iZz/N9CyzhoBy4gsUVLK5gbQ7Jgf79neHiL3AFwtK+x9O/hSA9NPxxEhe47/E8XsRCrMRG7MTkVfIqeZW8Rl4j7/SZfnAqMyUc/ehTZhY48v5nm8Mctj2hKExreWFOnJwgHJSDcXAOwWFzSA5FYTrQC1xBcAXBFQRXEFxBcAXTgXKO+3SgF4rCdKAXFgfh0Ad23ql+YrXu/78XrtlU01humMbyQi9cs1umsbygHPql9YM86VHie8+yJ4k/3uB+OLXmdJjWUTcIB+Uwjjlr5jLnheDQb1/NK+7nWOv+D/Vxjwp/fLZBPxeSHgf+2ImDeBMncYH7Z6PH8/JqgnBQDufl7X44KPMhv1+IDmvC5pAcqkP3svmQ3y8sDsJBORgH5zAVzOuRzSE5FAX9cVgcTg/qh2rSk8Nel4N4EydxgU+v+XgRC7ESGzF5jbw2L27ODJsX191oPhx49zM6mQ8H/sK8iTnBODiHeRPnaPvmkByKQvw4LA7CoStYc7p0m/mCcwgOm0NS2P2+zltwLmBmeEZ6cvhjJw7iTZzEBT4N5uN+TWtOs+4vX1AO85ru9ziHeU1z9HJzSA79rt4d3xcuX1gchINyMA7OYSqYs6Q2h+RQCDNm/IXF4Zy88+9kzxLPfJ/0KPHHmziJC3w60MeLWIiV2IjJu8g7vWeecs388IwIyUwJ7+ntMyX8hX4T5+nNTAl/wTn0mziPZWZK+AvJoShMh3lhcRAOU4FOMA7OIThsDkmhm878tNJDwjMbKjMkvGXeKHMOwWFzSA5FwX8cFgfhoBy4AucKnCtwrsC5AucKpvPM1dt8IPCeq4j52N8tc+Cnv8yzo/nY3y+0Z54dzcf+fqEo9GXMFxYH4aAcjINzCA5cweYKNleQXEFyBckVJFeQXEFyBckVJFeQXEFyBcUVFFdQXEFxBcUVFFdQXEFxBUUVzMTxF86ZPPdVZqx4zz2sGR7+QnIoCuvHYXEQDsqBPXPB88K8ApuwOSSHojAXPC8sDsJBOUwFMWEq2BOCw+aQHIrCtKMXFgfhoByMw1RQE4LD5pAcikI/2trzyGkGjucXn2QGjr8wFczRnr71gnMIDptDcigK07deOP/szA2+njf+WImN2ImDeBMn8fnnbu4m9pzxx4tYiJXYiJ04iPs19+9uygwYf6EoTAd7YXEQDsrBODiH4MAVTAebB2AzovzCdLAXFofxzPs13WieUs3E8ReKwnSjFxYH4aAcjINzCA5cwXSjecg0Hzp8w3zo8BcWB+GgHIyDc5gKYsLmkByKwvSzeR43Hzr8ha5gHs7l9LMXjEOfUzYcxBs8LWue5vUM8vyqmcyg8fsflP+raSsvCAflYBycQ3D4d57k0G/GPLGaceIvLA7CQTkYB+cQHKYCn5AcisK0lRemgjlQczk0j3tmnPgLxsE5TAU5YXNIDkVhLod8js9cDr0gHKaCOfHmQukF5xAcNofkUBSmzbywOAgHrmBzBZsr2FzB5gqmzczzr5ll3ve0nMuheeQznzG875k8nWUe1M3HCn+hKExnme46Hyv8BeGgHIyDc9iooKZlzD8M80nCX5ilc4JyMA7OIThsDsmhKEzLeGFx4AoWV7C4gsUVLK5gcQWLK1hcgXAFwhUIVyBcgXAFwhUIVyBcgXAFwhUoV6BcgXIFyhUoV6AsnSY0/3jNTPKex3gzefwF4+AcggP92zOTx1+gf/3mY4X3/FA3Hyv8BeHQFcwDsXLjBZxDcOAKnCtwriB+HBYH4aAcuIJg6ekh8zvQ0pPLHy9iIVZiI3biIN7ESUzeJG+SN8mb5E3yTp+Z55gzqjwfySMzkDwfOyMzkPyFOWwxQTgoh37T5jHkDCR/IThsDsmhvqAzkPyFxUE4KAfj4ByCw+aQHLiC6UD9vFTnE4q/IByUg3FwDn1gh6UP4BpWYiN24iDexElcYKX1dRF33f3kT+dThb9gHJxDcNgckkNRmOaSI50rnBeEg3KYCmTCVKATgsPmkBymAuswbeeFxUE4zLGrCcbBOUwFPmFzSA5FYdrOC4uDcFAOxsE5cAXBFQRXEFzB5grmCifn/JsrnJwXN9cxNUdhrlZqDuP8HNRPD3UmlL8gHPol1By5+QnpBecQHDaHpDCd51Yw/aXmAE9/eWGWnsM4/eWFzSE5FMKMHn9hcRAOysE4OIfgsDkkB65gcQWLK1hcweIKFlewuILFFSyuYHEFiysQrkC4AuEKhCsQrkC4AuEKhCsQlp4mNJ/TpDOXvPvZiM708Rc2h+RQFPr2cV/SaA8ffyzEIxnjtJkXnMPo7/ds+v4kLrCT28nt5O57M4+N2ImDmLxOrr7v0neCteeNPw7iTZzEBe5LlseLmNbvS5bH83bVBOcQHDaH5FAUpqG8sDic8y1/I+2G8gXj4Byiw5qwO8iE5FAUurt8YXXQCcJBORiHeQ9yQnDYHKaCOeOrEGbM+AuLg3BQDsbBOQSHzSE5cAWLK1hcweIK1lSwJ0wFOaE9/XRT55OIsx+c6nz4cPbjXp0PH/6CceiX0L+oqDM1/IXNITkUhf7B6AtCFegsLROCwyytE5JDUbAfh8VBOCgH4+AcggNXYFyBcQXOFThX4FyBcwXOFThX4FyBcwXOFThXEFxBcAXBFQRXEFxBcAXBFQRXEFxBcAWbpf1zk8yps2flOUV3cigK+eOwOJxO18/htD+u+GMjHskYp828sDmM/n5P4ftPl/l4EZO7yF3kPg3m4yDexEkMb48Xf3zWnE3Rg8IfJ3GB+wH340UsxEpM6/fd2cfzduWEzSE5FIXpIS8sDsJBOcz5NtLbUG4IDptDV9BP0nXmg7Ofl+vMB39hcRAOXUE/CNeZD/6CcwgOU8GekByKwvSdfiqu80nEXxAOysE4OIfgsDkkh6LgXIFzBc4VOFfgXMH0nXv+Td+ReXHTXWSOwvQQmcM4baMfkuuMEX8hOPRLmIuSGSP+QlHoH3m+sDgIB6MK9iw9B3g6yguz9BzG6SgvLA7CQTkYB+cQHDaH5MAVFFdQXEFxBcUVFFdQXEFxBcUVFFdQVMEMGH9hcRAOysE4OIfgsDkkB65gcQWLK1gs7UdEcx0zg8PZv1yiMx78hcVBOCiH7nQ+7MRBPJIxTpt5oShMm9H5nr4zc7+/fyh6rMTkVnIruXu273ESF7h/TnpMXiPX6Rnzd6F0BodTb0gORWFaxguLg3BQDsZhTog9IThsDsmhKMylygtdQT+w1xkcTpsTYi5VbA7o6TnzR6a054Y/DuI63H1gRoPT5hBOV3lBOIxj3sxpMS84h36V83NdjxDPX+nSHiH+uMCnv8wfI9OZEs6+F60zJfwF5zCOmrA5JId+J+fmYI8Qz59t0x4h/liIz7sV8zpOA/l4Eydxfdwzwh8vYiHul9cP/3Q+S/gLziE4bA7JoShM53ihD2I/NNWZIf7CVDDlzE89LziHqSAmbA7JoShM73lhcRAOysE4OAeuQLgC4QqEK1CuQLmCucrpZwTqc5XzgnFwDsFhc+hTYFY+3cau5VzJfKzERuzEQbyJk7jApx19TF4nr5PXyevkdfJOH+qHuTqTxdmPX3Xmh7N/AVJnfvgL/Qb2M1+d+eEvOId+A2NOybnCeSE5FIXpRS8sDsJBORgH58AVbK5gcwWbK0iuILmCuRCK2UjTqV4wDs4hOGwOfWDnhOoeNP2zx4g/NmInDuJNnMT18cwP55yxMz/8BeEwr2lPMA7zmnJCcNgc5l2tCUVh2tMLi4NwUA7GoSvox6k6H1P8hc0hORSFaU8vnN0zN2l7ytj0shMH8SZO4gKf7vPxIhZiJSavknf6Tn8ErM5kcfbzTJ2PHM5+nvkXFod+E/cc7fkZ6gXjMG/ieOZnqBc2h+RQFOaC6IXFYSqY02UuiF4wDs4hOGwO/b4On9Zjc3ukP4b4YyN24iDexElc4Gk58yhsRoq/IBz6Nc0TwBkp/kK/prmYmpHiL2wO/a7ORc+MFL8wLeeFxUE4KAfjMBXMWTIt54XNITkUhfnZ64Vz8s6PuT03bOuyEwfxJk7i+rjHhz9exEKsxEbsxPPifMK8uPlfpsPM5e3MFH9h3sScoByMw7yJNSE4bA7JoShMh3lhcegK5qHfzBR/wTg4h+CwOfT7Oq+tm8vcAe6B4I+DeBMncYHtR7yIaf2+pHk8dd/gHILD5pAcisK0lRcWh3nndIJyMA7OYSqwCVPBnDBzffNCUZifs16YCua8miufF5SDcZgK1oTgsDlMBXPw5srnhmlDLywOwkE5GAfnEBw2B65gcwXJFSRXkFxBt6Gah1QzMVzzkGomhmueJM1ccM2jqBkFznl4NKPAXzAO3t8zR67v4Hxhc0gOhTCjwF8QVDAzvjW3AmbG9wuzdExIDkVh/TgsDsJBORgH5xAcuILFFSyuQLgC4QqEKxCuQLgC4QqEKxCuQLgC4QqUK1CuQLkC5QqUK1CuQLkC5QqUK1CuwFjaP1fNE7wZGq4XkkNR8B+HxeF0unkC2JPBHxvxSG4IDpvD6GtC4ftPl/l4EZM7yB3k7kucx0G8iZOYvJtcp2fodKOe5P04iQt8usLHi1iIlZjWP9clH/fbtW7YHJJDUZge8sLiIByUQ59v88/UTAB/IThsDlOBTpgKuofMoPAXFgfhMBX4BOPgHILDVPCbkByKwvSdufyZQeEvCAflYBycQ3DYHJJDURCuQLgC4QqEKxCuYPrOPKSaQeGah1QzDlzzJGmGfmseRc2cb81VznxE8ReCQ7+EeUAzH1H8haLQPzl9YXEQDkYV2Cw9B3g6yguz9BzG6SgvLA7CQTkYB+cQHDaH5MAVBFcQXEFwBcEVBFcQXEFwBcEVBFcQXMHmCjZXsLmCzRVsrmBzBZsr2FzB5go2V5BcQXIFydLThHR+YJjPLq75OWcGgr+wOAgH5XA63fyM0fPAHwfxSG5IDvUFm2ng6id21tPA8/3Ww8AfK7ERO3EQb+IkLvDpLh+Td5Hr9AztGyfWH0r8+PSFjxexECuxETsxrX8uRD7ut0tvKApzHfLC4iAclINxcA59vvVDUZsR4C8kh6IwDaUfx9mMAFc/TrMZAf6CcjAOU4FNCA6bQ3KYU6Y63L5zw+IwFfgE5WAcnENw2BySQ1GYvvPC4sAVBFcQXEFwBcEVTN/R2Q/Td3Re3HQXm6MwPcTmME7b6JtoNh9S/IXk0C/B5shN23hhcRAOysE4BFUw1y42B3g6yguz9BzG6SgvKAfj4ByCw+aQHAphRoC/sDgIB+VgHJxDcNgckgNXsLiCxRUsrmBxBYsrWFzB4goWV7C4gsUVCFcgXIFwBcIVCEtPE9J+fGcz51s2YTrNC8rBODiH0+n6Zrv1BPDHSTySCdNmXlgcRr8nKL7/dJmPnZjcRm4j92kwj09/+XgRCzF5nVynZ+gt9DSGj4VYiY3YiYN4E/P68351d5iPGv7C4iAclINxcA7BoU+4/rVemxngLxSF6SgvdAX9ANpmBrj6N2ZtZoC/YBycQ1fQT3VtZoC/kByKwm08OWFxEA5TwZzl03hecA7BYXNIDoUwM8BfWByEg3IwDlNBTJgK9oTx9CHpeV6dLdDjvB87cZ8qa7i/uR9v2szyfkE5GAfn0OejDG/iJB7JGOdC5YXFYfTzPar4fjViJya3klvJfXrH49M6Pl7EQkxeI1f3hLkandnd6kegNrO7X1gchINyMA7OIThsDsmBKwiuILiC4AqCKwiuYC46pqPP7G5N15oJ3Yp52dNJYk606RdzWSrTL17o1fYc0+kXL2wOyaE9e97q6RcvLA7CQTkYB+cQHDaH5MAVFFdQXEFxBcUVFFdQXEFxBcUVFFdQVMF8hvAXFgfhoByMg3MIDptDcuAKFlewWNq9pH/gs57w/bjA/YPQ40UsxEpsxLR+t5fHU7hNSA5FYRrMC4uDcFAOxmHeOp8QHDaH5DAVTKFzidLPbW0Ge78gHJTDVJATnENw2BymAp1QFKYpvTAV1AThoByMg3MIDptDcigK05Re4AqCKwiuILiC4AqmKeWcf/OTUM6Lm6aUcxSmKeUcxulD/UDa5vOBv7A59EvIOXLTh26YPvTC4iAclINTBdNg5pbBDPa+MA0m5zBOg3lBOCgH4+AcgsPmkBwKYQZ7v7A4CAflYBycQ3DYHJIDV7C4gsUVLK5gcQWLK1hcweIKFlewuILFFQhXIFyBcAXC0r7G6duhNlO/1Q+0zabTvCAclINx6E4nw0G8iVvSz7nNps3cMG3mhdbXFNYXNPf7+4LmsRGT28ht5J6LnssF7h+EHi9i8jq5Ts/QfrZoPb778SIWYiU2YicOYl4/ieft6k4xM71fWByEg3IwDs4hOPT51o/XbT4X+AtFYRrKC1PBFDoNpZ+b20z8fsE4OIepYE/YHJJDUZi+U3NUpu+8IBymgjnjp++84ByCw+aQHArBp++8sDgIB+VgHJxDcNgc/io4f9ZuQnXoF9fDv+cv2U3wDjphFqgJyaEonLbx9z0+YXEQDsrBODiHTRXoLB0TFodZek9QDsbBOQSHzSE5FAX7cVgcuALjCowrMK7AuALjCowrMK7AuQLnCpwrcK7AuQLnCpwrcK7AuQLnCoIrCK4guILgCoIrCJaeJqQ9G2I9y/v3P8wpupWDcXAOwaE73ZzV/Xj6cYFzJHOy5uIgHFq/prB+RP2+34mDmNxJ7iR3/YgXsRArMXmLXKdnyFxG9UDux0psxE4cxJs4iWn9cyHycb9d/QzcYjrFC8rBODiH4LA5JIc+3/rxusU0lBcWB+EwFUyh01D6ubnFNJQXgsPmMBXEhKIwfeeFxWEqWBOUg3GYCvaE4LA5JIeiMH3nhcVBOCgH48AVGFdgXIFxBcYVTN/pp/AW03fmOi+mu8gchekhc2ET0zb691Mtpm28sDj0S5A5ctM2XjAOziE4bA5FFexZeg7wdJQXZuk5jNNRXggOm0NyKArTVF5YHISDcuAKkitIriC5guQKkisorqC4guIKiisorqC4guIKiisorqCogp7YRVgchINyMA7OITiQtEd1ZX7y3dOD5pn4nk7zQnDYHJJDd7o+q3sc9+NFPJKcoByMw+insG4z7/s3cRKTW8mt5O4G81iJjdiJyavk6p4xN497RPdjJTZiJw7iTZzEBe5W8Zi8Tl4nr5P3/9d2brvS5MaVfpe+9kXyFCT9KoZhtOX2QEBDEtrSAAND7z61GUXmUgsZXLuC/43Mr3/vlVF5WMlDZLDAcQsct8BxtWui6+pVPUbX1at6jC6YV3USXQmv6iQTCoIgVISG0AG0ozJh3Oq6Dl3VcCYkhIxQEAShIjSEDqCGMwEjaBhBwwgaRjAMR/QhGH4z2xXaDdr9bg+vme0A7QjtcYH1iR0+M9sF2gLtCu0G7b7aI1l3tfU3V4WIkBAyQkEQhIrQEDpAuBAwAjUizT9o2hmakBEKwjiOLjU17djockpTx5mQEDJCQRCEitAQOoB2bCZgBNqx0WSEph2bCRmhIAhCRWgIHUA7NpoO0bRjMyEiJASNICsUBI2gKFSEhjDuKf0TNat3O0BbD6I3m/ZqdIGwqf28QTsyukLa1HF0+Xek3EZ5/0O722oXb+GKR1FTmCAIFaEhdAA1hQl4HDUFXVduagoTMkJBEISK0BA6gPZCdC26aS9kQkRICBqB3g/aC9FV3aa9kAkVoSFoBOPadu2FTAgIEUEjuBQyQkHQCEShIjSEDqAmMSEgRISEkBEKAkYQMIKAEQSMIGIEESNQYxlVN3NXY9Gl8a72oWvN/W0S4xbvb5N4Q0BICHK7WdenX9/XXZ/xCQEhIiQE8LmeC4Ig6HGSQkPoADp40Se7F/C5XiJCQsAICkZQMIJSERoCOG2XCwEjEDzoMA19lkbC7GyPDsdsB2hHaCdoZ2gXaIP+MJXZ1lOnN4l6yhvUUyYEhIiQEDJCQdC7TO8Y9ZQJDaEDqKeoe3b1FF1l7+opExJCRhgR6Pp7V0+ZUBEagkbwZRblUk+ZEBBGBGOZvlzqKRMyQkEQhIrQEDqAesqEgIARBIwgYAQBIwgYQcAI1FPGymi51FPGsm+51DnGEm65tOMx1mbLpc4xQdWaQkPoAOopEwJCREgIGaEgCAJGkDCChBFkjCBjBBkjyBhBxggyRpAxgowRZIwgYwQFIygYQcEICkZQMIKCERSMQE1pjJHLpaY0oQOoKU0ICBEhIRSEL+ms94QOkMZws1w6DJqQEDJCQfj6BVlvo+FNs92grQfRm1WtaUJA0MNHhXT//XCm2S7QhmM3OHaDYw9TereHJ812gHaENhy3w7GGzyR9xIbNaHtk3652gHaEdoJ2hnaBtkC7QrtBG44b4LgBjhvguAGOG+C4aitjdrgEtZWRbFCC2spY9y9BOyRjdb8E7ZBMiAgJISMUBEGoCONWH2vRJajhvEENZ0JAiAgJISMUBEGoCBhBwggyRpAxgmE46d1O0M7QLtAWaFdoN2j3uz2MJukZGj4z2xHaCdoZ2gXaAu0K7fGbx0p7CWowb1CDmRAQIkJCyAgFQRAqAkagRjTW+kvQmZoJASEi6HH0PtbOUNf7WB3nDeo4EwJCREgIGaEgCEJFwAi0M9T1SdLO0ISAEBESQkYoCIKgEeiDrZ2hCf2GqJ2hCRpBVYgIGkFTyAgFYdxTRdsV2u1ua39nLC+XUXg3hrFgWEbe7g1twLigowrvC6LCuLn174eTzHYD4YRHUVOYkBAyQkEQhIrwD8fpI7Jxq42c2xsCQkRICBmhIAhCHaCnMDeEDlAuBI1AFDQCvZwlIWSEgqAR6LUtFaEhdADRCPSGkoAQETQCPYmSEQqCIFSEhtAB6oUQECICRlAxgooRVIygYgQVIxjGEoLersNYQtDbddhHCHoZm8At3ipCA+gXQrrdbGTevtT0Ao9nfEFD6DeM/NobwOfSFRESgh5HFAqCIGgEVaGhADhtChcCRhAwgoARhIxQEAShImAEEQ86TGOsLZeRf7vaBdoC7QrtBu1+t0cvZLbHfTaWQMvIvb0hIWSEgiAIFaEhdAD1mwkjgrGkW5L6zYSEkBEKwohgLAOXpH4z1l1LUr+ZoBGM2z6p30wICBEhIWSEgiAIIwL9bWo373a/22o273aAdoR2gnaG9jiy3rlqM+92hXaDdr/bajHvdoB2hLae9ayQEQqCIFSEhtAB1F8mBISIgBE0jUCfhlYQBKECqA1FPV9d1fTiqA1NKAiCUBEaQr8hq0FNCAgRISHoQ3wpFARBqAgNoQOECyEgaARVISFkhIKgEQSFiqARRIUOoF2cCSOCou0I7QRtPUhSUKlxqfPbl94QECJCQsgIBUEQKkJD0AjGayGrMU0ICBEhIWgEegbVmHTEk9WYJmgEeg7UmCZ0ADWmCQEhIiSEjPAVQdSzO3xptiu0G7T73R62NNsB2hHaX0dWSx1pv6tdoC3QrtBu0O53e9jSbI/fnPUmVl+akBAyQkEQhIrQEDqAGtMEjECNKetToMY0ISMUBD2O3l9d1fTi9IiQEDJCQRCEitAQ+g1FjWlCQNAIRCEhZISCIAgVoSF0ADWmsbxaihrThIiQEDSCqlAQNIKmUBEawrinxoXTBODZDtDWg3SFIaVtdZ/3P6jHTMA/UY+ZIAgVoSF0gIzHUY+ZoI4ZFBJCRigIglARGkIHUI8Z655Fk3cXRISEoBHohdLOz1j3LJq8u6AiNASNQO9C7f9MCAgRQSPQC6d9oAkFQSPQG0+7QRMaQgfQntCEgBAREkJGKAgYQcUIKkZQMYKGEajnFL0P1HNEf5w6i+hVUGcRvYzayyn6hKjNTIgI4yeIXjm1mQkFQRAqQrtB1Fk0Ak30DWNBs2im7wKVLgqCUBEaQgdQ/5gQECJCQsgIGEHACAJGEDCCgBFEjCBiBBEjiBhBxAgiRhAxgogRRIwgYgQJI0gYQcIIEkaQMIKEESSMIGEECQ+qJqQvL83kDWPVtGi+7oKK0BA6QIEXkebrLogIepyqkBEKgkbQFCoKNAR4FWrC7wKMQDACSQgZoSAIAkYgeFD1EO3jalrvAkGoCA2hA6hTTAgIeBztnUwYP2EsIxdN610gCBWhIXQAtZoJAUFHjuoHajUTMkJB0AiigkagUavVTOg3aFrvAo0gK0SEhJAR9EbqCoJQETSCotAB1JEmBISIkBAyQkEQhIqAEQSMIGIEESOIGIE6kq7Bam5w0IVSTQEOujqpub5BVz01wTfourpm+C7ICOMn6IqmJvkuqAgNoQNo52dChAi0V9P0AqvVTFBpvYxqNRM6gFrNhIAQERJCRigIgoARFIygYASCEQhGIBiBYASCEQhGIBiBYASCEQhGUDGCihFUjKBiBBUjqBhBxQgqRlAxgooRNDzoe+5G70Q1IV2X1ZzeBR1ArWZCQIA5lYqzOhVndTS1N+iSr+b2LqgIGkFVgFmdhrM6DWd1Gs7qNJzV0QTfBQVBECpCQ8AIAh5UP6dO2i7QFmhXaDdo97utH1K/2wHaEdoJ2nDcCMeNcNy3oTSFhtAB3lbzhoAQERJCRigIgoARvD/D1na/2++PsLUdoB2hnaCdoV2grd+va7tCu0G7322t8PBuB2hHaCdoj9+sC8hN7WiCIFSEhtAB1I4mBISIkBAwArUjXZvWROIFFaEBqOnooo/mFQddydC84gWCUBEaQgfQbtCEgBAREgJGoA6k68yaV7ygIjSEDqDeNCEgRASNQB8R9aYJBUEQNIKq0BA0gnGHaF7xgoAw7inRdoJ2hrYeZNzMmiAcda3unSD8bkdoJ2hnaBdoC7QrtBu0+91+f5qtbThuhONGOG6E40Y4boTjRjhuhONGOG6C4yY4boLjqtG8z5B2YyZ0AO3GTAgIESEhZAQ8zujtRM0S0FziBQ2hA5QLISBEhIQwpmt1IVtziRcIQkXQCLKCRjCeJs0lXhAQIoJGoDegThhPKAiCoBEEhYbQAXTaWNf8NR95QURICBmhIAhCRWgIHaBhBA0jaBqBXuBhSTHoLTaMJ+oiu6YYz5uv4xPZI0K5XVVzh9/rt5ohrCCaIbwgIESE2yFFM4QXFAQ9TlaoCA1BI9C/CRcIhIAQETCCgBEEjCAIQkVoCB0gYgQRD6rblIi2BdoV2g3a/W7rFm3vdoB2hLZe5TdkhIIgCBWhIXSAfCEEhIigETSFjFAQBKEiaARdQVdorgHqNxM0gqoQERJCRigIglARGsLoS4XR1lpV73aAdoR2gnaGdoG2QHv04fQ3abXed7vfba1I824HaEdoJ2hnaOuaWFQQhIrQEDqA+suEgBAREkJGwAjUeaKaQKsIDaEDqA1Fvb+6qunFURuaUBEaQr9Bk4sXBISIkBAyQkHQCJpCRWgIHUB7PRMCQkRICBpBUSgIglARNIKu0AF0YWqs0otmIS+ICF/3VNMAdLeld7tAW9dxx80c1Jb0zKj7zH+oCP/wJx1APWZCQIgICQGPox4zQZeek0JFaAgdQD1mQkCICAlBI8gKBUEQKoJGoBfqvf49bvDwXgB/Q0CICBqBnmvt00woCIKgEUSFhtABtE+T9Ppqn2ZCREgIGaEgCEJFaAgdoGEEDSNoGEHDCBpGoJ6T9T5Qz8n649RZsl4FdZasl7GrgD4hajMTBGH8hKxXTm1mQr9Bk4MXBISIkO8INO1XvyIRzftdoNLjMkb1jwkBISIkhIxQEAShIjQEjCBiBBEjiBhBxAgiRhAxgogRRIwgYgQRI0gYQcIIEkaQMIKEESSMIGEECSNIGEHCCDJGkDGCjAdVE9KXl2Yix5EpIJpvvCAgRISEAC8izTdeIAh6nK7QEDqAWs1YwRbNN54CEhESAkYgGIFgBFIRGgK8jGO9EDCCigcdRfTqu93v9iiUN9sB2hHaCdoZ2gXaoK+dkpFyIJpZvKAD6EBpQkCICAkhI4xzV/TJV4eZUBEagkYwzEtTk+NIUxBNTV4QERKCRiAKBUEQKoJGEBQ6gBrRBI2gKkSEhJARCoIgVISG0AHUiCZgBBEjiBhBxAgiRhAxAjWikQ4hmsYcRzqEaL6ylmARrQg8FrpECwLPtkC73e0x91v1b7VTM7IaRPOJFwhCRWgI41nQ9pjone0AbT2I3jNqJhMygh5eb5NRyHf+fYV2gzYcW+DYAsceA6PZTtDO0C7QhuMKHGsMet6XYgxuZrtAW6Bdod2g3e/28I7ZBv3hHbOtp0tvCO1gTCgIglARGkIHUP+YoHeU3iHqHxMSQkbQCDRQ9Y+q96f6x4SG0G/QzOE41sNFM4cXRISEoBGIQkEQBI0gKjSEDqD+MSEgRISEkBEKgiBgBAEjCBhBxAgiRqD+MZazRXOHY9Ufpy6hj7MmD0d9dWi+cNR7VvOFFyQE/QldoSAIQkVoCB1Aux7vCHQw1PQCq6NMGNJNL6M6yoSG0AG0hzIhIESEhJARCgJGUDCCghEUjEAwAsEIBCMQjEAwAsEIBCMQjEAwAsEIKkZQMYKKEVSMoGIEFSOoGEHFCCpG0PCgowOj73xN+I1Nb1F1mgkNoQOo00wYTqd39SjlO9sJ2noQvVnVZiYIgh5eAxuFfOff99XWMr6zHaAdoZ2gnaFdoC3QrtC+jzvyfVf7S1MHgyNzd7UrtBu0+90eq9SzHaAdoQ36Y2pktvV0iYIgVISG0AF0YDMhIEQEvd+qQkYoCIKgEWigaiijvrhocvAEHdhMCAgjAp1Y1OTgBRmhIGgEelXevvOGhjAi0DlRTQ5eEBAiQkLICAVBECpCQ8AIBCMQjEAwAsEI1HfGAolocnDUWX9NAY5dr4J6SNfLqLYxqmmL5vYuKAj6E/TKqW1MaAgdQD1kQkBIEIH2XbpeYHWUCePD40sv43CUCcNRFgSEiJAQMkJBEISKgBF0iEBTgBcEhIiQEDJCQRCEitAQMIKAEQSMIGAEASMIGEHACAJGEDCCgBEEjCBiBBEPOuZndXJSk361WrJoau+EdCEEhIgwnC5rO0O7QFsPEhUqQkPQw2tgIyHm/fdjUDTbEdpw7AzHznDskRAz2xXaDdr9bhc4boFjjZ1Mgv7mMc6Z7X63xzhntgO0I7QTtDO0QV/0fBWFitAQOkC9EAJCREgIesOJQkEQhIqgEVQFjWD4huYALwgIEUEj6AoZoSAIgkagV+JtPG/oAGo8Qe9yNZ4JESEhZISCIAgVoSH0GzQHeEFAiAgJISMUBI0gKmgE4+nQTN+kK9ojnze8/79GKt5sF2jXuz06MHo5NUc3hTdkhIIgCBXh61nQ22HU6Z3tsRw923qQN0SEhKCHbwoF/l6gXaENx05w7AzHHt4x2xHaCdoZ2nDcDMfS2jC67qy5uwsiQkLICAVBECpCQ+gAo9Ohe8aI5u4uiAgJISOMCHSlVXN3ky6hau7ughGBrqNr7u4E9ZgJASEiJISMUBBeEWgHZmTuzma7m301v7xlNsPdjHcz3c3XEbXPNRJ6Z1PuZr2b7W721fzykdkMd1N/oz5z6iETMkJBEISK0BD6DZrcuyAgRASNQBQyQkEQBD3OuJ/au2zVpZAQMkJBEISK0BA6gHZEJgQEjEBrz+jarFbvXVAQBKEiNIQOoF2ZCXpGm0JESAgZQSOICoKgESSFhtABvhxJR0kju3c2493UI2SFl46Ow7TS7vzv6icT8C/UTyZUhIbQAdRPJuBx1E8m6HXRG0j9ZEJBEISK0BA6gPrJBI2gKkSEhJARNAI9Vdpn0WVYTdhd0BA6gPZZst6B2meZEBESgkagT6H2WSYIwohA15g1YXdBB9A+y4SAEBESQkYoCIKAEXSMoEMEmrC7ICBoBFlBIygKepyqoGrjMmqaru52KpqnuyAh6E9oCgVBECpCQ+gA6irvCNQ7dA1Ts3EXDGld0NR83AUNoQOod0wICBEhIWSEgoARJIwgYQQJI8gYQcYIMkaQMYKMEWSMIGMEGSPIGEHGCApGUDCCghEUjKBgBAUjKBhBwQgKRiB40HenRi+jmpCuK2oi7oKG0AHUaibAS6jXiJAQ9Dh6w6rVTBAEjSApNBSA16Am4i7ACBpG0DCClhEKgiBUBIyg40G/PERXBUaC7mzWu9nuZn8360jZnc1wN+PdTHdTT1lRKAiCUBEaQgfQUc6EgKD3nSgkhIxQEDSCqqARNIWG0AG07zJBI9Bfqn2XCQkhI2gEWUEQKsKIYCwgVy0APEH9Z0JAiAgJISMUBEGoCBhBwgjUf8Z6dtXM3jTWl6vm7ybRS/LlJe39F201v6xjNr9uE/25ag6i10UtYEJD6ADqBxO+bkQ97ldnZTbT3dQj6MXWjsoEQdBj6/X9co/55301v5xjNu+j1vuo9T7ql2PMZrmbcjfr3byP1u5DfD3+o3RBHbmws1nvZrubfTW/nvPZDHcz3s1b96v3MJvjxFS9Ptp3mFARGkK/4V2Kd0JAiAjj7hyLxPVdjndCQRAEjSAqaARJoQOoR0wICBpBVkgIGaEg6M3RFSpCQ9AIxiXTbNkFASEiJISMUBAEoSI0BIwgYQQJI0gYQcIIEkagHjEWiavW7E1jAbtq/m3SJ3jW39Xr8y7A+4ah1vRivUvwvkEQKkJD6ADa35gQECJCQsAICkZQMIKCERSMoGAEghEIRiAYgWAEghEIRiAYgWAEghEIRlAxgooRVIygYgTaYWl6+2uHZYIgVISG0AG0wzIhIryk9aWqybOp6dOjA5s3aNdjQkCICK8foO/kkWE7m+Vu6hH0RlVbmtAQ9NgjqpFdq38+cmtnM97NdDfz3Sx3U+5mvZvtbvbVDPfRwn2IL18Z46g6MmVns6/ml2/MZrib8W6mu5nv5q2rXjFmCqpmxi5oCB1AvWJCQIgICUHvq65QEAShIowIxipv1czY1DVq7U9MCAgRYUQw5vSqlvJdUBAEQSPQC/D2lzd0APWX8Vlr1dTaBREhIWSEgiAIFaEhdADBCAQjEIxAMALBCAQjUH8Zq8lVU2tT1wdBXaTrZfzyiqwn6sspZrPczbqaXw9/Ug0dq3S9rDoimVAQBKEivE5D0jM/9njV5tji9d38OkK+9F7RnQEmJIQ8QG+PL4eYfy53s97N+6h9HXVkxc5muJvxbqa7me9muZvrEJrgmrSZ7ma+m+Vuyt2sd7Pdzb6a8dbV/oQ29cS8ISFkhIIgCBWhIXSA4RB5LKlVTWZdEBESgkZQFDQCURCEitAQNAI9r/lCCAgRQSOIChmhIGgETaEiNIQOUC6EgBAREkJGKAgYQcEICkZQMALBCIZD5LE6XLUCbw7644YP5KBXQXcZCXoZqwp0hYAQEcZPCHrlRgdiQUEQhIrQAEafYUag+4kEvcC6ocgEldbLqFuKTKgIDaED6LYiEwJCREgIGQEj6BhBxwg6RtAhAk2PXRAQIkJCyAgFQRAqQkPACAJGEDCCgBEEjCBgBAEjCBhBwAgCHnQkuTVtqvAbBKEiNIQOMJaIuzbD3Yx3U4/whoxQEMaxx6piHWmx87+3u9lXM99HzfdR831ULbOizXw3y92Uu3kfLa9DaDnTPFZdqxYtzerhWrR0gSCMczYqSlctWprHol3VoqUT9KJPCAgRISGo9LiAWnQ0j3WtqlVHF0SEhJAR9CfoL9XLPqEiNIQOoO+XMSdZNYVxQUQYESQ9iXrxJxQEQagIDaED6PtlrOBVTWFcEBESQkYoCIJQETQCvcD6Fkl6gfUtMiEhZAQ9TlEQhIrQEDqAvkUmBISIkBD0oHrviCBUhIagB9VbTN88EwJCREgIGaEgCEIF0JfN+75uASEiJIQMj5m+hiYIAj6N+hqaoLeynip9DU0ICOM4Y72hap7h+2mUtx+8oSAIwohgLDFUzTPMY4mhapZf1oljzfJbMCLQKXJN+VswItAxu0RwF4ngLprztyAgRIShljUcfTQnFITxe7LGpo/mhIYwIsgaqD6aEwJCREgIGaEgaAQatT6aExpCB9Cu34SAEBESAhik1gNdIAgVoQHIhaBqeu/oM6cvCU3/W9AB9JnTe1TT/953oqb/zSunT5Y+C5rKNw9aO0DDcFpAiAjptnV5P1lvKAiCUBEaArwk5P1kvSEgRIR0vzE0lW9BQRCEitAQ+g2ayvd+Y2gq34KIkBAyQkEQhIrQEOA1pUl+CzCCgBEEjCDAa0rLeS4QhIrQEDpAvBACQkRICBhBxAgiRhAxgogRqCPpW7MmeFHWlBAyQkGAF2VNFaEhwIuy5gshIESEhJAR4EVZc0VoCB2gwIuyloAQERJCRigIglARGoDAi1IzBxckhIwAL0rNHFxQERpCB0B7qmhP9W1Pb0gIaut6U+iLX12s1oYAL7DaLgT8m4Z/0/BvOv5NDwhq+Hon9oSQEQqCIFSEhtBv0Iy/9+tQM/4WRISEkBEKgiBoBEWhIcBrt4ULISBEhISQEQqCIGAEASMIGEHECCK8djVLcEFCyAgFQRAqQkOAF79mCS7ACBJGkDCChBEkjCDBi1+zBBc0BHjttnwhvN+0f//7v/z065//8PNf//jnP/3HX3/75Zef/vV/13/4n5/+9d/+96e//PzbL3/660//+qe//frrv/z0f3/+9W/j/+l//vLzn8b//evPv73+9fVa++VP//X6vy/B//7jr798tf7+L/dfX89/2r7W+Mcft5rWnxf+778SsPTve376+/j892F8GzsEXiuf4UkhPSvUtn7B9fgL8vPff9W6jG+Fr5p3dWnUf5AozxKvicj6VnjNPMYHAessjCQMPQuvCbmnX1ENBRn7kKjEV/GehyBsiV6WxGv17EGiGxKthRVFez1qDxLBuKNeU/brZF65LIX4j+fia8r0SULC/B2vAc2jgBlDn7d1CiE+Shi35dfeSvNMFLgc35Lo6b6kn/2Q+654rVM8/xAxoqi9rksa5FHCuLOqTId6dT4+EegyT0SvH0UQrjxDCFfrH52HntbV6PkxCv7pyNcnz+goRjCf0Vo/8JqxzqYKr3nWTzy7XOtkvmYqHxWKFUS77+3cH03X1uix3xqpPWlUv2F9vbmdhhW707DsGCjDSsFtWLYEZVjmD+EMK2W3YaXiNCxLgDIsS4A0LPM8cIb1jcfj0bHMx/S1aHLbxRXCRxpjIfStkeEZ4W2v3GbxWgz4xPYk3Hd3LB8ptGXer1mox86qcSZyrdMq8us+/0RijI5WGO3RN/MB38x+38xe38x+3yx+3yx+38x+3yx+3yxe3yxe3yx+3yx+38wHfNN+TPvtm692+MDz6nrCXsvYHzlWy/c9UdqTgiR/V8/W4Lp6UvyWJeK2LKlOy7JjoCxLutuybAnKsswfwllWjW7LqslpWZYAZVmWAGlZ5nngLOsbj8ejZZmPKdnVszX8Xb12j5Fb6x/Mavb1mPdUPvn7NUDu5XE2rxlnIV4yT0K82mNvtWXnvGgr/onRJt6ZUfNMjLLyM4jHyYbW/BOb7cDMZvfPbHbvzKZ9OteE/9fGih/dm1Hu26JeH0mk9Tr/+vj+SaIfmEHqB2aQ+oGRUPePhLp3JNT9I6Fw+YdCGw2qY9H9Y6Fw+QdD4fKOhkwFbt778o+H7HNBznwfGBD1AxNJ/cBEkm1epS3zquEj/xvJDyqRS/6kh9GmQO/XB3//egbXiuMVP1HoAgKPE1HBWpV5vUjWa0CyodGc3ZwQur+fE+Ll7ejYZ6OuR+S1KP7Y5wsx+peBdyJUfynEfGAp2Jr3ZdeCo3gXg+0ouFejta7BvhptDW492Pwt5LvRXOYh340pet+NlgL3brQU2HejeS64d+N3HpXHl+PmoeVWhjcWVK5lQfL4XgrJsNMiK44iLXykQdtYNi+N3O+4LuVDkZrXcnmvH4tIu0Wer83mnHCunA9MgobsnwUN2TsNuomCc+XsnwjdaHCunP1ToaH450JD8U6GmgqcKxf/dKh9LkhXzgcmRDcP7RFXXndYbM/5NrZGK6ur3Z4nbYIE/5TLRoSbcwnmJDFrY5L9NmbNmnM2ZkfB2Zi1psTamK3B2Zj5W0gbq5ffxmrw2pilQCYcBr+NmeeCtLFvPCrPNmY+tOTUy0aEm3vZ+Fhbg+R+feiFMN7vrX00e3JfmEuepx1asB6W9WZI4Tl3PrTonfyw1wPIyQ9rqYmc/DDPxthXSyVi+/CMjj1YVCNd7VnjQBJ8MBec2NeTteLEvp6sFSfu9WSe01EL9X1O2/XZdcn5vj1K/EyjXPOnvCJ6XJMN3bhNcwkzjowav7soJ1aMQpcDt4c1o07fHu2H3h54WZJ8eGn7fLckebaxaK0apXbbx6uT+pQzYEr0sDpiPT69Js2Xgqxb4+rt+XdYt2he6/45P6/7R3PZiXkpxEv8L4V4Ve9LwT4b5Wr3AyvPv6V7z4b1DQ19NkL4wWcjyzobJXx2f5W1apNLfb5HQ/a/ImM4MBEVg38iKgbvRJR9Tu9pxizROKcHJveitQZFn9MY/OfUmos+cU7rfU6ND0vtaXX40qnlUB5Ph/V16FXTyj+6MIn2cxH57PGvaf6aXJ8XS6O5gkSm/sd4IBUqJn8uVEzeZKjNhWlL4yvPTj66uq/R4Jpbew37H0+q+bEO+zFbOmGq6YCppvpDr0yI90s3xP74yFi3O/mxzOZGpebnYvbnRW00uG/rkv9r4Jj9iVExexOjTAVqfs5UIOfn7HNBfmH3DTd9/sTO9nXyWxH7BcM9LSUeeOdaIvQ79xsij+9cqz81suL0t7wmFJ9PiOWm7R7c9uu5I1MOzEnFcmBOKhb/nFQU75zU5tKSb21ThH1rn/guKcqBTKko5cCVkR96Zdi3tvnI1DWPkmp7fuzEPeqvJ0b91T/qLwcKu2