From 252ae8814dc6a5c2f112bb2bc95b4ad9858a2975 Mon Sep 17 00:00:00 2001
From: "zly.idleness" <zly.idleness@gmail.com>
Date: Mon, 23 Sep 2024 19:07:53 +0800
Subject: [PATCH 1/4] 0923

---
 src/transformers/modeling_utils.py | 728 ++++++++++++++++++++---------
 1 file changed, 503 insertions(+), 225 deletions(-)

diff --git a/src/transformers/modeling_utils.py b/src/transformers/modeling_utils.py
index d40697666360..fac3a45244b2 100755
--- a/src/transformers/modeling_utils.py
+++ b/src/transformers/modeling_utils.py
@@ -122,7 +122,8 @@
         set_module_tensor_to_device,
     )
 
-    accelerate_version = version.parse(importlib.metadata.version("accelerate"))
+    accelerate_version = version.parse(
+        importlib.metadata.version("accelerate"))
     if accelerate_version >= version.parse("0.31"):
         from accelerate.utils.modeling import get_state_dict_from_offload
 
@@ -158,7 +159,8 @@ def is_local_dist_rank_0():
     import smdistributed.modelparallel.torch as smp
     from smdistributed.modelparallel import __version__ as SMP_VERSION
 
-    IS_SAGEMAKER_MP_POST_1_10 = version.parse(SMP_VERSION) >= version.parse("1.10")
+    IS_SAGEMAKER_MP_POST_1_10 = version.parse(
+        SMP_VERSION) >= version.parse("1.10")
 else:
     IS_SAGEMAKER_MP_POST_1_10 = False
 
@@ -219,7 +221,8 @@ def get_parameter_device(parameter: Union[nn.Module, GenerationMixin, "ModuleUti
         # For nn.DataParallel compatibility in PyTorch 1.5
 
         def find_tensor_attributes(module: nn.Module) -> List[Tuple[str, Tensor]]:
-            tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
+            tuples = [(k, v)
+                      for k, v in module.__dict__.items() if torch.is_tensor(v)]
             return tuples
 
         gen = parameter._named_members(get_members_fn=find_tensor_attributes)
@@ -237,7 +240,8 @@ def get_first_parameter_dtype(parameter: Union[nn.Module, GenerationMixin, "Modu
         # For nn.DataParallel compatibility in PyTorch > 1.5
 
         def find_tensor_attributes(module: nn.Module) -> List[Tuple[str, Tensor]]:
-            tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
+            tuples = [(k, v)
+                      for k, v in module.__dict__.items() if torch.is_tensor(v)]
             return tuples
 
         gen = parameter._named_members(get_members_fn=find_tensor_attributes)
@@ -272,7 +276,8 @@ def get_parameter_dtype(parameter: Union[nn.Module, GenerationMixin, "ModuleUtil
 
     # For nn.DataParallel compatibility in PyTorch > 1.5
     def find_tensor_attributes(module: nn.Module) -> List[Tuple[str, Tensor]]:
-        tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
+        tuples = [(k, v)
+                  for k, v in module.__dict__.items() if torch.is_tensor(v)]
         return tuples
 
     gen = parameter._named_members(get_members_fn=find_tensor_attributes)
@@ -441,7 +446,8 @@ def shard_checkpoint(
     weight_map = {}
     shards = {}
     for idx, shard in enumerate(sharded_state_dicts):
-        shard_file = weights_name.replace(".bin", f"-{idx+1:05d}-of-{len(sharded_state_dicts):05d}.bin")
+        shard_file = weights_name.replace(
+            ".bin", f"-{idx+1:05d}-of-{len(sharded_state_dicts):05d}.bin")
         shard_file = shard_file.replace(
             ".safetensors", f"-{idx + 1:05d}-of-{len(sharded_state_dicts):05d}.safetensors"
         )
@@ -487,9 +493,11 @@ def load_sharded_checkpoint(model, folder, strict=True, prefer_safe=True):
 
     if not index_present and not (safe_index_present and is_safetensors_available()):
         filenames = (
-            (WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_INDEX_NAME) if is_safetensors_available() else (WEIGHTS_INDEX_NAME,)
+            (WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_INDEX_NAME) if is_safetensors_available(
+            ) else (WEIGHTS_INDEX_NAME,)
         )
-        raise ValueError(f"Can't find a checkpoint index ({' or '.join(filenames)}) in {folder}.")
+        raise ValueError(
+            f"Can't find a checkpoint index ({' or '.join(filenames)}) in {folder}.")
 
     load_safe = False
     if safe_index_present:
@@ -525,8 +533,10 @@ def load_sharded_checkpoint(model, folder, strict=True, prefer_safe=True):
             error_message += f"\nMissing key(s): {str_unexpected_keys}."
         raise RuntimeError(error_message)
 