yeW64TU7O/E2NrcJ0Y87eQnZha/Z2Y2rydGEuB68RYCmwnxjwXZCfmGy8Xo07Aidompnn0staAujxPxlgfPpEZbKYG/diai1FkBttGhMtg24lQGWy7c8L1Y6wvoOi3ZY/+t2VP3relHQXnypsPGihXtjU4VzZ/C+nKvfld2foOinNlS4FzZUuBdWXzXJCu/I1H5dmV7Yf2gCu/BoVrIed6/rQhWYtSpCubGqwrp6v5XXkjwrnyToRy5d05oVw5heh35WTVuyNdOVmLU5Qrb6IgS2mJ25U3GpQr27+FrKZlLk2R5bSslSmunla8nK5sKrAVtcxzwbnydx6VR1fePLQnXDnEtaIUnuscJLNCmG4lp4F8bRH3WSDr/sjBWJU2NcZsr2rE56ILyZofZxOcNyLcnFBKJ/w0HfDT5PbTdMBP0wE/TQf8NB3w03TAT7PbT7PbT/MBP00H/DSd8FPzoSUTnDciZOka2sfkk+I1r4dsndSQHhWS9VFT7mG9Gl529qhRLuccbLLq4rFzsMmak+bmYDdnI63Mq16u59+SDww9yoHUq1QOpJ+m4k8/TcWbfrqJgnu3yOV/t9ga3LvF/C3ku8VcjyLfLdZyFPdusRS4d4u5JEa+W8xzQb5bvvGoPL9b7IeW66tvLOhO3OzPdZuStRrFzqCYnzWxNmZWvGNnUGwRcgZlI8LNoGzOCefK9UAdqVT9haRS9VaS2kTBuXI7UI28+VOm7N9CunI7UJC8uSuSN3dJ8nagJnnzp0x951F5dmX7oT3iymvyo1zX87y2tXLCunI/UPYn9XzAlW0R0pU3Ipwr9wNVjFI/kDWVuj9rKl/erKlNFJQrj73Vna680eBcuftzQPJV3K6crWlYypVNBcqVTQXSle1zQbryNx6VZ1fu8Qe7crnPSLmevzfN1sqJtFVgT1p/mtXO1lJUrNeavKjxDkO+EUWPK4r+OJ9kStRrPbL1Kp9JhFWtsIZyfSQR27zB6mth47OruiZhXlf1cbUiRzOp767cCArhdwrBfVHNIOoq7Ywfuv8+CGsaeqQOqP29rPCjIMoqKYP1J8M3FO5CwvXxZ/BX9LnaWI5mbZy6srwrLB0JL/G19/hycXghpd9JmFknaT0hAk/INyTC1ZfxXZB9n78jIUvitbT3IGFfkrBu7xLC4zRDthZJao/rrdjTk4Pn5LdOO4r73dwfO8SmRLtaXjPYvXwkEdYAoYXePpJI693cUmmfWefyi9dVfZzNz9ltndlvndltndlvndltnfkHWydc0edvjnP2W2f2W2f2W2f2W2f+0dZZb+tsxs5cyZwLqGsuoD+NWHPxW6cZRV11UkqNj6ZlSvRVq6D08pGEXGVKyCX1I4l4zUdVYuwfWWeQ+0F7rrSdxW2d4rdOcVun+K1T3NYpP9Y6/+GKPr8MxW+d4rdO8Vun+K1TfrB1xnsgEFP7KGOhxJXM9tJ4vqzVTHa6luWE50KbpsZrvvKe/8Tp3O9IrIsSLyx7842zkdYvKUYptM0Zbf3WMM5oP3BGu/+M9h98RldxuVdTPjujScKt8dxrsxZQXi+ydUajfKZBntHmvkfNLKWV5/QaaD5P51h19kpZ08ClyPNotXm/FM39wJeiubu/FN2cjZrW2TA6oNZHTexC0k6EWnnJ/UAtk9z9tUxy99Yy2UTBrbx0f6XyjQa3zWz3Vyovl79Sebm8lcpNBW6r2ctfqdw+F+Rms994VJ53m7UfWm7lZWNB604veIf9zoKKVW6PXA83NVgbK+aHGuR6+EaEWw/fiVDr4btzQrlyCQdyR0vw546W4M0d3UTBbf0d/bmjGw3Kle3fQrpy9OeOlujNHTUVOFeO/txR+1xwrvydR+XRlTcP7QlXlliWoz4PjUvy546aGrQrpwO5oxsR0pXTgdzR3TnhXDkdyB0tyZ87WpI3d3QTBefKB8rtlQPl9uzfQrrygXJ7xV1ur7jL7ZUD5fbKgXJ733lUnl05hR/uyvf0Wg2PKwvF+rap1JWZU4yasKZGuOr9Y67anhZrirXqxC1clVK8axx2FNTClS1BLVyZEtzClS1BLVxtLivc6Fd7/ininpWy4wh3AtqrbcRxYEapnKizVw7U2SvuOnu7i9vCfXE/nC+sZXUL6/NX98Vc/An3prVf7f5RIG19bFpafJ41sNL5uXzNUqPbgMwoqHxNU4LL17QlqHxNW4LK19xc1aveV1Wer2pzrpwXa+WHvajNuXJeNhsTESvndhDMyrmtwKyc81f0uV5IacW7cm5KcCvnxSpnx62cmxLcyvlGglk531ySe82nPa/5FGvpicvXLN1vnXYUTL6mKcHla9oSVL6mLUHla26u6qqAW9pz0lHpbuvsfuvsXuuUy2+d3W2d/QdbJ17Rxyk4udzWaUpw1imX2zpNCc46NxJ+67yLepSejEQKU2NZ50vj+bIGc7McKkVGNgtERELHRoJJ6DDPxutBvge+4dMzuua7XhrGGa0Hzmj1n9H6Y89oWPfX63F97EOL9cVSTqu2Gsx2ZV6g5zQtuOfH4YREd16zRHdesx0FNT1kS1DTQ6YENz1kS1DTQ5uL2uZt0V/Lk08S5m4y1OSQHUVdO2y8nrfH05kOfOYt6cB+4pL8+4lL8u4nbp/Sktdc/Ws2/ZPu58vVlgeH/vj9rKTmfuKTu/9pR8E98aYE98Sn5n7iTQnqid9d1XRf1cfRu1irONSgQrK4L6oZBDWoyM07qLCDYAYVtoJ7UIFXND7vLSTmji3coMKSIAcVJbsHFWaVLW5QYUu4BxUS5b4kVkayqUFleIu1ScoJDbILXNx592aW+EqtDuF5W3gRs3zBcq1qdaLF3I6Qy/EWc+2I6zAJXYrh+dUsB3YU2olw3S45sJOoVP9OolK9O4luoqDyVsQsRsflrWw0qLwV+7dweSsjI+wxDi5vRao51UTkrZgKVN6KqUDmrdjngstb+c6j8pi3snloubyVjQWtXkc1vnO2NeLKSKwxP8/QmPXs0qqI+Wo+zxQ168qQBd83IlzBd2kHCo1J8xcak+4tNLaJgrPC7i80ttHgrLD5C41J9xcak+4tNGYqcFbY/YXG7HNBWuE3HpVnKzQfWrLg+0aEK/hu+1he+cw9x8fFvnrZ46d5e8DFzfHTKNqHrr6Skmrs4fmXGK+ol3/OM5r746aspgS7yX29DnRNa/B3TWvwdk03UVB+XIO/a7rRoPzY/i2cH9fg75rW4O2amgqUH5sKpB/b54Lz4+88Ko9+vHlouR3MbQMau6XpZUkxfmZi93f+NT9PqtVY3CYWywETiwe+C6nR/11Ijd7vQjZRcCaW/N+FbDQ4E4v+70Jq8n8XUpP3uxBTgTOx5P8uxD4XpIl941F5NjH7oT1hYnkVsqn5OcvS1hjfwKhGef4upOYDpSB2ItQ0Yc0HSkHU7C8FUbO3FMQmCs7Gsr8UxEaDs7HsLwVRi78URC3eUhCmAmdjxV8Kwj4XpI1941F5trF8oBTExoLktqD6OD1XrWJvXD9qF4asMJ6/W95orC/1aunGT0nuLqG9uRPZJZQTXioHvFTcXioHvFQOeKkc8FI54KX1gJdWt5dWt5fWA15aD3ipnPDSzY5sB7qE98NS5Xk3Rluj3pNz1ZicawcKOOxEuC5hO5CwV5s/Ya82b8LeJgrOxswviUgbszU4GzN/C2lj1gIUa2Pmx0SUjVkKnI1ZCqyNmeeCtLFvPCrPNtYOVDywLaitJMbXwR6Tear1KQ610mEqsCbWLvPCcFVoNiJcFZqdCFWFZndOKE9u14Hv69vl/76+Xd7v6zdRUJ7crub25I0G5cn2b+E8uVmLUKQnN+sDI8qTTQXKk00F0pPtc8F58ncelWdPth/aI558Z+IY33S30J2ebCrQnhzDAU+2RUhP3ohQnrw7J5wnxwPD/Rb9w/0WvcP9TRScJ0f/cH+jwXly9A/3W/IP91vyDvdNBc6Tk3+4b58L0pPjgeH+5qE94sllLUE3+TAXp5e1FN6fNVo+MNzfiXA2lg8M91v2D/db9g73N1FwNpb9w/2NBmdj2T/cb8U/3G/FO9w3FTgbK/7hvn0uSBvLB4b7m4f2hI31Nq/sawT72dLL6w/70nguB9BK9y69mBLs0kuziunRJibRb2LWSeVMzI6CMzFzep00MVuDMzHzt5AmJs1vYvZuToyJWQqcidl7OXEmZp4L0sS+8ag8m5j90B5YenlNWd77w+TPll5eA6M5Ln3ZyGf9uZbWE/c6Hc/9OasCXBr/qnd660/J6rZED+vB789lI8xfMrY2fv+S9Nn3Q62sSjOtpMdEz1ET8vG1UFZtgFziU7WwZk6tk58PbUS4z4daOzHObwfG+c09zm8HxvntwDi/HRjntwPj/H5gnN/d4/zuHuf3A+P8fmCc306M882Hlvx8aCNCfj5kW+G9gZlc12d2OiovvTXKY857tzYyan1Om3Yo4Rvj7xTSj1QYyZvveZPrSWFzNte0SSvPu9L168BHpRsR7q3QrwMflfbL/1FpD96PSjdRUG+FHvwflW40qLeC/Vu4t0IP/o9Ke/B+VGoqUG8FU4F8K9jngnsrfOdReXwr2A8t+VbYiJx4K9SVatFqfHZ0cysmqjibLSGrEuNrca4+h2GcD66SWI/mds1M0Sk7CqqSmC1BVRIzJbhKYrYEVUlsc2OUeD/28tnN1dJ6279GhB9qlL40nitRdOv7I67TkcSrYP6Ovort9/6cmtnNAnHU/gfdX2/PjoLa/8CU4PY/sCWo/Q9sCWr/g27vQ3fd6QjXFT97TsJL5K5rFtJj1lzPJ3qT+cDXpD37vybt2fs16XfOaimfXpu7QtprdeJxdrAX98CppB/pQOGKUD0Pb/h/+iXW96TURgK9uMtDbqJgNhIwJbiNBGwJaiMBW4LaSGBzXdN63F7tnD68z/N1d9KNGiU7lXTHkp9zErpViI+718X7xt90rldJ7Vaz8TtMM13j2deY5bFXa0pcdS1svtr5+aG1l3yq3K+pKvUjFa5GeK/B3c/37+BkR8H1800Jrp9v1iPk+vmmBNfPty8qUyO8m+tFVMlLOwpuGNoObH3d24Hl+978y/e9eZfv7VNK1QjfmE9ficOvtjGCMwtoXT3f74RePlXhdrDszV2wvDf/AKq5C5bbEpz9NHfBcluCsx/7snI7WPburrlrx0HuYDl2LnFbUD8xn9/98/mvIat7Qn9zdaktLG0XCteagX6126N/vH6NtRBH7mK56RXePf76nOXxisSayo5pqXy1P4ukr68P2usaGJFYc6ZkxeuXivv9b/6a12T56lpe1q+xFqD4XxPCD/41a/3otWKWjV9zoDTPVoWypK8JEr8nhSvIAVMK3tyTXRzUMuPXbI97nXEnQi00bn4Ot9L4mv7xJ6C8RLwZKLYEtdhoS5CrjZvzwS03fuuxeVxv3D3CB/K0e1jJdK+l7/psSdZmUNx8xkaDtjX7WyDuM8CdCvcd4FaF+hBwe15Io7bWqXijTvWAUZvLRJxR23GQRp2vA0Zti5BGbf4c1qhzOmDUObuNOme3Ued8wKjN88Ea9TceG8Oo7Uf4iFGvk9JDCc9GbX6nRBp1CSeM2qyZRxu1rcIa9UaFNOoSThh1qSeMurQDRl2626hLO2DUEg4YtYQDRl3aAaOWfMCopbiNWorbqKUcMGrJB4z6G4+NYdQl/HCjvk9sqMYg3yxhRxp1jSeMuuYTRm2rsEa9USGNusYTRl3bCaOuJ+Zj2+U26toPGHWLB4y6xQNGXfsBo27lgFE3cRt1E7dRNzlg1K0cMOpvPDaGUduP8BGjXps99HgZc8v9R4vEq67smFc7l8eT8g0ViR+qtGUnr3aUz1ReI6JpKK/244f3LxXrZiEzF8PVD+zm81pD8m/n8xIJbqe2z+z9QfGr3T+9yj3cm7P2xzXar+VPK5Za7nWV+pEG29PZ/Jq198urbbyLNyprc8NXu9cP71nuA7Dd3ca9j0M4MMO1EeHex/bPId/HIRyY4QrBPcNlSnDvY1OCfR/b54N9H3/DHI33sXnTk98+7VS4j5+2r54Kr57H9NvXHWu919cN+xr/3G/1752TZSev9mPJhdfjbQ55Qr2HPOExH3mrImdU1hP46u1cH6tcJ1RKB5Xy4R0XwCKD1WUyVSKoxPJs+dbmUuzLNB2Y37V/TbqvT0n1+fmxtmWq9z7B7Wnr943NrhRD/CXfc+o7hbUnt9l3azBqDlbS6gu/2uW5r2V9oMUbwUZFzqhwRrBTuU6okEZgXyN49UhO8UOVcqfTC2588/tfZH2vFS8YgD2PeYL5uVbvd/f++aVuxkFqbM5Irfd5rc+jlWCVxqPMZBOHwDMoJX34a3q8O8PWOGOjIvf8Y2/PsZizQpQ92hKUPbJzU5aEPenO2mNpJ+xxoyJnVDh73KlcJ1RIe7SvEWuPtgprj9aiE2uP4rc2Mw7WHu0zwtqjWTyQskc7DtYebRXWHjcqpD2aq5ucPZoSnD2Sa6yWhJ08wtpjrSfscaMiZ1Q4e9ypXCdUSHu0rxFrj7YKa4/WLkysPTa/tZlxsPZonxHWHq2vkTh7tONg7dFWYe1xo0LaY3YPrm0Jzh6zf3BtJ0Gz9tjlhD1uVOSMCmePO5XrhAppj/Y1Yu3RViHtMV7JbY/x8lubGQdrj/YZIe0xWt96cfZox8Hao63C2uNGhbRH82sTzh5NCc4eyW9eLAn7k8K8aja/2sn4kM9WWZuSvNqPHwWHaH2PRE5wmxrsBLf9a0pZt0gs8mwC1idarV1rgwNjP8OdyFp8a+25Ut82ErlFHqsZbU/KKo0Solztw1MrKd4qWT5Vud+jUZqhYn/Xm9e9EqqkT1XWg/xqP6/iRbMGIXnrx3rg1jd/zV0A56v9/A41u6CvOcHVQ/mqBWHccnYw9ZZprXz4kzp8vt3TZ1/5Xx2KHPWQPhS5lwGvLuX55Bo3/8uv58/JEapmyjcCeZ0xua9PkOdA7CIfZHkOq/QBtT3grngCl8Yc84E9W3cqZBrzToVKY95UcuAyhKL5pRWZIbQR4aoi9wMJu9Fc9CIThGI29xhmEoRMCS5ByJRgE4Ts80GWRv5G1ZHn0sj2I8yl625EyJF+LOXASH+nImdUqJH+VuU6oUKN9DeXiBvob0TYcb61sRM7zhf/GN2Mg9PYnBB2mG8teTHD/E0Y3Ch/I8IN8nci3BjfdmlqjG9LUGN89l3RjVpKZtWwu+RXNVyx+hO5TQ2yqKW1wwtZN8yUuLdPlQY9GvmGRL9Wln6P9TOJ1a+SXh4l7DJ9ywVfCo92bG8JBwX44/NVNTfK4oYjdhjrLdeNwXw0V5Oues/ZXK8+2vNUyUaGrHdqfW/HjWnaiWInsbUTYxpbhR3TbFS4MU3z7yz59dXLgTFNP7DTS/PvLfkK5MBHiLG7P0I0JcgxTT/wEaJ9PsgxzTeKuT73ytqBoh4bEXJMk+y1JHJMs1ORMyrUmGarcp1Q4cY07UBux0aEHNMks44hN6ZJVhVDckxjxkGOadqJzI4UnB8ZbMIgxzTtQF7HToQc03R3UrAtwY1pyHeFMaaxC7FTY5pkVeojxzSmBjmmqcU9pjEluDGNKcGNaWwJakyz2ZGiwI4U9XlHCmtahh2SWOVzmL0iNxuNtHtv+uvZvtKBOzT571CrzhO51YhZKoq7Q00J7g61JQ7codhJvcpnm+d9FVfut0p/LquZrHUi8kbPJ7YjzQeqeCWzHh85LNuIcMOyfKCIVzLPKzksS7l6h2WmBDcsMyXYYZl9Pshh2Te2GHvuWOYTu3DmA7twmhtzce+YzW/hviBP5URu605Fzqhwo8OdynVChRsd2peI+4B8I0J+P54k+XsT5ooX15uwfwz5+XjyrlZtnJEaCtkS1FCI9WdjKGTvqlnC6tTIY9pXqv7SAqYGe2tEd0cz+5d3sn95J/uXd+xtdO+bq3Zj+1pDI8XVP0yvXs1TGKZEktW1S+3pl4TU/N1Uq84y9660t2jmZgzaAQdtfge10ivJGQNTgntMTAnuMbElqMfkOtIF6teJLtBGRc6ocF2gncp1QoXrAl0nukDXkS5Q96dZmxrkA3yd6ALly1n0ogfvZLCtwHSAbAWm/9NMQ2UdIF8n0v52KnJGhXKArcp1QoVygM0l4hxgI0I6QA7+KVVTg3OAzY9hHcDaJohxgNa9n7HZCowD2AqUA1jTul9zbcsBajccIJ7oA+xU5IwK5wDxRB9gp8I5gHmJ2nXnM7VL5DMRSIp6TaqG59/j7wNk/6dWmx8T74vTouEAydkH2ISRVjf+1Y7xs9+Syv1b0uMHkxuRfK00s/byvScRM5WQMjRTgTI0O52RMbR8ouJVTicqXu1U5IwKZ2g7leuECmdo+UDBq40ImfWTs7/eVT5Qys+Mg9PYnBAy6ydnZ7mrTRhc1s9GhMv62YlQWT/NKpjFmaKpQJmiqUCZornlKW2K5USdq52KnFHhTHGncp1Q4UzRvkSkKdoirCmKv8pVPlDAL4u7FMzmhLCmKM4iV5swSFO0RUhT3IhwppjcQ9/kHvom79C3ml/f0qZYT2QA7FTkjApnivVEBsBOhTLFzSXiTHEjwppi89e2ygfK9plxcBqbE8KaYnOWttqEwZniRoQzxZ0IZ4rWzCZniqYCZYqmAmWK7cjwuR9ZEehHVgT6kRWBfmRFoB9YEdhcItIU24nhc7n8hQDKgWJ9ZhykKbYTw+dyOVOrNmGQptgODJ93IpQp1u4dPtsKjCnaCpQpSjqwSFLCid2GdipyRoUyxa3KdUKFM0U7uZJbJNmIkIskJfoTAk0NbpFk82PIRZISnV8BbsLgFkl2ItQiyUaEWyQZP9lnaNU79LUVKEPLJwo7F/PzKNrQNipyRoUztJ3KdUKFM7R8oK7zRoTt5Vm7QLG9vOz+rNmOg+zl5RNVnYulQpliPlDUeSNC9vLygZLOtbhNsbhNsbhNMZYTvTxzhxXaFDcqckaFM8WdynVChTNF8xKxvTxbhO3lWXs/sb08c/8orpdn/xi2lyfBaWh2GGQvbyPC9fJsEbKXl7wbHNkKlKEl7/ZG9aonDE1O7I25U5EzKpyh7VSuEyqcoZmXiDU0W4Q1tOrfQcDUIA3N/jGsoVVnGssmDNLQNiKcodkipKEF7webtgJlaMH7uaa0Ex8slXYijWWnImdUOENrJ9JYdiqUoW0uEfe5wkaE/FyhdH/dVVODM7TNjyE/Vyjdudoq7upV4i5eJe7aVaOgqXviSq4TE1c7FTmjQjnAVuU6ocI5gByoU74RISeu5PJPXIm/Hp8dB6exOSHkxJUE58TVJgxu4mojwk1c7USoiSup3m6RrUCZYnV3i4p5m1/37YW7ZX1HItQlgdtcfRrFY10RsTZl+9oba2oIVDv4Jw3nitUuithXFGAb/6QhPzYKOBf56VyIWY2LK0Axfu6TBleAQqypvwMSXM0FW4KqubCRYGouSPJvKCDWzBDZsTU1yI5tdm8oIP6KM+KvOCP+ijNirddzGwrYEtyGApLd1Ts3YVAbCoj1NZPcNQ1ffavnnRdtkbDOhwQoNfc7EbFWYousMuJF2mca9HYCks0uILmdwEaF3E5gp0JtJyBmcSOybqWUA3UrNyJU3Ur715B1K6UcqFspxV230pTg6laaEmzdSvt8cHUrJZlJO1zdys0jzG0nsBFhpx7kRBLgTkXOqHBTD3IiCXCnwk092JeInHqwRdipB7MqIDn1UN1ZzXYc5NSDfULYqYfqrLeyCYOcerBFyKmHjQi3nYDt0lQNTVuCqqHJviuM2QdrnMqOaA4UBxR/cUCxSpOQIxpTghvRmBLciMaWoEY01modOalkS1CTSnwUzxMp1nZTlPOYda/IUxH9pyIeOBXOWSm5gn9GyfrajhykXvmHSpAPqinBPai2BPOgFuvzFnKzjHr5l0pNDc5/i/V5HFfD2JTgLqstQV3WjQR1WZv/SavBPfFaWv6hEuQlae4nbSNBXZJqTv9S1cLNJBj2SQvuCmql+p80/8ZJxb9xUvFvnGQn8oWa1/kMVZ668zuR1Rl/tZ9Tk8zkVfbuiO6Z/c2PaX1NM37pPQdiL3XENTDIj1XYd5HUekfSymc/p99zSK9Jqad3ZCnWoO/q1z0l1kP6UOSe8Lxeo8jHE5vcs/x2IOHuTb7aQZ4DKe7p9WJ9lXdCg56ir+nEjr8bFXKKfqdCTdEXqwYTO0Vf84Edfzci1BS9/WvIKfqaD+z4W7N7x19TgpuiNyXYKXr7fHBT9MWcuiWn6DePMDdFvxEhp+jNL8noKfqdipxRoabotyrXCRVqin5zibgp+o0IOUU/nlTnFH0V/46/ZhycxuaEkFP0VZyZU5swuCn6jQg3Rb8T4abobZempuhtCWqKnn1XPEuUkt1T9LX6i/+bGuTQxPpkkpuityW4gaspwQ1cbQlq4JpO1Oqq9cT3fTsVOaPCve52KtcJFe51lw7U6tqIsK876zsU9nVnfyLEve6au1z25oSwr7vurP+/CYN83aUDtbp2ItTrrmTvR8+2AvOysxWod535vQZtiv1E/f+dipxR4Uxxp3KdUOFM0b5EpCnaIqQptstf/79d/vr/7XKXy96cENIU2+X8cHoTBmmKtghpihsRzhTNrUQpUzQVKFM0FRhTzP3ExEgLJz6c3qnIGRXKFLcq1wkVyhQ3l4gzxY0Ia4rRX/+/RX/9fzMOTmNzQlhTjM76/5swOFPciHCmuBPhTPHy1vuyFShTvLz1vsxq7Fxa10aCSev6RhSPaV3NWi0iP5tsyfni30VBfTZp7tlzIgrms8lc3d/TbiSoG4OP4vlkZGe+X67+Z6T6n5F64BkpztF+tqaUyYSsZi27cyv3WeSHSnAToLYENQG6kWAmQHPxp563A3X8mr+On7mHFDevbUtwl7W4U883EtRlzQeeNCtbgHxMsvxQCfKSZP+Tlv1PmpVyRCYZt+qvi25qkE9aMm5QLvXRlCAva/I/acn/pMUDT1p1p/PnKD9Ugrwk0f+kRf+TZpXoIZOMW/On85sa5JMW/E9a8D9pwf+kBfeTlvqJr4dbP1FceqciZ1S4GbidynVChZqB21wibgZuI8LOwFnLLOwMXHcXHbPj4DQ2J4ScgeuX8xu+TRjcDNxGhJuB24lQM3D58mYm2QrMDJytwMzApXoigaXbFfpIU9ypyBkVyhS3KtcJFc4U64EElo0IaYp98zkxY4rjWw+nKfbgToLZnBDWFKNzsnQTBmmK9UACy06EMsXUvAkstgJjirYCZYpWsiZvivFEVt9ORc6ocKa4U7lOqHCmaF8i0hRtEdYUkz+rryd/Vp8ZB2mK9glhTTE75/k3YZCmaIuQprgR4UxRvAkstgJliuJNYEnJ6suwlf97PpHVt1ORMyqcKeYTWX07Fc4U7UvEVf7fiJCV/3vxf0ZtanDzX5sfQ1b+78WZfJIsR+QcwFSgHMBUoBzAKmkU4muWeJ7M+JoYf7oiG5E1n/hqy/OtIf7p1S7u6dXNjyllvR5ikefby6qjnq/14s7hevxK/xsimCbwrQtc7qIDX1kcn52TUbltimT5UOQ2+PhcBDeZWcF0N76e+BZ1pyJnVLg31k7lOqHCvbHsS0R2420Rthvf/N+i9ub/FtWMg+zG2yeE7cY357eomzDIbrwtQnbjNyJcNz66X+LR/RKP3pd4tGZHeFPsJ7bv2anIGRXOFHcq1wkVyhQ3l4gzxY0Ia4rdvX3P62z5t+/p/pW0zQnhTPH1Y5zb92zC4ExxI8KZ4k6EMsVoXRjKFG0FxhRtBcoUy4E9zV6Pw4HUgK2KnFFhTHGvcp1Q4UyxHNjTbCPCmeLr9zS/KQa/oV3+lbTNCWFNMTpTAzZhkKZYDuxpthPhTNHKx+NM0VSgTNFU4EzRrHb8evrX/dXj88NizcBxMzW2BjdTs/sx90cpl3Vz2CLrTn+1++OvMevi5HZXYsv9+ZQcqCv3Oq3mtqhUXbmdCFVXLh6oK/cKxHJUrq5cvMylK6aunC1B1ZWzJci6cpvzwdWViyfqytk3fLnCPQd/hfCZCLz+S07po+f3Nbu6vo8LV398dYtzMWB3PqiFtFdH6UDK1VZFzqhwnc2dynVChets2peIW0jbiHALaa8Yq//9WdzVqjc/hltIi5c4MwM27kp1rS73Uhrt8YaE9T3719tqmUDthglIOWECGxU5o8KZwE7lOqHCmYB5idp1lzRul8hnIlAX+dUteXz5Xf7Kf7YGaQL2j4n3xWnRMAHvNlSbMNL6nuTVjvGz35LK/VvS49LtRiRfa8G05fB4n7mLfkV30a/oLvoV7Yol7GjRvwuVrcHe6PnAaHEjwo0WrV0x6dGiNUajR4utHxgt2iLcaNH8Nexo0VyoYUeLViUXcrTYuRoq9cMo6NGivXDFjRbtDa3J0aJ5w7OjRVuEHC2azy87WvTWdNmdD260GK4DSSxbFTmjQnUUtyrXCRWuo2hfInK0aIuQo8UQ/LOtpgb5/rR/DDlaDMG71mq7Kzda7N4yWbTHGxLBqh7O7dBiarA7tLyuyYFN1Hcq3A4tWxVqh5YYDvSNQkz+vtFGhOsbhQN9oxD9m6i/RLybqNsSXN/IlGD7Rvb5IPtG4UTfyH6EuR1aovXU0F7ST3iJ2V+kvcRWYb1ko8J5ifU1KO0l6cA4ayPCeYn5a1gvyQfGWSG7x1mmBOkl+cA4yz4fpJfE64SX9B/sJXTeVDiylBWOLGWFI0tZ4chSVjiylGVfIjJvyhYh86ZCcVcPeGn4c57MOFgN84SQeVNBnNUDNmGQeVO2CJk3tRHh8qZsl6bGa7YENV5j3xUfjtd4X5QDBQS2KnJGhfPFncp1QoX0xQMFBDYirC9W8ftiPeBpVfwaBwoIxNC8aQLhQAGBjQjriwcKCGxGwpwvmhKcL5LjcWMjvV78q1mh1wOjrBOrWeHAapb9a9hR1onVrOBfzQr+1axwYjUrHFjNCl38oyz7hidXszYi3GrWLhJuHSleJz5x2anIGRWqS7JVuU6oUF2SzSXi1pE2IuQ6UrzcJdltDW4dafNjyHWkGPzDLP+4xL+OFNzrSMHaPJKc+zU16LnfaFfwI+d+Nyrk3O9OhZr7NfsCbK/EnHJleyUbEa5XYv4asldiJlCwvZJo7WLF9UpMCa5XYkqwvRL7fJC9ktYO9ErsR5ib+92IkHMcMR0oJLBVkTMqXIciHSgksFXhOhTtwDezGxFyjsPMgyLnOGJ2FxKw42BLcp/4ZjZmZ3LLJgxujmMjws1x7ES4OQ7bpak+li1B9bHYd4XRx5JwYqBVjgy0ypGBVjky0CpHBlrlxEDLvkTkQMsWYQda5cBAy7/31ebHsAMt73pWsGpQUx5gKzAWYCtQDpBOrIpHkRMOsFGRMyqcA+xUrhMqnAOkA6viGxG2Z2TV0WN7RtW9Ta8dB9kzSidWxaO1lEW5SDqwKr4RIXtG6cCqeCjeySdbgTLF4p56ivGEKbYTH7zuVOSMCmeKO5XrhApnivHA7ksbEdYUe/SbYk9+U7QXkihTjPGEKXbnNsKbMEhTjAd2X9qJcKZozY9wpmgqUKZoKjCmeGRNLl35gCfuVOSMCuWJW5XrhArliSeW5I6syKXg3rrV1uAGikcW5FJwVhYOl3euyFagnv7LO1NkTeWTi3HmagC7FmcWFabX4jYq5FrcToVai+uXfykuxexfituIUEtx5o8hV+LscvrkSlyyil1wK3GmBLcSZ0qwK3H2+eBW4szcL3Ihzn58uXU4W4McV6V0Yly1U5EzKlwfIp0YV+1UqD6EfYW4YZWtQY6qUvaPqlL2j6pSdo+q7PNBDqpSdg6q7Ci4MZWtwQ2pNhrcApxtztQCnC1BLcCxr4hniSPlFe2N32hDLEcGVeXIoKocGVSVA4OqE9UVjxRXTHJgUCXuQdWR2ormDn+MmVXvNHP1zjJX7ySzVcWMLqqYjnx2lY58dpWOfHaVjnx2lU58dmVeIbKmoq1BllRM5kdX5KNvfjBFPfr2byErKqbqLDJlR8EVVNxoUPUUbQ2unGLxTg0V78xQcddSPDItbGZl0Ta2UZEzKpyN7VSuEyrkbiT+Hkw8Mi3cs9/GunsjzHhkWrg7F/6T99FP3kc/eR/9EznV+TqRU71TkTMq1JO/VblOqFBP/oGM6hP51Pny51Pn4M+nNuPgNE5kU2dvqcADudQHMqkP5FEH75fiwfudePB+JW7clRLm30u8/z7Tf17mwyWtPPx5zFZ+QrumQoOX8ncU4toAOcKj+S2FtcgRy/WZwroRIizFfUvhWk92+CyGtHb6fnUPPlNY62ep948Ubr/OMX+msMZGOXx2JnOaVzPn64Nbus/3cA0fPBG1rD+vn/x5XH/e4vf/vMX5+m/5kz/nHkZjkF7W2ZMrPy2yx5ysOenU8nJV6GSH9nsR65PK1NaqdIJi8OH37znrE6g4tp9WkeuKhog1YxmuNWgI/1DY+p9+kOkPq5MZ22dPVrrqWtpOn3nUurjp+jCGdX+mlD66wXKoy2Gey2nmXA/cYLmduMH6gRvMLFz3jRvMOrHX6lu++rH1+dTSInCXfUuktXWnvpb9PxZZCQOtxfxxJHKLlH8Q+fcX/fyHP/72H7/++Q8///WPf/7T/7z+8O9fWr/98ef//PWXN/733/70B/jXv/6/v8x/+c/f/vjrr3/8P//xl9/+/Idf/utvv/3ypfT1bz9d7//5t9fI8jUSe/1vC//+Lz+l13959XpTerWD/nN+9b++/rd+/aegf9HK+Iv673//CvL/Aw==", 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ZbAabw+awOWwOm8PmsDlsDpvD5rAFbAFbwBawBWwBW8AWsAVsAVvClrAlbAlbwpawJWwJW8KWsBVs0x96B0x/GGxb/2ry9IdBAzowgAmsizn9YXABBbiBCjSgAwOYQNgWbAu2BduCbcG2YFuwLdgWbAs2gU1gE9gENoFNYBPYBDaBTWDbsG3YNmwbtg3bhm3DtmHbsG3YFDaFTWFT2BQ2hU1hU9gUNoXNYDPYDDaDzWAz2Aw2g81gM9gcNodteok1bqACDejAAH62/qyCnk082L3k4AIKcAMVaEAHBhC2gC1hS9gStoQtYUvYEraELWFL2Aq2gq1gK9gKtoKtYCvYCrZ6tp5svLiAAtxABRrQgQFMIGwLtgXbgm3BtmBbsC3YFmwLtgWbwCawCWwCm8AmsAlsApvAJrBt2DZsG7YN24Ztw7Zhm15SjQmsh9NLBhdQgG3bjQo0oAMDmMB62L3kYB9bNgpwAxVoQAcGMIH1sHvJQdgcNoete0lPAPUo5UUHBjCB9bB7ycEFxDmb/tAndfrDYD2c/jC4gALcQAUa0IGwJWwJW8FWsBVsBVvBVrAVbN0fxBoTWAflNx8rMriAAtxABRrQgQFsmzfWw+4PBxdQgBuoQAO2LRo/W38uRc9aXqyH3R8OLqAAN1CBBnQgbAKbwNadYCrrTtCfhtFTlRcN6MAAJrAedic42EeRjQLcQAUa0IEBTGA97E5wEDaDzWAzHEVv6d2fPtNbuj/MpccjL37/7Bsykh6PvGhABwYwgfWwt/TBBewiV+MGKtCADgxgAuthb3/ta9zb/6AAN1CBBnRgABNYDwu23v7a57e3/8ENVGCv+22RHnmUb3BIeuTxogA3UIEGdGAAE1gPF2y9pb9JKOmRx4sbqEADOjCACWzb11Z65PHiAgqwbdWowM/2jTRJjzxeDGC3q/lv62F/yz/4rfsNOkmPPIp0Zb15DyawHvbmtf4wpt68BwW4gQo0oAMDmMB6aLD15v2msaRHHi9uoAIN6MAAJrBtfap7z3ufkt7zBwW4gQo0oAMDmMB6GLAFbAFb7+6prHf3NwEkPQh5MYH1sHf3wQUU4Ab2UfS93rv7oAMDmMB62Lv74ALqU/R3ae97p79Lf5+wIPPhXwcX8CsyBjdQgQZ0YAATWA97Sx9cQNgWbAu2BduCbcG2YFuwCWwCm8AmsAlsApvAJrAJbALbhm3DtmHbsG3YNmwbtg3bhm3DprApbAqbwqawKWwKm8KmsClsBpvBZrAZbAabwWawGWwGm8HmsDlsDpvD5rA5bA6bw+awOWwB22z/ahTgBirQgA5s225MYD3spnBwAQW4gQps26/RgQFMYD2cpjC4gAJsmzUq0IAODGAC62JPSl5cQAFuoALb1h+yN71kMID5cLpGNX4rfLMdMh+KdtCBAUxgPez+cHABBbiBsHV/+D7pQXr68WIAE1gPuz8cXEAB9tnJRgUa0IFtk8YEtu27YXom8uIC9jfsXmFeHgwqsBf7vh/3yKPkfD6iADdQgQZ0YAATWA97ox9sW9fQG/3gBirQgG3r+6E3evUV6o1+sG3f98Ieeby4gALcQAUa0IFt6xPVG/1gPeyNfnABBbiBCjSgA2FL2BK23ujV17g3+kEBbqACDejAALZtPgCzLvZ45MUFFOAGKtCADgxgAmHrFw3f76tKj0deFOAG+vfZl7/G+vDbWT3yeHEBBbiBCjSgAwOYQNh226RxAQW4gQo0oAMD2GenGuthb/SDC9i23biBbdNGAzqwr0UfRXeCg/VwPojUGvuf9Vmfjx4dTGA99B9wAQW4gQo0YNv62PrjSA8msB72h5Ie/Gyr74f+YNLVR9EfTXqwbdloQAcGMIH1sD+o9OACtq1PVG6gAg3owAAmsB72B5weXEDYCraCrT/qdPU17g87PRjABNbFnl28uIACbFs0KtCADgxgAuthfxDqwQUUIGyrbdloQAfGw97+32+hSs8j7v6M455HvGhABwYwgfWwN/rBBRQgbL3R+7OTex7xogMDmMB6qD/gArbt17iBCjRg2/q69ccQH2ybNdbD/jDig30t+ij6A4kPbmCv+/XqHiHc/QZFDwvufteyhwUvBjCB9XA+UXhwAQW4gQpsWzU6MICfrd92s/mM4cb5lOHBBRTgBirQgA4MIGy9eXefyd68BxdQgBuoQAM6MIAJfLYeFry4gG3Txg1UoAEdGMAE1sPevP3ZyT1YeFGAG6hAAzowgAmshwJbf0fvt417sPDiBirwW/f7BU/pYcHd74b2sOBFAW6gAg3owAAmsB4qbL15+63VHha8uIEKNKADA5jAtn0bvYcFLy6gANvW160/Ufxg2/ou6S19MIB9Lfoo+pv7YH9zP9jremOv0Feo93y/4doDgBfrYe/5fnexBwAvCnADFWhABwYwgfUwYUvYEraELWFL2BK2hC1hS9gKtoKtYCvYCraCrWAr2Aq2erYeALy4gALcQAUa0IEBTCBsC7YF24JtwbZgW7At2BZsC7YFm8AmsAlsApvAJrAJbAKbwCawbdg2bBu2DduGbcO2Yduwbdi6P/RTgh4WvLiAAtxABbbNGh0YwATWw+4PBxdQgG2rRgUa0IEBTGA97P5wcAEFCJvD5rB1L+k3KHpY8GIC6+H0ksEFFOAGfrZ+j7+HBS86MIAJrIfdSw4uoAA3ELbuJf22fA8LXgxgPuyu4X1Zuj98v/ArPQB40YEBTGBd7AHAiwsowA1UYNui0YEBTGA97P5wcAEF2DZtVKABHdi2bExg2767pAcALy5gX/k+iukPgwr8Fvs+KVd6km/3W8E9yXdxAxVoQAcGMIH1sDf6wbb1sfVGP7iBCjRg26yxbX0UvdEPtu3rMD3Jd3EBBbiBCjSgAz9bvwXak3wX62Fv9IMLKMANVKABHQibw+aw9Ubvt4J7ku+iADdQgQZ0YADb1leoN/pgb/SDCyjADVSgAR0YQNj6RUO/T9uTfBcXUIC9bl/u+VMlvZ3mj5V8WPPnSgYXUIAbqEADOjCACWzbtwt7Ou/iAgpwAxVoQAe2zRoTWA97ox9sWzUK8LP1+789nXfRgH3l+yj6hcDBfNjbv9/y6jG73e+G9pjdxQAmsB72Rj+4gALcQAW2rY+tN/rBACawHvZG77dLe8xu97uhPWZ3sW3aqEADOjCACayHvdEP/rFpv+/ZY3YXN1CBBnRgABNYD/tz5g/CFrAFbP23Gvr93+q/1nDQgQFMYD3sv9twcAHb1leo/3rDQQUa0IEBTGA97L+ncnABYeu/qtJvzvZI3kUDOrDXjf5jRb1CNm6gAg3owAAmsB7231I5uICwzV8sqkYFGtCBAUxgPey/r3Kwbd4owA1U4Gf73vTdPWZ38bN9b/ruHrO7WA/7FX/1UfQLgYMC7HWlsffFYD2cPT+4gALcQAUa0IEB7HqtsR7231s5uIAC3EAFGtCBAYTNYHPYHDaHzWFz2Bw2h81hc9h6z3/vK++ew7u4gALcQAW2rW+C3vMHA5jAeth7/uACChDr9j5evcl6Hw/2Pj64gALcQAUa0IEBbFvffb27G3sO7+ICCnADFWhABwYwgbAt2BZsC7YF24JtwbZgW7At2BZsApvAJrAJbAKbwCawCWwCm8C2Yduwbdg2bBu2DduGbcO2YduwKWwKm8KmsClsCpvCprApbAqbwWawGWwGm8FmsBlsBpvBZrA5bA6bw+awOWwOm8PmsDlsDlvAFrAFbAFbwBawBWwBW8AWsCVsCVvClrAlbAlbwpawJWwJW8FWsBVsBVvBVrAVbAUbeslCLxH0EkEvEfQSQS8R9BJBLxH0EkEvEfQSQS8R9BJBLxH0EkEvEfQSQS8R9BJBLxH0EkEvEfQSQS8R9BJBLxH0EkEvEfQSQS8R9BJBLxH0EkEvEfQSQS8R9BJBLxH0EkEvEfQSQS8R9BJBLxH0EkEvEfQSQS8R9BJBLxH0EkEvEfQSQS8R9BJBLxH0EkEvEfQSQS8R9BJBLxH0EkEvEfQSQS8R9BJBLxH0EkEvEfQSQS+R6SXVuIAC3EAFGtCBAUxgPUzYEraELWFL2BK2hC1hS9gStoKtYCvYCrbuJd/T6t0DgBcdGMAE1sUeALy4gALcQAW2bTU6MIAJrIfdSw62TRsFuIG9rjX2Ct9rufmLqd/T6t0DgBcFuIEKNKADA/jV+32g8Z6/qjrY/eHgZ9tdeveHgxuoQAM6MIAJbNv+sPvDwQUU4AYq0IAODGACYTPYDDaDzWAz2Aw2g81gM9gMNoetO8Hua9x7/mAAE1gPe88fXEABYt3e8wcN2La+o3p3D/buPriAAtxABRoQ6/buPpjAtvX927v74AIKcAMVaEAHBjCBzzZ/ofXgAgpwAxVoQAcGsG3RWA97dx9cwLZlY9uq8Vu3//Tx+Wutgwn81u2/hHz+Qutu/Crrv0J7/ibrYAATWA/nb7Na4wIKcAMV2LY+4vk7rYMBbFsf5vy11sb5e62DCyjADVRg2/pEzd9uHQxgAuvh/A3XwQUUYNt+jQo0oAMDmMB6OH/XdXABBdi2vsbz910HDejAz2bzzxJYD+fvVQ4uoAA3UIEGdCBs/X3e+j7rPX9QgBuoQAM6MIC0bh9F37+95w8uoACxL2bPDxrQgQFMYF2cvwh7cAEFuIFxd5bNH2EerIfzh5gH192QNn+MeXADFWjAPlGzQgAT2Ceqy+mN/v1q2e6pv4sKNKADv3W/X47YPfV3sR729v8e4+6e+rsowM/mXW9v/4MGdGAAE1gPe/t7H1tv/4MC3EAFGtCBAXytzfS1NpvtP7iAAtSH8024i+zN23+Dfv647MENVKABHRjABNbD3rwH+zx4owA3UIEGdGAAE1gP5483D8KWsCVsCVvClrAlbAlbwlawFWy9pb/n/rvHAi8q0IAODGDb+pz1lm7sscCLCyjADVSgAd+6Peqn35P43aN+FzdQgQZ0YAATWA/7RfrBtlWjADdQgQZ0YAATWA9ndw/CtmHbsG3YNmwbtg3bhm3DprApbAqbwqawKWwKm8KmsClsBpvBZrAZbAabwWawGWwGm8HmsDlsDpvD5rA5bA6bw+awOWwBW8AWsAVsAVvAFrAFbAFbwJawJWwJW8KWsCVsCVvClrAlbAVbwVawFWwFW8FWsBVsBVs9W/x+wAUU4AYq0IAODGACYVuwLdgWbAu2BduCbcG2YFuwLdgENvSSQC8J9JJALwn0kkAvCfSSQC8J9JJALwn0kkAvCfSSQC8J9JJALwn0kkAvCfSSQC8J9JJALwn0kkAvCfSSQC8J9JJALwn0kkAvCfSSQC8J9JJALwn0kkAvCfSSQC8J9JJALwn0kkAvCfSSQC8J9JJALwn0kkAvCfSSQC8J9JJALwn0kkAvCfSSQC8J9JJALwn0kkAvCfSSQC8J9JJALwn0kkAvCfSSQC8J9JJALwn0kkAvCfSSQC8J9JIeN9RvXm73uOHFBNbFHje8uIAC3EAFGtCBAUwgbAu2BduCbcG2YFuwLdgWbAu2BVv3km8ybve44UUBbqACDejAACawHm7Yupd8I4S7pxQvbqACDejAtlljAuthd41vmnD35KF+Q1G7Jw/1G5LbPXl4MYH1sPvDwQUU4AZ+9X6/A7178vCiAz9bdundHw7Ww+4PBxdQgBuowLZpowMDmMB62P3h4AIKcAMVCFvAFrAFbAFbwpawJWwJW8KWsCVs3Qmyr3Hv+YMbqEADOjCACXzr9uThxQVsmzca0IEBTGA97N19cAGxbu/ugwpsWzQ6MIAJrIe9uw8uoAA3UIGwCWwCm8AmsG3YNmwbtg1b7+5vUnL3aOJFBwawbdX42b7px91DiNqTRT2EeFGB37rfPOLucUP9Rv12DxZq9dXsfXxwAxVowK+y6mvR+/hgAuth7+ODbesj7n18cAPb1ofZ+/igAwOYwHrY+/hg2/pE9T4+uIEKNKADA5jAtn1NbAYLDy6gADdQgQZ0YAAT+Mdmv77G3/f5iwsowP1h/7Nvz180oAMDmMA6qP1ZfxcXUIAb2DZpDGAC6+H6ARdQgBuIdVcfxW50YAATePeFzrjhwQUU4AYq0IAODGACYest/e0s7Y/yu2hAB8bZkNqThxfrYb/4P7iAfaJ6Bd1ABX4nanU52qfEGuuh/YALKMBv3dUX9tv+Fw34XYDVl+Xb/hcT+NlW1/tt/4sLKMANVKAB29bH5gFMYD2MH3ABBbiBt7XpzBgedGAA8+Hs+cFu0F1kb97vc1x0pgkPdmXxYW/egwsowA1UoAEdGMAEPltPE15cQAFuoAIN6MAAJhC2BduCbcG2YFuwLdgWbAu2BduCTWAT2AQ2gU1gE9ikbd4YwATWw/0DLmDbsnEDFWhABwYwgfVQse7s42p0YAATWA97dx9cQAFuoAI/2/eRAtoTghcDmMB62Lv74AIKcAMVCJvD5rA5bA5bwBawBWwBW8AWsAVsAVvAFrAlbAlbwpawJWwJW8KWsCVsCVvBVrAVbAVbwVawFWwFW8FWz9YTghcXUIAbqEADOjCACYRtwbZgW7At2BZsC7YF24JtwbZgE9gENoFNYBPYBDaBTWAT2AS2DduGbcO2Yduwbdg2bBu2DduGTWFT2BQ2hU1hU9gUNoVNYVPYDDaDzWAz2Aw29BJBLxH0EkEvEfQSQS8R9BJBLxH0EkEvEfQSQS8R9BJBLxH0EkEvEfQSQS8R9BJBLxH0EkEvEfQSQS8R9BJBLxH0EkEvEfQSQS8R9BJBLxH0EkEvEfQSQS8R9BJBLxH0EkEvEfQSQS8R9BJBLxH0ko1estFLNnrJRi/Z6CUbvWSjl2z0ko1estFLNnrJRi/Z6CUbvWSjl2z0ko1estFLNnrJRi/Z6CUbvWSjl2z0ko1estFLNnrJRi/Z6CUbvWSjl2z0kj29ZDVuoAIN6MAAJrAeTi8ZXEDYFDaFTWFT2BQ2hU1hM9gMNoPNYDPYDLbpJdoYwATWw+klgwsowA1UoAFhc9gcNoctYAvYArbpJbtRgQZ0YAAT2LbvteeeXjK4gL1uNPYK2fitsPs26v4w2P3h4AIKcAMVaMCv3m9QRHvG8GIC2/aV3jOGFxdQgBuoQAM6sG3WmMB62P3h4AIKcAMVaEAHwrZgW7AJbAKbwCawCWwCm8AmsAls3Qm+qUrtycOLBnRgABNYD3vPH8S6vecPbmDbojGACayHvbsPLqAANxDr9u4+6MC2ZWMC62Hv7oMLKMANVKABHQibw+awBWwBW8AWsAVsAVvv7m+gTnvG8GIC62Hv7m9SUnvy0L5hV+0ZQ9PeAf2a4KADv3W/IS7tGUPTvnd6d2tfzd7H2ue39/FBBwYwgV9l35io9tzgxQUU4AYq0IAODGAC2/adhx4svLiAAtxABRrws33jnNqDhRcTWA97Hx9cQAFuoAINCJvAJrAJbP19/psC1R43vCjADVSgAR0YwATWQ4VNYVPYFDaFrb/PfyOa2uOGFwOYwHrYneDgAgpwAxXYxzbowAAmsI/tu+17YPHiAgpwAxVoQAcGMIGwdSf4Rkq1RxMvKtCADgxgAuthYt3e898HNmmPJl7cQAXa7Q82nWAwgAmsh/3d/+ACCnADFQjbNIWvrfg0hcEFFOC+jcmnKQwa0IEBTGDdfubTFAYX8LN9c7ra84hmLZ7tPxjABNbD3v7fcK72POJFAW6gAg3owAB+tm84V3se8WBv/4MLKMANVGDb+pT09j8YwATWw97+BxdQgBuoQNgUNoVNYevt730tevsfXEABbqACDejAACYQNofNYXPYHDZ/3wB7HvGiAwP4vgH2EOLF/jbeR9xb2vve6S19sB72lj64gALcQAUa0IGw9Zb+Jme1pwkP9pY+2LZqFOAGKtCADgxgXuy5wYvfCt/AjPaEoH1jw9oTghe/FWI1JrAe9j4+uIAC3EAFGtCBsPXu/iZqtCcED/buPtg2bRTgBirQgA4MYD7cWLd37Ddyoz31Z994jvbU38VewRsTWA97xx5cQAFuoAIN6EDYFDaFzWAz2Aw2g613bD/a6qm/iw5sW98lvWMP1sPesQcXUIAbqECs2xsy++7rl+PRt1y/HD/4rZB9Afpb80EDOjCACayHvY8PLqAAYUvYEraELWFL2BK2gq1gK9gKtoKtYCvYCraCrZ6tJ/kuLqAAN1CBBnRgABMI24JtwbZgW7At2BZsC7YF24JtwSawCWwCm8AmsAlsApvAJrAJbBu2DduGbcO2Yduwbdg2bBu2DZvCprB1f/hm4LQ/b/CiAg3owAC2LRrrYfeHgwsowA1UoAE/2zfNoj31dzGB9bD7w8EFFOAGKtCAsDlsDlu/oO95jZ76u7iAAtxABRrQgW3bjQmsh91LDi6gADdQgQZ0IGzdS3qopGcBD3YvObiAvW5flu4P36iU9tTfxbrYU38XF1CAG6hAAzowgG2rxnrY/eHgAgpwAxVowLZ5YwATWA+//uDf1JT2LOBF+XA1bqAC+67uo5j+MBgPd68rjb2CNirQgA4MYALrof6ACyjAtvWxqQIN6MAAti0a29ZHYT9g26xRgBuoQAM6MIAJ/Gw9LdQTghcXUIAbqEADOjCACYQtYAvYom19jWMDFWhABwYwgfUw29ZXKBdQgBuoQAM6MIAJrIcFW7WtT3UJcAMV2Ov+udzWU3/+jYRYT/1dFOAGKtCADgxgAuvhgu3b8/498LKeBby4gQo0oAMDmMC2xYe95w8uoADbthoV2DZpdGAA+67uo5B6OJ1gsNfdjb2CNQYwgfWw9/zBBRTgBirQgG3rY+s9fzCB9bD3/MG29f3Qe176KHrPH2ybNxrQgQFMYD3sPX9wAT/b7hPVe/6gAg3owAAmsB72nj+4gLAFbAFb7/nd17j3/MEAJrAe9p4/uIACbFtfod7zBw3owAAmsB72nj+4gAKErff87lNdBnRgXOwRQv+eSVgPC/r3TMJ6WPCiAR0YwATWw97zBxdQgLD1nv/ec7QeFrzowAAmsB72nj+4gH12snEDFWjAtkljANu2G+th7/mDfS36KLYAN7DX1cZeoc967/mDCyjADVSgAR0YwAS2rS9s7/mDCyjADVSgAR0YwATC5rA5bA6bw+awOWwOm8PmsDlsAVvAFrAFbAFbwBawBWwBW8CWsCVsCVvClrAlbL3ntW+53vMHE1gPe88fXMDPZn1z9Z4/qEADOjCACayLPRZ4sVdYjQ7sFaQxgfWw9/zBBRTgBiqwbbvRgQFMYD3sPX9wAQW4gQqETWAT2AQ2gW3DtmHbsG3YNmwbtg3bhm3DNv3h+9Ys0x8GF7Bt1riBCjSgAwOYwHrY/eHgAsJmsBlsBpvBZrAZbAabw+awOWwOm8PmsDlsDpvD5rAFbAFbwBawBWwBW8AWsAVsAVvClrAlbAlbwpawJWwJW8KWsBVsBVvBVrAVbAVbwVawFWz1bD0AeHEBBbiBCjSgAwOYQNgWbNNLtFGAG6hAAzrws32fAWQ9AHixHnYvObiAAtxABRrQgbAJbAJbd43vSabt6Q/e6MAA5sPuBN9DQutBPf8ePloP6l10YAATWA97zx9cQAFuYNu6ht7zBx0YwATWw97zBxdQgBsIm8PmsDlsDpvDFrAFbAFbwBaw9Z73vuV6zx8MYALrYe/5g22LRgFuoAIN6MAA5sPCur2Pv2eA1oN6F3uFagxgAutiD+pdXEABbuBn+54BWg/qXXRgABNYD3sfH1xAAW4gbAu2BduCbcG2YBPYBDaBTWAT2AQ2gU1g6338PVu0/jjBg/2a4OACCnADFWhABwYQtn5N8D1QtB7qu7iAAtxABbbNGh0YD7sTfA/+rMf3/Hu8Zj2+59E3Qe/5gw4MYALrYe/5gwv41ft9NoD1+N5FBX627NJ7zx8MYALrYe/5gwsowLZpowIN6MAAJrAe9p4/uIAChC1hS9gStoQtYUvYCraCrWAr2Aq27gTfUyProb6LCyjADVSgAR1I6yawHvae/36H33p876ICDejAACawHgrW7d19UIBti0YFGtCBAUxgPezdfXABBQjbhm3DtmHbsG3YNmwKm8LWu/t75GA9vndRgQZsWzV+tu9xlfWgnn+fDWA9qHdRgN+63xMm65E8/x4gWQ/fefXV7H18cAEFuIFfZdXXovfxQQcGMIFt6yPufXxwAdvWh9n7+KACDejAACawbX2ieh8fXEABbqACDejAtq3GBNbD3scHF1CAG6hAAzrwjy1+fY2/7/MX62LP7F1cH65GAW6gAg3owAAmsB6uHxC21TZpNKADA5jAeig/4AJiXemj2I0KNKAD377omb2L9bD3/MEFFOAGKtCADoStt3TvrB7Ju7iBCrS7IXsk72IAE1gPrU9Ur2ALKMDvRK0ux/qUWGMAE1gP/Qf81l19Yb/tf3EDvwuw+rJ82/+iAz/b6nq/7X+xHn7b/+ICCnAD29bHFgZ0YAATWA/zB1zA19o8N1CBBnRgPpxvwl1kb95vasp6+O5iABNYF/uj/C4uoAA3UIF9HqLRgQFMYD3szXtwAQW4gQqEbcG2YFuwLdgENoFNYBPYBDaBrbf09yv61oN6FxNYD/cPuIBt63O2N1CBBnRgABNYDxXraq9QjQ78Vuhnlj2od7Ee9j4+uIAC3EAFfrZ+ktmDehcDmMB62Lv74AIKcAMVCJvD5rA5bA5bwBawBWwBW8AWsAVsAVvA1rv7+3U866G+iwsowA1UoAEdGMAEwlZt240LKMANVKAB29a3Z39zP5gXe3wv+tltD+pFP7vtQb3oh009qHcxgAmsh73nDy6gAL96+0lmD+pdNGDbdmMAE1gPe88fXEABbmDbrNGADgxgAuth7/mDCyjADYRtw7Zh27Bt2DZsCpvCprApbAqbwtadoJ/H9vDdRQFuoAIN6MAA0rr1sPf8wbb1HdW7+6ABHRjABNbD3t0HsW7v7oMb2La+f3t3H3RgABNYD3t3H1xAAW4gbAlbwpawJWwJW8FWsBVsvbv7OXoP3100oAM/Wz/67pG86KfVPXwX/VS5h+8ubuC3bj9K7jG7+H4txnqgLvqRbw/UXRTgBirwq6wfK/VA3cUAJrAe9j7u55A9UHdRgG2rRgUa0IEBTGA97H3cjyT7w/UuCnADFWhABwawbdJYD3sfH1xAAW6gAg3owAB+tn6E2sN3B/v7/MEF7GPrf9Z7/qACDejAACawHvaeP7iAsPX3+X4O2WN2FwOYwHrYe/7gAgoQ6/ae7wcyPWZ30YEBxL7oPT/Ye/7gAgpwAxVoQAcGELbe0rOzeksfVKAB/W3I2dKDCayD3hN3F/tEWaMAN/Czfc+HvGfr4nv84z1bd7Ee9vY/uIDfut/DG+/ZuosK/I7ie5bkPVt3MYCfzbve3v6Dvf0PLqAAN1CBbetj6+1/MIAJrIe9/Q8uoABva/P+IL6LBnRgAOvhfBPuInvzfoNv/psX6YMJrIfzIn1wAQW4gX0e2tab96ADA5jAetib9+ACCnADYXPYHDaHrbf09wDJe4ruYG/pg23ro+gtfXADFWhABwYwHybW7W36PWTxnoyL7ymX92TcxQAmsB72t+aDCyjADVQgbAVbwVaw1bP1vNzFBRTgBirQgA4MYAJhW7At2BZsC7YF24JtwbZgW7At2AQ2gU1gE9gENoFNYBPYBDaBbcO2Yduwbdg2bBu2DduGbcO2YVPYFDaFTWFT2BQ2hU1hU9gUNoPNYDPYDDaDzWAz2Aw2g81gc9gcNofNYXPYHDaHzWFz2By2gC1gC9gCtoAtYAvYAraALWBL2BK2hC1hS9jQSxZ6yUIvWeglC71koZcs9JKFXrLQSxZ6yUIvWeglPXEX32+sek/cXayL/fF8FxdQgBuoQAM6MIAJhK17yfdU2Xs676IA26aNCjSgAwOYwHrYveQg1u3+8D129p64i+8RtffE3cVe4Tt9PXF3cQEFuIEKNKADA5jAz/b9kqn3xN3FBRTgBirQgA4MYAJhM9gMNoPNYDPYDDaDzWAz2Aw2h81hc9gcNofNYXPYHDaHzWEL2AK2gC1gC9gCtu4P37iA98TdxQTWw+4PBxewbX3bd384qEADOjCACayHhXV7z2fftL3nD/YKvZ16zx+siz1Fd3EBBbiBCmybNjowgAmsh73nDy6gADdQgbAt2BZsC7YFm8AmsAlsApvAJrAJbAKbwDb94Xtxuqc/DC5g27xxAxVoQAcGMIH1cPrD4ALCprApbAqbwqawKWwKm8FmsBlsBpvBZrAZbAabwWawOWwOm8PmsDlsDpvD5rA5bA5bwBawBWwBW8AWsAVsAVvAFrAlbAlbwpawJWwJW8KWsCVsCVvBVrAVbAVbwVawFWwFW8FWz6a/H7Bt1ijADVSgAR342b7RAu9Jvov1sHvJwQUU4AYq0IAOhG3BtmDrrvFNqLhOf4hGBwYwgb1CH1D3h4MLKMANVKABHRjABMKmsClsCpvCprApbAqbwqawKWwGm8FmsBlsBpvBZrAZbAabweawOWwOm8PmsDls3R++8QbvSb6LCayH3R8OLmDb+l7v/nBQgQZ0YAATWA8T6/aer76Nes8f7BWyMYH1sPf8wQUU4AYqsG3V6MAAJrAu9iTfxQUU4AYq0IAODGACYVuwLdgWbAu2BduCbcG2YFuwfXs+v1Ee7/m+iwsowA1UoAEdGMAEwrZh27Bt2DZsG7YN227bagxgAuuh/oAL2DZt3EAF9rrfHupJvvx+nd97ki+/X9z3nuS7uIEKNKADA5jAr95vRMh76u/iAn621aV/e/6iAg3owAAmsB5G23bjAgpwAxVoQAcGMIH1MGFL2BK2hC1hS9gStoQtYUvYCraCrXrdvsYVwATWxZ7ku7iAAtxABRrQgW377qie2bu4gALcQAUa0IG0bgLrYe/ub/rGe5LvogA3UIEGdGAAE1gPN2wbtg3bhm3DtmHbsG3YNmy9u7+BJO9P37u4gAJsWza2rRq/db+5Fe+hvov1sPf89/ED3kN9+Q2KeH+iXkpfzd7H0ue39/HBetj7+OACfpVJH0Xv44MKNKADA5jAetj7+OACtq3PQ+/jgwo0oAMDmMC29ZnsfXxwAQW4gQo0oAMDmEDYCraCrWCrtvWF7d190IAODGAC62IPAF5cQAFuoAIN6MAAtu3XWA+7ExxcQAFuoAIN6MAAfrY9WA+7ExxcwM+2V+MGKtCADgxgAuthd4KDCwhbd4JvpMl71O9iABNYD3vPH1xAAWLd3vPfdJP3WOBFBwYwb3/oscCD3QkOLqAAN1CBBnRgAGGbpqCNG6hAA/ptTDFNYTCB9TB+wAWU289imsKgAvtEdWW9/fspbc/3Heztf3ABBfitq31z9fY/aEAHBjCB9bC3/8HPpn3v9PY/uIEKNKADA9i2PiW9/Rv7Q/suLqAAN1CBBnRgABMI24Jtwdbb/xs98p4FvKhAAzowgAmsh739Dy4gbAKbwCawCWzyvgH2LODF9w2wZwEvLuAG9ibrI+4t3UMaPcl3UYAbqEADOjCACayHBpvBZrAZbAabwWawGWwGm8HmsDlsved7FKI/cu+iAtsWjQ4MYALrYe/5gwsoQKzbu/ubHvOe70vty9K7++C3gvUV6t19cAMVaEAHBjCB9bB390HYCraCrWAr2Aq2gq1gq2frj9y7uIAC3EAFGtCBAUwgbAu2BduCbcG2YFuwLdgWbAu2BZvAJrAJbAKbwCaw9e7+BvW8JwQvJrAe9u4+uIBt08YNVKABHRjABNbD7g+WjQsowA1UoAEdGMAE1kODzWAz2Lo/WDQq0IAODGAC62H3h4OfzftMdn84uIEKNKADA5jAetj94SBs3R968K3nBi8q0IC9bl+W7g89EdazgBc3UIEGdGAAE1gPuz8chK37Q89S9bDgRQUa0IEBTGAdjB4WzG8OL3pY8KIAN7Bt0WjAtmVjABPYV94+nP4wuIC9WDX2DdO22eiNs9EHF1CAG6hAAzowgJ/tmwiLngU82Bv94AIKcAMVaEAHBhC2DVtv9OhyeqMfFOAGKtCADgxgAuuhwWawGWwGW2/06GvcG/2gAwOYwHrYG/3gAgpwA2Fz2By23ujRN21v9IP1sDf6wQUU4AYqsG199/U7AgcDmMB62C8aDi6gADdQgbB1U8i+CbopHExgPeztn31ZeqN/j6ijpwkvBjCBdbGnCS8uoAA3UIEGbJs1BjCB9bA3+sEFFOAGtk0aDejAALbNG+th94fvqVz0NOFFAfa16KPo/nDQgL3u1656QjC/X26PnhC8+P233/PC6FnAiwH8Vqgusnf3YO/ugwv4lVMt7i190IAODGAC62Fv6YML+JVefUC9pQ8q0IAODGAC62Fv6YMLCJvD5rD1lq6+Fr2lDwYwgfWwt/TBBRTgH1t9v3kePQB40YAODGAC6+G3pS8uoABhy7b1lU8DOjAeVq/bl6V6hb7tS4EGdGAAE1gXe6jv4gIKcAPb5o0GdGAAE1gP1w+4gG3bjRuoQAO2LRoD2LZsrIfyA/aV76PoLX1wA3vdaux7p239bfzgAgpwAxVoQAcGMIFfvd/jquihvosLKMDv7Hw/+0cP9V00oAMDmMB6aD9g2/pamAA3UIEGdGAAE1gP/QeEzWFz2LxtfVncgA4MYALrYfyAC9i2vh96zx9UoAEdGMAE1sPe8wcXELbe86tvrt7zBw3owG9d6cvyfcMu6buk9/xBBRrQgQFMYF3sob6LCyjAtmmjAg3owAAmsB72nj/YttUowA1UYNus0YFt88YE1sPe86uPQhZQgL1uNPbVbJvUw/0DLqAAN1CBBnRgALvebKyHvecPLuBn+54zRA/qXVSgAR0YwATWw97zu69F7/mDAtxABRrQgQFMYD102Bw2h633/O7L0nv+oAEdGMAE1sPe8wfb1vdD7/mDG6hAAzowgAmsh73nD8KWsPWe332f9Z4/aEAHfutqX6He89o3TO/5gwo0oAMDmMC62MN3FxdQgG2TRgUa0IEBTGA97D1/sM9ONQpwAxXYtt3owLZpYwLrYe/53UfRe/6gAHtda+xd2Lbe84O95w8uoAA3UIEGdGAAu95o7Hq/3d3DdxcXUICf7XtvMHr47qIBHRjABNbD3vMHP5v1Oes9f3ADFWhABwYwgfWw9/xB2Bw2h633fL+L0sN3Fx0YwATWw97zBxewbd64gQo0oAMDmMB62Hv+4ALC1nve+nL3nj9oQAd+63pflt7z3ndq7/mDCjSgAwOYwLrYw3cXF1CAbZNGBRrQgQFMYD3sPX+wz041CnADFdi23ejAtmljAuth73nro+g9f1CAva419i5sW+/5wd7zBxdQgBuoQAM6MIBdrzfWw97zBxewr0U2bqACDejAACawHvaej74WvecPCnADFWhABwYwgfXQYXPYHLbe89GXpff8QQM6MIAJrIe95w+2re+H3vMHN1CBBnRgABNYD3vPH4St93z0zdV7/qACDdjr9mWpXqHvkt7zBzdQgQZ0YAATWBd7+O7iAn62fhewh+8uKtCADgxgAuth7/l+a7VH8i4KcAPbthoN2DZpDGAC+67uo+g9f3ABe93d2CtYYwLrYe/5gwsowA1UoAEd2LY+tt7zB+th7/mDC9i2bNzAtvUB9Z4/2DZvDGAC62Hv+YMLKMAN/Gz9/kP/kduLDgxgAuth7/mDCyjADYTNYXPYes/325o9qHexHvaeP7iAAtxABbatL1bv+YMBTGA97D1/cAEFuIEKhK2/z1ef6n4P72AC62F3gurL/e35P2+y99b6Nv3jIE7ietwTeI8XsRBvYiU24vGu5iBO4gKvH/EiFuJN3CcsGw3owACOU5oLLOPczYtYiPsa9SF1hzhowFnw6+c9XveHvXkTK7ERO3EQJ3GB9Ue8iMfbB6ubWImN2InHW83t7TcJewrvso03mhexEG9iJTZiJw7i8fY5tAL7j3gRC/EmVmIjduIgJq+TN8gb4+2bIYR4EyuxETtxECfxePs65o94EQvxJlZiI3biIE5i8tZ4+1rUIhbiTdzrS98b0zz6zbac5nF4EQvxJlZiI3biIE5i8k7z6Hc7c5rHYSHexEpsxE4cxHPeqrnA00AOL+Lx7uZNPF5tNmInnuvVxyVJXODpM/1WZp5+MuzEQZzEBT79ZHgRC/EmVuKp35udOIiTeK7X13Ny+snhRSzEm1iJjdiJ27v7ek0/OVzg6SeHF7EQb2IlNmInJq+T18k7/aTfRMrpJ4eFeBMrsRE7cRCPt++f6SfD008OL2Ih3sRKbMROHMTknX6y+96YfnJ4EQvxrN/Xbl6M9Jt3Of2kuaafHF7EQryJldiInTiIk7i9/e5pTT85vIiFeBMrsRE78XijOYkLPP3k8HhXsxCPV5qV2IhnX/RxTT85nOA96+/m2V/tnX5y2ImDOIkLPP3k8CIW4k089WuzETtxELf3G9GNmn4yPP3k8CIW4k2sxEbc3n6Ts6afHE7iAk8/ObyIhXgTK7ERk9fJ6+SdftLvDdX0k8OLWIg3sRIbsROPt++f6SeHCzz95PAiFuJNrMRG7MTknX7S777W9JPh6SeHF/Gs39dufrjpN0Vr+snhupy/6SeHF7EQb2IlNmInDuLxZnOBp58cXsRCvImV2IjHa81BnMQFnn7yvUGav+knh9v7vWucv+knh5V47pM+ruknhwO8e/3vTeD8zc872t7pJ4eN2ImDOIkLPP3k8CIW4qlfmpXYiJ24vd/bw/mbfnK4wNNPDi9iId7ESjzerm36yeEgTuICTz85vIiFeBMrMXmdvE5eJ6+TN8gb5A3yBnmDvEHeIG+QN8gb5E3yJnmTvEneJG+SN8mb5E3yJnmLvEXeIm+Rt8hb5C3yFnmLvAXvmv7zvWefa/rP4fFG8yZWYiN24iBO4gJP//mGfHNN/zksxJtYiY3YiYM4iQss5J3+873vnWv6z+FNrMRG7MRBnMQFnr50mLybvJu806++UeNc068OO3EQJ3GBp18dXsTj7Ws9/eqwEhuxEwdxEhd4+tXhRUze6VfR98P0q8NG7MS9fva1m/7zvdOea/rPYSU2YicO4iQu8PSfw4uYvNN/vjfzc03/OWzEThzESVzg6T+H57xVsxBvYiUeb1/f6T+Hx9v31fSfwwWe/hN9XNN/DgvxrN99Y/rJ9557yvSK7xlAyvSKw0psxE4cxElc4OkVhxcxeRd5F3kXeRd5F3kXeRd5hbxCXiGvkFfIK+QV8gp5hbxC3k3eTd5N3k3eTd5N3k3eTd5N3k1eJa+SV8mr5FXyKnmVvNMrvvelU6ZXHC7w9IrDi1iIN7ESG7ETk9fIa+R18jp5nbxOXievk9fJ6+R18jp5g7xB3iBvkDfIG+QN8gZ5g7xB3iRvkjfJm+RN8iZ5k7xJ3iRvkrfIW+Qt8hZ5i7xF3iJvkbfIW/Du3494EQvxJlZiI3biIE5i8i7yTr/6fgki9/Srw5t4XkdlsxMHcRIXeHrU4UUsxJt4jnE3G7ETB3ESF3h61OFFLMSbmLzTo75fUcg9PepwECdxgadHHV7EQryJlZi8Sl4l7+lR3lzg06OGF7EQb2IlNuLxVnMQJ3GBT48aXsRCvImV2IjJ2z1q/fr+7B51ucDdoy7v5r523XPW92wxe5T0cRIXOH/Ei1iIN7ESGzF5c7zWnMQFrh/xIhbiTazE45VmJw7iJB7vd317GvXxeKNZiDfxXC9tNmInnvW//duTpn+4mvG9WOk1j9JrHj2vc+bfJnGv+T27yZ4yfbyIhXgTK7ERO3Gfq9X1dw+5XOD9I17EQryJldiInZi8m7ybvEpeJa+SV8mr5FXyKnmVvEpeJa+R18hr493Nm1iJjdiJg3i81lxg/xEvYiHexEpsxLR+zDp9n4cQzzp974USG7ETB3ESF3h6yOHx9j08PeTwJlZiI3biIE7iAk8POUzeIm+Rt8hb5C3yFnmLvAWv/X7Ei1iIN7ESj7eanTiIk7jA60e8iIW4vd9z9uxZ18dG7MRBnMQFnv5zeBELMXmFvEJeIa+QV8gr5N3k3eTd5N3k3eTd5N3k3eTd5N3kVfIqeZW8Sl4lr5JXyavkVfIqeY28Rt7pP988QNr0n8NKbMROHMRJXODpP4cXMXmdvE5eJ6+T18nr5HXyBnmDvEHeIG+QN8gb5A3yBnmDvEneJG+SN8mb5E3yJnmTvEneJG+Rt8hb5C3yFnmLvEXeQn/w6T/ffEv69J/DSmzEThzESVzg6T/fbEb69J/DQryJldiInTiIk7jAQl4hr5BXyCvkPf3Hmp04iJO4wKf/DI83moV4EyuxETtxECdYaf3TT7LZiGedvtannwwncYFPPxlexEK8idv7/b5e+vSTw04cxElc4OknhxexEG9i8jp5nbxOXievkzfIG+QN8gZ5g7xB3iBvkHf6ye69MP1kePrJ4UUsxJtYiY14vH3fTj85nMQFnn5yeBEL8SZWYiMmb5G3yDt95pszyRn4vbyIhXgTK7ERO3F7v7mXnIHfywWe/nN4EQvxJlbieOd5hnjXN8eSM8R7WYg3sRIbsRMHcRIXeJN3k3eTd5N3k3f6yTerkzMDfDmIk7jA83rm8Hi1WYg3sRIbsRMHcYKN1p9+0s/cZ6b38qzjzUGcxAWefnJ4EQvxJh5v31fTTw47cRAncYGnnxxexEK8ickb5A3yBnmDvEHeJG+SN8mb5E3yJnmTvEne6SffL3zmzPQenn5yeBEL8SZWYiMebzUHcRLX45kBvryIhXgTK7ERO3EQJzF5F3kXeRd5F3kXeRd5F3kXeRd5F3mFvEJeIa+QV8gr5BXyCnmFvELeTd5N3k3eTd5N3k3eTd5N3k3eTd7pP9+8Vs7M8GUh3sRKbMROHGDDfZvn/ZlfsxIbsRMHcRIX+Lw/M7yIhXjq12YlNmInDuIkbu83n5YzA3x5EQvxJlZiI3bi8fZ1mf5zuMDTfw4vYiHexEpsxE5M3iRvkrfIW+Qt8hZ5i7xF3iJvkbfIW/DOzPDlRTzebN7ESmzEThzE7e0ZuZkZPjz95/AiFuJNrMRGTOtPP+n5upkBvjzrSLMSG7ETB3ESF3j6yeHx7mYh3sRKbMROHMRJXODpJ4fJq+RV8ip5lbxKXiWvklfJa+Q18hp5jbxG3uk/PfM5M8OXg3i81lzg6T+HF7EQb2IlNmInDmLyOnmDvEHeIG+QN8gb5A3yBnmDvEHeJG+SN8mb5E3yJnmTvEneJG+St8hb5C3yFnmLvEXeIm+Rt8hbz1szY3x5EQvxeLVZiY3YiYM4idv7zdfVzBhfXsRCvImV2IidOIiTmLxCXiHv9KVvlql+p/94cxIX+PSf4VlnNwvxJlZiI3biIE7iqb9d038OL2Ih3sRKbMROHMRJTF4jr5HXyGvkNfIaeY28Rl4jr5HXyevkdfI6eaf/RN+3038OO3EQJ3GBp/98s4U1M8aXhXgTK7ERO3GAk9affvJ9WELNzPDlWaeanTiIk7jA008OL2Ihbm/2/T/95LARO3EQJ3E9npnhy4tYiDexEhuxEwdxEpN3kXeRd5F3kXeRd5F3kff0E21O4gJPP/lmMmtmhi8L8SZWYiN24iBO4gJv8m7ybvJu8m7ybvJu8m7ybvJu8ip5lbxKXiWvklfJq+RV8ip5lbxGXiOvkdfIa+Q18hp5jbxGXiOvk9fJ6+R18jp5p/9888A1M8aXgziJCzz95/B4o1mIN7ESG7ETB3ESF3j60mHyJnmTvNOXvrmRmpnh9c0V18wMX17EQtz/9puFK5le8X2ARc1s8Po+U6JmNviyEwdxEhd4esLhRSzEm5i8i7yLvIu8i7yLvEJeIa+QV8gr5BXyCnmFvEJeIe8m7ybvJu8m7ybvJu8m7ybvJu8mr5JXyTs94Zvrq5kNvqzERuzEQTzebC7w9ITDi1iIN7ESG7ETBzF5jbxOXievk9fJ6+R18jp5nbxOXidvkDfIG+QN8gZ5g7xB3iBvkDfIm+RN8iZ5k7xJ3nkN88031swGXw7iJC7w9JDDn1d+3Su6h1zexEpsxE4cxElczd/39JkNvryIhXgTK7ERO3EQJzF5F3kXedd4d/MmVmIjduIgTuICy3i9eREL8SZWYiN24iBO4gJv8u7xRrMQb2Il7vW/9z9r5n7lmy2smfu9LMSbWImN2ImDOIkLbOS18UqzEG9iJTZiJw7iJJ7z9r1un7nfy4tYiMfb19eVeLx9X7kTB/Fcrz4uL3D8iGdNa55/29clkrjA+SNexEK8iZXYiJ14vH28mcQFrh/xIm6v9P0zPeRwe6WPcXrI4fFmcxAncT2eWd/Li1iIN/F4rdmInTiIk7jA00MOL2Ih3sTkXeRd5J0e8n3eS82c8OUCTw85vIiFeBMr8Xij2YmDOIkLPD3k8CIW4k2sxOTd4+1rMb3lcBIXeHrL90y/Zu5Xvs9OqZn7vRzESVzg6SGHF7EQb2IlJu/0kO+DoGvmfi8ncYGnhxxexEK8icf7azZiJw7i8fb1nR4yPD1k930Vi1iI53r1cU2fOWzEs+b3vWNmfWX3dZkecliJjdiJgziJCzw95PAibq/28U4POazERuzE7dW+f6aHaB/X9JDmmfWV7/NeamZ9LwvxJlZiI3biIB6vNxd4esjhRSzEm1iJjdiJg5i8i7xC3ukh38xGzazv5U2sxEbsxEGcxOP9ruPM+l5exEK8iZXYiJ04iJOYvPO6RftazOuWw0K8iXt963tjesj3uSI1s7uXF7EQb2IlNmInDuIkJu/0kO/Zcc3s7mUh3sRKbMROHMTjXc0Fnh5yeBGPt6/v9JDD4+37anrIYSee69XHNa9hDhd4+sz3vKxmFlesr8v0k8NBnMQFnn5yeBEL8SZW4vZ6H+/0k8NBnMT12KeffM9My6effM8Ha2Z3L7f3e55bM7t72YidOIiTuMDTTw6PN5qFeBMrsRE7cRAncYGnnxwmr5BXyDv9pJ/pzOzuZScO4iQu8PSTw4t4vNW8iZXYiJ04iJO4wNNPDi9i8k4/6WdeM+t72YiduNfv51Azuyv9vvfM7l5WYiN24iBO4gJPPzm8iMk7/aSf0czs7mUjduIgTuICTz85PN7eI9NPDm9iJR5vX9/pJ4fH2/fV9JPDBZ6fifrZ6MzuXhbiWT+bp58MF/j0k+FF3Ov0s4yZxb2sxEbsxEGcxPV4ZnEvL2IhHu9qVmIjduIgTuICTz85PF5pVuJZfzc7cRAncYGnbxxexEI8x5XNSmzEThzESVzg6RuHF7EQk3eTd5N303HN3v8+V7vi7P1hJTZiOj/K69D5MTo/RufH6PxMfzisxHRdjK6LkdfIa+R18jp5nbxO3ukP1ffJ9Id+vjCzuFLz3/T63+/S1szcXl7EQryJldiInbiPq98Pn5nbywWePnB4EQvxJlZiI3Zi8iZ5k7xF3qL7reh+K7rfiu63ovut6D4vus+L7vPTN76+l6dvDC9iId7ESmzETjzHO5zEBZ6+cXgRC/EmVmIjdmLyTj/p971n5vbw9JPDi/hbf/f7aTNDu7/fGa+Zob1c4O4PlxexEG9iJTZiJybvHu9uLrD+iBexEG9iJTbi8f6agziJC2zj1eZFPF5r3sRKPPfJsBMH2Gd9b551+jq6Ek+dfb18avv65MzBTl+d2dfLgTWT1s9FLMSbWImN2Il5/T5v/f7zzLIe7n19eREL8SZWYiNub7//PLOsl5O4Hs8s6+73mWeWdff7yTPLenkTK/F4rdmJgziJx/vdJzPLenkRj9ebN7ESG7ETB3ESF1h+xIuYvEJeIa+QV8gr5J0+0O8Pz+zr7veuZ8Z19/vSdfZ1NDtxEBf47Nn+t7Nn+/20mUe9HMRJXGBDLypbxEI86/f9MHvzsBGPt+8BC/q3SYweWE5eJ6+T1zexEhuxE5PXyXVe//f5DyN24iBO4gLP9/fDi5jWn+/vh+dc9T0wfeCwEwdxEhd4+sDhRTz3T98P0wcOK7ERj7drmz4gfR9OHzhcl78/qfHj0ObvXeEvCIfNQTmM3SY4h+DQBXxvwn+hKExLuGFxEA6bg3IwDs4hOHAFiysQrkC4AuEKpj98741/YSqYg5tO8P2G4vfHPWY1nzALyITNQTnMIeQE5xAckkNRmO/7NwhVMN/V91zgaR039NI6l3Gaxw1FYdrHDYuDcNgclINxcA5cgXEFxhU4V+BcgXMFzhU4V+BcgXMFzhU4V+BcQXAFwRUEVxBcQXAFwRUEVxBcQXAFwRUkS+eniu+dni/M0nOPTn+5oShMh7lhcXjv2Xxhc1AO45kbdvrMDcFhKpjaqrDAzJu+sDgIh81BORgH5xAckgNXsFg6PzJYTAgOyaEozI8HNywOwmFzYM+853DDnESbEBySQ1GY7nLD4iAcNoe5E32CcXAOwWEqmEKn1Wh3lxkrfWFxEA5TQU1QDsbBOUwFc31ORzqhKExHst+ExUE4bA7KwTg4h+CQHIqCcwXOFThX4FyBcwXTkWxPmArm4Kbv2FyF6S42l3Eaiq0JxsE5zCHMlZuGckNRmJ94blgchINSBfNKxuYCT6u5YZaeyzit5obFQThsDsrBODiH4JAcqAL5/TgsDsJhc1AOxsE5BIfkwBUsrmBxBYsrWFzB4goWV7C4gsUVLK5gcQXCFQhXICyV96TyC7209z06M6ovLA7CYXN4zyu/YBycw3jWhORQFKbV+NSmixZQ4bA5cAXKFShXoMEhORQFPK/9AldgLD0jHT6hKJzhjRMWB+GwOSgH48CeM8NxwpxEnVAU5rXLDYuDcNgclINx6DvRbUJwSA5FYVqNT6HTajwmCIfNQTlMBTnBOQSH5DAVzPWZjnTD4jAVzF6YjnSDcjAOziE4JIdCmEHVFxYH4bA5KAfj4By6gpAJXcG8NJzJ1B02oVebFz8zd7rjNyE4JIc+hOgrN7OnLywOwmFzUA5OFcyrmugLPOOlL8zSOUE4bA7KwTg4h+CQHIrCtJobuALlCpQrUK5AuQLlCpQrUK5AuQLjCowrMK7AuALjCowrMK7AuALjCowrcK7AuQLnCpwrcJaeSbI1YZaee3RazQ2bg3IwDm8+7wvBITm0J+eGnVZzw+LQFeTUlpsWSOVgHLiC5AqSK8iiUD8Oi4Nw4AqKpWdMtTftnUc9QThsDsrBODiH4PBPnqIwr11yT1gchMPmoByMg3MIDn0npk4oCtNqblgcpoIpdFpN+gTlYBycw1QQE5JDUZiOdMNUIBOEw+YwFeQE4+AcgkNyKArTkW5YHITD5sAVKFegXIFyBcoVTEeq3iUz8LprDm76Ts1VmO5ScxmnoWRNKArTUG7oQ6i5ctNQbtgclINxcA5JFcyrmpoLPK3mhll6LuO0mhuMg3MIDsmhKEyruWFxEA5cQXIFyRUkV5BcQXIFyRUUV1BcQXEFxRUUV1BcQXEFxRUUV1BUwYy9vrA4CIfNQTkYB5Keydb1mzBL5wTlYBycQ3B4vxfyhaIgPw7jqQnCYXP4KtDf1CbGCziH4MAVCFewuYK9OAiHzUE5cAWbpefX/WTC6qrP/yIcNgflYBycQ3BIDkWhe8gLXIFxBcYVGFdgXIFxBTYV7AlTQbenmXDVn08QDuOZW8yVw3jmdvFZba62F4X+aUrPbdl95wXhsDm051yS7jsvOIfgkByKQv44LA7CYXPgCpIrSK4guYLkCpIrKK6guILiCoorKK6guILiCoorKK6gqIIZj31hcRAOm4NyMA4kPZ9eW78Js7RMEA6bg3IwDs4hOCSHoiA/DlyBcAXCFQhXIFyBcAUyFeiEqaC/n87wq66YMJ6csDmMpyYYB+cQHJJDUdAfh8VBOGwOXIFyBcoVKFegXIFyBcYVGFdgXIFxBcYVGFdgXIFxBcYVGFfgXIFzBc4VOFfgXIFzBc7S88EEc+WmV8nc19ORbjAOziE4JIeiMB3pBvZMR7qhD0HmTpyOdINxcA7BITkUhelIN0wFc5NPR5qH0DNZ+4JyMA7OITgkh0KYAdsXFoepICZsDsrBODiHqSAnTAU1oSisqcAnLA7CYXNQDsbBOQSH/hXw+cloZnFvOB+wcsLiIBw2B+VgHPpX0OentvO5uTckh6JwPmrlhMVBOGwOfQ7mTYuZzX3BOQSH5FAUpqXdsDgIh82BK5iWNu8FzEfpvhAcksI0rhlGmBFenbeFZ4b3BecQHJJDUZj2dMPiIBw2B65g2tPMRsw47wvBITkUhXnFdcPiIBymAp2gHIyDc5gKZmdNf7thKpidNf3thsVh7qpZ7XzWygnKoT3z8D7wEStfUP5f/uk/Sw6FMDO5LywOwmFzUA7GoU/IPLyf0dwXkkNRmFZzw+IgHDaHqWBPMA7OIThMBTphKuibfOZ0X1gchMNU4BOUg3GYCtaE4JAcpoK+32bC94XFQThsDsrBODiH4JAcuALlCpQrUK5AuYJpNfMQegZ+dR4bz2SvzvOsGeHVeSg4c7s6j/VncPcF49CHME+xZ3b3heRQFKa73LA4bKpg2sY8hJ4x3xdm6bmM0zZOmLZxw+IgHDYH5WAcnENw4AqCK0iuILmC5AqSK0iuILmC5AqSK0iuILmC4gqKKyiuoLiC4gqKKyiuoLiC4gqKKpg54xeEw3RlmzBLx4SiMK3mhsVBOND3nxkafsE4jCcnBIfkMBV0j5/R4buALA7CgSsQrkC4AnEOwSE50Pfg+QDdF1h6Pp5yzsH5fMoTgkNyKArnMypPWByEw+agHLgC5QqUK1CuQLkC4wqm78zT/5lP1nn6P4PIOg9tZxL5hT6J83h6ZpFfKArTauaJ9IwjvyAcNgflYBycQ3BIDkUhuILgCoIrCK4guILgCqYjzUP1GW1+ITkUhelINywOc4Hn9j8fYDlXYVrNPIifMeUXFgfhsDkoB+PgHP7JkxzmEL7duGZe+YXFQThsDsrBODiHrqAPe828svZD9TXzytoP1dfMK7+wOAiHzUE5GAfnEBySw1RgHaYj3bA4CIfNYSrwCVNBTHAOU4FOSA5FYTrSDYuDcNgclMO0jd8E5xAckkNROI3rhMVBOPR9fS7w+YDvE4yDcwgOyaEonI/5PmHOwQnCYXNQDsbBOQSH5FAUpqXdwBVMS8s5B9PSblAOxqE9OffbtKecizXt6YbNQTkYB+cQHJJDUZj2dANXMC+Ycm7lecF0g3IwDs4hOCSHojD9LWfXT3+7QThsDlPB7KzpbzdMBbOzpr/dkBzmrupwPpT3hsVhPDlhbsv2zFjz/V8W/2fTam5wDsEhORSFaTU3sGdazQ19QvpN8zUDzy8YB+cQHJJDUZhWc0NX0I+n1ww8v7A5KIepQCZMBXtCcEgORWF+zuon32sGnl8QDnNT1ATlYBymApsQHJJDUZh3qW9YHITD5qAcjANXYFyBcQXGFThXMK2m5tJPq6k5uG4o9psT3w3FfnPl5sVPzQ073eUG4bD738zF6u7ygnFwDsEhKXRDuRXkLD3XNJXDLD2XMZ1DcEgORaF+HBYH4bA5KAeuoLiC4gqKKyiqYAaeX1gchMPmoByMg3MIDsmBK1hcweIKFlewuILFFSyuYHEFiytYLD1NaE+YpW2CcwgOyaEobPr+MwPPLwiH8fgE5WAcpoKYELxAcqDvgDPw/AJXoFyBbg7KwTg4B65AWXr+LslvwuagHIyDcwgOyaEonL9PcsLiwBU4V+BcgXMFzhU4V3D6Tk3oC7zmf+lWY/2gd83A8wt9Gfsp6ZqB5xecQ99Ia+63aTU3FIXpOzcsDsJhc1AOxsE5cAXJFSRXUFxBcQXFFUxHWnMfTEe6wTg4h+CQCPu8dlkT+jL2e4Nrnz/FdoJxcA7BITkUhfMH2U6Yg7MJysE4OIfgkByKgvw4sEfmJPqEzUE5zJHWBOcQHJJDUcCfYvvC4iAc+lz3m6Vr5qJfMA7OITgkh6KAP8r2hTkHMUE4bA4j/U2Yg5s7ZH6AOuH8PbYTFgfhsDkoB+PgHIIDV2Bcgc8FnpPYHclkqu6+YzK38ryquaFvJJkT0n3nheTQN5L0NptP431hcRAOm4NyMA5TwRzCdKQbkkNRmI50w+Iwp3fu6/OnZedIz9+WPSE5FIXz52VPWByEw+YwBzfSaSg3JIdCmI/pfWFxEA6bg3KYkxgTnENwmAp8QlGYlzg3LA7CYXNQDsZhKsgJwSE5FIXpSDcsDsJhc1AOxoErmI60p7bpSDcUhelINywOwmFzUA69afdckulINwSH5FAUpiPdsDgIhz4H/SEFa+aiXzAOziE4JIei0I3rhcVBOHAFNhWsCcbBOQSFaU/9rHrNkLSdg5v2dINxcA7BITkUhWlPNywOwoErmPZ0rum0pxucQ3BIDkVh2tMNi8NUsCdsDsrBOEwFs53nBdMNU8Hc8fOC6YR5wXTD3FWz2uliJ2wO46kJvVo/VF+GP339hcVBOGwOysE4OIfgMN/ajrQo4I9gf2FxEA6bg3IwDn2k/Rh8zWD1C8mhKMwA0JzRmZg+p2rmol9IDkVh/zgsDsJhc2DP/DjWswRrPvH3heCQHIpCt5oXFgfh0BXMa4oZn37BODiHqcAmTAU+oShMq7lhcZgK5j6YVnODcpgKZIJzCA5TwVwsKwrThG5YHITD5qAcjINzCA5cgXMFwRUEVxBcQXAF057mJeiMXJvNbTlNyObKTas5N/m0mhuUQ6ARz1y02YT50eoG4bA5KAfq11bOITiM54RCmOnnF6YCn0D9eqafX1AOxsE5BIfkQN8xZkj6hcWBK1gsnT/HNC+yZvr5haIwQ4Q3LA7CYXNQDsbBOXAFwhVM3+mn/2s+E9j66f+a6WebdxZm+vmFPonz0+5MP78QHPok9lPsNdPPN0x3uWFxEA6bg3KYCvYE5xAckkNRmO5yw5zeOQfn72DPOTh/CPuE4JAcisL5Y9gnLA7CYQ5OJziH4JAcisI0hxsWB+HAnmkOPrflNIcbpoK5Kea1yw3JoSjMa5cbFgfhsDnMDTs30vzdpRucQ3BIDkVhfgK7YXGYazoXeF673KAcjINzCA7JoRBm+tn6N+/XTD+/IBw2h5GuCXMj2YSiMC9kblgchMPmoByMg3MIDlzB4gqEKxCuQLgC4Qrm9c48hJ6xZpvBghlethksmOHlG6bvzAP/GV5+QTj0SZzn6DO8/IJxcA7BITkUhelINywOwoErUK5AuQLlCpQrUK5gOtKMKcznFb+wOAiHzUE5zAX2CX0Zf3O/Td+5YXNQDsbBOQSH5MCe6UgzTzHzyi8Ih81BORgH5xAc5iQeaVGYjnTD4jAV1ISuYJ7+z7zyC8bBOXQFObfyvBK6oSjMT1MzbTIDzy8Ih6lgbv95jXSDcXAOwSE5FMIMSb+wOAiHzUE5TAU6YSqwCePpqzADz2seNszA8wvGYW6XPWEW6Esy88ovbA7KwTjMbTm1zauaG5LDeKaC6S43LA5dwTxhn3nlu8C8cXODceAKNlewuYJ54+aEeePmhsVBOHAFytJuG79zFbo5vCAcNgflYBycQ3D4J0+fxBkfmHnlFxYH4bA5KAfj4Bz6vp6RgxlrfqEoTHe5YSqYszPdZR7Rz1jzC8rBOEwFPiE4JIepoPf2jDW/sDhMBbMxprvcoByMg3MIDsmhKEx3uWFx4AqKK5juMmMKM9bsMxcww8s+EwPzWci/md+ZeeUXlMPcLr8Js0BfkhlRfkE4bA7KYW7LNcE5BIfx6ISi0K9QXpgKbILQArI5KAeuQLgC4QokORSF7i4vLA5cwWZpt43f/Jg0g8gvLA7CYXNQDsbBOfyTJznMSew7ZAaRX1gchMPmoByMg3OIDjkhORQF/3GYCmpCVzBPsWdE+QXlYBy6gnm+PSPKLySHqWDu6/hxWBymgtklsTkoB+PgHIJDcigK3V1eWBy4guQKkitIriC5guQKciqYLZNTwWyZGs9cuekUay5JOYdZba7P9JAb6gWZeeUXFgfhsDkoB+PgHIJDcuAKFlewuILFFSyuYHEFiytYXMHiChZXsLgC4QqEKxCuQLgC4QqEKxCuoNuT90+7MvPKLxSFbk8vLA7CYXMwDt02+l02mUFk759cZcaNX9gclINx6PbUv7oiM278QnIYj3SY9nTD4jAV7AmbFpgXPzcYB67AuALjCro93dDt6YXFQThwBc7S7js/nUK7u7wgHDYH5WAcnENw+CdPUZju0s+DZSaMXxAOm4NyMA7OITjMnTi3y3SXE+rHYXGYCuZU1VSQE5SDcXAOU8FsjOlINxTCTBh7P/2XmTB+QTh0Bf30Umb2+AXj4ByCQ3IoCtORblgchANXsLiCxRUsrmBxBdOR+mGdzFiz7zm46Tv9iFFmRNn7XWqZqWTvB7AyU8kvFIVpKP0UTmYq+QXhsDkoB+MQVIHO0jlhcZil5zJOq7lBORgH5xAckkNRmFZzw+LAFRhXYFyBcQXGFRhXYFyBcQXOFThX4FyBcwXOFThX4FyBcwXOFThXEFxBcAXBFQRXEFxBsHSa0LmM04R07tFpNTcoB+PgHLrZnaW71bxQFKbV6Nyw02puEA5TgUxQXsA4OAeuoLiCogpmKvmFxUE4bA7KwTn00tNhZ6j4hc1BORgH5xAckgN7+rXLC3MSdYJw2ByUg3FwDsEhOfSdqCOdVnPD4iAcpgKfMBXEBOPgHILDVJATisJ0pBumgj1BOGwOU8FcuelINziH4JAcisJ0pBsWB+GwOXAFxhUYV2BcgXEF05F69FFmeNltDm76js2Jn+5ic+WmofRzZ5nZ4xumodzQhzAvJ2cq+YXNQTkYB+eQVMG8qplXXDNu/MIsPZdxWs0NxsE5BIfkUBSm1dywOAgHrqC4guIKiisorqC4gqIKZvb4hcVBOGwOysE4OIfgkBy4gsUVLK5gcQWLK1hcweIKFktPE/pNmKVrgnIwDs4hOEyzWxOKwryLc0N7fKTTam7YHLoCP//GeAHnEBy4gs0VKFcwbwPdIBw2B+XAFShLu4f8+k0ymaHiF5SDcXAOwSE5FAVnz/yYdMOcxD1hc1AOxsE5BIfkUBSm1fhIp9XcIBw2h6nAJkwFPsE5BIfkMBV0q5lx4xcWh6lAJmwOymEqmNt/OtINwSE5FIXpSDcsDsJhc1AOXEFxBcUVFFdQVMHMK3s/9ZWZV/Z+0Cszlez9+FNm9tj72a7MuLH37xTLjBu/sDj0IfQTT5lx4xeUg3FwDsGhqIJ5VdPP52TmiF+YpWOCcXAOwSE5FIV5VXPD4iAcNgeuYHMFmyvYXMHmCjZXoFyBcgXKFShXoFyBcgXKFShXoFyBcgXGFRhXYFyBcQXGFRhXYFyBsfRrQlVz60wPirlFp9Pc4ByCQ3KYXtfNfyaHX1gcxjPS6TQ3KIeuIM+/cV4gOCQHriC5guQKutO8sDkoB+PAFSRLvxZSOXvk6xOPjdiJgziJ63FPCT9exELcp66fScuMCL9gHJxDcEgORWH6S57Qt9+RTn+5YXNQDlOBTpgKbEJwSA5FYd4Y7mdqMvPCLwiHqWBNUA7GYSqICcEhORSFaUM3LA7CYXNQDsaBK9hcweYKNlegXMG0oX4ULzNj7DUHN82m5sRPS6m5ctNFejJAZkT4BeHQh1BzsaaL3GAcnENwSArz49CpYF7K1FzTeSlzwyw9l3EazA3BITkUhXkpc8PiIBw2B+XAFQRXEFxBcAXBFSRXkFxBcgXJFSRXkFxBcgXJFSRXkFxBcQXFFRRXUFxBcQXFFRRXUFxBkbQniWta/gwSez9IlxkXfiE4JIei8HWamo7f08KPhXgkY5w2c4NxGP35N0H/PokLLOQWcgu5vwbzWImN2InJK+T6ekbNa+geD37sxEGcxAX+esLjRUzrf69LHn+nK/pjF2Smgl9wDsEhORSFbigvLA7SQSZsDsrBOEwFe8JUoBOSQ1HwH4epYI7UhcPmMBX8JhgH5zAVzE3uyaEoxI/D4iAcNgflYBycA1cQXEFwBckVJFeQXEFOBbM5ciqYm7G7S6y5ct1DYs0l6R7yQq+25vp0D3lBORgH5xAckkMhzIjwC4uDcNgclINxcA7BITlwBYsrWFzB4goWV7C4gsUVLK5gcQWLK1hcgXAFwhXIVKATNgflYBycQ3BICvvHobvGb3hWtgnBITkUBf1x6Na0hoV4E4/EJxgH5zD6mJD07wtsP2JyG7mN3F9TemzEThzE5HVyfX2m5m32ngh+HMRJXOCvkTxexEJM639N5PGcrprgHIJDcigK00NuWByEQ99v8/biDAK/YBycQ1cwb63NIHDMOMIMAt8wfeeGxaErmKmFGQR+QTnMOcgJziE4TAVzk0/fmTCDwC8sDsJhc1AOxsE5BIfkwBUsrmBxBYsrmL4zz//n05JjHuzPZyLHPNSeTz6OeZY/w8MxT6dmePgF5dCHMI+uZ3j4heCQHIrC9JAbhCrYs7RMcA6z9J6QHIrCdJQbFgfhsDkoB+PgHLgC5QqUKzCuwLgC4wqMKzCuwLgC4wqMKzCuwLgC5wqcK3CuwLkC5wqcK3CuwLkC5wqcKwiWTg+a22160IxNzFDwC0VhOs0Ni0M3urlzchMr8UjmZp02c0NwGL1PKPz7+hEvYnIXuYvc/QbOZScO4iSGt6eFH39rzu7t8eDHSVzg/pno8iIW4k1M638vRB7P6coJwSE5FIXpITcsDsJhc5j7rSYYB+cQHLqCuZNmXjjO2ZqGcsPiIBy6gplNmOHhF4zDVDAn5PSdE5LDVNA3+Qwcv7A4CIfNQTkYB+cQHJIDV2BcgXEFxhUYV2BcwfSdebEwA8cxz/9nrDjmif0MD8c81J7h4Rd6tXkmPcPDLziH4JAcisL8zHTD4iAcNgeuILiC4AqCKwiuILiC5AqSK0iuILmC5AqSK0iuILmC5AqSKyiuoLiC4gqKK5jXOzZ3/LzeucE5BIfkUC/sGTh+QTh015DhWfmEojCvVm5YHIRDt6Y9rMRGPJITgkNyGL126Ldrzr/vt2suCzG5hdxC7n675nIQJ3GBN3k3ufptnP6JevcQ8OMC99s1lxexEG9iJab1vybyeE6XT0gORWF6yA2Lg3DYHJTD3G8xwTkEh+QwFWSHee1iU/W8drlBOGwOXUH/GLpnRPgF5zAV2ITkUBSm7/jc5NN3bhAOm4NyMA7OITgkh6KQXEFyBckVJFeQXEFyBdN3fO6/6Ts+m2O6i8+Vmx7ic0mmh9wwq831mR5yQ3BIDoUwI8IvLA7CYXNQDsbBOQSH5MAVLK5gcQWLK1hcweIKFlewuILFFSyuYHEFwhUIVyBcgXAFwhXMa6R+Yr/nk49fCA7JoSjMa6QbFofNobuGDvfK/WvnewaJX1gchMPm0K3Jho3YiUeyJiSHojCtqUcedk8R33/fb9dc3sTkNnIbufvtmstJXOB+6+YyeZ1cX5/JnAv1NZPLXy95vIiFeBMrsRHT+tNEYq7QNJEbisI0kRsWB+GwOSiHvuFiDnCayA3BITlMBd1RZmA4emhgz8DwC8Jhc5gK5kin8dzgHKaCPSE5FMIMDEc/TdkzMPyCcNgclINxcA7BITkUhcUVLK5gcQWLK1hcweIKpvH08+k9E8fRz9v3zBVHPzvfPT2cOf9ElNiIA7z7Lp1TNo2hH6jvmft9wTg4h+CQ32JTyC6w/ohH4hOEw+Yw+phg9O+dOIjJreQ2ctsiFuJNrMTkNXJ9bSGnZ/YA72MlNmInDuIkLnDQ+t06Ls/pOmFzUA7GwTkEh+RQFKZ/9OzAningF4TD5tAV1Nyf0z9q7s/pHzcEh+TQFdTcitM/blgc5hzkhM1BOUwFc72mf9wQHJJDIcwU8AuLg3DYHJSDcXAOwSE5cAXTP/q5954p4Ogn13tmfbMfQ+4Z741+jL3nQ4ejRyb2zPq+sDhIL7AmbA7KwTg4h+BQVMGepWXC5jBL7wnGwTkEh+RQFPolyQuLg3DYHLgC5QqUK1CuQLkC5QqMKzCuwLgC4wqMKzCuwLgC4wqMKzCuwLkC5wqcK3CuwLkC5wqcK3CWTg+auzJm5ROMg3MIDsmhG93ch/kjXsQjOWFzUA6j9wlO/z6Ik5jcRe4i99dgHm9iJTZi8hZcPaeb/Qsxez5WOH8nbA7KwTg4h+CQHIrCmhsiJywOwmFzUA7GoSvox/575nyznzHv+Vjh7AfBu4d+U+effC3n8SL+Tt5ctR74fezEQZzEBe6XK5cXcR9ePwHeM+n7gnIwDs4hOCSHojBdZ83Znq5zw1Qwhztd5wblMBXUBOcQHJJDUZiuc8PiIBw2B+XAFRhXYFyBcQXGFThXMF2nn+TumQh+YXNQDsbBOfQtMKf6exEUdXgRC/EmVmIjduIgTuICJ3mTvEneJG+SN8mbc2AyoU/tvBE4HxCcMrunFoc5gXNP1+agHOYEjqecQ3BIDoUwHyr8wuIgHDYH5WAcnENwSA5cweIKplP1U6g9E8MvbA7KwTg4h76ww9IXUIZn4ZxgHJxDcEgORWH/OCwO7JledEMfQD993zPu+4JzCA7JoShML7phcegK9hz29KJ+HrvnI4Wzn6fuGQR+wTkEh+RQFKYX3bA4CIfNYSqYCz+96AbnEBySw1Qw13d60Z7TO73ohqnAJmwOysE4OIfgkByKwrSpNbyIhXgTK7ERO3EQ990813Xa1PC0qcOLWIg3sRIbcR+znhAckkNRmAZ2w+IgHDYH5WAcuIJpYDrHPg3shkKYzy1+YTw6YVazCcEhORSFaUY3LA7CYXNQDsaBK5iXTf0sdc9HFb9QFOTHYXEQDpuDcpgK9gTnEBySw1TQO2k+qviFqSAnCIfN4bun8rARO/FI+tVCTxqfN3Vmnvj9D/xfTVu5YXEQDpuDcjAO7Jm2ckOfjHmkOVPDN0xbuWFxEA6bg3IwDl3BPO2cqeEXkkNRiKlgLlRMBXODzw9gN2wOymEqmLtwfjS7ITgkh6mgd+JMDb+wOEwFc+PNT2c3KAfj4ByCQ3IoCtNmblgcuILiCoorKK6guIJpM/MMcSaNc54Uzjxx9u+O7pkaznmYNoPCOQ8uZ1D4heTQhzDP3GZQ+IXFQThsDsrBqYJpGfOD+EwAvzBL2wThsDkoB+PgHIJDcigK0zJu4Ao2V7C5gs0VbK5gcwWbK9hcweYKlCtQrkC5AuUKlCtQrkC5AuUKlCtQrsC4AuMKjCswrsBYOk1ovnnNpwznPJWdTxl+YXNQDsaBvvfMpwy/kBzGMzfstJobFoepICfQd7/5lOEXjANXEFxBcAVB339nuPiFxUE4cAXJ0q+HnGcjPTR8+esgjxexEG9iJTZiJw5i8ha8PSn8eBEL8Sbu0zkPTmdKOOP8L33S5tHnzALfMK1lnnbNLPALwqFP2jzXm1ngF4yDcwgOyaEoTAe6YXEQDlyBcAXCFQhXIFyBcAXTgeaZ4wwTv7A4CIfNQTn0hZ0T+nWW8wB0RolzHmbOKPELwmEWjgnKwTjModWE7/45+q+vPC7w1zvOQ/kZFs6ce2Ne5txgHNpx7qd5mXNDcujTNw/8elj4PK7vWeHHQvxtg/nJuseBHwdxEhe4fza6vIiFeA5vjnvayg3GYQ5vTvu0lRvm8M5qRWHayg19EedF6Xy88Aubg3IwDs4hOEwFc7nmFcwJ8wrmhsVBOGwOXw+aNwx6cnh+L2H35PDjelzday4vYiHexEpsxE4cxEncBzc/mdY0oHlzZT6SOOcZ3Xwk8Qt9EueZ1owYvxAc+iTO86kZMb5h2swNi4Nw2ByUw1TgE5xDcEgORWHazA19Xmv4z8rzoRa7J4cfB3ESF/hrMY8XsRDPMc3Jn/5yg3GYY5qa5oXLDd8x1TwpnJHhG/qFywurw1zXfuHywuagHIyDcwgOU8HcJVYU/MdhcRAOm4N+JyeGv5Psh5O4wF8HeryIhXgTK7EROzF5g7wxBze3c87BzQ2YcwhzTVM5zEmcBdI5BIc5iXO1syjUj8PiIBw2B+UwFcztUs4hOCSHekFnSviFPq853OdvD/fC/XHSOp82/EJyKArdX15YHITD5qAcjANXsLiCxRUsrkC4AuEKZCqQCVPBHKmMxyaMxyckh/FEh/3jsDgIh81BORgH5xAckgNXoFyBcgXKFShXoFyBcgXKFShXoFyBcgXGFRhXYFyBcQXGFRhXYFyBcQXGFRhX4FyBs9T7Tl7Ds/Lc4l4U4sdhcRAOm4NyMA7sieDQRyBzI07TOWGazg2Lg3DYHJSDcegKZO7xaUcy9/i0oxuKwrSjGxYH4bA5KAfj4BymgtlY045uKIQZOH5hcZgKfMJUEBOUw1SgE5xDcEgORWH61g2Lg3Dobzu/YSU2YicO4iQusPyIv293/Q6X9pzx402sxEbsxEGcxHPME6aD3bA4CIfNQTkYB+cQHJIDVzAdbM+xTwe7QThsDu3pR2M6E8e15+JMN7phcRAOm4NyMA7OITgkB65gutGeW3deHd0gHDYH5WAcnENwmApkQlGYfnbD4jAVzE6afnbDVDA7afrZDc6h76nDSVzgaVn9NE97Bnk+o1Rn0Pj+D8X/1bSVG5SDcXAOwSE5kGfGiV/ok9FPrHTGiV/YHJSDcXAOwSE5dAX9lEtnnPiFxUE4TAUyYSrYE4yDcwgOU4FOKArzcuiGxWGuQk3YHJTDVGATnENwSA5FYdrMDYuDcNgclANXsLmCzRVsrmBzBdNmdO6DaTM6Bzcvh2yuwrzosbmM01n6QZ3O5w+/sDj0IdhcueksNygH4+AcgkNRBdMybC7wtIwbZum5jNMybnAOwSE5FIVpGTcsDsJhc+AKgisIriC4guAKgitIriC5guQKkitIriC5guQKkitIriC5guIKiisorqC4guIKiisorqBIOjPJ55vXzCRXP8bTmTx+wTkEh+RA33tm8viFxWE8PmFzUA5TQUxwXiA4JAeuQLgC4QpEOGwOysE4cAXC0t0vkn7DQryJldiInTiIk7jA/YbPZfIqeZW8Sl4lr5L39Jma0Ec2L/9mIHk+dkZnIPmFPmn9hFNnIPkF49AnbX5+n4HkF5JDUZgXLTcsDsJhc1AOxoErcK7AuQLnCoIrCK5gOpDPdZ8OdINyMA7OITj0hZ0T+nWW+bx97ZHkx04cxElc4K9zPF7EtP7XNh5P3bPlpmvc4ByCQ3IohPmQ4RcWhzlzMWFzUA7GYSrICVNBTUgORWHazg1dQT+E0JlefmFzUA5TgU1wDsGhK+gHbDofWXzDtJ0bFgfhsDkoB+PgHIIDVyBcweYKNlewuYJ5hdMPq3RGnCvm4OZ1TMxVmFcrMZdxfg7qp4c6E8ovKIc5hLly8xPSDcEhORSFeYVzg1AF019iLvD0lxt66ZzLOP3lhqIw/eWGxUE4bA7KwTg4B67AuQLnCoIrCK4guILgCoIrCK4guILgCoIrCK4guYLkCpIrSK4guYLkCpIrSK4guYLkCoqlXxOav02jM5dcObfodJobCmGmj19YHLrT/YY3sRKPRCY4h+Aw+j2h8O/Xj3gRk3uRe5G735u57MRBnMTkFXL1+y7zvlbPGz9O4gJ/XeHxIhbiTUzr90uWy3O6bEJwSA5FYXrIDYuDcNgc5n7zCcbBOQSHqSAmTAXdQ2bM+IXFQThMBTVBORgH5zAV6ITkUBSm79RcvOk7NwiHzUE5GAfnEBySQ1EIriC4guAKgisIrmD6Ts19MH2n5uCmu9RchekhNZdx2kbN3py2cYNzmEOYKzdt44aiMD8Y3bA4CAelCua1S80Fno5ywyzdl3HGgV9YHITD5qAcjINzCA7JgStYXMHiChZXsLiCxRUsrmBxBYsrWFzB4gqEKxCuQLgC4QqEKxCuQLgC4QqEKxCuYHMFmyvYLO2fm+ZZRn+EscwH42t/VjHC4iAcNoev061ZuH9IuuzEI1kTkkNRsNHLhIV//3WZx5uY3EZuI/fXYB4ncYG/7vKYvE6ur2f4qa0fcB/uB9yXF7EQb2IlNmJav9+dvTynSycUhfxxWByEw+agHIyDd7AJwSE5FIWaCnzCVBAThMPmoBymgrkVyzkEh+QwFXQf7U8iRlgcpoKasDkoB+PgHIJDcigK68dhceAKFlewuILFFSyuYHUF84y7P4n4+8u+HaQ984y7Z4q/P/M7oReYh+Q9RoyQHPoQ5lF4jxEjLA7CYXNQDk4V7Fm6L3BMR7lhls4JwmFzUA7GwTkEh+RQFKap3MAVGFdgXIFxBcYVGFdgXIFxBcYVOFfgXIFzBf7/a3u3nVl240r3XXTti+QpgvSrNAxDrVZvCBAkQ5Y30Gjo3buKo8gcWkYG4y9y3tjxral/ZORpFA9BJmcgnIFwBsIZCGcgnIFwBsoZKGegnIFyBsoHfZtQQfNA4UFwRYXTDEgMmaEwyFsMwm+jmXGluB8E8+AKmxkQGPrh+87FudcRj79/u8yMC8V07EbHbnTst8F84l5HPONAcaQ4UVwofmuie11hGZjKr7CMAYEhMiSGzFAYhKE/EOhsVFjGgEYQL4bAEBmQgQCQAc4tIgOc3NtzCnqjvW54xvWO37ZSMFxS4Srp8w+JITP0Y/SNinOFxQxQhn6WGIbvJcQFAzG9hHjGgeL3s4FBrgp7wVh0hb0MUAYc4/P3jQD2MqBfSUyx9hLignGxXkI840zx+2rFz/++Utzu+G0fMw4UR4oTxZlinB7OFc4xQBkqQyOAcwwIDJGh30T0rXsN8Q09A0yN9hriG5ShZ4BJ0wqH+QBaOQMCQ2RIDJmhMAiDMnAGlTNonEHjDBpn0DgDtHIwoVvRyhkgDMpQGdoNvcK44M3rlcQlfOJMcaFYKFaKK8Xtjt+GNONAcaSYjhvouIGOG+i4gY4b6LjwIUzmNvgQpl8b3KYvgMwNbjMAFxACaOEMUAZcwAZoBGjhDAgMkSExZIbCIAzKwBkkziBzBpkzyJxB5gzQEMJEU4NTDRAGZagMjaD0GwvltwdljAn3MuIZC8VKcaW43fHbm2YcKO7nhInwhobNgMyAc8JDDnsagHPKgMrQCGBPmEFtsKcBkSExZIbCIAzIAOcDexrQCGBPAwJDZEjvi4On+e1BWT6xUlwpbnf89p8ZB4ojxYniTHGhmI7b6LjwnYJ3CL7T5zPLhT5Un898QWLoF7HPZ5YLfagBwtAvYp/PLBf6UAMaARpEAwJDZEgMyCABCoMwKENlaASxX9eK+H39esli6dsQz1goVoorxe2O36Yz40AxzukDiSEz4JwEIAw4JwVUhkYAy/mcFCxnQGRIDJmhMAgDMsBTAssZ0AjQOBoQGCLD++GNOLfuQOETK8WV4nbH3YFGHCiOFCeKM8WFYjqu0HHhPYrHGd6jeADhMIrnBw4zoF/E3tgtFxxmgDD0i6i423CYAY0ADjMgMESGxIAMcApoAA0QBmWoDI2gm07AQ9XNJeAKdBMZcaW4zbhXA884UBwpThRnigvFyLsClKEyNALYyoDAEBkSA65cAxQGYVCGnkHv6JQAW+nTaSWgfTMgMESGnkHFmaLlM6AwCAMyUEBlaARo+fRZuBLQ8hkQGRJDZigMwqAMlaERZM4gcwaZM8icQeYMYEN9kqoE2FDFycFsKu4CLKXiNmIEp08elYARnAHC0E+h4c5hBGdAI8AIzoDAEBkyZYAWTMMNhosMgDRuI1owAwJDZEgMmaEwCIMyVAbOoHIGlTOonEHlDCpnUDmDyhlUzqByBpUzaJxB4wwaZ9A4g8YZNM6gcQaNM2icQaMM4nUxBIbE8P6R6EPXJcKD+nReiXCaAYEhMiSGt9P1Me3SK4NnLBTjIBlQGRoBbKbP2JVeFzz+/u0yM04U07EjHTvSsXsTZ8SV4nbHvYkzYjpuomO9PSPh0e+VvCN++8KMA8WR4kRxprhQTPrvdsmMcblwt+AUH0CzZEBgiAyJITMUBjxvONmPoXygMjSCj6E0wDuD0KfTSi8UviExZIbSAWfae04TlKEyIIPuo/HjOx8IDMgAT7wmhsxQGIRBGSpDI+i+MyEwcAaVM6icQeUMKmdQkQHeh4oMcHINx8FdaFDDbWwQwLvZlKEy4BT6netbFN8QGCJDYsgMcmfQK4Bf5tFvcK8AvqFL931AS68AviExZIbCIAzKUBkaQbwYOIPIGUTOIHIGkTOInEHkDCJnEDmDxBkkziBxBokzSJxB4gwSZ5A4g8QZJM4gcwaZM8icQeYMMh/0bUIJTZe+d/HrH/oj2guCb0gMmaEwvJ0OTZ9eDzzjSjEO0h/WXg18Q2DA4TMg3X//dpkZF4rp2ELHFjr222BG/PaXGQeKI8V0XKVjvT0jKR79tzHMOFKcKM4UF4qFYqWY9dsdN1wu3C04xYDIkBgyQ2EQBmXA84aThaEAMgxlQGBABhWADBogMxQGYegZ9Hmz0kuAb2gE8J0ByKAAIkNi6Bn0WbiS4TsDhEEZKkMjgO8MCAyRITFwBpEziJxB5AwiZwDf6QtDS4bvYLQmw10i7gI8BEMVGbbRJ95Khm18ALYxAKeAOwfbGJAYMkNhEIZKGRRI4wbDUQZ06YTbCEcZUBiEQRkqQyOAqQwIDJGBMxDOQDgD4QyEMxDOQDgD5QyUM1DOQDkD5QyUM1DOQDkD5QyUM6icQeUMKmdQOYPKGVTOoPJB3yaUMASY4UEJjyicZkBhEAZleDsdRg17BfAn7gXAM8ZBIiAyJAYcPgEK/b1QrBRXiunYgY79NpgZR4oTxZliOm6gY709I+VPnCjOFBeKhWKluFLc7jiRPhoifca3FDREBiSGzFAYhEEZKgMeOBwUjjIgMEQGZKAAZFABhUEYlAEZNEAjKBdDYEAGGZAYMkPPoE8LlwLjGaAMlaERwHgGBIbIkBgyA2cgnAGMJ+NJh/FkPOmwl4xbov1xwtXtDZYR6x3X/qgExPhj3Bu8/wMKgzAoQ38ekVZvqXzibhIjxkFwdDRUBiQGHB63uRX6e6FYKaZjt/vYvdR3xoHiSHGiOFNcKL6P1Yt3E37hBI2OPgVaBI2OAYkhMxQGYVCGytAI0OgYwBlEziByBpEziJxB5AzQ6Ogr1Yqg0VFw1nCSPpVZBE7SJx+LwC/63GER+MUAqCmgMjQC+MUAHAeXF34xIDFkhsIgDMpQGRoB/GIAZ1A4g8IZFM6gcAaFMyicQeEMCmcgnIFwBsIZCGcgnIFwBsIZCGcgnIFwBsoZKGegnIHyQbuXoH3XK3xnHCiOFCeKM8WFYqGY9SvFPXFM76Kwd0JgiAyJITMUBmHolw7zwyjsndBuQGHvBGQQAcggARJDZigMyCADlKEyNAL0hDATjsLeCZEBGRRAZigMwqAMlaERwJQGBIbIwBlEziByBpEziJwBTAlTu6gMDpiZRf1vwOynwpTQV1b4EJpfCh8a0AjgQ5gxRf3vhMiQGDJDYVDKAAaDKVcU9k6ANG4jDGZAZigMwqAMlaERwGAGBAbOQDgD4QyEMxDOQDgD4QyEM1DOQDkD5QyUM1DOQDkD5QyUM1DOQDmDyhlUzqByBpUzqJxB5YP2Ng665Kj6DZjQVjjNgMxQGIShOx2enN6YGXGbcYXNYJ67wmYGRAYcXgGZ/r5QLBQrxZViOjYaPZ84UBwpThTTcQMd6+0ZCYPdvXx3xoniTHGhWChWiivFpN87QiPG5WqAyJAYMkNhEAZlqAz9ecP0eoWhDAgMkaFngOn1CkPBvDkqficIgzL0DDADjYrfAfCdAYEB16ACEkNmQAYZIAzKUBkaAXxnQGCIDIkhM3AGwhkIZyCcgXAG8B0M16IsOGDaG8W/ARPiKPENmJ9HVW+oeHZgGwMCQz8FzOGhqndCZigMwqAMjTJA2wVzuSjXnQBp3EY4ygBhUIbK0G5oMJUBgSEyJIbMUBiEQRkqA2cQOIPAGQTOIHAGgTMInEHgDAJnEDiDwBlEziByBpEziJxB5AwiZxA5g8gHfZtQwqQoankD5sRRsTtBGJShMnSn609OL9idcaAYBymAxJAZcHgBCP29UlwppmMXOnahY5dIcaI4U1wopuMWOlb3DLzKKMgdcaFYKFaKK8XtjrsljJj0e0NkxLhcFZAZCoMwKENlaAQwlAF43hogMiSGzPDOIH6epG4o8XO1uqFMqAyNoLdXImage53uDZEhMSADBRQGYUAGeOJbZWgTpG8EfENgiAyJITMUBmFQhsrAGQTOIHAGARkIABkoAMdpAKjVDhECGRAZEkM/hT5VLr1g9wZhUIbK0AhSoAwSpAOgMEA6ApShMjSC3naZEBgiQ2LIDIWBM8icQeYMMmdQOIPCGRTOoHAGhTMonEHhDApnUDiDwhkIZyCcgXAGwhkIZyCcgXAGwhkIZ6B80G5CisdNoYxHVJWhMjSCejF0p8OT041mxIliHAQPK2xmgDDg8AVQ6e/bHXeTGTEdu9GxGx27G8yIC8VCsVJ8H7eX7s74rdkHYKSX6M64UCwUK8WV4nbH3SxGHCiOFNNxAx030HEDHTfQcQMdN9BxI26gAvAIVgAewQbodyri/OEkA5ShMjSCPo08ITBEhv6o906voPR2QmEQBmWoDI0AhjMgMEQGziBzBpkzyJxBNxzBNex+M+J2x91tRhwojhQnijPF/QYLYqFYKa4UtzvuHjPiQHGkGOecAJmhMAiDMlSGRgC3GRAYIgNnACPq9QeCkt4JwqAEMKKI61WhhucYjjOgMAiDMlSGRoCGzYDAEBk4g4YM8Ca1wiAMylAZ2g0oz50QGJCBABJDZigMPYM+3S4o453QM+hz5xLRsPkAGjYD+jOVEUeKE8U4SARAqt9qFOtO6Ckn/A0cp0//Si+5jX1MVnqZ7YwTCSc+CkxhQGVoBDCFAYEhMvBxYAp9XlkiTGGAMChDZWgEaIUMCAzIAGeKVsiAzFAYkAGeB7RCMm4nWiEDGgFaIQN6Bhn3Fq2QAYkhMyADPFAwiQHKgAxwEWESH4BJDAgMkSExZIbCIAzKwBkoZ1A5g8oZVM6gcgYwlozHFcaS8bjCPjJuI0zi84jDJAYkhsJQbzdDlW3sk9WCWtoJiSEzFAbyuXQpQ2XAcfqzg5LbCYGhZ9DX0QpKbodAyAyFgTMInEHgDAI5LUpuJwSGyMAZRD5oN40+1SwomB1xpDhRnCkuFAvFSjHrtzuGp/RpbUGd7ITIkBgyQ2EQBmXol66vERbU0w6ApwwIDMggA5BBAWSGwiAMyEAAlaERwFMGIANcHXjKgMSADBRQGIRBGSpDI4CnDAgMkSExcAbKGShnoJyBcgbKGcBTCt4aeIrgrYFzoAWe0PAQ3B84x4Cuht9Q1ONOCAyRITFkhsIgDMpQGSgD1ONOCAyRITFkhsIgDMpQGTiDwBkEziBwBoEzCJxB4AwCZxA4g8AZwJT67LCgHndCYIgMiSEzFAYl6O2aXuokGR0k+UBmKAzCoAzvM8APEKpxP3H3phHjIB+IDIkBh6+AQn8vFCvFdOxMxy507G5KI44UJ4ozxXTcQsfqPoMfTZTfjjhSnCjOFBeKhWKluFLc7ljpuErHVTqu0nGVjqt0XKXjwlb6hL6g3Db2YgNBuW3s8/6Cotqo+Bs0SAZkhsIgDMpQGRoBDEfxEsBwBkSGxJAZCoMwKENlaDeU62IIDJEhMfQLXREXioVipbhS3O64W82IA8X9BiviRHGmuFAsFCvFleJ2xzCYPtMuBQYzIDIkhsxQGIRBGSpDI0icAYxIkRtGagYkhszQj4OxMtTqxj73LKjVnRAZEkNmKAzCoAyVoREUzgCNoT6tLajVnZAYMkNhEAZlqAy4ov23H7W6EwJDZEAGCZAZkAGecDSGBihDf6YK4nbHMKtPjIN8AFK41bCfD6DtUnFD4TgVL0D3FXQwUZ77iWEXH+HGR4EpDCgMwqAMlaHdINfF0C9mn2AXgSkMSAyZoTAIgzJUhp5Bn0YXQStkQGCIDMggApBBAhQGYVAGZJABjQAmMSAw4FY1QGLIDMgAFxEmMUAZKkMjgEkMCAyRITFkBs4gcQaJM0icQeIMMmcAY+kThIKi3thnPwWlu5gTF/mYhAAawcckPhAZyu1mqLzFxLGgvnYAGiIDAkNkIJ8TyQyFAcfBs4P2yIDKgAzwuCj5HOprJ0QGzkA5A+UMVBiUoTKQ00rlDCof9LPCB7FQrBRXitsdo/TtEweKI8X9lxhToKi9nVAYhEEZKkO7AbW3EwJDZEAGAsgMhUEYlAEZKAAZ9EcYtbcTkEEBRIbEkBkKgzAoQ2VArVCPUb7yiQPFkeJEcaa4UCwUo8INcaW43TGWHn7iQHGkOFGcKe7njGlsbMk7QRkqQyPo/jIhMESGxJAZOIOMDAJAGSpDIyg4Dp6vAjXcHNjQAGWoDI0ABjUgMESGxJAZOAMYVMCjC4MaUBkaAZohAwJDZEgMyCABCoMwKAMywHOgjaAiAzyBNTBEBiyCQJwpLhTjIP3nFNvwJkxhYhveCYkhMxQGYVCGytBuQLXuBGQQAZEhMWSGwoAMEgAZZEBlQAb9AcXWvRMCQ2RIDJmhMAgDJg4QV4rbHX/mjhAHiiPFieJMMQaKEQvFSnGluN3xZyobcaA4UoxzFkBmKAzCoAyVoRHAmAYEhsjAGcCYMJ+Aot8JwqAEBUMceL4KxjJwc0pmKAzCoAyVoRF8hnA+EBgiA2fwGcbBtf6M43xAGJShMjSCz2DOBwIDrmgDJIbMUBiQAZ4zGNMAZIA3Ccb0ARjTABRdIY4UJ4pxkAJA7V+P4T6ff4DHDOA/gccMqAztBhTvTggMkSEx4N4ooDAIgzJUhkYAjxkQGJBBBSSGzFAYkEED9Aww74ni3QmNoNvMhJ4B5j1RvDshMWQGZCAAYVAGZBABjSBdDIEhMiSGzFAYhEEZOIPEGWTOIHMGmTOA52ASFBXAWCcu2JY3YT6ywVkwgtvQyukLjKXBZgZkBpwC7hxsZoAyVIZGAJsZECkD+AcmNFHpO6FLY3qqfUZXPtAIPuMrHwgMkSExZIbCIAycgXIGyhlUzqByBpUzqJxB5QwqZ1A5g8oZVM6gcgaNM2icQeMMGmfQOIPGGTTOoHEGjTNodwaKGt8JsIALAOkIqAyNAFYzIDDcP0R6hcSQGXCcBBAGZUAGGdBIIF4MgYEziJxB5AxiYRAGZagMnEHig8JD+g+LXp9u0wcqQyP4dJs+EBgiQ2Lg43y6TR/ARRSAMlSGRgB3GRAYIkNiwJOogMIgDMqADCoAGSBrWM2AwBAZegaCZxQtmgGFQRiQQQFUhkYARxLcRjjSgMiQGDJDYRAGZagMjaByBpUzqJxB5QwqZwBHErwlcCTBycF3BHcB7iK4jTAUwUsLQxkgDDgF3DkYyoB2A0p+JwSGyJDvDFDqm/qCYUWt7wRM8vTbiGrfCYEhMiSGzFAYhEEZKgNnEDmDyBlEziByBpEziJxB5AwiZxA5g8gZJM4gcQaJM0icQeIMEmeQOIPEGSTOIHEGmTPInEHmg37GbjIA0v0ZRU3vhMAQGRLDPaaigUZ1NNCojqK0N/UpX0Vt74RGAKvpM7YaaFRHA43qaKBRHQ3CGQhnIJyBKENlaAR6MXAGygfFNx6RJz4t+4krxe2O8V3ZTxwojhQnijPFhWI6bqXjVjouDEVxg9GQGRAYIkNiyAyFQRiUoTJQBvHz+UjEgeJIcaI4U1woFoqV4n7qFXG7Y3w08hMHiiPFieJMcaEY5ywAZagMjeBjRx8IDJEhMWSGwsAZfOxIAZWhEXzs6AP9OH3SR1FXnPpMhqKueEJlaASwlgGBITIkhsxQGDgDOFCfZ1bUFU9oBPCmAYEhMiSGzIArimcG3jRAGSoDMuivFeqKJyADPOHwpgGJoT9TuHH4Su0nFopxkO4rKBDGpqKKAuELdw37gn/iQrFQrBRXitsdfz4siThQHCmm41Y6bqXjVjpupeNWOm6l4zY6bqPjNjpuo+M2Oi6MBlcI+/JOCAyRITFkhsIgDP90HDyKFdAIPrU0HwgMkSExZIbCgAwaQBkqQyOAvfTJfEUtMTbGVtQST0gMmQFVDxEgDMpQGZBBdxRs3zshMCCDBEgMmaEwCIMyVIZGAEsaEBg4g8wZwJIabjAsCS8hio8THD997AX/UiJDZtDbVVE7nPCThArhCZEhMWQGckhUCE9Qhl6NiBcDFcIDestlQi9IvPA3Sh6NCuEJmYEzUM5AOQOtDPQrgQrhCYGBM6h80N6I+VzQ3ogZcbvj/m3rEQeKI8WJ4kxxr7S88Aj35ssEZagM7QaUCU8IDJEhMWQGZJABwqAMlaERBGRQAMhAAJEBGSRAZigMwqAMlaERxIuht6UC4khxojhTXCgWipXiSnFvw/XHOOOj1584UBwpThRnigvFQjHOuQIqQyNAafGAwBAZEkNmKAzCwBmgvLgvkddPffEHUGA8IDD04wQ8X6gfDrg53YYmNILefJkQGCJDYsgMhUEYOAMYFLqXqDEeAIMaEBgiQ2LIDIUBGeCZUWWoDI2gIgO8STUwIAO8STUxZIb3M4Uf7l6EPGO944aDKKDb0icW/od2AyqDJwSGyJAYMkNhEAZlwJVpgEYAjxkQGCJDYsgMhaFn0Of2FGXCEypDI4DH9PlyRaVw7pPPikrhCYkhM/QM+oS4olJ4gjJUBlyDfuNQKTwhMCCDDEgMmaEwCIMyVIZGAM8ZEBg4g8wZZM4gcwaZM4DnRDwH8JyIk4OzYG4BlcI54jYWCBSAMlSGfgoYZEdx8ITAEBkSQ2YQygD+kXCD4R8DII3bCP8YkBgyQ2EQBmWoDI0A/jGAM6icQeUMKmdQOYPKGVTOoHIGlTNonEHjDBpn0DiDxhk0zqBxBo0zaJxBowxQsDwhMESGxFAYYEIRAOn+jKLeeEJiyAyFgX6IUG88oTLgOP2BRb3xhMCADARAP4WoN55QGDiDyBlEziDSj7GkiyEwRAbOIPFB3x6CPQu1VwvPOFKcKM4UF4qFYqWY9XHtulugsnhCYIgMiSEzFAZhwAPYAJWhEXwc5gM9g17NoChNzr1MQVGaPCEzFIaeQcbTiIbMgMrQCD5GhKvzMaIPRAZkgBsGIxpQGIRBGSpDI4ARDQgMkYEzqJxB5QwqZ1A5g8oZwIgy3hQYUcabArvJuI2tP1KfWCmuM+5VyDPuj2lEjBwbQBkqQyNA02VAfxcS4khxorgfpNdEKIqJJwhDPzwm4Hsx8fz7dsfxopiOHenYkY7dO0YjLhQLxUoxHTfRsXqnBxOvvRx4xkpxpbjd8buVMeNAcaSY9Lt3jBiXC0mggTFAGSpDI4B/DAgMkaE/USgqwM6+EwqDMCCDAkAGAmgE8I8BgQEZKCAxZIbCgAzw+MI/BlQGZIAnHv4xIDBEhsSQGQqDMChDZeAMKmdQOYPKGVTOAP6B6WzUDmdMQKN4OGNuG8XDWXAbYQyfZwftkAGFoZ8C5sNRLzyhMrQbUC88ITCkOwMUAmdMZ6MQeAKkFdAI4CgDAkNkSAyZoTAIgzJwBoEziJxB5AwiZxA5g8gZRM4gcgaRM4icQeQMEmeQOIPEGSTOIHEGiTNInEHiDBJnkDiDzBlkPmhvwOClR8Evvu6t2Mt3AJxmQGCIDN3p8OSUTHGhGAdpAGWoDFgd3n8+eh3w+HsJFEeK6dhCxxY6dp8qGrFSXClud6x0XKVjvT0DX4LRXrk743bHfZZ6xIHiSHGiOFNM+n1oZMT9cuH3vcIpBjQCtCcGBIbIkBgyA5bU462HoQxQhsqADPqThOLg3PcXVxQHT4gMiQEZCKAwCIMyIIMAaATwnQHIQAGRITFkhsIgDMpQGRoBfGcAZxA5g8gZRM4gcgbwHUyQoDg4Y9QfJcAZc6ko9M2YpkVtb0bTELW9E5ShnwJmWVHbOwC2MSAwRIbEUCgDtF0wC9jgKB+Ao2BiocFRBkSGxJAZCoMwKENlaATCGQhnIJyBcAbCGQhnIJyBcAbCGQhnoJyBcgbKGShnoJyBcgbKGShnoJyBcgaVM6icQeUMKh+0j89i/AFFvxljsijtnRAZEkNm6E6HJ6d3dkasFOMgeFhhMx0qNvKdgMM3QBx/X/s+vjPOFBeKhWKluFLc7rgXxIw4UEzHDXSst2fgW6e1l/LOOFAcKU4UZ4oLxUIx6/fr1T7QCGAVAwJDZEgMmaEw9Aeuz2hX1ABPqAyNAI7Sp5AraoBzwxWCowxIDJkBGRSAMChDZUAGVwcYz4DAgAwEkBgyQ2EQBmWoDI0AxjMgMHAGwhkIZyCcgXAGwhnAeBreDhhPw9vR7aVcuI29MRNwe3op3oiV4nbHvQFz4RC9U1Ouzz8IgzJUhkbQp6P7yuba9+mdcaQYB8Ez091jQmHA4fGYdP8Yf18pbjPuRbwzDhRHihPFmeJCsVCsFNOxAq54ASSGzFAYhEEZKkMj6I2OCYEBGSCdmBgyQ2EQBmRQAcigARpBQgYCCAyRITFkhsIgDMrwygCm0it3P+HbX0YY7jDeYbrDfIflDl9HhMH1gt4R1jtsM3x7yQjDHcY7THfYzzF8oDAIgzJUhkbQPWRCYIgMiYEzEGSAcxZhUIZKoDgOnieFGm6GFgZhUIbK0AjqxRAYIkNi4AwqMsCjCicaoAyVoRG0iyEwRAZkkAGZoTAIAzLAm9MqAzLobw52750QGF4Z1E+Y7jDfYT9Cn/2tvVgXbSLstDv/uzD8019UhkYAPxkQGCIDHwd+MqBflT6tXFF8O0EZKkMjgJ8MCAyRARkkQGYoDMKADDIAGRRAI8gXQ2BABgJIDJmhMCCDAFCGyoAM+kOHgt0JgSEyJIbMUBiEQRkqA2cgnIFwBsIZCGcAv0l4DuA3CScHV0m4C3CVhNuoEMDTC4sZUBj6KaBtjVLdCZWhEcBiBgSGRBnAOxJuMLxjAKRxG+EdH4B3DAgMkSExZIbCIAzKwBk0ygCVvBMCQ2RIDJmhMAiDMlQGziBwBoEzCJxB4AwCZxA4g8AZBM4gcAaBM4icQeSDwoTww4US3dLnFSsKcQfAagYEhshAP0JjZ+APFAYcpwKUoTIgg+7jKMQdAjkwRAbOIHMGmTPIwqAMlYF+iLE/8AQ+6NtDFGm+fWKEbYZvjxhhuMN4h+kO8x3eumiH9NnkilLdCZWhEfRBlAmBITIkhn7J+gx0RanuBGFQBmSQAMig2wdKdScEhsiADPAQou0yoDAIAzK4AJWhEcB/Mu4T/GdAZEgMmaEwCIMyVIZ2Ayp7JwQGZFAByKAB+nH6pHDtVbp9dLP2Gt0Rxjt8PyYZIf4wAhoBLGBAYIgM7wexIMx3WO4QR0gAZagMODayervH58/f3jHCeIf3UdN91HQf9e0YI9Q7rHfYZpjvo+X7EO/XX3BB36/4CNsM36/3CMMdxjtMd5jv8NZ9tx5GiAuD+4y2w4BGgLbDgMAQGRJDZuhPZ58krqiWnaAMlQEZ4LGDRxQ8dvCIAZEhMfQMBA8fPGKAMCgDMsC9gEd8AB4xoGfwuYjwiAGJITMUBmFQhsrQCOARAziDxhk0zqBxBo0zaJwBPELwTsAj+gR2Rf1t6ZPEFVW2pc87VlTZToBaBShDZWgEaG8MCAyRITFkhsLAGQTOIHAGgTOInEHkDCJnEDmDyBlEziByBpEziJxB5AwSZ5A4g8QZJM4gcQaJM0CDpc+/VlTZTqgMjQANlgGBITJkhpc03mwUz5Y+y1tRPDshMiSGzPA6AbzxvcJ2hHqHOEIANALY0gAcOwLi/PO3KY0w3+F9VLmPKvdR31Y0wjbDtw2NMNzhfTS9D/H2FbRWeqXsCMMdxjtMd5jvsNyh3CHp4tLgTYVXfABeMSAwRIbEkBkKQ3+u8AuFytgJlaHdgMrY0md5KypjS58IraiMnZAYMgMyqABhUIbKgAy6j2Er3wmBARk0QGLIDIVBGJShMjQC+MuAwMAZRM4gcgaRM4icQeQM4C99NrmitLb06duKAtrSJ3Zrr5nNn/8ud6h32Gb4fvnR9xL0VSpuK3okA5ShMjSC/pFX3NT+jddPGO8QR8Dh4A4DCgOO/fkbvf+83mGbodxHlfuoch/17QwjzHdY7lDu8D6a3IfAUkKE5Q7lDvUO6x22GWKRIMJwh7cu9lBBiAuDW47WxABhUIbK0AjgEAMCQ39mMKWGYtYJmaEw9AwwUI7NdwtG47H57oR2AzbfndAzwLQiNt+dkBgyA65BBQiDMiCDDGgEcIgBgSEyJIbMUBiEQRk4g8AZRM4gcgaRM4BD9C2wK3bgLZgVxFa70te5VuypWzBFiI10CyZQsZPuhMxQukAACIMyVIZG0BsQEyJlkCGNG5yFAdK4jfio4oBGgM8qDggMkSExZIbCIAycQeEMCmcgnIFwBsIZCGcgnIFwBsIZCGcgnIFwBsoZKGegnIFyBsoZKGegnIFyBsoZKGdQ+aC9yA2PXoUwntBaGRpBuxgCQ1+3ijDdYb5DHAEPahMGZcCxBdDGn1fstYIw3GG8w3SH+Q7LHcod6h3WO7yPhl1V/vGPf/ndn//6h9///U9//cu///1vf/zj7/71/87/8J+/+9f/8X9/9x+//9sf//L33/3rX/7rz3/+l9/9/7//83/1/9F//sfv/9L//99//7fXv77e6D/+5X+9/v9L8H//6c9/fEf/+Jf7r6/nP63v0t/+x69Xdf558f/9e3gHf9/y09/H578P/SszXeA1sxyeFNKzgtZ5BtfjGeTnv39/Tzx+FN6fOtepof8kUZ4lUq+v7Qqvwdb4IGBdhb4jGa7Ca1Ty6SzUUHh/cHZIvD+w+pCELdHKlHhNOD5INEPi9WjPLOprxP5BIhhP1GsOY17MK5epEP/5Wrxntp8kJIzzkJQeBcwc2nisX7Mb8VHCeCxDHwnGlSh0O34k0dJ9S787kfupeM2yPJ+IGFlo03lLgzxKGE+WynCo1w/eNwJNxoVo+lUG4cojhXDV9tV1aGnejZYfs/C/Hfn65h3tH2Qa76jqF17TNzeBwms8/RvPLte8mK/BuEeFYiVR72f7NVr5eB6mRovt1kj1SUP3Des9E71pWLFtGpadg8uwUtg2LFvCZVjmifgMK+Vtw0pl07AsAZdhWQJOwzKvg8+wfvB6PDqW+Zq+5gVuu7hC+Eqjb17y0cj0jvhtr9xm8Rqu/sb2JNxPdyxfKdRp3q8hjcfGqnElXlNQwypec076jURfsDbTqI++mQ/4Zt73zbzrm3nfN8u+b5Z938z7vln2fbPs+mbZ9c2y75tl3zfzAd+0X9N2++Z7VdsXnqfzDXtvavWNY9V8PxOlPilI2m/q2Rq+pp6UfcsS2bYs0U3LsnNwWZa0bcuyJVyWZZ6Iz7I0bluWpk3LsgRclmUJOC3LvA4+y/rB6/FoWeZr6mzq2Rr7Tb1695FrbV+Marb5mrdUvvn72UFu5XE0rxpXIV4yLkK86mNrtebNcdFa9gdGq+yOjJpXom8COJJ4HGyodX9gsx4Y2Wz7I5ttd2TTvpxzwD8G/e7ZjHI/Fnp9JZHmz/n7i1pPEu3ACFI7MILUDvSE2n5PqO32hNp+Tyhc+12hhYarYdH2+0Lh2u8MhWu3N2Qq+Ma9r/3+kH0tnCPfBzpE7cBAUjswkGSbV6nTvDR85X99QzxI5JK/aWHUIdDa9cXfv97BOeN4xW8UmpDA40BUsGZlXj8k82dAsqFRN5s5IbT9dk6I125Dx74aOl+R16T4Y5svxLg/DbwScbWXQswHpoKtcV/vXHCU3clgOwvfT6M1r+H9abQ1fPPB5rk4fxvNaR7nb2OKu7+NloLvt9FS8P42mtfC99v4k1fl8cdx8dL6ZoYXFlSuaUHy+LsUkmGnRWYeRWr4SsNtY9m8NXL/xjUpX4pontPlTb8WkXqLPN+bxTXxuXI+MAga8v4oaMi7w6CLLHyunPcHQhcaPlfO+0OhoeyPhYayOxhqKvhcuewPh9rXwunK+cCA6OKlPeLK8wmL9bnextaoZTa16/OgTZCwP+SyEPGNuQRzkNhrY5L3bcwaNffZmJ2Fz8asOSWvjdkaPhszz8VpY3rt25iGXRuzFJwFh2Hfxsxr4bSxH7wqzzZmvrTOoZeFiG/sZeFjdXaS2/WlF1J/v9X61ejJfWMueR52qMF6WeYvQwrPtfOhxt3BD3s+wDn4YU01OQc/zKsRdVpHrF9e0djG85XSVZ81DhTBB3PCyfvzZM04eX+erBkn38+TeU1TifOa1uu7+5Lz/XiU+J1G39kQGuW5+C404zHNfVu3rpFZ4zc35cSMUf9E7fbjYY2oux+P+ksfD74tSb68tW38tiR5trFozRqletvHq5H6VDNgSrQwG2ItPv1Mmj8KMh+Nq9Xn87Ae0Tzn/XN+nveP5rST50chXrL/oxAv3f1RsK9G34B0vLDyfC5t92pYa2jcVyOEX3w1ssyrUcJ3z1eZsza56PMzGvL+T2QMBwaiYtgfiIphdyDKvqb3MGOWaFzTA4N70ZqDcl/TGPavqTUWfeKa6n1NjYWl9rA6rXR6byn2eDms1aGXpll/dHER7fci8t3rr2mcTdbnydJoziA5S/9jPFAKFdN+LVRMu8VQixtTp8a7zk6+uruv3uAcW3t1+x8vqrlYx7uYLZ0w1XTAVJP+0jsT4v2jG2J7fGWsx925WGbxoLrG52Ler4taaPjW1qX91cAx7xdGxbxbGGUquMbnTAXn+Jx9LZwr7H7gps9L7Gxfd64VsX9gfG9LiQd+cy0R92/uD0Qef3Ot9lSvisO5vAYUny+I5ab17ty267khUw6MScVyYEwqlv0xqSi7Y1KLW+v81TZFvL/aJ9YlRTlQKRWlHLgz8kvvjPdX23xldI6jJK3Pr51s9/r1RK9f93v95cDGLov31teI0bzfiLE1fI0Y81ycjRjV/UaM1t1GjKXga8RYCt5GjHktnI2YH/y4GPsEnNjbxDSPVuYcUJPnwRhr4ZOzgs3UcL+25mSUs4JtIeKrYFuJuCrYVtfE146xVkC5fy1b3P+1bGn319LOwufKiwUNLle2NXyubJ6L05Vb3Xdlax2Uz5UtBZ8rWwpeVzavhdOVf/CqPLuy/dIecOVXp3BO5FzPSxuSNSnldGVTw+vK6ar7rrwQ8bnySsTlyqtr4nLlFOK+KydrvzunKydrcsrlyossnFtpybYrLzRcrmyfi3M3LXNqyrmdljUz5dtPK16brmwqeHfUMq+Fz5V/8qo8uvLipT3hyiHOGaXwvM9BMncIi2kW5L3j9l0i8/nIwZiVNjX6aC804vOmC8kaH/cWOC9EfGNCKZ3w03TAT9O2n6YDfpoO+Gk64KfpgJ+mA36at/00b/tpPuCn6YCfphN+ar60zgLnhYhz6xq3j8k3m9e8XrJ5UUN6VEjWoqbcwvxpeNnZo0a5Nsdgk7UvnncMNllj0r4x2MXVSLPyqpXr+Vzyga5HOVB6lcqB8tNU9stPU9ktP11k4fttkWv/t8XW8P22mOfi/G0x56Ocvy3WdJTvt8VS8P22mFNizt8W81o4f1t+8Ko8/7bYL62vrb6woLtwsz3v25Ss2SjvCIq5rMlrY+aOd94RFFvEOYKyEPGNoCyuic+V9cA+Ukn3N5JKuruT1CILnyvXA7uR1/2SKftcnK5cD2xIXrd3JK/bW5LXA3uS1/2SqZ+8Ks+ubL+0R1x5Dn6U63oe17ZmTryu3A5s+5NaPuDKtojTlRciPlduB3YxSu1A1VRq+1VT+dqtmlpk4XLlfMVtV15o+Fy57deA5Ktsu3K2hmFdrmwquFzZVHC6sn0tnK78g1fl2ZVb/MWuXO4rUq7n9abZmjmROjfYk9qeRrWzNRUV9ZqDFxrvNOQHWbQ4s2iP40mmhF7zldWrfCcR5m6FGsr1lUSs4wHT18TGd3d1DsK87urjbEWOZlHfvXMjKYTfKITtm2omoXNrZ17o/tskrGHoXjoA+3tZ4VdJlLmlDO8/GX6gcG8krI+n4b+jz7uN5WjujaOzyltp6kj8Elrmmbz8/P5RS7+RMKtO5oOlQsugfyDx+kWbP6253g9n/olEuT9xc7UnCXMwfV9C22j/aZNvBOociefS7h8ItGs8VryO+ycC8w1tz3fCuTfhdwIh3ssoYvjqKrzaBXq3C+qThDnF48vCkohzzXKkX+KfCMS5I2HM3wik2ZVI+pVAvhehh+8E5nxwTu07geueEvpK4J5TKt89j/80p1S/k5iv5UstfCchdxb6XRbx/iROzN89kbPCOMpXz0O8F57I492wRpTzvY9JfBwwyOZ3ltL8tWmZ5tVC/Y2GVWqd6uxQJlovFn7TLizmVkw1zK4xDaD8N41qPlhz4Vr4p503fns25k1ptCfUVz4zi2kSbYf9E4E5+pLCdxnMkZeUnzKwG2RhNm5LCI+TDNmcPmrTrLSlp/5bttYyOdvYdhZ3z7w9DoeZEvWar0i9WvlKIszhwRpa/UoizZ55TaV+13GavYXXXX2cy88adjtO1ieKnDfVTMLVcbLWHzk7TmYSro6TqXCg40R39HnHkWxNbDg7TuYKKF/HqV7bHad6bXecbAlXr6emXyrh6jhZAq6OkyXg6jiZAp6OU/VtH/ydgLPLUtN2x8lceeHLwpJwdZxMAU/HyRJwdZwsAVfHyRTwdJxsAUfHyRTwdJzMh8nXcbIlXB0nW8LVcbJfLFfHyXwiPR0nU8DRcSrWPIer41TM+Rpfx6mYO+P5Ok7FWvrj7DgVa2s8f8fJvCmejpPpM56Okyng6TiZAvsdJ707Ts9bFpRgrzrWWQfQnmari7VkyNfGtrPQ+YoUjfUxC0uizZ+u0spXEnKVISGX6FcS8RqWJzG2rzpOQe5m9vNXtkqMmx2nYk32OG+qmYSn41TMzwa5Ok52Ep6Ok62w33H6pzvanu9o2+04mRK+jlNJYbfjZEr4Ok4LCU+vp9gb321LeDpOpoCn42QKeDpOtoCj42QLODpO9n1wdVlsCVfHqeTteS9TwtNxsgUczVRbwNHzMgU8PS9TwNPzsgUcPa+FwLrnZQs4el720+jqeS0kPD2vhYSn57V4Mz09L/uJ9HScrLmeMn/+5XkTu2LN9Hg7TtZMj7fjZG0f5e04yXWg42TfFEfHybYJR8fJFnB0nGyB7Y5TvEuAYnocyV5ozOvw0nhu1Im5Ye180yU8f2LL1AhN78pnLuT+iUSbP14Xb3j/g6uR5pkU4yMoiys637OXxvMV1bh/RTVuX1Fb4sAVnZ+VeYXy3RVNEm6N+nw1rGc0xnlFo3yn4b2i28+o6Z75Nk95LOQs1tKeUmYB+Ot3S541dj+eVE58PKnsfzxpcTU0zathDD/VA1sjrURcay7Kia8nlQNfTyrbX09aZOFac1Ha/pqLhYZrzYV9Lr41F6Xtr7kobXfNhangWnNhKjjXXNjXwrfm4ievyuOai8VL61tzsbCg+aQXfsJ+Y0FyJXME3LMSztTw2pjYy2F8K+EWIr6VcCsR10q41TVxubJcB776IWH/qx8Sdr/6scjC5cpiLWhxuvJCw+XK9rn4XFnMneZ8rizWZ5RcrmwquFzZVHC6sn0tfK78k1fl0ZUXL+0JV5ZYpqM+d43F+hKL15Xjgc1vJMoBV7ZFnK68EPG5cjywl4+kA5/8krT/yS9Ju5/8WmThc+W0v0f9QsPnyua5OF057e9RL2l3j3pTwefKaX+PevtaOF35B6/KsyvH/Mtd+R5e0/BYVyDZrNOYa3KL8TU4sT+BpPfJXFqfSjUkq/nz4ChbkVx3KxzsLFxlK7aEq2zFlPCVrdgSrrKVxW2lB/2qz6dStkel7DzCvfT8FRt5HBhRknJgbyUp+3srSdndW2l1c+f81Tv+brxQy2wW6vN+uyLmWjy9r+krbl8lUuc2k6+h2udRA2uFjG+nBtlfcGRn4dqpwZTw7dRgS7h2arAlXDs1LO7qpfddfRycl+0FR7K/4Ei2FxzJ/oIj2V5wJNsLjvx39HmncNlfcCT7C45kf8GR7C84kv0FR7K/4Eh2FxzJ7oIj2V1wJLsLjmR3wZHsLziS/QVHsr/gSHYXHMnugiPZXXAkuwuOZHfBkewuOJLdBUeyv+BI9hccyf6CI9lfcCS7C45kd8GRbi840gMLjvTAgiM9sOBIjyw4kt0FR7K74Eh2FxzJ7oKjRYPsrviozxUfaq2Q8e3UoPsLjhZZeHZqMCV8OzXYEq6dGmwJ104Ni7s6v3xb6vOCI91ecKT7C450e8GR7i840u0FR7q94OgHd7Q939HtBUe6v+BI9xcc6f6CI91fcKT7C450d8GR7i440t0FR7q74Eh3Fxzp/oIj3V9wpPsLjnR3wZHurhfS3fVCurteSHfXC+nueiHdXS+k++uFdH+9kO6vF9L99UK6uwROdxccqf1JQE/H6cCCIz2w4EgPLDjSIwuOdHfBke4uONLdBUe6u+DIbpDdn/Iq7XnB0UJjmtVL47lRd2DBke4vONL9BUfm1Xi1J+9J7/DtFZ21Li+N5yt6YMGR7i840v0FR/YVDfP5kpDk+Uxk8+fQEmg51Wmfj1OJak3X+EpDVNt2X9TMwlUaYku4SkNMCV9piC3hKg1Z3NQ6nqxW4uNNtT4C5CsMsbN4zXSPLDQ8Xs5q+ZazLERPLDTSAwuNdHuhkX1JS57dsZLzN4NPEubb+rq4j1/N0Ja23/i2/dUMOwvfG29K+N54S8L5xpsSrjd+dVfTfVcfZ+7rtfvVjHptfzXDTsIzpFiv7a9m2El4hhRthe0hRb6j8XkzhnptfzXDlPANKdZr+6sZpoRvSHEh4RkPrCH+UgnPkKIp4BlSNAU8Q4q2gGNI0RZwDCna98E1mGdLuIYUa9z+aoYp4RlStAUcAzi2gGNM0hTwjEmaAp4xSVvAMSa5EFiPSdoCjk6Y/TS6xiQXEp4xyYWEZ0xy8WZ6xiTtJ9IxpFitcVHXHkavqdvtIcWa9r+aUdP+VzNqOvHVDPumOIYUbZtwDCnaAo4hRVtgd0hRotwNMmsvElPDtbdLzeXXavgGwBYS2/vDzE1VQqjp+TyspmW4m5bGEFotZrfHt7tLLWF3uGRxLlzUH5/P5cC2CCsR16BLtb485B10qeanh3yDLrXo5qDLIgvXitVqzQ45V6wuNFwrVu1z8a1YreaSIN+K1SpWTbpnxaqp4Fqxaio4V6za18K3YvUnr8rjitXFS+tbsbqwoDnmoMb+5rZGnHsRaMyP8zPVWsnymrwbVvgK27OG2Vmv9+uS27Ot2yJt9i5ecaqPIvmAFWrZt0Jr9NhnhXYWPiu05je8Vmhr+KzQPBenFVrtOa8VmvvTuaywxl0rrHHfCs1r4bTCH7wqz1ZovrTlmoNk7zh8J9I/+v0RyY9LPW0fy7PX0nJ8LPStzVzJNi2Zbu6rb/1lFvVLV59dUY3t2dWtaY6Xf44rml8zlE8X1JJ4DUvcfqz12Y/biaZpO9A0bdtN03agadoONE3bgaZp22+atmu/adqu3aapqeDyY1PB6cf2tXD6cTvRNLVf2nb78SsOXzVN0xwn1hTjdyZ27/Cr+XlssFnb0vlMzJTwmlgzN5VzmlgLedvEmvkBHo+JLbJwmVgz94TzmdhCw2Vi9rk4TcycWHKaWAy7JmYp+EzMUvCamHktfCb2k1fl0cQWL+0JE8tzGFzz8/4KtkZJ4+nQ8rwjVLPWCXmHCVcirmHCZi708dqYtSmd18ZS2rUxOwufjVkzT14bszV8Nmaei9PGzJknp42ltmtjloLPxiwFr42Z18JpYz94VZ5tzH5pTwwTFrktSB+H53pt3WZncJGGzDSedyxdaMxpWi3NOJW23SS0JNxNwnLCS8sBLy3bXloOeGk54KXlgJeWA15aDnhp2fbSsu2l5YCXlgNeWk54qf3SnmgS3i+LSmzfmZjeg3P6PDjXRA80CeVAuX6TAzvQN93fgb7p7g70iyx8Nqb7O9AvNHw2pvs70Dfd34G+6e4O9KaCz8Z0fwd6+1o4bewHr8qzjdkv7YkmYZ1LGF5m9FjM06wvJLlmOkwFt4nVA/vPL0R8+8+vRFz7z6+uic+T24H951vb33++td395xdZ+Dy57e8/v9DweXLb33++tf3951vb3X/eVPB5ctvff96+Fk5P/sGr8uzJNf9yT74rcYzdXMNl7WznMmVbwuvKLxXdt+WVis+XlyouY15eF5czhysc6PW/VPa7/S+R3X7/Kg+XO79E9nv+KxGXPy9Ox2fQL5H9zv9LZLf3b0u4PNqWcJr04nr4XPpHr82jTa9e4SM+PVdxaZUv63P6kAk02rNG6K/5vlFHPWFrsZ2wtXQdsLUUtm0tXQdsLaUDtpbSAVtL1wFbS3LA1pJu21rSbVtLesDWkhywtdhO2Fr85UMCrY77W6/ru+mZ1x+2qfG8YdDr+pfd+RlbwztBg3XF+6aW6wFTy23b1Ow8nKZmjsR7Tc0WcZqaeTpeU7O+p+Q2tVK2Tc2ScJqaOfflNTXzenhN7QevjWFq9it8YLrmNcx5f00+fzddU+Ncb1h5Oe2PNNJ8917Xw2jvWd8ASn2NBZ742tLjVTU1Wpgm0B63mrLPJc/V7zWn71Yd1TJ3p6slReN6GA6Qy70bZYnl+XrYO5W4lh2tVHzrjsKlR4YG9MTQgO4PDeiJoQE9MTSgJ4YG9MTQgJ4YGtD9oQHdHxrQE0MDemJoQI8MDZivsHMV0krFuQzJNsc6f7Tkur4z2P691I9Gyc8Ga+3EV+fuR42+Ahh/O5hs7cN3QELn1j+vWcsnicUFnaMttYgxst7iiV+KFk/8UrR84pei7a9RfYnI9i9F21+l+t4N5cAvRdtfp7o4HecvRbj2V6q+RHaXqtoSvl8KU8L7S2FfD+8vxQ9eG+OXosUTvxQt/upfCp1lHFXjs8uHYIwO+vZ9XWjIvU3dayTaSMS4JL5tSl8a21/JWeTh2qh0oeHaqdTW8G1VutBw7VW6ekBKvE1AvnzKapotgZrjtyJzR8dmfF/rvaPabpMkWF8gckqYp9LmB31bey4CfeWx/ZHll0bZf2Pi9meWbQ3fd5YXGq4PLS80XF9atm/uaxbyLn64rvjlO/OerJ7j+q+p/PQsk060OUM6sIL1pbK/hPUlsruG9UeXtpSv79C9M9trxqMZd2i7oxVS+6WW9J6quq8JP/v/7WSytZbV9QHDEHLcNyU7D88nDG0N3zcMFxqujxguNFxfMVzd3nTv9nmlnL595PN1t+eNbVKWMunOJhs1EMHaDdD52Jfd9sCiGZ7mLs+arTMx/XX2gl89nOfWr6lx6Zw7fcXZeIPtqSSV++dLRb+T8X2r5KWh+32CUvfNxMzD2ScwNZx9AnN7RGefwNRw9gnse+v5ZEkI5lyUaxPORR7OzqtYlRzeCqggJ4oFghwoFgiyXSxgX1fXl0tWbtRmRfN7YMbo9Jlbe73+9P6laOVrGb2LZF6zEs/PirXjmdeP9ECPy8zD6UemhtOPLA2vH5kaTj+y7y69fVc1zqaGfUdSe2f3eyQ6XFYm6YQn1RPzBKEemCcIdXueYHWP693ZqtYvRjLvD+2bf1XDTqxZqVeq96V9xe278du7a6DNaDhaq4pCTFPlHX+XSZsrJGqrRrvR2jnMuzX3S2W7WWCeTbvm2bTLPBs5cjb6i89mzkq1cBmj/O3AHkJLFZ8txetEoUu8DhS6xGu70GWRh2/6Ml4HCl0WIr7pS/t0nNOX8TpQ6BKv7UIXU8I3fWlKeKcv7evhnL78yWvzPH25eIUPFIu3MOv3WojP4wMxyPaIh63htbVoL1FyLlZcqDgXK65UfIsVV9fFadTWPJffqGM6YNTmFJPPqO08nEYd5YBR2yJOozZPx2vUsR0w6nRtG3W6to06XQeM2rweXqP+wWvzbNSLV/iIUc+L0kJ5nmaO5nopp1GnE4sVo7m5n9uobRWvUS9UnEadTiy/jPnE5GzMByZnY96enF3k4TTqrAeMOusBo875gFGX64BRl7Bt1CVsG3UJB4y6XAeM+gevjWHUSX+5Ud8XNuhzJz+ae+05jdrUcBu1XCeM2lbxGvVCxWnUi+viNGo5MSLbv0i9bdSyPSK7yMNp1HKgcnsh4jRqOVC5HfVA5XbU7cptU8Jp1Hqgctu+Hl6jlhOV24tX+IhRz69StGjUSEX91SLx0llC84rzY3nST1QkfqlSp5284ijfqYTr/pzwe/n3o4o56+Wtd4y1nHDquv/doZeIbju1fWXvVcyvuH17l1u4vyLbnudqYzO/4Do/cfyaV9GvNLwtncXZzI/UvGLjDi9U5lcYX3HTL59Z5+qyxdPm/D1uJ0a42okRrrr/ialXJgdGuNK1PcJlSvh+j00J9+9xOzHC9QNzNH6PzYfeu5JqoeJbSbX86VH66WnGj4b1uz4f2Ff/5/5V/9k1mXbyip+3eUhmde2rD3l3ecJz5fJKRc6ozDfw1dq5vla5TqiURirlyycukEUGo8lkq0RSiUWfz0i3f0xNDe+PqX026b4/Jenj+5Os70fp/UHj+vSN+oXNzpJDPpOfOfVd2trSttkbEovOSppt4Vdc2vMFLSeMYKEiZ1R8RrBSuU6oOI3Avkf00yM5xS9Vyl1uL/yFnt+ekbXKK17UAXvu8yRzjVdrd/P++UfdzMOpsbgiqvd11efeSrJ26HOZySIPoXdQnlclLlRavBvDVj9joSL3+GOrRjMn7tqjLeGyR+/YlCVhD7p77THnE/a4UJEzKj57XKlcJ1Sc9mjfI6892ipee7Qmnbz2WPatzczDa4/2FfHao7ltocse7Ty89mireO1xoeK0R3N202ePpoTPHp1zrJaEXTzitUdJJ+xxoSJnVHz2uFK5Tqg47dG+R157tFW89mh9Lsprj7pvbWYeXnu0r4jXHq1VST57tPPw2qOt4rXHhYrTHtN259qW8Nlj2u5cL4qgvfZY4wl7XKjIGRWfPa5UrhMqPntc3COnPS5UvPZY2749tn1rM/NwaiyuiNcerbVeLntc5OG0x4WK0x5XKk57NFeb+OzRlPDZo3PNi2mP5pLCPLeJflfQGUvfbJX5pZRX/Lw6OFvrkZwD3KaGd28N+2xKmY9ILFKfMzE6XbVe8ysLxocXVyJz8q1WY9u/VSZyixj7Hy0uytw95V2EV7+8tJLirZLlW5X7dzRKfVZZrOvN81kJKulblfkiv+LnWbxsbmbofPTDgW1l7LO598h5x+05E6sJ+pqzni2U994Qz4/cIhm9ZWotX55So+XbLX251v9qtBdSC+lblXsi8GryeEbZ2tbw5djjhF7/u/thkZ9kEq5pCu/9WOU5E3vfD++GHdYWCL5PGa52UfDVMud44AuzKxVnLfNKxVfLvNjRwVcnlM31Vs46oYWIr07IPh1nnVA2576cdUI5md9E9tQJmRK+OiFTwlsnZF8PZ53QTzYgMXos9mvsq9tdqTj7/DmHA33+lYqcUXH1+Zcq1wkVZ5/fvkfePr+t4uzzZ+sDU84+f877/XUzD2+f374izj5/tua/fH1+Ow9vn99W8fb5Fyq+Pr9t164+vy3h6vN7fzSsPr+9o9i9G5ga5ij7ld2mhrfDYn1Xxr2jmKVxf+ZVKjVv5Cca7ZqV+y3qlxqzmSWtPGvYu/lNP3xJPFuz/Yk62uI/Gve27vdR7Dzmb14zOvnZnGW69B7LuV6NtuchlIWMd59UayGes6MjJ7ZByZpPdHRsFW9HZ6Hi7OjIgW9eZj2wQHEh4uzoyIFvXuZ6YIFirtsLFE0JZ0enHligaF8Pb0fnB7u/Gs00ObDlx0rF29Gxp5q8HZ2FipxR8XV0VirXCRVnR0dO1H4sVLwdHXOjQ2dHx9rm0NvRMdeMOTs6cqL2o1y7yxAWeXg7OnKi9mOl4uzo1O3KYVvC19Gp25XDi63cXR2dYu3n5+zomBrejo6E/Y6OqeHs6Jgazo6OreHr6Cy+cVHoGxdqfOPCGrZx91OsvXY8n61cfcGkzq9GX9ezlYUDT2o48KRa20J5v2Bibi3lfFJNDeeTamuceFK57XqVLz/e9/rTuWz1FbfnLmixppW8D3w68YHUxQeifD22Ett+j20h4uyxpQN7fxXzyjp7bCWl3R6bKeHrsZkS3h6bfT28PbYffM/MaG+mEx8DXag4l7CbnwBz/uIsTse3+rzkE3WxKxU5o+LqOi5VrhMqzq6jfY+cq88XKs7V5yW3/daFOUfmbF3YZ+NcfV6257dsh3R1kmwJVyfJ69NWJ8n+omcJs4kj+fly7m9NYGp4H45Y95ue8cBsUDwwGxQPzAbZ3/O9HzFt1ld0DZEUZ4sxvRo5zydjaSSZbb1UH0+myIGWq7Vfs/OH0/5mtG9QQQ6YqRwwU6tQ0/3NaNl/X0wN5/tia7jel9UH352NIpUTjaKFipxR8TWKVirXCRVfo2hxj5yNooWKt1FU98u2TQ3ne7w4G2+jqO7uohGu7ZFjW8LVKLIlXI2iyzRXtxG0ExWEKxU5o+IzgnaignCl4jQC+x55jcBW8RpBOzD22vbHXhdn4zQCsT4/5DKCS7fXx9kSLiOwJXxGYA0AvwfkphFoezaCXjO1bQQrFTmj4jKCpcp1QsVpBOY9qtddElUvkS9VqLDqNfb6OL4nBxZyyYGFXIuziff9qdEwgrDbIljkkWbT/hXH+OXZpHKfTXpekblQydcsV6s5PD9tZlWiz9pMCZ+12bWRLmuLJ/bVknhiX62VipxR8VnbSuU6oeK0tnhiX62FirN4SNL+vlpyYMtAMw+nxuKKOIuHXu/Frj3GE/tqLVScxUMrFV/x0GXtzuW0R1PCZ4+mhM8ezU+suu0xn9hXa6UiZ1R89rhSuU6oOO3Rvkdee7RVvPZY9vfVkgNbBkrZ33xmcUW89lh299Va5OG1R1vFa48LFac9hv2OcdjvGIfdjnEzl/u63VFOlA+sVOSMis8d5UT5wErF5Y6LW+Qzx4WI1xtlf1MtObBfoJmHT2NxQbzWqJt7ai3S8DnjQsRnjCsRly82a+zTZYu2gscVbQWXKcqRHnU9MWuwUpEzKj5TrCdmDVYqPlOUAx3qhYjXFOv+rgNyYJdAMw+nKcqR7nTbLMpapOE0RTnQmV6J+ExRd7vStoLLFHW3I91yOzCDoteJzxytVOSMissUlyrXCRWfKdp1mb4JlIWIc/5Er/1KQlPDN3+yOBnn9ImGzcWFizR8sycrEdfkyULEN3fSym7n11ZwGVrZ7vrGEztKq7nSym1oCxU5o+IztJXKdULFZ2jxwIbSCxFnK0+tz085W3ka95dLq/0ZLFcrL57YTlotFZcpxgO7SS9EnK28eGAv6Za2TTFtm2LaNsUQTrTyzE+7uE1xoSJnVHymuFK5Tqj4TNG8Rd5Wni3ibeVZH53ytvLMD1f5Wnn2yXhbeVk3Dc1Ow9nKW4j4Wnm2iLOVF3e/rGQruAwt7n5XqbZ0wtDKiY9yrlTkjIrP0FYq1wkVl6HZt8hpaAsRr6HJ/qcLTA2foS1OxmtoslnWskjDZ2grEZehLUSchnbtLvW0FVyGdu0u9Kx6Ym2T6ol6lpWKnFHxGdpK5Tqh4jM0PbC0aSHiXNDQ14buGlrd3t91cTLO9QxaN2db6/aOWHV7Q6y6vR9WLSe2Rdd6ZOCqHhm4qkcGruqRgat6YOBqcYt8A1cLEe/AVTswcHVgnz8zD5/G4oJ4B67a5sDVIg3fwNVCxDdwtRJxDVxV2W0W2QouU5TtZlE2H/Prfrz4M10/kQg6Jfj7Wt9m8VgsWa2vwb0/yjU0hPZH+K3G7ozVKovYZhZkG/8ti/hrs6BrkZ+uRbVa3M4tK2owbqtvy4p6yS+V8O3QYEu4NmhYSHj2Z1CrN+fceqNaI0POhq2p4WvYqrWLrm/nDVvCdVttCddtXUi4bqu1L4TvmwW2hO+TBTVubwW6SMP1xYJqLW+Se1tECdfzZpG2SJjXQwJtVfcbEa3W7rkydygvUr/TcH+toCazCej8WsFCxfm1gpWK62sFapXtebe+rOnA1pcLEdfWl/bZOHe+rPnAzpc1b+98aUr4dr40Jbw7X9rXw7fzpd0ldW58uXiFfV8qWIg4hx5qOVEEuFKRMyquoYelynVCxTX0sLhFvqGHhYhz6KGa2wn6hh5q2a5qtvPwaSwuiHPoocrmhiyLNHxDDwsR39DDSsS3jM52adcyOlvCtYzO+1vxLKHWt4K8PZoDmwnW/c0E1RqfdvZoTAlfj8aU8PVobAlfjybuDiotJDyDSj/I4nkgxXpAXc4jun0pbAnXpfBn8Xwp6uaolFmC6R1RslbbOTupsf5SCeeLur9Fru7vkKvWAJ3zuxu17U+VmhpO/7XWdPv2PjYlnLfVlPDdVlvCc1vF+qSg902zBrZ8r4nor5Xw3RJbwnVLFhKuWyL724ybRTDON83U8L1pItu7jJsSztsq2wa6kPDdVmuYImie1zOopMerYYvMxvgrfi5NMotXvU9HCPtPh3kytc1hxrfecyL2N5rj7Bjkx53bV5mo3pnU8t3ptHsM6TUo9fQbKebOe1e77iGxFtKXIveA5/XqRT5f2O1RfjuRcLcmX3F4HOc3S5Sdw+uvAZVfq+Eeom/xxAeFFyrOIfqVimuIXsqB7wm3eOB7wgsR1xC9fTbOIfqWDnxOuKXtzwmbEr4helPCO0RvXw/fEL2UA18TXrzCviH6hYhziN5cSeYeol+pyBkV1xD9UuU6oeIaol/cIt8Q/ULEOUTf8v6XhFve/5KwmYdPY3FBnEP0rWxWTi3S8A3RL0R8Q/QrEd8Qve3SriF6W8I1RO/9rXiWkLz/EeFW9j8PYGo4uyZl+xvCtoSv41q2vyC8kHB1XOOJDQybnFjft1KRMyq+n7uVynVCxfdzFw9sYLgQ8f7cLWZjXD939hIh38+dbu+fvbgg3p873fwywCIN589dPLCB4UrE9XMnaXcDQ1vB82NnK7h+664TK4TsHSHdplhPfBJgqeIzxZXKdULFZ4rXgRVCCxGvKbb9DwK0tv9BgNa2d81eXBCvKbbNhdOLNJymeB1YIbQS8Zli2N3A0FZwmWLY3cDQ/Cye1xRftnBg4fRSRc6oeExxrXKdUHGZ4uIW+UxxIeIzxXiF7c8AvP5X24Zm5+HTWFwQnym+Etn8CsAiDZ8pLkR8prgScZlisdbEukzRVvCYoq3gMkXdLutaSHjKun6QxdPT9VK39qJyLZt8Z7j3jK+y8CybjOYHfE5k4Vk2WfZLH8t+6WPZL318GfRmvV/ZL30s+6WPRQ68I2mzt1/2Sx/fP5i7M/cl/loJ3wBo2S99LPulj8UqffSNa7/aDtv7+NkavnHtYlYMusa1y37pY9kvfSz7pY95v/Tx1c4Ou69J3i99zPulj3m/9DHvlz5mq/TRV2T8uiW6/6aV7X3R837pY94vfcz7pY95v/Qxx7T/psl2OX+O8kslnLckbm8QsZBw3RLry8m+IuPX+3Ptv2m6Xc6frXEr55sW9g007Bto2DbQZA4Su0fg9MDm0ksVOaPiG4FbqVwnVFwjcItb5BuBW4h4R+CsaRbvCFzd3nTMzsOnsbgg3hG4urmGb5GGbwRuIeIbgVuJuEbg8rU7AmcreEbgbAXPCFzScMIU7R36vKa4UJEzKj5TXKlcJ1R8pmjfIqcp2iJOUwzm0imfKYZr29DsPJymaF8QpymGa3OwdJGG0xRtEacpLkRcppisXzyXKdoKHlO0FVymWA5U9cUQDlT1LVXkjIrLFJcq1wkVnymWA1V9CxGvKca4b4ox7Zti3C6CWVwQrynGzXH+RRpOUywHqvpWIj5TtIZnfKZoKrhM0VRwmWKyRiOcO//HkPIJU0z5hCmuVHymuFK5Tqj4TNG+Rb6d/xcivp3/Y8jby6htDd/41+JkfDv/x5A3i09SvnYdwFRwOYCp4HIAqzIgxBzmDYk5Pd3VlcgcT3zF8vxolP3hVVPD+XjZJ1PK/HmIRZ4fL2sf9XzNH+4crsdV+j8Q4TKBH93gcm868K7i+O6aSIq3SJYvRW6Dj8+b4KbrRMllKO3EL1ZpJ36xViq+X6yVynVCxfeLdR0ouVyIeJvxsr0WNQbZXotq5+Fsxl8nSi6Dbq5FXaThbMZfB0ouVyK+Znzc/hGP2z/icfdHPJpzEm5T1HrCFLWeMMWVis8UVyrXCRWXKS5ukc8UFyJeU6xl3xSr7JvigZk0+4J4TbFufr5nkYbPFBciPlNcibhMMbbd707bCh5TtBVcplj0hCm2E6UBKxU5o+IzxXaiNGCl4jPFcmBj8YWI0xTjtV8aEK99Q4sHZtLKgY3FX4lslgYs0nCaYjmwsfhKxGeKuruM21ZwmaLWbVM05zVeb/98vlp8flmCbI/UmBq+kZrVydyLUi7r4bBF5pP+itvj2Vgfmwu53jux5fZ8SaydMZz7ysVoThj59pVbibj2lbPPxrev3CsPy1F9+8q9RKypK8++craEa185W8K5r9zievj2lYtirqrw7StnP/ClLzEdE0chfCdCP/8lp/TV+/saXZ3r48LVHn+6ZffHbnE9fBNpMZ0ouVqpyBkVV2NzqXKdUPE1Nu1b5JtIW4g4J9JiTvu/n3l7t+rFyTgn0mLerAxYuKuraWVLuNpWXo+3JKr9azVNQJthAiWcMIGFipxR8ZnASuU6oeIzAfMW1eve0rheIt+J0L7Ir2ZJeD6fum8C+zv/LU4m3jenRsMEdj9DtUgjzfUkrzjG784llftc0vPUrS2SrzlhWnN4fM7ybs2kreBytFy2DS2f6C3uf4XK1vA+6PlAb3Eh4ustmnVB3t6i1SZy9xa1HOgt2iK+3qJ5Nt7eotYDvUVt271F9Y1d65dZuHuL9sSVr7doftTW21s0H3hvb9EWcfYWzffX21tMu/NFi+vh7C3WE0UsKxU5o+JrKNYTRSwrFV9D0b5Fzt6iLeLtLbYDo61tf7TVPhlvb7Htvju2u/p6i7o9Pen1eEPC/I6P7wstpob3Cy0xXQc+or5S8X2hZani+kKLObfgbRu9/nf7baOFiK9tFA60jVLY/4j6S2T3I+q2hK9tZEp420b29XC2jcKJtpH9Cvu+0BKt3YHcXtJOeEkMJ7zEVvF6yULF5yWWiNtL4oF+1kLE5yXm2Xi9JB7oZ6W43c8yJZxeEg/0s+zr4fSSeJ3wkvaLvcRdN5WOTGWlI1NZ6chUVjoylZWOTGXZt8hZN2WLOOumUt7fPSDl/ZonMw9vQap5QZx1UynvbrVqp+Gsm7JFnHVTCxFf3ZTt0q7+mi3h6q95fyu+7K/5fbGc2EBgpSJnVHy+uFK5Tqg4fTGf8MUTGwgk2d9AIMm+p5l5eH3xxAYCSXbLBEI+4Yv5hC/mE75o9oR9vmhK+HzR2R+3PqRX9mezzHJydy/rxGxWOjCbZZ+Nt5d1YjYr7c9mpf3ZrHRiNisdmM0K9jC4r5dlP/DO2ayFiG82a5WJbx4p1RNLXFYqckbF1ySpJ5a4rFRcTZLFLfLNIy1EnPNIqe1vyW5q+OaRFifjnEdKbbeblfbnkdL+PFLankcK1mPqHPs1Ndxjv9newc859rtQcY79rlRcY7/mwlBvqyRb2wl6WyULEV+rxDwbZ6skmx+PcrZKsvUVK1+rxJTwtUpMCW+rxL4ezlZJrQdaJfYr7Bv7XYg4xzhyOLGRwEpFzqi4GhRLleuEiq9BUQ+smV2IOMc4ctzfSMDc2t85PpHj9rrbxQVxjnHkuFncskjDN8axEPGNcaxEfGMctkv7yqBNCV8dtPO3wmhjWeXt7o5WTic6WisVOaPi88V0oqO1UvH5on2LnB0tW8TZ0coHvn2V9799tTgZZ0cr785nBd31AFvBYwG2gssB0olZ8VziCQdYqMgZFZ8DrFSuEyo+B0gHZsUXIt6WkbWPnrdlJNuf6bXzcLaM0olZ8WxNZblcJB2YFV+IOFtG6cCsuLmzp88Uy+7cj63gMsV44utLWU8seF2pyBkVnynqiQWvKxWfKcYDX19aiHhN0ZrA8Zqitn1TtCeSXKYYT3x9KdfNzwgv0nCaYjzw9aWViM8Urc8q+0zRVHCZoqngMcUjc3KvqfITnrhQkTMqPk9cqVwnVFyeeGJK7siMXG77n241NXwdxSMTcuXa3Fk4XLtL5m0F19t/7S6Zt4bynZNx5myAdy6umLsje+fiFirOubiVimsuznrxvVNxxZyscU7FLURcU3HmyThn4oq5DMo5E1esUjbfTJwp4ZuJMyW8M3H29fDNxJkrh50Tcfbr65uHszWc/aoST/SrVipyRsXVhliqXCdUXG0I+w75ulW2hrNXVeJ+r6rE/V5Vidu9Kvt6ODtVJW12quwsfH0qW8PXpVpo+CbgbHN2TcDZEq4JOO9PxLPEke0VSz7RqVqpyBkVnyHmE52qlYrLEE/srnhkc8WS9ztVpoavU3Vkb8VSNjtVujvMrLujzLo7yJwXWwC7NlUsR5ZdlSPLrsqRZVflyLKrcmLZlXmHnHsq2hrOLRWLuejK+eqbC6Zcr759Ls4dFYu1f7bn1bez8G2ouNBw7adoa/i2Uyy7Q0Nld2SobO+leGJYuGg6YWMLFTmj4rOxlcp1QsX5NZL9Fkw8MSxc6v6HME0N7+cVTrRg6ubEf9p99dPuq592X/0TNdWlnqipXqnIGRXfm79SuU6ouN78AxXVJ+qpS9uvpy5tv57azMOncaKauuxuFXiglvpAJfWBOuqguzNjujsxpt944L+96Pd/+NPf/v3Pf/3D7//+p7/+5T9ff/aPt9Lf/vT7//nnP37wf//XX/5A//r3//Mf41/+59/+9Oc//+n/+/f/+Ntf//DH//Vff/vjW+n9b7+7Pv/nf7x+y15N2df/jfJv//K79Povr7uS0ivO+Neo73+t8fVftP+X+nobX/+31dd/CZB4dTDe/ym0938K+LvWVZv+2z/eJ/L/AA==", "file_map": { "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "4": { - "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn(T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", + "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", "path": "std/array/quicksort.nr" }, "5": { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/higher_order_functions/execute__tests__force_brillig_true_inliner_-9223372036854775808.snap b/tooling/nargo_cli/tests/snapshots/execution_success/higher_order_functions/execute__tests__force_brillig_true_inliner_-9223372036854775808.snap index 3564b74225c..8325e236a6c 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/higher_order_functions/execute__tests__force_brillig_true_inliner_-9223372036854775808.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/higher_order_functions/execute__tests__force_brillig_true_inliner_-9223372036854775808.snap @@ -44,7 +44,7 @@ expression: artifact "debug_symbols": "tdvLjh03soXhd6mxBslrBP0qhmHIttwQIMiGWjrAgaF3bwbJf+3yoGR1FnoifirtXJGZO5jX0l9Pv7375cu/fn7/8fc//v30w49/Pf3y6f2HD+//9fOHP359+/n9Hx/nT/96uuKP4k8/pDdPZayhXntIe8h7KHuoe2h76Huwpx/yHHwPYw3t2kPaw0wpcyh7qHtoe+h7sD34HsYa+kypc0h7yHsoe6h7mCltDn0Ptgffw1iDXXtIe8h7KHuYKX0ObQ99D7aHmWJzGGvwaw9pD3kPZQ91D20PM8XnYHvwPYw1jJky5pD2kPdQ9lD30PbQ92B7iO/omuPYY7oukEAG8WWlQAUNdGDAwThIF4jkHMiggAoiuQQ6MOBgHORIroEEMiigggY6MBDJLTAOygUSyCCS57eZVg9boAMDDsZB9G/yQCw1AtHfsXujNxeiOzcSmEVz7Lroyhz7J5ouxzpH2+VYn2i1HCWi2XKUiF7KUSIap0ROdEmJnOiTjQwKqKCBDgzEbIrViLYJ5GibjQQipwQa6MCAg3EQTbKRQAYFkJxITiQnkhPJieRMciY5k5xJjiYpNdBABwYcjINoko0EIrkFCqiggQ4MOBgHcczcSIDk6LrSAxU00IEBB+Mg+nAjgUi2QAEVNNCBAQfjILp3IwGS4whbPFBBAx0YcDAO4ni7kUAGJBvJRrKRbCQbyUZyHIPLCCSQQQEVNNCBgZlcr8A4iBm3kUAGBVTQQAcGSI4ZV+OsGjNuI4EMCqiggQ4MRHIOjIOYgxsJZFBABQ10YIDkmIN1zq8Sc3AjgQwKqKCBDgxEcg2Mg5iDGwlkUEAFDXRggORCciU55mBtgQwKqKCBDgw4GAcxBzciuQcyKKCCBjow4GAcrOubBZI7yZ3kdZ1jgQY6MOBgHMQc3Eggkj1QQAUNdGDAwTiIObiRAMlOcszBOgINdGDAwThYc3AhgQziau0KVNBABwYcjI0ac3AjgQwKqKCBSF5XxAYcjIOYgxsJZFBABQ2QnEhOJCeSM8mZ5ExyzMEW19oxBzca6MCAg3EQc3AjgQxIjjnYSqCBDgw4GAcxBzcSyCCSa6CCBjow4GAcxBzcSCADkmMStRbIoIAo2gMNdGDAwThYNw4LCUSyBciJCdI8bn/4sPPhmBcbBfSzeMyCDQfjIGZBG4EEMigg7lOiRaPnNxyMjbY63AMFVBAfToFxEG28cVa+pQwKqKCBDgw4OJvTMjmrRaN65sOZD+ezw1u5QDmLrz5caKADAw4ouvpwIYEMSK4kV3IqOZWcRk4jp5HTyGnkxHF+bWBzMA6iD3vcB0cfbjTQgQEH4yD6cCOBDEg2ko1kI3ndzq5bbAfjIJp2I4EMCqggkmugAzsY5ESL9hZo/CQ+HN/p6tUF3+jXCexXBwYcjIPVogsJZFBABSQnkhM5mZxMTiYnk5PJyeRkcjI5hZxCTiGnkFPIKeQUcgo5lZxKTiWnklPJqeRUcqJXY4f36NWNBDIooIIGOrCDOIp2D2RQQCw+Ag10YMDBOFjdu5BAPDy5AuSsRy0pHtzwYefDqyEXCuhn8Wi/DQfjIBrSciCBDAqInHjKFJ254WBsWBxFY8UsjqIbFcSHa2AcRItunJW3lEEBFTTQgQEHZ3MskxMNuapnPpz5cD473MoFylk8+nCjgQ4MOKBodOZGAhmQXEmu5FRyKjmNnEZOI6eR08iJPlwb2ByMg+hDi2d20YcbDcTmrAd4BhyMg+jDjQQyKCCS45lfdOZGB+SsR3/xk/XUb/2kAVbDWY1B4CBwELi6bsHB2PC4Xt1IIIMCKmjAATmJnEROIieRk8hJ5CRyEjmZnExOJieTk8nJ5GRyMjmFnEJOIaeQU8gp5BRyCjmr/eKh6mq/hQbOV+DVgIPzDXq7QAIZFMBqNHI6K9/5cOfDvYIGWOfOOhuLG9tubLux7bSf035O+7kZINlIdpKdHCfHyXFynBwnx8kZ5AzWkO51utejRT2eQ0eLbiSQQQFzcU+BBjow4GAcRNNuJBDJOVBABeREi66fRGfun8SHS6CCdlAILAQWAqPrNjow4GAcVIrGEXKD5EpyJaeSU8mp5DRyGjmNnEZOI6eR08hp5HRyOjmdnE5OJ6eT08np5Bg5Ro6RY+QYOUaO0wlOJzidEA250QCd4HyDTic4nTDohEEnDL7cOIlv8MWNkzPfjJy1n8r62WmGqSo1ybXEQEkpKUlZKlKVmtQlk1QjqUZWjay8rLysvKy8rLysvKy8oryidS7KK8or5/p/viG6pCRlqUhValKXzqX9lEuzxlj/Gt19lKQsFalKTeqSSS6pRleNrhpdNbpqdNXoqtFVo6tGV42uGqYaphqmGqYaphrr+nbtDeuS9pWphqmGq4arhquGq4arhms7XNvh2g5XDVeNoRpDNYZqDNUYqjFUY6jGUI2hGoMa6/XkUZKyVKR69sZ6R3nU9a8muaQaSTWSaiTVSKqRqtQk1UiqkVQjqUZWjawaWTWyamTVyKqRVSOrRlaNrBpFNYpqlHPvltb7y6MqNalLJrk0UL2kJKlGVUpVSlXKOk3FG8v1VvQoSVkqUpWa1CXlrbOTL2mJmJc+lrpkUuy/a2mgNS+3Yv+t19Km5HXC2qrSuXWbGsgvKUlZij2+Xoqvhx9bJjla82gtsebRVpWa1CWTXBpH633tUZKyVKQqNalLJrmkGkk1kmok1UiqkVQjqUZSjaQaMVN8K0tFqlKTumSSSwOt89uWahTVKKpRVGPdO6xfFVg3D0vrvnYrSVkqUpWa1CWTVKOqRlONphrroYstdckklwaKs9pRkrJUpCqpRleNrhpdNbpqmGqYaphqmGqYaphqmGqYaphqmGq4arhquGq4arhquGq4arhquGq4agzVGKoxVGOoxqDGet3p+3dFLilJWSpSlZrUJZNcUo2iGkU1imqs3u1LVWpSl0xyaaB1P7KVpCypRlWN1btjaaDVu1tJylKRqtSkLpmkGk01ump01Vj31b5UpCo1qUsmuTTQuuPeSpJqrJtu//r1zRO/Effz50/v3sUvxD37Fbkf/3r68+2ndx8/P/3w8cuHD2+e/u/thy/rQ//+8+3HNX5++2n+6zz/vPv42xxn4O/vP7wLfX3zWPp6edEU7/fXwqk/Fm+3lu93lndWfp5GXrn8uLF8jpfre/n5hbxqeS83li/xznotPxv+lcvbjeV7/H7ZWn6+OXjl8nf233wkeZafD/Feufyd79+1/HzAc2P5Uei/Ue+s/2jMn/l0487yxv6fTzXu9O+jgfI80L6UEL+X+GJEfszBee68E5G0F+al+r2t6I+teHE/5uvVW/GtiNduRXK+inkB/+IKlG9swzW0CY+DSRl3VuDOwSTHu+hzMH3xYJb7NwLinfEO6PdWoPzzCnzraBa/LruPZreOJmbsgPmm5s76W33swV5fm2C3zuhpMBEm7+zFuA8kYd6kvTYh262Ex4VJbvm1Cb3dShjainLd2g+lPhJafW3Cva0oOsXMpzv+yoR63fouajIl3OuH5wnt3pVur0qw+sqEdt05288zjLai3TpC/S2h3kvoRQnWXpnQrztHmPkoaTwu+/2VCfNocychlaqEWl6b0O8d5fLjGJXvHGHqcPqhjmdH+/K9Ae1KfJltvhe6E3D1fw5o37p6LO1xhHr5rPnNiFfvyPS4+IlnYncSLtPl1/OmLt9/5n7Mq/TsbuK/CBiPbbjsTsDjCnI+er61BuOxBv1GQEn6Iku6d7b6rm761pxqiXk9eeOLrK1eCmjtTkAp/xxgr98L9j89OM13SOVx4XDrUrQWNWSt9x7QdL6LyRstPR9ulscjnjuzsrou4eq4tR//lnDnZFcep4ky7kzs+fVxlJ+XgP1OQGsKsDuT6rLrEXDn4NbKs0unW5exzR+XTvcupZ8njFtnmWGalcOfzanvD9Ajs/z8m/zugPw44c8Xi+VOgK5h5xq0167BS5sw0re+h8dtVWv+QkJc7r988XfpWUW6bl3Cpseju9T+fiH+0/zb21/ff/rb/7j/Glmf3r/95cO789ffv3z89dm/fv7/P/kX/sf+n5/++PXdb18+vYukx3/bn3/8OG9v+5t5krWf5iuN9YMy/15T/DWtv87bvnnLmH76Giv0Hw==", "file_map": { "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "5": { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/higher_order_functions/execute__tests__force_brillig_true_inliner_0.snap b/tooling/nargo_cli/tests/snapshots/execution_success/higher_order_functions/execute__tests__force_brillig_true_inliner_0.snap index fc03bf5f815..5243ba1ba7a 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/higher_order_functions/execute__tests__force_brillig_true_inliner_0.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/higher_order_functions/execute__tests__force_brillig_true_inliner_0.snap @@ -44,7 +44,7 @@ expression: artifact "debug_symbols": "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", "file_map": { "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "5": { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/higher_order_functions/execute__tests__force_brillig_true_inliner_9223372036854775807.snap b/tooling/nargo_cli/tests/snapshots/execution_success/higher_order_functions/execute__tests__force_brillig_true_inliner_9223372036854775807.snap index 743225eced3..2e64aeeac23 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/higher_order_functions/execute__tests__force_brillig_true_inliner_9223372036854775807.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/higher_order_functions/execute__tests__force_brillig_true_inliner_9223372036854775807.snap @@ -44,7 +44,7 @@ expression: artifact "debug_symbols": "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", "file_map": { "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "5": { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_false_inliner_-9223372036854775808.snap b/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_false_inliner_-9223372036854775808.snap index 07e635bd67e..eeb35b4f10b 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_false_inliner_-9223372036854775808.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_false_inliner_-9223372036854775808.snap @@ -231,15 +231,15 @@ expression: artifact } } }, - "bytecode": 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", 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", 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", "file_map": { "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "4": { - "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn(T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", + "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", "path": "std/array/quicksort.nr" }, "5": { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_false_inliner_0.snap b/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_false_inliner_0.snap index 0494d0fab96..daefa81fc65 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_false_inliner_0.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_false_inliner_0.snap @@ -223,15 +223,15 @@ expression: artifact } } }, - "bytecode": 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8edL1vCOWp/x6cELpSh2xPOeQ59z2nENPOGJN6rr9LY5Y73bE4n1vUb04w7B92lPvf98TH0gozdLf96a9z5Vk0CBtFX8l/e56OohXoc8zgJMQ7leK3xXZTMYOMGidMCzAIG8mcQOCHVij756JvJmsUB7SXwOsYUaBDwF9H89vDSqjgPWVcmSP0R9zgfYgbVXBD+QhB7J4OdAfOKZnxHfcHxVBT0P8jnkXZzO9UfjBBqu/ngz2c4zN/VxBvqrN/RzxHOfaYdord1rB+5wjludi/IAj1mOOWJ78mtSNr6eC5tmPk7qR8zQUTOqGaVI3q5M6Vj0NnZ78esURy5NfngZrT35NqsHHc257joljR6zbYQ695ohlAe9GHax+lg5WbX9R9GDVyv85OFj9hzmmCgLuqFvvW9vQQGBJ6dH8wJ8K0mV5+FAHBoXiNEN/Y3uz+r5joYfL5SypvSYHIDM+414Q9w/Wp9wvvwF7vR+k4PmR9q+dyIff3cBLak+FbeLA+2o/yv8bFudN6n6xReWZB8Ow5kpixd239/q0Fmg31t8I0MrtyNL11IcnWbpnQrGsjcYb5GWZ8a7qQSwOpnXSIHWqHmX/PO38VfUo2Xm7Bhk8zWN6k/oIRYvKMw+GYZ2nIIPI62mQwWRkG2+VIIOs15x0/qp6EMvkonr8yjAm5WyKH78qejZ1d76weTx+VRH0jNp/fWlN11l0/2XlPwj7r6/IPzcFXbaONwkb117l9DpLeaiTGUZkHXHggWUcC0pvqCaD4xb7j3n4hsBYKHsuVi3IuwXKU4+gWh6Od34c+IJo41kFsbS98rAglheAR1geP2epRt/tBPqjrG2kLuiJ/BDoMQfHx2R57EyPeRi8i88HMdBYjfLQWZ91aHxgjmUUPtKIY42Tsnngg+s/XMDmoewzxguW0W+CcbCcT4rItqUjtgMlgnYVfF71K+tm2K+8lnMAOfvMaZTd6ZcLPADYFPWwPApdilCPukfWh0v3C88p7BeeUyroaqhfys63sg+fGg/LzincFzAvrDz2K+p819Ob/9eo/D7MwRWag6hfnFVfF+WhlcvovpjTHeLheWvTy7dgm/bqt16b/snS6DYtwe9wP6P2AfxwitKHELtKdKkA6kqHVLpnIxldN+rZ96Wj6w7RuizKK/1S6dD228VksI2O4+VQPdpkSZ0p8fqCcppt3Lhm8fqCaxY/yK0e8i46do1PZR81nxT+sj6G/GV9DPnL+tik8JflguGyXGAbVFXQqOwONh6Lypt5QUdoDisnbtRT2C6B+3l+hB3tEqyvKpuokmEs33Dcsi57XvfPn3LcP88LeuI+MFL+EfaQzj7sEXY1r1Fnx3HISc1r40XZR9hx/PEj7Njf/Ai7/e5n88bHPdvf2A7ZnCft4nZD0GN010XeaR4n3N09PD4+Otxqbx1sbuy3r4TkHn7HclI9NKfsjJEf99tUjxPyA4SzkLdEeTXIMxrV44SRHrLcLMJ/rL8lyrPeWrQvW6IevqtSFMseFFRrJa/diFVUJ7DfKl8wtKOPQydguzm2eykZXTfOmyJ9GaJVrZchezzaV3h/ogJ0TMo52jy0n9el0DnaPwroGqpvQmf2c4IexbsV4l2kvUeXd125l+g9M/MOdbiG4J19908ceadsyyEs5VvE7cXy6nGPC8mIi5fz9LddvGTCVqicTVoryw5Ns0MIfs+Q+oYdlFfoe26oDShOw377HvouS3GdItsbrPRiUkovb5BR6eUNMh4M8aHRJchjBfwy5GFfcVKKtPGp7AZZHdQWXQzttxhxKnK/tXkjg0ltZJYpD/uUX+XGPm1R3h2i3apPy26ATtpvSjgV7Tf7bVbdd+bgNoZn0145r37b2V3vvshkCrI5bHCahXwsX815w4Yj64+rJ6TzeGevfby5d7y3tXd42DnYWyX8G3Xn/y9FqH9vZ3P3YKNzsLO/tbm3uT2y/mycfJEcVlhZxjych1XKY8dNzFMOVJEdMoNR2pSjR5XK4+cscZS2Vs4opTiUdZhThrMQlnJ84/bOifKIx+25BO35dC6wihw2j1tpNtpKO58F+k85fOJ3of5jR9d5wFGKH2/k5oBeHjuIx4EF/uf8Q/bfl+ef1bzlOY308pwOOd2e13n71Y7zVs0rHkO4gcd1lvt9CepVY47Ld9fYJLyZrFH5NrT/p/N5HtlxZD3yJjFocMI2Wf0ezvccoTK2833IYJOlkAGMx+ZpjGmGFVn+By9UIK/Z4BzD8TtL5jjMhm2PetS8VRd80Hn5+hBjBa4bqMstQD6W/29h3bgnsG6EHNDOog8Ub2I432PbYl9aKOLkrw5QeZ1muYp5aAgzjMjOt9013PbEuEZhnQ3gEZbHz1mq0XfvCqzhynCG34XmXoP4E+eAp7zdge0+RQ9QcVxwUvYDPCz6TKuHy+WYVhxjq5SHfbpGecNk2yNDLoMMk20NyMfyHwDZ9jjJNvw9yzYlc5RM5MtdKBM4cFRV1Gd5M6K+7Pem03O7noPx/4WV3m+xDOqdar8RWQZ01291eINjAuvHv7F8Uec4xirqHBd3vvd4sTyCF3whoSXKYxtDznf227i6d3lHEbZvF3UUCdnMWQYqm3lRGejpHIbzbtjFbfwtyr4iOhi2jR21bsVLSZ8IrPsel5JCWGqvpRw1kNdZmoU8R153Mh781kqPjgHepf15qE/Npf28wLzraT8vlFNMU/BCYd0zoVjWRrXHuEC/C60vow51Q3Me1/nrEex1vA/A8c62vPN6gfjHAvKg7AViFaRh0hyreX30uARX9mwY18evXO3hcjluh7oYUMTOPG7HErYzF3Us+XuR7cyj9lA/N+RMY9geiu3FVv4XYA+1lxdSsojlFPYpy6mQPXHc/W20le3vXwn0d1l766KgpyLoU/3ITm2xbTyKn8uCHubX54hHLeCPWiP4XFrZllqBeptUL55/cFuSRPOW+8LaOZcUs0tY+X8uzlq5f5EPSBefAyma6yVprheg+fMBmpcDNCs5YX2BY3g5UJ71E8bni7bYhtCZMY/JfxWwq6jzPJzDb01v/h/XuTpsS1gM8ETpy4rnym7MWKpu5ZNgvFiJzIvWCF6sEP0rojzKDtZPmgGs1ghesL1tLTIvVkfwYo3oVy9Iok2mRbxYCWCN4sW1tJ8XFyPzYm0ELy4S/RdF+bUAL1YFVuwzsAbV03KsB+cA+zOuOdaDPOULVyuO9eBY5XOJi471INb1tFcmy0NbZIPqxO8WBK34e16rXpfr4tmStTbfj3uBfoPya5byrOwlwPtD88PbZ/7cRcspe6TxUq01vFfgdRQxlKzDdY5944yXc4m2v/MFPyv/pXk7UfdRZz5sO8IznwblzYh6kaeKbxeJPsv7aui7/3y+v83Kl62ojWoB6LL9ZNxAY+2Omp+WlH2f98PoP81+1+gTz2eRr4M8lkl3Qh77a98FeXhJipOypRgPMz6/tYAtJfQwkrqoxXOJ+wzzQucMuP7wGoh7hzXKUxdrWe4N09FZTln5Pw5z8Qtg/84S2uGND1m5dwNvsZ4szea/OSu9Xe3X1KXISjIoz0J7q5b4PdoLmT9LAf7UAvyJFKC3yx9lU1dBB0LyDcfyMvEn5LOp6sa9DsrKG2XS3u9D4w1pnM1/M07/OeW7VUkG50bI/1idjwwLnI9YyOMQP41GxU/EmM1/E9kvubA/IvNAnUMgPzlwujqXVLrGLOWpOzlK17A1AO25Me6ibO4e722sb+0dH7Sv7G5sbJW5i6Js25Uh/ydJMR09FKw9hh9hluzMMnR2F9v3msch2kvVuUs1GRyHocCt74f1+LT3KKqCnsjzuvRDJhXKU3sXdXY3Q3lqT1YRNCi90XhR9gwOxx/7Ds2IdkzKXQ+jrexdj48ExibydUZ8Fxqb3Je4zuEZ3MfndZ2o83JACWyHlf8O2N/9Iu0PQ/02bpnC/VZUpnxX5H6rCPpUf/ADLLH9JBQ/lwQ9zK/vo3HW1acEj9SYU/4ZjUC9F6hedZam9B4OjITl1b1FLF8nGqz8DwjbDPcv8gHp4rM0RfNCSZoXCtD8lwI0LwVotjL4GX3p1Jjh8jPUHsbntU75Xs8IfB6TPyT26YxZTfQc5rO0cdwbqwZ4MiogDfsozwSwVN0hH+XY/tqNEbwo4q+t7pwom9BySV7wWdo4zliRF2XPWJkXoTPWUby4lt5avKgLLKXvIQ+UPEnEb1DvwvKfAb3q79MZCv8Gxy3bJq3szwPezwpslsFJMrjWZYlt8mrdRP1S7XHt/EXdb2sABuuTuF4YnuUpG0OUOBbHnaOdjSt7m4