-    weights_only_kwarg = {"weights_only": True} if is_torch_greater_or_equal_than_1_13 else {}
-    loader = safe_load_file if load_safe else partial(torch.load, map_location="cpu", **weights_only_kwarg)
+    weights_only_kwarg = {
+        "weights_only": True} if is_torch_greater_or_equal_than_1_13 else {}
+    loader = safe_load_file if load_safe else partial(
+        torch.load, map_location="cpu", **weights_only_kwarg)
 
     for shard_file in shard_files:
         state_dict = loader(os.path.join(folder, shard_file))
@@ -556,7 +566,8 @@ def load_state_dict(checkpoint_file: Union[str, os.PathLike], is_quantized: bool
         return safe_load_file(checkpoint_file)
     try:
         if (
-            (is_deepspeed_zero3_enabled() and torch.distributed.is_initialized() and torch.distributed.get_rank() > 0)
+            (is_deepspeed_zero3_enabled() and torch.distributed.is_initialized()
+             and torch.distributed.get_rank() > 0)
             or (is_fsdp_enabled() and not is_local_dist_rank_0())
         ) and not is_quantized:
             map_location = "meta"
@@ -571,7 +582,8 @@ def load_state_dict(checkpoint_file: Union[str, os.PathLike], is_quantized: bool
             and is_zipfile(checkpoint_file)
         ):
             extra_args = {"mmap": True}
-        weights_only_kwarg = {"weights_only": True} if is_torch_greater_or_equal_than_1_13 else {}
+        weights_only_kwarg = {
+            "weights_only": True} if is_torch_greater_or_equal_than_1_13 else {}
         return torch.load(
             checkpoint_file,
             map_location=map_location,
@@ -607,7 +619,8 @@ def set_initialized_submodules(model, state_dict_keys):
     """
     not_initialized_submodules = {}
     for module_name, module in model.named_modules():
-        loaded_keys = {k.replace(f"{module_name}.", "") for k in state_dict_keys if k.startswith(f"{module_name}.")}
+        loaded_keys = {k.replace(f"{module_name}.", "")
+                       for k in state_dict_keys if k.startswith(f"{module_name}.")}
         if loaded_keys.issuperset(module.state_dict()):
             module._is_hf_initialized = True
         else:
@@ -627,14 +640,17 @@ def _end_ptr(tensor: torch.Tensor) -> int:
 def _get_tied_weight_keys(module: nn.Module, prefix=""):
     tied_weight_keys = []
     if getattr(module, "_tied_weights_keys", None) is not None:
-        names = [f"{prefix}.{k}" if prefix else k for k in module._tied_weights_keys]
+        names = [
+            f"{prefix}.{k}" if prefix else k for k in module._tied_weights_keys]
         tied_weight_keys.extend(names)
     if getattr(module, "_dynamic_tied_weights_keys", None) is not None:
-        names = [f"{prefix}.{k}" if prefix else k for k in module._dynamic_tied_weights_keys]
+        names = [
+            f"{prefix}.{k}" if prefix else k for k in module._dynamic_tied_weights_keys]
         tied_weight_keys.extend(names)
     for name, submodule in module.named_children():
         local_prefix = f"{prefix}.{name}" if prefix else name
-        tied_weight_keys.extend(_get_tied_weight_keys(submodule, prefix=local_prefix))
+        tied_weight_keys.extend(_get_tied_weight_keys(
+            submodule, prefix=local_prefix))
     return tied_weight_keys
 
 
@@ -730,7 +746,8 @@ def _load_state_dict_into_model(model_to_load, state_dict, start_prefix, assign_
     # PyTorch's `_load_from_state_dict` does not copy parameters in a module's descendants
     # so we need to apply the function recursively.
     def load(module: nn.Module, state_dict, prefix="", assign_to_params_buffers=False):
-        local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
+        local_metadata = {} if metadata is None else metadata.get(
+            prefix[:-1], {})
         local_metadata["assign_to_params_buffers"] = assign_to_params_buffers
 
         args = (state_dict, prefix, local_metadata, True, [], [], error_msgs)
@@ -742,8 +759,10 @@ def load(module: nn.Module, state_dict, prefix="", assign_to_params_buffers=Fals
 
                 # In sharded models, each shard has only part of the full state_dict, so only gather
                 # parameters that are in the current state_dict.
-                named_parameters = dict(module.named_parameters(prefix=prefix[:-1], recurse=False))
-                params_to_gather = [named_parameters[k] for k in state_dict.keys() if k in named_parameters]
+                named_parameters = dict(module.named_parameters(
+                    prefix=prefix[:-1], recurse=False))
+                params_to_gather = [named_parameters[k]
+                                    for k in state_dict.keys() if k in named_parameters]
                 if len(params_to_gather) > 0:
                     # because zero3 puts placeholders in model params, this context
                     # manager gathers (unpartitions) the params of the current layer, then loads from
@@ -756,9 +775,11 @@ def load(module: nn.Module, state_dict, prefix="", assign_to_params_buffers=Fals
 
         for name, child in module._modules.items():
             if child is not None:
-                load(child, state_dict, prefix + name + ".", assign_to_params_buffers)
+                load(child, state_dict, prefix + name +
+                     ".", assign_to_params_buffers)
 
-    load(model_to_load, state_dict, prefix=start_prefix, assign_to_params_buffers=assign_to_params_buffers)
+    load(model_to_load, state_dict, prefix=start_prefix,
+         assign_to_params_buffers=assign_to_params_buffers)
     # Delete `state_dict` so it could be collected by GC earlier. Note that `state_dict` is a copy of the argument, so
     # it's safe to delete it.
     del state_dict
@@ -801,7 +822,8 @@ def _move_model_to_meta(model, loaded_state_dict_keys, start_prefix):
     # dematerialize param storage for keys that are going to be replaced by state_dict, by
     # putting those on the meta device
     for k in loaded_state_dict_keys:
-        submodule, param_name = find_submodule_and_param_name(model, k, start_prefix)
+        submodule, param_name = find_submodule_and_param_name(
+            model, k, start_prefix)
         if submodule is not None:
             # selectively switch to the meta device only those params/buffers that will
             # be next replaced from state_dict. This a complex way to do p.to_("meta")
@@ -830,7 +852,8 @@ def _load_state_dict_into_meta_model(
     is_safetensors=False,
     keep_in_fp32_modules=None,
     unexpected_keys=None,  # passing `unexpected` for cleanup from quantization items
-    pretrained_model_name_or_path=None,  # for flagging the user when the model contains renamed keys
+    # for flagging the user when the model contains renamed keys
+    pretrained_model_name_or_path=None,
 ):
     """
     This is somewhat similar to `_load_state_dict_into_model`, but deals with a model that has some or all of its
@@ -869,14 +892,18 @@ def _load_state_dict_into_meta_model(
         # To reproduce `_load_state_dict_into_model` behaviour, we need to manually rename parametrized weigth norm, if necessary.
         if hasattr(nn.utils.parametrizations, "weight_norm"):
             if "weight_g" in key:
-                new_key = key.replace("weight_g", "parametrizations.weight.original0")
+                new_key = key.replace(
+                    "weight_g", "parametrizations.weight.original0")
             if "weight_v" in key:
-                new_key = key.replace("weight_v", "parametrizations.weight.original1")
+                new_key = key.replace(
+                    "weight_v", "parametrizations.weight.original1")
         else:
             if "parametrizations.weight.original0" in key:
-                new_key = key.replace("parametrizations.weight.original0", "weight_g")
+                new_key = key.replace(
+                    "parametrizations.weight.original0", "weight_g")
             if "parametrizations.weight.original1" in key:
-                new_key = key.replace("parametrizations.weight.original1", "weight_v")
+                new_key = key.replace(
+                    "parametrizations.weight.original1", "weight_v")
         if new_key:
             old_keys.append(key)
             new_keys.append(new_key)
@@ -897,7 +924,7 @@ def _load_state_dict_into_meta_model(
             continue
 
         if param_name.startswith(start_prefix):
-            param_name = param_name[len(start_prefix) :]
+            param_name = param_name[len(start_prefix):]
 
         module_name = param_name
         set_module_kwargs = {}
@@ -954,9 +981,11 @@ def _load_state_dict_into_meta_model(
 
         if param_device == "disk":
             if not is_safetensors:
-                offload_index = offload_weight(param, param_name, offload_folder, offload_index)
+                offload_index = offload_weight(
+                    param, param_name, offload_folder, offload_index)
         elif param_device == "cpu" and state_dict_index is not None:
-            state_dict_index = offload_weight(param, param_name, state_dict_folder, state_dict_index)
+            state_dict_index = offload_weight(
+                param, param_name, state_dict_folder, state_dict_index)
         elif (
             not is_quantized
             or (not hf_quantizer.requires_parameters_quantization)
@@ -970,9 +999,11 @@ def _load_state_dict_into_meta_model(
                 param_device = "cpu" if is_local_dist_rank_0() else "meta"
 
             # For backward compatibility with older versions of `accelerate` and for non-quantized params
-            set_module_tensor_to_device(model, param_name, param_device, **set_module_kwargs)
+            set_module_tensor_to_device(
+                model, param_name, param_device, **set_module_kwargs)
         else:
-            hf_quantizer.create_quantized_param(model, param, param_name, param_device, state_dict, unexpected_keys)
+            hf_quantizer.create_quantized_param(
+                model, param, param_name, param_device, state_dict, unexpected_keys)
             # For quantized modules with FSDP/DeepSpeed Stage 3, we need to quantize the parameter on the GPU
             # and then cast it to CPU to avoid excessive memory usage on each GPU
             # in comparison to the sharded model across GPUs.
@@ -1008,7 +1039,8 @@ def _hook_rss_memory_pre_forward(module, *args, **kwargs):
         try:
             import psutil
         except ImportError:
-            raise ImportError("You need to install psutil (pip install psutil) to use memory tracing.")
+            raise ImportError(
+                "You need to install psutil (pip install psutil) to use memory tracing.")
 