fHxwcbV65M41jEuw//GwFd2yOORWTd+YD36JjUHn2O8jz8a8vu39H2Xmb/rvwAbuUYHf/a0bYUut87bP/+b0luqzvWKJvZ59DK/ztYC17OKz+tr/+t6M//xUB/e/jzK32hyL4/9n2pk+6/5xf629UE/qg1gvf96p5WM1DvMtWr9v1qb6T28ZVkUBdR5dmXxcovAQ2fHmJ7QD4gXbzvVzQvlqR5sQDNrQDNjQDNSk5YX+AYbgTKs37C+MPuzqKsqwv8AT8iaCPv++cFzTiHed8/jr3uQoAno/a6fFc8xF9VtzobPau7yc0RvChyNxllB+snywGs5ghe8L5/NTIvVkbwYpXoV35o6O/SJF60AlijeHEt7efFOPyJkRdl/YmZF8qfWPlNVIb8b/XwdyF9gONANR3rUbEN+K67Rz3KRzu2T/AKlOG+Z79dLp+I3/A6aeW/Ju+sbH39Ywv9uEv0G5QrbJexsn8C8L5WYPP6lKWydhm8z2v7hkWiy3eOtjeVb7Yl9qPEOaD8KNnfG/0o2Xca/ShxfHBSe198KPGDY/Z5VP4Pau/FMgv3OquUpx7VZP1hmE7F49fK3y90qrI+aKhnzea/GaftWNl7Q2ugstMomYv+wMyfeoA/IZ/HSOfohX0ei8SEVfZddX+E/SdV3TgfY/g8jsNHr4g9AMuHfB5D9yunPo9n5/OI+gufVRRdL/gsm9cL7lO+U4J3JGZEGbahWPkXQKanq8XbfBI/T7M5xjir2Vnf2Ns4PDzs7G8ebu6MPqtxPytqH21eOThutzf+4Lhof6dz1vXvHx5sd462Nrf3Ou3tvcOts66/vXF4sLff7nQOOocb+5sHJzkrY/00S3Hv+ax3xnmfQN1xUnpAZcj/hsV5Vlc90XLmqk/b2kX0hiyFzkD4fFRhzZfEWhR5Mfp0LtBurL8RoFX5a19PfXiSpXsmFMvayGtNlu6jPBzj/CCYWl/Lrl+sB+B+0c7hoqxZO5v7652t/Y2DrePj9v7hSe4mxLozYP1zK98Z+Fug+8S4M6B4x2d64/bXYN4V9df4KUfeFblXh/Qr/ZL12s+Afe2XFvrrw3HO98NvxXH+85HHeSMZLjez6j67oOss6str5X8F+vQfiv5VvmPoH8Z4vxo4I0ReM41Z4jPC2Pcn1V52mJ8hz93QvFc2SMYK+aNnic8II/kpBGM5qxgHFfobyxeN5dwoyQs+IzxvftKhR9GXS/LiWnpr8UKdySs/KPQvYEw+S2RZk4jfDLO7VgWtLF+x/L8Bmbm0OLxtrKOc13Xv30XWUSLLuyP2DcUUur+pfENrlIfn3eyLivsQlIWc1LkaxhEq8/i9OufiMyNsE6/lc/mAydoym38O7WtwDiSJnjdqjeB6l6DexZL1hsYi8pbPe2cEzahnee9Ttw522xvtvYOdg+0rx4dHO2dtWzzY2tvc2j3eWb+ye7R/uF/KtmhzlO9fnpQWSp24Z+u9tQ3Hg1rbeO4jbypD/k8SbT+07+qE5dy2dqhtqm+VPQh5MAxrpiTWosiL0afVZHi7sf5GgFZlszJZc1qeZOmeCcWyNhpvkJdlxruqB7GGyV6PetSe1+qZdawHsWxfrs4gDSPymUtX77P1EfU+rLMG7cHy+DlJBu857Obrq9L71Ll46KyiIuhZTKLKxR1l27Wk9KUq5anYB0qX4jUDbQZvS/vzcM99f9rD4KR0QiuX0VzkLZvbgb9fn/bnIX+/Ke1hcJryt5cX4q+NUcVfG9tT/p6cv9+W9spxGsXD10r4W1ZEO9Q94BnKK3pmyDpfpLPy4JpXE7SWXfMeCax5uP7PiO9CNn4+X50V9GN/m/2zRuWfBPr+cO4oHPmcflf5AFpSc4F1MJwLPIeWRJuVrDE5X3SeGC/K2k9wvHMsWuxD9pEzLOV7k6Xr6c3/a1T+JejPP5L353Tv3c+jJOmXXwnlTffeg1jTvXcvTffe/fWc1d77pPNX1YN5tveOLLe2lA9hQm3EfTavbbweYx77Z2Ae+mfz/gbPad6S9jA4qTUR7+qU0b9vZf7y/hz5+41pD4PTlL+9vBB/ef+I/C27Py/L39A+pezep0J5FYF5Vnsf67Nhex+jrZoM9jH62tbou58J7H1YXvN3ob1PhfgzKfsU1jeL7FN4XHAatRd5w1oPl8sxrTjGeC+Cfcrvm0bywb+i5jfTzPsizBv2/h8nxUO8S/Wmiz1cLsf0qHtNke3ypcch+7ejjPxI2ivHSfEJ3yB5uASf1FhTd25Z1kWay11Zp/xWlf9/NRkc+8h/9jf4lwFZp+4j4HchWcd3JyL5/OypsWJJzTeWdeiTwbKO/XDsMyc1/qy9GV8/VmL8qXc5la5hdE9aHHn2/VG+akV5iPeay8g6FbMo8ppQehzyOoY2vo+mvXKcRq0JD59yrE3aeOL5iuOJ56t6l+AkY+0zrR4ul2Na1ZxU49CwItujSo9D9rlXb4QX5aF9l2H+5Tt6uFyO6VF+55H3hKV1Ex5ryN+Ppb1ynEbt375Qgk8hm7yymUXmYaciaFU2aKu/ngz2dwwbtDqfQv5wXy4IWjm+Upbw7IXzZsR31QDW2xyxzLZwnvY9OAY4jVrjFi/1cLkc06POqvgscb3ew/4jl/p/XxH4sc+his4rq/+sznZCZxxJMjivlL1J+ZCh/XbY/A2dL2PeNzpimV1u0tYi1rGHvaPBadRaVGZe4dg32mqUdx3m1RvGP692brd5xevVaeaC5xx9myOW53y/3xFrUmVHyD/rNLLjjzrLjodAdrwzx576gwyXnUVl29QfZDBv6g8yWVjWxqk/yOh6MG/qD3IzTf1B+unCvKk/yNQf5Hbg7/vTXjlOo3j4rgK67O3Aww+kvXKcRvHwswV4WMQvScU0LfsWGt+zi2Qf7Z7VGw/xrB7r5HgeuD9D/nO8jR+HPRGf1ZeNnafiCp6nsz+OP4Fnf2V9avDsr4xfEo6xMmd/k/JmHPvbIA/Lnv3h229lzupxrDGfIvmMlB6HdcrDdn0w7ZXjNMov5OET+m/xG/fIp0mLB4ixhXjsh+IB/h8BWYcyckZ8F5J1i8SfSG8wH6qxYknNN5Z16r0yy8M4PGVlnbW3rF8SjjF+/wX71OiO7ANcWtbxebl6s7coD/F9gDKyDvUclnWRfDDd1twsfSjtlePk6YOp3slTuiLrLbHjSyi9Duvku7ZF9brfC8i6smcQ84KeRfG7qz68Oea12g97o63GboXaFEl+Fz6Dt/rrSVTdrmtPV7EqkT8hH1Z+gwLn4p9Oe+U4b0Z8Vx2CxXP0tFiTul6H3lQuu57gmlzmDF6tyWqPkf27mv+9fqrU2VHrLs9J7D+/PtrYLTonrf56EnXMtEPjG/kT8lO137aSsF6CeVjPsqinJfKeS/2wXnTEesAR60lHrGcdsd7niOXZj89PKNajjlievL8dxr1nGz370XN8PeyI5ckvz370lBOestBz3Hu28TFHLM82PuWI5TkfX3HEesER6yFHLM9+fNURazomymG9lmOx3eXVXMGOey6zcaD2lpWkv+7lSHVXqD7jH36H9Yf2KHWRd5o43BvtjfWt9aP9vaOjzc3t7b2yfW3l1bu6av9lvG7F4fWO8QnfPsXY81mahbxlyqtBntGY2QQuE/1x3jXe2CnCf6xfzbX70l45r75U59hx+3J983z3ZfiNatWXq0n/XEd5yDEOEjc6Nzbj2Y87U/txL0W1H9s6YJ95rStr8513xGL7caQ4HaXPI0P+K2XtxxhX56R3I7tyi/K+Z6mHbXe4Ivu+lT6zDI1Z9n1DPlcoD9eeD6e9cpxG+b49eLmHy+UsxYjJFXmMT2zsBhz/ZWI3KDmszqD5rH6GaL2a/71+ulSavzx+lXwpykP77jSxGzjWyqTwaZh8yNK3pr1ynEbxqUzsBjXW+P5alq7m/6+fKnW2uQ8csTfi+RPsbvD8Q35Glm+F7wdP6nvo6lzYftuivCy9Pe2V47wifnWY944JxGL5dFqsGUcs9s251deUk94PNtr4fvDnQC+d3g/u519F8LeobLPvpveDo7R7ej/YAcvayPLC8NX/SaL3FVwPYt1H9cw41qP2b6edv6oezHtjevP/ePrbxl5kv+9t4xHGIU2If6gfsm7Ed78wD9dn7hO8n2l6E6+tvBZwUmsk+pKXieWN/GW7AftBYx7b1jAPeXNG8Vujv9ncyhUIjzeb5wQ9jKXi82bJZAnH372U0xf7vg6/cYt1qbHB/R/7zqTq/9CdyaL9f3eg/8vemZwT9Ezanb4i9yKVvZPXuhbk4T6Rk5JpeBewzJtTOP4alIf9bW3iN8v+i5xJWTs3L+gy/yXNt1hvFKj5xjaNGrVDvZVXo7IbRH+sOy+KfqsL+Xs73IMvO/7xLOCXC4x/dRbA67Z610Kt92xXOK9x6a8F5HbZuPSzgp4K5ak3RLJkOjOfi903AfPwvMRIVnOP7zZgHq5153nujTvOBM+9ovcRv/mM5l7o/Z4s8dyz8ns092LtTdTcs7rU2OD+H/d9VH7vvGj/Pxzo/7Lvnc8KeiLLrdJxqlluqTeOytjKs/R1aX8e+mFZ7LJKMphGybSTvvdVRqaFzinOazyJDwfGdNlznFlBT1l9gucTYqJd7nra3x4r/+oEyMDbwQ/I5vFJ5upZ6B+huRrJJtGdq8Z7HOvqjZtqMthXqOPV6LvvPqO5qtbPkP5h5b+H5l4kH1I59zgeRkj/iG2zVf2vYhGV7f8fON/6R2mbXRFfOJZNSm6hzY71jxXIi61/xDtf6p25T0pcoRh9Z/Frue94zHLyOF+6HeYGx7bFuYFxUDhNgm6u/Gvjxt3oyXvjPcp7dT+gmgz2Fe5/avTdzwXkvbqvoO52KXnPe7VRuvm1tJ8+K/+/T8B6H3leHql9qiU1v3he8j0szEP5xfNyFephubcGebFjTsecl3HudvbmpfULjmusk+cl9iPudWr03T+b8Hn5+Vt/Xh6rPaklNfd4Xq4QXZiH7eJ5iXNv0vfAobkX6X5ld+4Z73HsYp0897CvUCep0Xe/e0ZzT+3hcO69Oe2nz8p/keZepHu4cu5ZXUXuCsSRCRvbFaovSQb98bD+s7oroMZG6K5AS9DaSgbHJ8evaIl6WqIehfVOR6ynHLGedMR61hHrEUcsT94/NKF0veSI5dmPLzhieY7Vxx2xPPn1qCOW5xyaVDnxoiOWJ+89x5cnXc87YnnKrwcdsTzpSh2xPOeQ59z2nENPOGJN6rr9LY5Y73bEeo2wiurFF+D7p97/vic+kFCapb/vTfv/vm9IxV9Jv7ueDuJV6PMM4OD3FcKz/CKHgrGMJNxpuKHEOvlQsAV52IG8Ib4/tyCcz0PB9ro6iLCkjDW8UVslujBvLennR5L0jxnkDSZsb8bX71jo4XI5S6d9tKslaLU8bD/3C7f/av73+imT0WE8xHGLdRpt1WSQ52gsq9F3DwfGLR48zYjveNzWBD2MhfTjWGNnBiv/eE7fomhXbEOO1aXGBvf/SiS6Qv2PdRptZfv/mUD/l13gaoIe48/FKPxpdy9g3JEMJsu7BHXzZfTLkMeXk14HWDY+Le9OyCt7+GO8KHsBCcffJcrD/rY28Rr123klWTe/SvMK5WX272r+9/qp0uaOcojww+89wjIneBf3kkm7cBA9DsgSyfE6GJBFOcGri0YckAUvVxymvXKcNyO+qwawnnPEesAR62VHrIcdsZ52xHrJEetRRyzPfnzMEctzrD7viOXJr6ccsTzHxCuOWF78UuvlabBCezzHtfjK+IKXtY9OulZOWvCyomslH0ieZty+0xHLc54/6Yj1rCPWI45Ynrx/aELpesER60VHLM+1clLH6uOOWJ5j4sEJpctTD/bUUyZ1THjOxyccsSZVrr7LCUvpcaehiw+MZgWW2uOX1XNOfMDEhK0NIeBL6PfX00HcCv02ob/XRuR/icDKmNPI3Y5Pe0gwJ+pXUapYsV8gzKv53+unTEbHqIgLHKWq6I373wsYictGqaoJeiLf3naNDH/SCFZljcTGi4znJ41SxVHjsb8NiyNQXcgbFNlzXB64WF1qrk4jCvbyQhHlLuV8U3O17AKkIgreChHleFxwUvMRo8aVecUBx1iRqHHNZJC/PDZjO0kYncOcJIy2ajLYH8iTGn33VYGxibJ5RnwXGpv8ElSc18U3DtVNRj/8TXnLrUJti3NjpN2uUH3WD/gd1q9e24thoFMvrqkX2tQhKb8Eh4fXnjcGPL38PT1IX3HEes0R6wEnLDXXT4PVcMLybGOWPMfqy45YDztiPe2I5XnDwnM+2hxS+xGW83FuyxeX81b/Wb381xB8Db381xS0tigvSzx31HrSFPUorHc6Yj3liPWkI9azjliPOGJ58v6hCaXrMUcszzHxqCPW045Ykzq+TM7bXg5lOe9Vxn0DvAHtYX0I1y52JnwxsJdTsrwR4N2SoMfkPNqFzQkzqy5d7q9zEeicod9WksFo5lb+E60e5mtkc7oV7UPffkb2oUg2itIRJ9g+hE7TbB9Cm2RZ+5C1N+PDG9Z6uFyOacUx1qI87FOjW0WVsP3wWUVaUhFdQpGWikZ0+b7A2CwbVUJFmFG8mzR5fNKIHJ9y5N2SoGeUPP6fSsrjBWqHlT8CefxDdBYR6XxoYm3OeAb0sYs9XC7HtIZszriGsNyI46Tf2Z466fcIqlB9Rg/zh/e7dUFrS+RxH9ZFPXVRT0vksePzabBecsR61hHrEUesBxyxXnbEetgR62lHLM8x8agjlmc/vuqINR0T4xsTvKdHOcs6ZOxXjdTeUPkdld0bfj6gQ5a9xL4k6BmlQ35hWddZVIe08veCDvlbU5+vG6msz9fvBMaCh89XQ/yuMuR/q4e/43qwH9iHa9axHmVXCl2uOWk9yDe2P0XST0u/TsP9qV6bVT52zDfc1+ynvXKc1D7JeJGN1Qcv93C5nKXbgYcHaa8cp1E8fNelHi6Xs3Q78PAo7ZXjNIqHny3Bw1v5pal3pL1ynBQPMeDLa6s9XC5naZFod+ZhJ3IfbVaSfv4aX7hNPIYrgh9F1hmsq05Yzm1rh9oWWveQZuTBMKyZkliLIi9Gn1aT4e1Wc0rRyu3I0vXUhydZuscR602OWNZG4w3yssx4V/UgFr8sPeNYD5ZhGXnS+avqwTzbT0V+iaYTea/bnUPqRc/QfuKkej7WVU+iyvx2qG1If+i8le28ZV8+VViRdbpun9YC7cb6GwFauR1ZYrl4Up5k6R5HrDc5YrFcjLHfzBLLxTnHetSrxDH36Vliuci2gqv53+unSxuR/UP2WG/HpPR21unZLwPz2P8Z89Dn3l7c8DjjND5l+v7PlNi788uSV/O/10+XOpN2F5H7j307MK/oq6H8khH6GCBfOY06oy7Tf5PC32H7f7UOIX9Zbk0afydF/jB/kffMX+Q983dS5I+y+fM5AtsiME/tCZr0N/JNnU3MUd441lPTQ4wGHG+8zz6vr3J/Mh9Y6gyk7Eu/VUGP4h3r4uf1leS/6Mi7mqCHsZB+HMtzRJ+V/8s5fVn+32z214fj/N705v/KJ5jH+Xn1Cf6RQF+V9QmuCnqUbQXPff9aU9eJ577Yp/PUDiv/E9Cnf0P0r5Kzs4RleT+e/yayfVnGQbC6lkQb/xfoqy+saL5VE21ve2va36ZIsmWTx2uSaJs2391Ud8BxDFv7lNxkLFU3jtv7iBeRfKY3Wc4O4wXvvdQdCpS9deLFUgBrFC/enPbzItKd7s1Q25AXvI9ZFuUbAV5cCGCN4sW19NbixYLAaiaDc8vkYFVgIn+UrEnEb4at01VBK8t8LP+rIMff2OqnP6Q3xdY5ra1Fdc6G4BO217779ch6kzoTrgz53+rh77gexLqe9tfTcKwHscymamMB5wmPhXHf6ViG9mB5/Jwkg3c6/q/AWFByYDnAu5qgJ+7rqO0237nCpO5cVSgP47yxHQEflGA7wh2Qh+siJ2UPMF5kPP/yu3q4XI7bgePP2tRMBvl71g+HGC1FHw5ZFe1Ikt7YtO/+Q2Bslo2RoB4O4bssWcI9w+/TnqEOdPK6jmOL9z5V8BX9r1oaszoEk+88xplH4Xt3LUEP36urU7usD2eSsJ5t5dXjPauBeleo3iz/p4kGdb8a660TDdbOuSHlm0SDlV8GGj49pH+RD0iX6cUhmpdL0rxcgOa1AM2tAM04zrnvcAy3AuVZb2J8ftgJ26DGEs87K38ntJH3sU1BM+rtvI+NJEM3Q/KsyGNXWB55bu1TejhjqbpxHPI+NtIDWZssO4bxgmOzronyKDtaxIuVANYoXvA+Ns6jUz1erI3gxUWi/6IovxbgxWoAaxQvrqW3Fi+aAkvp08gDJU8S8RvUHbB8G3SDrVY/Lv8Gx+0S5VnZXcDrCGyWwVkK2a5YR8Lfoo6k9mlm1+a9FY471ImUPSF0jtEgHtjv/gS0+4+3+mlQ50u4768Q1mz+L/vugqCL634j1H3tlHVnSdlYh/kT4m/R5s13l75OrIuRbRrdOazu9WCf8rquxqm6g6POMNlOhb/juy6xbTnqDEzdLSh7BvZ26MvT3qGaE/Qo3vFdxHHHimXeFbWDvduRd0uCnhCW8sXj9mJ5FaduZCD9OfrbBDITtkblTMBY2doQAltD8Ic58lXoe17UWdAN+00rgB9y/MwSC0910Q4virKx+OGA8BzHAQ8KkbIHPCw81eKqDjxYeEZqd/RgQE8HBEDZgDZzgp5JM36ygROVYTZwojLNDvBo/CzrbIbGzzKPDuD4W6U87O/Qi8pslD6vhtGPBMZtWcPosqBH8Y4X/XG/Rs68K/oa9Z9x5N2SoCeEpQ5P1DoYWvSt/MWC5UcqCQv0NysJ1hBe7M0SYmX5tI8thmuEww0ZpkzwyTD+Nkk0rfYdC6Aivy2KjXnq1LSo0mG/9Yxw9knYhf5ES5f53oB1FE+WVXvYChbp9DPowYCKD481ZUVXngNKuWEsVTf2N1tHI53QbIbahrwoIohUdFalBPKCgDzkxXTcJ+BGW9kT8B8KLAiq7/G70GLaCPCOF9PYp3rqdK0V4B0qKSuCd/bdjznyLuRdprBCi6k6HQktpqsFy596MR22c+bFFHcaPCmxga0hDTmPiykuVt4Lq4VyrlOd9pur+d/rJVNna2/nYG+n3b7SaR912lvsapsldLX1rn+7s93e3d3bPdg+OL7SOdgfVX/W/1/MR3Dkq7jrkcNTBU3KKmz0aa+zYl11wnJuW+m3w1tUnnkwDGu+JNakXsmPcXU4S+YO1EgG+XXaelRIN3WMwVdAxh3ujUP/FQ339usBZaFs6L+qoEddR0N3rn/a0nUO21xVqB1W/u+s9DD/BR1d4ljgK3RIMx+vqJCBkcNrrPNcw/7GOtlte060Eflk330h0N/qbeFQWIqqoCd0/TDu2ra1WXS9mNRwHyzP8Lcsn7K0l/bKnWYtydIzjljPOWK95Ij1rCPWi45YDzhiefL+MUcszza+7Ij1sCPW045YTzpiPeKI5dmPjzpiefLeky5PuepJ16TKwhccsTzHqiddTzhiTepa6zkfJ1V+efaj5zrkuT56yhxP3r/XEcuzjZMqoz15/6ojlqdcnVR9wlOPPnbEmlSdyXPcv+aI5TmHPHUmz73CpOqrnnLiQUesSV3TPHW5SbV1PO6I5alHTyq/PNft22Ev+rwjlqeMnlS5OtVNxqebfHOOpUJD8tnVPOTFOLvCa3ozos45aA+Wx89Z4mt835ef8amzq7KhtauCHsZC+ivEM6TPyv8tunkS50x4azsUyifu1bmt7QrVZ/xOiEdWf0PQY3TXRd5p/G52O0fb24dHx1vH60ftK1cGwicarfwdjoXsnwpHp87r4l6129owv7SZtIe/BHzN0izk1SmvBnn4TPlloj9OOMCtjSL8x/pbojyH4jjpU444bz2wKifEWk365wDKiUYyXNZgSEjzA/gJkIfsLI7+IGoMcyiN2FeMlc8gzrsiV4xV2Dd1PZ2xVN3qyYPIV2c3Q/IFecFPbKlbbypUrworcqEkL/gSwXkLj8m8CDkwj+LFtfTW4kVNYCl/MfbFZHmSJMNlGMuoXwK/rN9cGU4P+2Wpp0ybgXon5dr7ScM//lpAty0b/lGF6ig65tEX6UaZtPd75HWWZiHPkdcbWT2/tdKjY4B3aX8eXjqYS/t5EQrTVTaUImKxzoJ7iEXKw72PtQPnA88xpOckt+TNp9Lbr/1ga3v/oLO19wfa9Y1+GuXXbheB5lNoV9r/myXMozKIZ1gLkLdUAGspgLUYwKoXxArVjbTOEv6F/O+5IfiLVL6R/z0L5ecFPTXC//9A/i6u9pcxzH8PZf4jzLsbZdNeHdx3y5gnyuP84fLW1qzOhZyu7noF9Xivs8PoxrFg9SseVIb8j1j4ndVVJyzntrU9+ySEtVwQy/iJ/Zt9vuNs+nqD22D7IGwn1t+V2XHo6Y69ZhrmqdXvMfasrrMae6ptobGH5XnsKaxmQSzjpxpvjTg82GS5ngzhAdaPf6Ncx/XPfssy+ytW+3EUv3ANs7Woe9cR8uqUtwJ5F4je1XSQXsRqEr2rRK+tvWrMt0T9y1Q/1qXq53V5RZRfEeWzsXI38XRO/Bb713g6vcOn943qf8PiPKtreocvSrtv+zt8ke9t7aqw5Zb4vAZ5oc5HuA/QhoV7Pk4z9De2N6PrzQXC2qu7b2ybUeMl8vgtzV++j1YXPCnKQ2tTVvdeiacBlE1AzTleJ3jvz7JJYfAdNnWWYOV5vVLhJA2vRThZ4hCyVVFHC2jhdrYEzWzj4+iGmKfOtlUbZhLdBkVnNUDnjKCT25Cl6+nN/9H2gbof759Nv0BdSunLNSq/B/upg/wznwdifVm550gPXgBaYuy7+mw+RA/Wr2xE9jkyrZshWucFrYrHZdeq+bT/u0VRN/Jpnurus0tRHu5rZ6ke01txrCGW0cH2ow+Q3o42Lft9S9SPfcZ1qfqL2NOU/S0b24/nNHafSYS6PXVHnEfDZOh5lFsmz2PEQtnY3d2+srG/3tk5PDg+7GyWiYWi+FYJ8I15qs6QzvuTnX8mZ6DHk53zgh7ml9WP4+UdaX/ZhUDZD1LZeqDsh6jsUqDsR6jshUDZj+Zl1T7V9iEZKz8B+3/sL4xFofQ2jqnxPbA+fxLkhTq/5PGM54Xsl7Q8gh4Of2/lvx/GTJGggsgf9hNpQX0x9qejggS2iCctUb4peKKei2MsVXcoqGCkaKtdXqyM4AU/oaYCpmHQOH5+phXAWhnBi0l4cgV5UfaZkRXiRejJlVG8uJb28+KOyLy4OIIXdxD9d4jyFwO8WBNY6hy+MuR/q4e/4/VHBa5U0bNPW4+K7swRtT3qQZ5yNOw7HOvB/l2DMtnfl0Se/c3lE/EbXGOw/GdgTfuKtf4ypit8Dsr8vdX+ug1nLhkue9jf+9cB739b7S9jesSvQZmfpzKmP/xjKPMLVMb0hl+FMp8dQrvSLb4t7c+zsr8E+23msae+qZ4Rv5Ro+m2MKN4nlMftydJ70pv/s98N/i7j3/+5Orzc2gSV4zwOQpule9Kb/yve8QsdCms1gLU6BCs0V1vwmWXMqPayXmjzOCvzeRqvcZ6/bnfvLuAYTagdl6HuBuW9DvJWKO9OyLtIeXdBHspPTsr+a7zI5NIHyd6WCCyla1YoD/uR962sVyIGP/PHY8x0VCtvfEY/IyzDstfK/w7sGdL8cyvpbwu2sSXo5VcfQvxQbVb6SCiAM+sxuO5fpDxcq4fpS8P2WbxGdn+fE4r7LOVHaX2flXs3ySzUE2bz39icxHkRQ6/EOTkjeHOZ+HRZlMd1jv0g8ffWp4o/dwT4sxjgT+wnMFuivYuiTTxXsTzO+ybxR43zCv2NWOo5UMXPrh09GeQnYszmvxknP9WrMUX5iXG8+ff8vKryg0Yeh/jZtYUng/xEjNn8N5FftgnysxHgwajnann+KnlaZE1QdxjUeoHPT5puEuovdYeHbXz4W7Txedu5j/b3D/f2D7a3rxzsH2wcHJ91zPHD3U5nfe9w66CzsXu0dbQ9jTk+2Kapv9JwrKm/0mT5K93KMcevg548jpjj967pOsvGHH/9xR7mm3PMaczxwbgI9wf6+5zHHF8vul5MY44P5k1jjpfDmsYcH18bpzHHy2FNY46Pj65pzPFyWNOY47fGWjuNOT6+dWgac3x8bZzGHB+fXJ3GHB9fG6cxx8enM01jjpfDmsYcH99YncYcvzXW7accsTzlxCuOWNN43LfGun07xOP+/cC5ziTE474zP5+LHI97U/mmGX2R43EXfvN2HPG4tw6OtraPrhzvb25sH65vHVYI32jl73AsZP8mIx535/h8x+PuHBfhP9bfEuWn8bhvJhWP+0tyeTONxz2Nx428OE087mvprcWLmsBS/kOeMajXwU/n2sXh9HjFoI4jv3v6nPEN9SHF82oy2GfYX3wHbhfk120eg7qtYlD38S7tz0O/77m0nxfog10nPhWNG2114xjmeXGDV8ng2lI0bvS9oM9zDA5cq1nfiuTLtM7tmxXtQ9qqyeCcRt6wXHhLYLzPwO9mxHeh/QvrNTOC/gpgXUv76bPy99P+JZb/n7pDWhvSBuRHVbRB+T8yP2ojsN5KWDMBuuZGYH0DYSl53xC/M/oj+8Z3KlSftQO/w/rriR5vV33oaRfla5V4p+wJSo5V6PNJbSxqXJ0Gi32HkU773bD4RExHls67bHzCUTYqWdAQv6sM+T9JBueEqkf1QyguV+x7LzwusY+wTvajx3GJ45fjLTwf6KOyfvRK9i8muv+u+vBnR92lsMTxPtXYQT3yjWl/nrJZqHiqKEc5zdDfyIuMrt8tEU9VzYHpPazBcVlGBmBd03tYUdo9vYeVDLaV9z/n9R7WDziuH1VBz6h7WJ8aso8tew/r+Tt6mH+F7Ds4Fm73e1g/Gujvc34Pa6PoejG9hzWYN72HVQ5reg9rfG2c3sMqhzW9hzU+uqb3sMphTe9h3Rpr7fQe1vjWoek9rPG1cXoPa3xydXoPa3xtnN7DGp/ONL2HVQ5reg9rfGN1eg/r1li3bwcbjOccmlRZONUnxqdP3A73w96dn8tN6v2wD+b0Rb4ftjXG+2FbFarP+J0Qj8ZxP2x3r3PUaa8fHu8d73e2DrcqhG+08nc4FrJ/k3E/bKt9vu+HbbWL8B/rn94PGy5r1P2wD4M8nN4PG4416n4Ivz03vR82vR/GcyhJhs9bnpffCf5D/+Mdw+m53e+H/bmAPje9HzY598NsDPO8uMGrZHBtKXo/7FPgP8Z3IG5Ff7kfCYx3D385xip6P8zK/xjtX2L5jav7YXx3Cum1z5HvwOxUqL4k0Xsbq79OtHrLhNDYQBqrxLs5QSvP/yzZ+4EVkVdkDz3FOj1WyMe1yHhU9SgfZ3U/6o3pzf8zun6OZLz9Dn2n8bfX0l4+lv8s6D6/QLqPmkNLI/J5zakOoYd1+HHcSUGZvED0L4jyuN7yfmYugKXqDsW7OG97O9Z91d5O6Wq4TmRpNo3S7p2Mnk++vkcH86CWFm9vkhSzPyEPra9bVJ7XUebP4hj5UwnwR+mTSr6F5K66O6R03gXKU7KyImhQMof13BmBhbL1drnr+/8E9Fzkn+Ipr2UVQc/0rm+vzpPe9f29QB9N7/r2Uqy7vj8K8pHLWSpz1zemHpmle9N+mm7FObGWPzYca06M0sEvX9J1FtXBrfzdl3qYd+Wfi+rgys7BfE+SYuv0KJ3X2lJE51VY1UDdSiddCNSNdPG5Ktt5kc55qndO0FMTWEr/4HNoNeaK6h9oZ7Nx5f5G7UZ7d317Y6vdOTxqH3c2z/qN2o29/Z29jf29K4dXNtu7W5tl3qidystenSeVl1fGLC+/1kle/kmQl1cd5SXKgpDNIiTjQvJ1lIzj8yUl40J14z7rPqK1XpLWUXvaBaIV6VsqgBVaC0adGTKfQuen3nVzu+dE3VyW5yH3B+ZxjAa1zqg1S51FsjwZtsbxPfPZAJbXGve1kda4KzvHB3uHVw6PdzaP94739sqsMapP7XdqTlYpL6SvKHtZZHvfOs8BXE+Uf1c1GRzvOH9q9N1xYD0pex6sZF1kn4ZDdTZryfKWoW4+82pC3jzltSAPxwUnte9E36/PtHq4XI5pxTG2THnYp0Z3ZP/Szln5pIzat1wgXpw0NhHWVU8GZUWMs8RR63zIr4Jlt8JaKIk1qTHEFK3cjixdT314kqV7JhTL2hgzRlaWeK1fcKxH6cVnFVvMfmNyg/0usnRAZRuBskdUdjlQ9v15WRUbD/dS/z3tpeqAqc5YFqlOK//9sJf687SXUmcLah/F53+h8xvsJz4XUz7+yt6P+zpuz18EnYR9bK0M7u9CfqWr1P6r+d/rp0tdGbcCdau9BtaPf2P5FShj7bN+aAWwVN24J+Ez6bXIvFgdwYs1on9NlMc2rhAv6gKL/Rt/CubCj1zqL2Pz+yehzI9SGZvXfwfK/LVLPTqRLjX3P5D251nZv5ljRB6X0s8L+wVpsr1CC/Lflt78X+nH9ruMJ3/3UnHMt6T+mB9P/TG/Pi2HuZgMzlG/vmy3G8ngPLdkedjfvH/B+ca61kXI47X5DshrUt4lyON9Pya1JzI+ZbTMzfVwuRx+ZpkW8r3h9RZ/i+ttLHsB69e4l4thE7C2oU1A+axXk8H+Qp7y+vuPAjaBsjrtvKCH+VVEN1sIlGXdrB4oW1Q3+xzpZqh7Kd1sjuq08v8c1pF/ln9GWzbbhBPIG3bujetdUV3Ryn8+oFuhDUnNM76/hHaVGPoEygalTywTT5ZF+YbgSZN4orBU3Wj7Yt0qzhrQ40VzBC9aRH9LlMf1rEG8WA5gNUfw4rzr3E3iRUjnHsWLa2k/L86bzs28WBFYbI+0fPW/1cPf8TqBNLOtr+lYD84Btq+uOtaDPG1RPWuO9WD/sm57UeTZ31w+Eb9h24mVr+eXmbM17R2X+8vYmv4lUOYClbG1/C4os0xlbA2/DGVadoma6CmzD7uYYyyK9sbeh11MNP1F9mEXxO8ynnzp5eKYb4mA+fEImF9fEjPyPqzDeyZMas+0RHm4Z+L91GXIW6W810Eey947IY/l2F2Qh7zjNGqP9tbVHi6Xs6R0qQrloW7B+yfuM8zDtWeJ8tRZWDMZlPGrlIfzctjaOEynZnlo5bfyTkSdWt0FMD5k5d4NvOU1YTb/jY1rHFsxdAgc1zOCN3cQn+4Q5VGO8h1X/L31qeLPWoA/iwH+rETmj9pXLIo28bqK5XEONIg/apxX6G/EwvmIto8bZdLe741GxU/EmM1/M05+4j6tLD85lonS7SvJ4DhifTlLIX7ad4qfiDGb/2ZR0HBW/FwK8EDtYZCfPH+VPFVnQRcoD/VK7iPUbW0NQH0y1F9FbX+LgPu5yLa/28FX6BDWuamvUH/e1Fdo6it0ijT1FQq0G+uf+goNb+PUV2h0PSf1FdpP+2kadm70Z0l/OalPz/eC3eu788/n2afnL4g9MvOoqE9P7P2JOjvB9bTI/gT1AfbpaQawVN0hn57zdtbSIl7UBRafWf51mAs/SHbEFv0G5+yH0/48K/sjZDeMNJ6kDZjt8EaT6eK4NzM7aEJ5fJ6PZVnHxTqUjmuYGW//9hDectksfV06Htoi76GO1ZlTQjTi3OZ9AI4l1llwPPAah/a2BuWhbQ35w0ntLYxP4/K5+bMg96f77pup7L77s5H33SEs1PH4PqLRNyfKK93Gyv8KtOfT+WQq4k817pib7E9VNObmrwX6z8OfqkJ5M4neO/LdV/RZUnZt9s+y8t+eHz5lf/9T0k15D4p5SC/P6VCs1fM6b3/Tcd6qecVjCPVn3Ldxvy9BvWrMcflu7KpEz4th917/NbT/p/N5Hts38qxsUOpcBtvENqiT7smxrnoyOMdi2KBU20JvYqi4xCGspZJYkeV/MKYe8prP9TlmifrfsPg7bncoNlIsPyvWXZVtJPuu+rreb3i+h2x17BP0OKwbtRwzFCdBrRtn0QeKNzFsati2mDa1LFl/qn03r9PK71j5ebDujfs6w4jsK9xdw22/hmuUOnPnfTZ+zlKNvrszH6dqDVc+wvhdaO41iD+RzojXx2kzU3YudQZXdj20uurJ4LiLsR6qtoX6Ge0DfPausFolscbpcx6y3cTw080Sr4dsl1L/F60Hecg+8SF5FstmaLw3WxHKsxVBa5XK4+cs1ei7zcjybJQusfM6XecwXYLv8lj5rwFd4mtIl8C1jX0D1Z2JW3n9+pNn1N9Knsdd27Y2i64XVv9ZrRchuYl8VfZk+y3Lpyztpb1yp1lLsvSMI9ZzjlgvOWI964j1oiPWA45Ynrx/zBHLs40vO2I97Ij1tCPWk45Yjzhiefbjo45Ynrz3pMtTrnrSNamy8AVHLM+x6knXE45Yk7rWes7HSZVfnv3ouQ55ro+eMseT9+91xPJs46TKaE/ev+qI5SlXJ1Wf8NSjjx2xJlVn8hz3rzliec4hT53Jc68wqfqqp5x40BFrUtc0T11uUm0djztieerRk8ovz3X7dtiLPu+I5SmjJ1WuTnWT8ekm35xjqTslfHbVgrwYZ1d2Ljkr2oG0Vak8fs4Sx+r57cDZlTpjaQZ4V+Q+/DD/T74X1o3hkp9Bxj0T3toOxaeKG2dsa7tC9Rm/8TusX91nMLrrIu8074Tsdo62tw+PjreO14/aV650x6OKcYbfVaH+7J+Kf6LO6+LGY9naUDEnLgJfszQLeWuUV4M8ozGbO5eJ/jgxr7Y2ivAf62+J8ux7XLQvW6Iejkl0Gqz6CbFWk/45gHJC+dSx7I50Vh30O2gIHpb1O1jLZaOS3ep9pguCd0p2h+72o5/J5Tt1nUX9TKz8l4GfyV35Z3V/iX1WlS+p8nUN3cPlu7ZVUZ/lzYj6st/bXQ1u11dA//B9XOxz5lWW+D7ueYtvY+1Tfj+MperGsXre48AuEy8uCKzIPlCl71wuUh7qXey3jXKXfa2L3rksG0MP47z9zOUeLpfDz8PkonoPkGUf/hZlH/vM/deBOY/+Zapujv183u6dh+IdF3lXQ8Vci6tP9XixNoIXF4n+UfH8WP9YDWCN4gWvBeOIVYi8KBurkHkRins4ihfX0luLFw2Bpfbw7PvJ8iQRv+H3k6z8/aBvPXLncHrYr1fFL28G6o0d/5d5jvq14nk1Gewz7K8affeugH6txil+x/r1sqCn6JhHX9YbZdLe75HXWZqFPEdeb2Q8+C2IuzrAu7Q/D56ESebSfl5grF5eFy4JXlwSvFBYa5Sn7sawnQ/bgfOB5xjSU1RfaADuTom7eLgP5pi8qCdZubPax9pYK7qPxTGPOj6/A/Ws4z429MaxwlJ3btU8Q15naRbyHHndUfOsj3dpfx7q8zzPMO962s+LsrZdzLvHEetNjljWRstTd8WVvYPfJQnFyctSaM6jXaAamPOVpD+vaNyGGcpTb41PSiwPjDnG8zEUy+O/C8gDFTMHv+PxsSDombQYqLyfbkIe76eL7tExfh0ntZ/G+KhfflcPl8txO3D8sZ0jFG8kEu/Xed3GsYl1crwRXMew3aw7/6XA2PSINzLK5vqDpJ9gHABlc+W4IVb+h2EP8GV5fytZxHIK+5TlVCiuxLj722gr299/I9DfZeNuLAp6KoI+1Y/2+azuSip+qrjNzK+fovHZAv6oNYLjkzVFva1AvU2qF+PgcFuSRPOW+8LaOTekfINosPL/K9DwaaJBxQNSNp4QzfWSNNcL0Pz3AzQvB2hWcsL6AsfwcqA86yeM30w0T1DWqdhhPCZ/MWCTVXGdcA6zTbZJNF/N/14/XSr8DgbzROnLiufNZDR/Vd0qNt04412UfRMEZQfrJ80AVmsEL66l/bwYxxt0yIuyb9CF7HfsZxIrBtP1tFeG+ypk70Q5ouLe8vz/TdB1/sWd/bjD/I6yz7OUZ2X/JeD99p3D23dvWq5ck+qxdmMezl/Wv1g2IUbojDJLHHcS3wBTZ5rMayv/b8R6os7dQ++YNChvRtSLPFV847iYlvf/Q9/9zhBdOknK7/sXgK4fDPgwcH+GYrCqfb9hqJjnSq/iGOG/L9ZCZe8yutQ7PLjfns1/M86YgGVtfkpnaInyNi9D9kDFn/kAf2LH1xv1bkeRt69Qvi4Tf/D3s4Q1K7BQpwi9+xQab9im2fw3Z8XP+gj+FOEnjk/jkeJnjbBqAgt5HOKn0aj4iRiz+W8ix4ENvkGC44h9ABX/cTzzOwpY3vSLsn5f3Ecoe/G9K7ObZH2IsbcXk0G577lHn9T3YnC8Vob8b1icZ3XVk8G54Ni2dqhtSD/rrTjmeD1XWPMlsSK/G9Xt07lAu7F+ZYcs06eq3cqmqdbM09aj5MNiEnVc7aq1yhLvo5AXyo7OfTBszeWk7PrW3oyuNxew6zdFPXOUp8ZL5PFbmr+zlFcXPCnKQ9RN9kqcjSjfZTXneJ1Q66LSBRCD3ysyGioCg9cr1ef2O9XnvE+ZE3VEHg9dO7WNB7QXK/lapfI8lvh85O15Pyu7v5KhoffjhvX1MKxZgaV0POR1lmYhz3PNVD4JfbxL+/PQtsE+CWpPW1Y/s7qLjGO+VxRJ/q9zf+J4VPO0mgyOBxwL/B7EewPjUe271BgK6bpK5lQAg3Xa5OS8a/MXVt888UatQWoPUxlCo3MfdypUn/ENv8P664mWzVd96GmfRIawDOR5dh7eYjSskI4aeY+0VXQsWP2T9rap0pnYFoZynPuvrK/DpGOh/Im9J2HddNaxnpDMPKu93IJjPeoN1pAvSmQ7VvT3jP58YJ0vO7bVXcYG8MQS+hd93139ddaAzhn6rZXBdlj5v31XD/P7yb9IyccK0TxMD7ExUKPyn8rrWBQ0eva/em/S6loU7XOs+4rSry2d1I5geXhuwnJjmM8HJ7V3tnLZ7w7v7uFyOUtqjPDdXqW/Wt6MaIeSwRXKU3sGlnWIr+STnRdVksE5NkPYWTL7vWqz/Rbf+bW59eMgI+wM76zs94uBNnH9+DeWD/kzqzculbxnm5Tyg1aymrGzxDLFyn+GZEosH0YlU/gcsw5tULL/WtrfBiv/GyCHf5bkcOj+vDqzUn6e3A9IN/dDyH9LtaHrvyXGu7KHoM9plmYhz3MfmNHxydf36EBab9Cf9rdb+VWp+11WfpRPl/FM3XMqcoaIPGe/DzzfUuflqA9g+X8MfcR+Hyh32bdDzUkld/mcD8cW3sv+f2nOsqy5mv+9frq0p3whLKn1ltdinCe8FuN6y2vxMH9KTmotNl6UXYs95ASeFd6gLx2kaxzztu8eCtTLc3RGlOd5O2qem56t7oTy+MY5w75SReZMlu6j+pTfAs6ZkMyYEbi8l8Df4l4CbbRcB64FyM8ZyMfy/wHWs2vUlzH2t0wX9o3aE2XpWtpPB/sl437/HVR2MVD2g1S2Hij7ISq7FCj7ESp7IVD2o3lZPvNHPmW8m3t97/thaz/b4rFOK3/99T3Mxfyz0hWtL9UdlmFnLkiL8u1lG/xyXr/yI1d6sfInsPKj9GL22w7FUbLxuHaG9LHePi/oG+OZlZT1yAOW9aH2Kv6M8vNiW+owXYD5M861sBbgT1F/mRA/+SwY+YO84307yvBh+m0NMN8ItLPstfJfBXJl+/Uas5Jo+cA6s9E7BzTcF6DByr8B5muabwIjn2GVtiFVKE+9W6t02hrloU5b1ofFeJHx6XcL+F+oPq1SHaP6FPUYLo94PK6+BvrU9kGRz563lf5jic8yVJ+qdqnzoRnKU7p/0T5Fv6SifWr6XjYGbd3hu/v42fbFSufI0rW0l4/l7w2so62kv11IQ5ZYDo66k2T0hO4k8Tr/lgB9KyXpGxXTi9d5pH2V6LNx9BjI1m98fQ8faZkT7eU7JTb2HgG8b3p9fxkbg++FMg9QGZNZx1DmQSpjsusQyrxrCO1KL/62tD/Pyj6UY0S+MydtiDgWkCY7k1K8T5LBucNj6D3pzf+VbLffZfx75vXDyy1PUDnOY3mQJTtnVrxD3gzDagWwWkOwKslgvyXJ8L4r2l7eYz0LY/4TtMdqEC2Yp+6Dh2Qvtu1a2k+LlX+hpOwN2VE974MWjdFVVK6y3FdyVe0P7Ltx7A/64sinxdur+DPqfirfO8L7rTjemT8rY+RP6Ayg7NhV/FR6QysZ5F2T8nBN4PmsbJzqzhbP67lEn02gnQfLf7fQi5WtrQH4nxhiQ0Jaa4JWZZtC3DmQcTe+SwE37f0mS/P533buxuVxH4Dl/wLI1O+HcXKjrKgvK/dXA+UqQ/6/gSG+m037v1tMB8vPpIPlre56Okij5S1BHo7zLF3I/0Z+IZbRUaPyP0R9sgC/sd+3RP0LVH8f3eI7nGeMNSO+Q5vmp3Iau/Y3qNvbl+9GnYSP3zFtfxX2Rp729h+DcdyEMyOct4ah/k+Ss7O3W3kV0wPLW6wV+03I5v6tedlR9u6fJFml7meH7N3dc5y7e5g/VcLerXwjKvQ30hLyHbDynwnoYnivQmEaj628speG7Iej7m2H/D/Yd0LVrc7Jiq4zZc79sH7lI8H7BBvrvxjgvTd9fNYwL+gL1R2yozVL0hqKU4H1qz1ISC88o3OHHaUXIg9wzRjVXsWfUXsatp0Pi9vP/GmOkT+hc4fQfVXFn1F3ltl3Ro31SRs/cwH+jIpVWuTcKvRWiornqfiD95jPmj+e51aj4mew/5LyQ/I4t2KdxMr/Nuhjlbs1ZiXRa3LZcyumwcr/7tmfW5X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", 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", "file_map": { "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "4": { - "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn(T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", + "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", "path": "std/array/quicksort.nr" }, "5": { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_false_inliner_9223372036854775807.snap b/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_false_inliner_9223372036854775807.snap index 0fdf6812398..961ccdff05b 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_false_inliner_9223372036854775807.