         process = psutil.Process(os.getpid())
         mem = process.memory_info()
@@ -1020,13 +1052,15 @@ def _hook_rss_memory_post_forward(module, *args, **kwargs):
         try:
             import psutil
         except ImportError:
-            raise ImportError("You need to install psutil (pip install psutil) to use memory tracing.")
+            raise ImportError(
+                "You need to install psutil (pip install psutil) to use memory tracing.")
 
         process = psutil.Process(os.getpid())
         mem = process.memory_info()
         module.mem_rss_post_forward = mem.rss
         mem_rss_diff = module.mem_rss_post_forward - module.mem_rss_pre_forward
-        module.mem_rss_diff = mem_rss_diff + (module.mem_rss_diff if hasattr(module, "mem_rss_diff") else 0)
+        module.mem_rss_diff = mem_rss_diff + \
+            (module.mem_rss_diff if hasattr(module, "mem_rss_diff") else 0)
         return None
 
     def add_memory_hooks(self):
@@ -1084,8 +1118,10 @@ def invert_attention_mask(self, encoder_attention_mask: Tensor) -> Tensor:
         # /transformer/transformer_layers.py#L270
         # encoder_extended_attention_mask = (encoder_extended_attention_mask ==
         # encoder_extended_attention_mask.transpose(-1, -2))
-        encoder_extended_attention_mask = encoder_extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility
-        encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * torch.finfo(self.dtype).min
+        encoder_extended_attention_mask = encoder_extended_attention_mask.to(
+            dtype=self.dtype)  # fp16 compatibility
+        encoder_extended_attention_mask = (
+            1.0 - encoder_extended_attention_mask) * torch.finfo(self.dtype).min
 
         return encoder_extended_attention_mask
 
@@ -1099,7 +1135,8 @@ def create_extended_attention_mask_for_decoder(input_shape, attention_mask, devi
             device = attention_mask.device
         batch_size, seq_length = input_shape
         seq_ids = torch.arange(seq_length, device=device)
-        causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]
+        causal_mask = seq_ids[None, None, :].repeat(
+            batch_size, seq_length, 1) <= seq_ids[None, :, None]
         # in case past_key_values are used we need to add a prefix ones mask to the causal mask
         # causal and attention masks must have same type with pytorch version < 1.3
         causal_mask = causal_mask.to(attention_mask.dtype)
@@ -1108,13 +1145,15 @@ def create_extended_attention_mask_for_decoder(input_shape, attention_mask, devi
             prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1]
             causal_mask = torch.cat(
                 [
-                    torch.ones((batch_size, seq_length, prefix_seq_len), device=device, dtype=causal_mask.dtype),
+                    torch.ones((batch_size, seq_length, prefix_seq_len),
+                               device=device, dtype=causal_mask.dtype),
                     causal_mask,
                 ],
                 axis=-1,
             )
 
-        extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
+        extended_attention_mask = causal_mask[:, None,
+                                              :, :] * attention_mask[:, None, None, :]
         return extended_attention_mask
 
     def get_extended_attention_mask(
@@ -1165,8 +1204,10 @@ def get_extended_attention_mask(
         # positions we want to attend and the dtype's smallest value for masked positions.
         # Since we are adding it to the raw scores before the softmax, this is
         # effectively the same as removing these entirely.
-        extended_attention_mask = extended_attention_mask.to(dtype=dtype)  # fp16 compatibility
-        extended_attention_mask = (1.0 - extended_attention_mask) * torch.finfo(dtype).min
+        extended_attention_mask = extended_attention_mask.to(
+            dtype=dtype)  # fp16 compatibility
+        extended_attention_mask = (
+            1.0 - extended_attention_mask) * torch.finfo(dtype).min
         return extended_attention_mask
 
     def get_head_mask(
@@ -1188,7 +1229,8 @@ def get_head_mask(
             `[None]` for each layer.
         """
         if head_mask is not None:
-            head_mask = self._convert_head_mask_to_5d(head_mask, num_hidden_layers)
+            head_mask = self._convert_head_mask_to_5d(
+                head_mask, num_hidden_layers)
             if is_attention_chunked is True:
                 head_mask = head_mask.unsqueeze(-1)
         else:
@@ -1199,12 +1241,16 @@ def get_head_mask(
     def _convert_head_mask_to_5d(self, head_mask, num_hidden_layers):
         """-> [num_hidden_layers x batch x num_heads x seq_length x seq_length]"""
         if head_mask.dim() == 1:
-            head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
+            head_mask = head_mask.unsqueeze(0).unsqueeze(
+                0).unsqueeze(-1).unsqueeze(-1)
             head_mask = head_mask.expand(num_hidden_layers, -1, -1, -1, -1)
         elif head_mask.dim() == 2:
-            head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)  # We can specify head_mask for each layer
-        assert head_mask.dim() == 5, f"head_mask.dim != 5, instead {head_mask.dim()}"
-        head_mask = head_mask.to(dtype=self.dtype)  # switch to float if need + fp16 compatibility
+            # We can specify head_mask for each layer
+            head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)
+        assert head_mask.dim(
+        ) == 5, f"head_mask.dim != 5, instead {head_mask.dim()}"
+        # switch to float if need + fp16 compatibility
+        head_mask = head_mask.to(dtype=self.dtype)
         return head_mask
 
     def num_parameters(self, only_trainable: bool = False, exclude_embeddings: bool = False) -> int:
@@ -1407,11 +1453,13 @@ def __init__(self, config: PretrainedConfig, *inputs, **kwargs):
 
         self.name_or_path = config.name_or_path
         self.warnings_issued = {}
-        self.generation_config = GenerationConfig.from_model_config(config) if self.can_generate() else None
+        self.generation_config = GenerationConfig.from_model_config(
+            config) if self.can_generate() else None
         # Overwrite the class attribute to make it an instance attribute, so models like
         # `InstructBlipForConditionalGeneration` can dynamically update it without modifying the class attribute
         # when a different component (e.g. language_model) is used.
-        self._keep_in_fp32_modules = copy.copy(self.__class__._keep_in_fp32_modules)
+        self._keep_in_fp32_modules = copy.copy(
+            self.__class__._keep_in_fp32_modules)
 
     def post_init(self):
         """
@@ -1429,7 +1477,8 @@ def dequantize(self):
         hf_quantizer = getattr(self, "hf_quantizer", None)
 
         if hf_quantizer is None:
-            raise ValueError("You need to first quantize your model in order to dequantize it")
+            raise ValueError(
+                "You need to first quantize your model in order to dequantize it")
 
         return hf_quantizer.dequantize(self)
 
@@ -1488,7 +1537,8 @@ def _from_config(cls, config, **kwargs):
         if torch_dtype is not None:
             dtype_orig = cls._set_default_torch_dtype(torch_dtype)
 
-        config = copy.deepcopy(config)  # We do not want to modify the config inplace in _from_config.
+        # We do not want to modify the config inplace in _from_config.
+        config = copy.deepcopy(config)
 
         if config._attn_implementation_internal is not None:
             # In this case, the config has been created with the attn_implementation set by the user, which we
@@ -1497,7 +1547,8 @@ def _from_config(cls, config, **kwargs):
         else:
             attn_implementation = None
 
-        config._attn_implementation = kwargs.pop("attn_implementation", attn_implementation)
+        config._attn_implementation = kwargs.pop(
+            "attn_implementation", attn_implementation)
         config = cls._autoset_attn_implementation(
             config,
             use_flash_attention_2=use_flash_attention_2,
@@ -1508,7 +1559,8 @@ def _from_config(cls, config, **kwargs):
         if is_deepspeed_zero3_enabled():
             import deepspeed
 
-            logger.info("Detected DeepSpeed ZeRO-3: activating zero.init() for this model")
+            logger.info(
+                "Detected DeepSpeed ZeRO-3: activating zero.init() for this model")
             # this immediately partitions the model across all gpus, to avoid the overhead in time
             # and memory copying it on CPU or each GPU first
             with deepspeed.zero.Init(config_dict_or_path=deepspeed_config()):
@@ -1618,7 +1670,8 @@ def _set_default_torch_dtype(cls, dtype: torch.dtype) -> torch.dtype:
                 f"Can't instantiate {cls.__name__} model under dtype={dtype} since it is not a floating point dtype"
             )
 
-        logger.info(f"Instantiating {cls.__name__} model under default dtype {dtype}.")
+        logger.info(
+            f"Instantiating {cls.__name__} model under default dtype {dtype}.")
         dtype_orig = torch.get_default_dtype()
         torch.set_default_dtype(dtype)
         return dtype_orig
@@ -1670,23 +1723,27 @@ def _check_and_enable_flash_attn_2(
             install_message = "Please refer to the documentation of https://huggingface.co/docs/transformers/perf_infer_gpu_one#flashattention-2 to install Flash Attention 2."
 
             if importlib.util.find_spec("flash_attn") is None:
-                raise ImportError(f"{preface} the package flash_attn seems to be not installed. {install_message}")
+                raise ImportError(
+                    f"{preface} the package flash_attn seems to be not installed. {install_message}")
 
-            flash_attention_version = version.parse(importlib.metadata.version("flash_attn"))
+            flash_attention_version = version.parse(
+                importlib.metadata.version("flash_attn"))
             if torch.version.cuda:
                 if flash_attention_version < version.parse("2.1.0"):
                     raise ImportError(
                         f"{preface} you need flash_attn package version to be greater or equal than 2.1.0. Detected version {flash_attention_version}. {install_message}"
                     )
                 else:
-                    raise ImportError(f"{preface} Flash Attention 2 is not available. {install_message}")
+                    raise ImportError(
+                        f"{preface} Flash Attention 2 is not available. {install_message}")
             elif torch.version.hip:
                 if flash_attention_version < version.parse("2.0.4"):
                     raise ImportError(
                         f"{preface} you need flash_attn package version to be greater or equal than 2.0.4. Make sure to have that version installed - detected version {flash_attention_version}. {install_message}"
                     )
                 else:
-                    raise ImportError(f"{preface} Flash Attention 2 is not available. {install_message}")
+                    raise ImportError(
+                        f"{preface} Flash Attention 2 is not available. {install_message}")
 
         _is_bettertransformer = getattr(cls, "use_bettertransformer", False)
 
@@ -1773,7 +1830,8 @@ def enable_input_require_grads(self):
         def make_inputs_require_grads(module, input, output):
             output.requires_grad_(True)
 
-        self._require_grads_hook = self.get_input_embeddings().register_forward_hook(make_inputs_require_grads)
+        self._require_grads_hook = self.get_input_embeddings(
+        ).register_forward_hook(make_inputs_require_grads)
 
     def disable_input_require_grads(self):
         """
@@ -1844,7 +1902,8 @@ def tie_weights(self):
         if getattr(self.config, "tie_word_embeddings", True):
             output_embeddings = self.get_output_embeddings()
             if output_embeddings is not None:
-                self._tie_or_clone_weights(output_embeddings, self.get_input_embeddings())
+                self._tie_or_clone_weights(
+                    output_embeddings, self.get_input_embeddings())
 
         if getattr(self.config, "is_encoder_decoder", False) and getattr(self.config, "tie_encoder_decoder", False):
             if hasattr(self, self.base_model_prefix):
@@ -1889,10 +1948,12 @@ def tie_encoder_to_decoder_recursively(
             if hasattr(decoder_pointer, "weight"):
                 assert hasattr(encoder_pointer, "weight")
                 encoder_pointer.weight = decoder_pointer.weight
-                tied_weights.append(f"{base_encoder_name}{total_encoder_name}.weight")
+                tied_weights.append(
+                    f"{base_encoder_name}{total_encoder_name}.weight")
                 if hasattr(decoder_pointer, "bias"):
                     assert hasattr(encoder_pointer, "bias")
-                    tied_weights.append(f"{base_encoder_name}{total_encoder_name}.bias")
+                    tied_weights.append(
+                        f"{base_encoder_name}{total_encoder_name}.bias")
                     encoder_pointer.bias = decoder_pointer.bias
                 return
 
@@ -1903,7 +1964,8 @@ def tie_encoder_to_decoder_recursively(
                     len(encoder_modules) > 0
                 ), f"Encoder module {encoder_pointer} does not match decoder module {decoder_pointer}"
 
-                all_encoder_weights = {module_name + "/" + sub_name for sub_name in encoder_modules.keys()}
+                all_encoder_weights = {
+                    module_name + "/" + sub_name for sub_name in encoder_modules.keys()}
                 encoder_layer_pos = 0
                 for name, module in decoder_modules.items():
                     if name.isdigit():
@@ -1936,7 +1998,8 @@ def tie_encoder_to_decoder_recursively(
                         total_encoder_name=f"{total_encoder_name}.{encoder_name}",
                         total_decoder_name=f"{total_decoder_name}.{decoder_name}",
                     )
-                    all_encoder_weights.remove(module_name + "/" + encoder_name)
+                    all_encoder_weights.remove(
+                        module_name + "/" + encoder_name)
 
                 uninitialized_encoder_weights += list(all_encoder_weights)
 
@@ -1954,7 +2017,8 @@ def tie_encoder_to_decoder_recursively(
     def _tie_or_clone_weights(self, output_embeddings, input_embeddings):
         """Tie or clone module weights depending of whether we are using TorchScript or not"""
         if self.config.torchscript:
-            output_embeddings.weight = nn.Parameter(input_embeddings.weight.clone())
+            output_embeddings.weight = nn.Parameter(
+                input_embeddings.weight.clone())
         else:
             output_embeddings.weight = input_embeddings.weight
 
@@ -1963,7 +2027,8 @@ def _tie_or_clone_weights(self, output_embeddings, input_embeddings):
                 output_embeddings.bias.data,
                 (
                     0,
-                    output_embeddings.weight.shape[0] - output_embeddings.bias.shape[0],
+                    output_embeddings.weight.shape[0] -
+                    output_embeddings.bias.shape[0],
                 ),
                 "constant",
                 0,
@@ -1996,7 +2061,8 @@ def _get_no_split_modules(self, device_map: str):
                             "class needs to implement the `_no_split_modules` attribute."
                         )
                     else:
-                        _no_split_modules = _no_split_modules | set(module._no_split_modules)
+                        _no_split_modules = _no_split_modules | set(
+                            module._no_split_modules)
                 modules_to_check += list(module.children())
         return list(_no_split_modules)
 