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_false_inliner_9223372036854775807.snap @@ -219,15 +219,15 @@ expression: artifact } } }, - "bytecode": 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", "file_map": { "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "4": { - "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn(T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", + "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", "path": "std/array/quicksort.nr" }, "5": { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_true_inliner_-9223372036854775808.snap b/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_true_inliner_-9223372036854775808.snap index 07e635bd67e..eeb35b4f10b 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_true_inliner_-9223372036854775808.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_true_inliner_-9223372036854775808.snap @@ -231,15 +231,15 @@ expression: artifact } } }, - "bytecode": 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", 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", 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", "file_map": { "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "4": { - "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn(T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", + "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", "path": "std/array/quicksort.nr" }, "5": { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_true_inliner_0.snap b/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_true_inliner_0.snap index 0494d0fab96..daefa81fc65 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_true_inliner_0.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_true_inliner_0.snap @@ -223,15 +223,15 @@ expression: artifact } } }, - "bytecode": 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", 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", "file_map": { "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "4": { - "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn(T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", + "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", "path": "std/array/quicksort.nr" }, "5": { diff --git a/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_true_inliner_9223372036854775807.snap b/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_true_inliner_9223372036854775807.snap index 0fdf6812398..961ccdff05b 100644 --- a/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_true_inliner_9223372036854775807.snap +++ b/tooling/nargo_cli/tests/snapshots/execution_success/uhashmap/execute__tests__force_brillig_true_inliner_9223372036854775807.snap @@ -219,15 +219,15 @@ expression: artifact } } }, - "bytecode": 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", "file_map": { "3": { - "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn(T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", + "source": "use crate::cmp::{Eq, Ord};\nuse crate::convert::From;\nuse crate::runtime::is_unconstrained;\n\nmod check_shuffle;\nmod quicksort;\n\nimpl [T; N] {\n /// Returns the length of this array.\n ///\n /// ```noir\n /// fn len(self) -> Field\n /// ```\n ///\n /// example\n ///\n /// ```noir\n /// fn main() {\n /// let array = [42, 42];\n /// assert(array.len() == 2);\n /// }\n /// ```\n #[builtin(array_len)]\n pub fn len(self) -> u32 {}\n\n /// Returns this array as a slice.\n ///\n /// ```noir\n /// let array = [1, 2];\n /// let slice = array.as_slice();\n /// assert_eq(slice, &[1, 2]);\n /// ```\n #[builtin(as_slice)]\n pub fn as_slice(self) -> [T] {}\n\n /// Applies a function to each element of this array, returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.map(|a| a * 2);\n /// assert_eq(b, [2, 4, 6]);\n /// ```\n pub fn map(self, f: fn[Env](T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array along with its index,\n /// returning a new array containing the mapped elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let b = a.mapi(|i, a| i + a * 2);\n /// assert_eq(b, [2, 5, 8]);\n /// ```\n pub fn mapi(self, f: fn[Env](u32, T) -> U) -> [U; N] {\n let uninitialized = crate::mem::zeroed();\n let mut ret = [uninitialized; N];\n\n for i in 0..self.len() {\n ret[i] = f(i, self[i]);\n }\n\n ret\n }\n\n /// Applies a function to each element of this array.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// let mut i = 0;\n /// a.for_each(|x| {\n /// b[i] = x;\n /// i += 1;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_each(self, f: fn[Env](T) -> ()) {\n for i in 0..self.len() {\n f(self[i]);\n }\n }\n\n /// Applies a function to each element of this array along with its index.\n ///\n /// Example:\n ///\n /// ```rust\n /// let a = [1, 2, 3];\n /// let mut b = [0; 3];\n /// a.for_eachi(|i, x| {\n /// b[i] = x;\n /// });\n /// assert_eq(a, b);\n /// ```\n pub fn for_eachi(self, f: fn[Env](u32, T) -> ()) {\n for i in 0..self.len() {\n f(i, self[i]);\n }\n }\n\n /// Applies a function to each element of the array, returning the final accumulated value. The first\n /// parameter is the initial value.\n ///\n /// This is a left fold, so the given function will be applied to the accumulator and first element of\n /// the array, then the second, and so on. For a given call the expected result would be equivalent to:\n ///\n /// ```rust\n /// let a1 = [1];\n /// let a2 = [1, 2];\n /// let a3 = [1, 2, 3];\n ///\n /// let f = |a, b| a - b;\n /// a1.fold(10, f); //=> f(10, 1)\n /// a2.fold(10, f); //=> f(f(10, 1), 2)\n /// a3.fold(10, f); //=> f(f(f(10, 1), 2), 3)\n ///\n /// assert_eq(a3.fold(10, f), 10 - 1 - 2 - 3);\n /// ```\n pub fn fold(self, mut accumulator: U, f: fn[Env](U, T) -> U) -> U {\n for elem in self {\n accumulator = f(accumulator, elem);\n }\n accumulator\n }\n\n /// Same as fold, but uses the first element as the starting element.\n ///\n /// Requires the input array to be non-empty.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [1, 2, 3, 4];\n /// let reduced = arr.reduce(|a, b| a + b);\n /// assert(reduced == 10);\n /// }\n /// ```\n pub fn reduce(self, f: fn[Env](T, T) -> T) -> T {\n let mut accumulator = self[0];\n for i in 1..self.len() {\n accumulator = f(accumulator, self[i]);\n }\n accumulator\n }\n\n /// Returns true if all the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 2];\n /// let all = arr.all(|a| a == 2);\n /// assert(all);\n /// }\n /// ```\n pub fn all(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = true;\n for elem in self {\n ret &= predicate(elem);\n }\n ret\n }\n\n /// Returns true if any of the elements in this array satisfy the given predicate.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr = [2, 2, 2, 2, 5];\n /// let any = arr.any(|a| a == 5);\n /// assert(any);\n /// }\n /// ```\n pub fn any(self, predicate: fn[Env](T) -> bool) -> bool {\n let mut ret = false;\n for elem in self {\n ret |= predicate(elem);\n }\n ret\n }\n\n /// Concatenates this array with another array.\n ///\n /// Example:\n ///\n /// ```noir\n /// fn main() {\n /// let arr1 = [1, 2, 3, 4];\n /// let arr2 = [6, 7, 8, 9, 10, 11];\n /// let concatenated_arr = arr1.concat(arr2);\n /// assert(concatenated_arr == [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n /// }\n /// ```\n pub fn concat(self, array2: [T; M]) -> [T; N + M] {\n let mut result = [crate::mem::zeroed(); N + M];\n for i in 0..N {\n result[i] = self[i];\n }\n for i in 0..M {\n result[i + N] = array2[i];\n }\n result\n }\n}\n\nimpl [T; N]\nwhere\n T: Ord + Eq,\n{\n /// Returns a new sorted array. The original array remains untouched. Notice that this function will\n /// only work for arrays of fields or integers, not for any arbitrary type. This is because the sorting\n /// logic it uses internally is optimized specifically for these values. If you need a sort function to\n /// sort any type, you should use the `sort_via` function.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32];\n /// let sorted = arr.sort();\n /// assert(sorted == [32, 42]);\n /// }\n /// ```\n pub fn sort(self) -> Self {\n self.sort_via(|a, b| a <= b)\n }\n}\n\nimpl [T; N]\nwhere\n T: Eq,\n{\n /// Returns a new sorted array by sorting it with a custom comparison function.\n /// The original array remains untouched.\n /// The ordering function must return true if the first argument should be sorted to be before the second argument or is equal to the second argument.\n ///\n /// Using this method with an operator like `<` that does not return `true` for equal values will result in an assertion failure for arrays with equal elements.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let arr = [42, 32]\n /// let sorted_ascending = arr.sort_via(|a, b| a <= b);\n /// assert(sorted_ascending == [32, 42]); // verifies\n ///\n /// let sorted_descending = arr.sort_via(|a, b| a >= b);\n /// assert(sorted_descending == [32, 42]); // does not verify\n /// }\n /// ```\n pub fn sort_via(self, ordering: fn[Env](T, T) -> bool) -> Self {\n // Safety: `sorted` array is checked to be:\n // a. a permutation of `input`'s elements\n // b. satisfying the predicate `ordering`\n let sorted = unsafe { quicksort::quicksort(self, ordering) };\n\n if !is_unconstrained() {\n for i in 0..N - 1 {\n assert(\n ordering(sorted[i], sorted[i + 1]),\n \"Array has not been sorted correctly according to `ordering`.\",\n );\n }\n check_shuffle::check_shuffle(self, sorted);\n }\n sorted\n }\n}\n\nimpl [u8; N] {\n /// Converts a byte array of type `[u8; N]` to a string. Note that this performs no UTF-8 validation -\n /// the given array is interpreted as-is as a string.\n ///\n /// Example:\n ///\n /// ```rust\n /// fn main() {\n /// let hi = [104, 105].as_str_unchecked();\n /// assert_eq(hi, \"hi\");\n /// }\n /// ```\n #[builtin(array_as_str_unchecked)]\n pub fn as_str_unchecked(self) -> str {}\n}\n\nimpl From> for [u8; N] {\n /// Returns an array of the string bytes.\n fn from(s: str) -> Self {\n s.as_bytes()\n }\n}\n\nmod test {\n #[test]\n fn map_empty() {\n assert_eq([].map(|x| x + 1), []);\n }\n\n global arr_with_100_values: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2, 54,\n 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41, 19, 98,\n 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21, 43, 86, 35,\n 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15, 127, 81, 30, 8,\n 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n global expected_with_100_values: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30, 32,\n 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58, 61, 62,\n 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82, 84, 84, 86,\n 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114, 114, 116, 118,\n 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n fn sort_u32(a: u32, b: u32) -> bool {\n a <= b\n }\n\n #[test]\n fn test_sort() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort();\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort();\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_100_values_comptime() {\n let sorted = arr_with_100_values.sort();\n assert(sorted == expected_with_100_values);\n }\n\n #[test]\n fn test_sort_via() {\n let mut arr: [u32; 7] = [3, 6, 8, 10, 1, 2, 1];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 7] = [1, 1, 2, 3, 6, 8, 10];\n assert(sorted == expected);\n }\n\n #[test]\n fn test_sort_via_100_values() {\n let mut arr: [u32; 100] = [\n 42, 123, 87, 93, 48, 80, 50, 5, 104, 84, 70, 47, 119, 66, 71, 121, 3, 29, 42, 118, 2,\n 54, 89, 44, 81, 0, 26, 106, 68, 96, 84, 48, 95, 54, 45, 32, 89, 100, 109, 19, 37, 41,\n 19, 98, 53, 114, 107, 66, 6, 74, 13, 19, 105, 64, 123, 28, 44, 50, 89, 58, 123, 126, 21,\n 43, 86, 35, 21, 62, 82, 0, 108, 120, 72, 72, 62, 80, 12, 71, 70, 86, 116, 73, 38, 15,\n 127, 81, 30, 8, 125, 28, 26, 69, 114, 63, 27, 28, 61, 42, 13, 32,\n ];\n\n let sorted = arr.sort_via(sort_u32);\n\n let expected: [u32; 100] = [\n 0, 0, 2, 3, 5, 6, 8, 12, 13, 13, 15, 19, 19, 19, 21, 21, 26, 26, 27, 28, 28, 28, 29, 30,\n 32, 32, 35, 37, 38, 41, 42, 42, 42, 43, 44, 44, 45, 47, 48, 48, 50, 50, 53, 54, 54, 58,\n 61, 62, 62, 63, 64, 66, 66, 68, 69, 70, 70, 71, 71, 72, 72, 73, 74, 80, 80, 81, 81, 82,\n 84, 84, 86, 86, 87, 89, 89, 89, 93, 95, 96, 98, 100, 104, 105, 106, 107, 108, 109, 114,\n 114, 116, 118, 119, 120, 121, 123, 123, 123, 125, 126, 127,\n ];\n assert(sorted == expected);\n }\n\n #[test]\n fn mapi_empty() {\n assert_eq([].mapi(|i, x| i * x + 1), []);\n }\n\n #[test]\n fn for_each_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_each(|_x| assert(false));\n }\n\n #[test]\n fn for_eachi_empty() {\n let empty_array: [Field; 0] = [];\n empty_array.for_eachi(|_i, _x| assert(false));\n }\n\n #[test]\n fn map_example() {\n let a = [1, 2, 3];\n let b = a.map(|a| a * 2);\n assert_eq(b, [2, 4, 6]);\n }\n\n #[test]\n fn mapi_example() {\n let a = [1, 2, 3];\n let b = a.mapi(|i, a| i + a * 2);\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn for_each_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n let mut i = 0;\n let i_ref = &mut i;\n a.for_each(|x| {\n b_ref[*i_ref] = x * 2;\n *i_ref += 1;\n });\n assert_eq(b, [2, 4, 6]);\n assert_eq(i, 3);\n }\n\n #[test]\n fn for_eachi_example() {\n let a = [1, 2, 3];\n let mut b = [0, 0, 0];\n let b_ref = &mut b;\n a.for_eachi(|i, a| { b_ref[i] = i + a * 2; });\n assert_eq(b, [2, 5, 8]);\n }\n\n #[test]\n fn concat() {\n let arr1 = [1, 2, 3, 4];\n let arr2 = [6, 7, 8, 9, 10, 11];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1, 2, 3, 4, 6, 7, 8, 9, 10, 11]);\n }\n\n #[test]\n fn concat_zero_length_with_something() {\n let arr1 = [];\n let arr2 = [1];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_something_with_zero_length() {\n let arr1 = [1];\n let arr2 = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, [1]);\n }\n\n #[test]\n fn concat_zero_lengths() {\n let arr1: [Field; 0] = [];\n let arr2: [Field; 0] = [];\n let concatenated_arr = arr1.concat(arr2);\n assert_eq(concatenated_arr, []);\n }\n}\n", "path": "std/array/mod.nr" }, "4": { - "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn(T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn(T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", + "source": "unconstrained fn partition(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) -> u32 {\n let pivot = high;\n let mut i = low;\n for j in low..high {\n if (sortfn(arr[j], arr[pivot])) {\n let temp = arr[i];\n arr[i] = arr[j];\n arr[j] = temp;\n i += 1;\n }\n }\n\n let temp = arr[i];\n arr[i] = arr[pivot];\n arr[pivot] = temp;\n i\n}\n\nunconstrained fn quicksort_loop(\n arr: &mut [T; N],\n low: u32,\n high: u32,\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) {\n let mut stack: [(u32, u32)] = &[(low, high)];\n // TODO(https://github.com/noir-lang/noir_sort/issues/22): use 'loop' once it's stabilized\n for _ in 0..2 * N {\n if stack.len() == 0 {\n break;\n }\n\n let (new_stack, (new_low, new_high)) = stack.pop_back();\n stack = new_stack;\n\n if new_high < new_low + 1 {\n continue;\n }\n\n let pivot_index = partition(arr, new_low, new_high, sortfn);\n stack = stack.push_back((pivot_index + 1, new_high));\n if 0 < pivot_index {\n stack = stack.push_back((new_low, pivot_index - 1));\n }\n }\n}\n\npub unconstrained fn quicksort(\n arr: [T; N],\n sortfn: unconstrained fn[Env](T, T) -> bool,\n) -> [T; N] {\n let mut arr: [T; N] = arr;\n if arr.len() > 1 {\n quicksort_loop(&mut arr, 0, arr.len() - 1, sortfn);\n }\n arr\n}\n", "path": "std/array/quicksort.nr" }, "5": { From b099b1644bcb9e66e73227c13480359e4a32d56b Mon Sep 17 00:00:00 2001 From: Michael Klein Date: Tue, 29 Apr 2025 16:52:44 -0400 Subject: [PATCH 9/9] skip tests timing out --- tooling/debugger/ignored-noir-tests.txt | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tooling/debugger/ignored-noir-tests.txt b/tooling/debugger/ignored-noir-tests.txt index c32ecdc23f7..d049ddb8bcd 100644 --- a/tooling/debugger/ignored-noir-tests.txt +++ b/tooling/debugger/ignored-noir-tests.txt @@ -10,3 +10,5 @@ test_pow test_pow_and_show test_add test_add_and_show +test_sort +test_sort_comptime