@@ -2025,12 +2091,14 @@ def resize_token_embeddings(
         Return:
             `torch.nn.Embedding`: Pointer to the input tokens Embeddings Module of the model.
         """
-        model_embeds = self._resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
+        model_embeds = self._resize_token_embeddings(
+            new_num_tokens, pad_to_multiple_of)
         if new_num_tokens is None and pad_to_multiple_of is None:
             return model_embeds
 
         # Since we are basically resuing the same old embeddings with new weight values, gathering is required
-        is_quantized = hasattr(self, "hf_quantizer") and self.hf_quantizer is not None
+        is_quantized = hasattr(
+            self, "hf_quantizer") and self.hf_quantizer is not None
         if is_deepspeed_zero3_enabled() and not is_quantized:
             import deepspeed
 
@@ -2050,14 +2118,16 @@ def resize_token_embeddings(
 
     def _resize_token_embeddings(self, new_num_tokens, pad_to_multiple_of=None):
         old_embeddings = self.get_input_embeddings()
-        new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens, pad_to_multiple_of)
+        new_embeddings = self._get_resized_embeddings(
+            old_embeddings, new_num_tokens, pad_to_multiple_of)
         if hasattr(old_embeddings, "_hf_hook"):
             hook = old_embeddings._hf_hook
             add_hook_to_module(new_embeddings, hook)
         old_embeddings_requires_grad = old_embeddings.weight.requires_grad
         new_embeddings.requires_grad_(old_embeddings_requires_grad)
         self.set_input_embeddings(new_embeddings)
-        is_quantized = hasattr(self, "hf_quantizer") and self.hf_quantizer is not None
+        is_quantized = hasattr(
+            self, "hf_quantizer") and self.hf_quantizer is not None
 
         # Update new_num_tokens with the actual size of new_embeddings
         if pad_to_multiple_of is not None:
@@ -2073,9 +2143,11 @@ def _resize_token_embeddings(self, new_num_tokens, pad_to_multiple_of=None):
         if self.get_output_embeddings() is not None and not self.config.tie_word_embeddings:
             old_lm_head = self.get_output_embeddings()
             if isinstance(old_lm_head, torch.nn.Embedding):
-                new_lm_head = self._get_resized_embeddings(old_lm_head, new_num_tokens)
+                new_lm_head = self._get_resized_embeddings(
+                    old_lm_head, new_num_tokens)
             else:
-                new_lm_head = self._get_resized_lm_head(old_lm_head, new_num_tokens)
+                new_lm_head = self._get_resized_lm_head(
+                    old_lm_head, new_num_tokens)
             if hasattr(old_lm_head, "_hf_hook"):
                 hook = old_lm_head._hf_hook
                 add_hook_to_module(new_lm_head, hook)
@@ -2126,7 +2198,8 @@ def _get_resized_embeddings(
                 )
             if new_num_tokens is None:
                 new_num_tokens = old_embeddings.weight.shape[0]
-            new_num_tokens = ((new_num_tokens + pad_to_multiple_of - 1) // pad_to_multiple_of) * pad_to_multiple_of
+            new_num_tokens = ((new_num_tokens + pad_to_multiple_of - 1) //
+                              pad_to_multiple_of) * pad_to_multiple_of
         else:
             logger.info(
                 "You are resizing the embedding layer without providing a `pad_to_multiple_of` parameter. This means that the new embedding"
@@ -2138,7 +2211,8 @@ def _get_resized_embeddings(
         if new_num_tokens is None:
             return old_embeddings
 
-        is_quantized = hasattr(self, "hf_quantizer") and self.hf_quantizer is not None
+        is_quantized = hasattr(
+            self, "hf_quantizer") and self.hf_quantizer is not None
         if is_deepspeed_zero3_enabled() and not is_quantized:
             import deepspeed
 
@@ -2183,9 +2257,11 @@ def _get_resized_embeddings(
 
             params = [old_embeddings.weight, new_embeddings.weight]
             with deepspeed.zero.GatheredParameters(params, modifier_rank=0):
-                new_embeddings.weight.data[:n, :] = old_embeddings.weight.data[:n, :]
+                new_embeddings.weight.data[:n,
+                                           :] = old_embeddings.weight.data[:n, :]
         else:
-            new_embeddings.weight.data[:n, :] = old_embeddings.weight.data[:n, :]
+            new_embeddings.weight.data[:n,
+                                       :] = old_embeddings.weight.data[:n, :]
 
         # Replace weights in old_embeddings and return to maintain the same embedding type.
         # This ensures correct functionality when a Custom Embedding class is passed as input.
@@ -2236,7 +2312,8 @@ def _get_resized_lm_head(
         if new_num_tokens is None:
             return old_lm_head
 
-        is_quantized = hasattr(self, "hf_quantizer") and self.hf_quantizer is not None
+        is_quantized = hasattr(
+            self, "hf_quantizer") and self.hf_quantizer is not None
         if is_deepspeed_zero3_enabled() and not is_quantized:
             import deepspeed
 
@@ -2260,7 +2337,8 @@ def _get_resized_lm_head(
             )
 
         # Build new lm head
-        new_lm_head_shape = (old_lm_head_dim, new_num_tokens) if not transposed else (new_num_tokens, old_lm_head_dim)
+        new_lm_head_shape = (old_lm_head_dim, new_num_tokens) if not transposed else (
+            new_num_tokens, old_lm_head_dim)
         has_new_lm_head_bias = old_lm_head.bias is not None
 
         # When using DeepSpeed ZeRO-3, we shouldn't create new embeddings with DeepSpeed init
@@ -2282,7 +2360,8 @@ def _get_resized_lm_head(
         if is_deepspeed_zero3_enabled() and not is_quantized:
             import deepspeed
 
-            params = [old_lm_head.weight, old_lm_head.bias, new_lm_head.weight, new_lm_head.bias]
+            params = [old_lm_head.weight, old_lm_head.bias,
+                      new_lm_head.weight, new_lm_head.bias]
             with deepspeed.zero.GatheredParameters(params, modifier_rank=0):
                 self._copy_lm_head_original_to_resized(
                     new_lm_head, old_lm_head, num_tokens_to_copy, transposed, has_new_lm_head_bias
@@ -2299,9 +2378,11 @@ def _copy_lm_head_original_to_resized(
     ):
         # Copy old lm head weights to new lm head
         if not transposed:
-            new_lm_head.weight.data[:num_tokens_to_copy, :] = old_lm_head.weight.data[:num_tokens_to_copy, :]
+            new_lm_head.weight.data[:num_tokens_to_copy,
+                                    :] = old_lm_head.weight.data[:num_tokens_to_copy, :]
         else:
-            new_lm_head.weight.data[:, :num_tokens_to_copy] = old_lm_head.weight.data[:, :num_tokens_to_copy]
+            new_lm_head.weight.data[:,
+                                    :num_tokens_to_copy] = old_lm_head.weight.data[:, :num_tokens_to_copy]
 
         # Copy bias weights to new lm head
         if has_new_lm_head_bias:
@@ -2348,8 +2429,10 @@ def prune_heads(self, heads_to_prune: Dict[int, List[int]]):
         """
         # save new sets of pruned heads as union of previously stored pruned heads and newly pruned heads
         for layer, heads in heads_to_prune.items():
-            union_heads = set(self.config.pruned_heads.get(layer, [])) | set(heads)
-            self.config.pruned_heads[layer] = list(union_heads)  # Unfortunately we have to store it as list for JSON
+            union_heads = set(
+                self.config.pruned_heads.get(layer, [])) | set(heads)
+            # Unfortunately we have to store it as list for JSON
+            self.config.pruned_heads[layer] = list(union_heads)
 
         self.base_model._prune_heads(heads_to_prune)
 
@@ -2368,19 +2451,23 @@ def gradient_checkpointing_enable(self, gradient_checkpointing_kwargs=None):
                 Additional keyword arguments passed along to the `torch.utils.checkpoint.checkpoint` function.
         """
         if not self.supports_gradient_checkpointing:
-            raise ValueError(f"{self.__class__.__name__} does not support gradient checkpointing.")
+            raise ValueError(
+                f"{self.__class__.__name__} does not support gradient checkpointing.")
 
         if gradient_checkpointing_kwargs is None:
             gradient_checkpointing_kwargs = {"use_reentrant": True}
 
-        gradient_checkpointing_func = functools.partial(checkpoint, **gradient_checkpointing_kwargs)
+        gradient_checkpointing_func = functools.partial(
+            checkpoint, **gradient_checkpointing_kwargs)
 
         # For old GC format (transformers < 4.35.0) for models that live on the Hub
         # we will fall back to the overwritten `_set_gradient_checkpointing` method
-        _is_using_old_format = "value" in inspect.signature(self._set_gradient_checkpointing).parameters
+        _is_using_old_format = "value" in inspect.signature(
+            self._set_gradient_checkpointing).parameters
 
         if not _is_using_old_format:
-            self._set_gradient_checkpointing(enable=True, gradient_checkpointing_func=gradient_checkpointing_func)
+            self._set_gradient_checkpointing(
+                enable=True, gradient_checkpointing_func=gradient_checkpointing_func)
         else:
             self.apply(partial(self._set_gradient_checkpointing, value=True))
             logger.warning(
@@ -2427,7 +2514,8 @@ def gradient_checkpointing_disable(self):
         if self.supports_gradient_checkpointing:
             # For old GC format (transformers < 4.35.0) for models that live on the Hub
             # we will fall back to the overwritten `_set_gradient_checkpointing` methid
-            _is_using_old_format = "value" in inspect.signature(self._set_gradient_checkpointing).parameters
+            _is_using_old_format = "value" in inspect.signature(
+                self._set_gradient_checkpointing).parameters
             if not _is_using_old_format:
                 self._set_gradient_checkpointing(enable=False)
             else:
@@ -2435,7 +2523,8 @@ def gradient_checkpointing_disable(self):
                     "You are using an old version of the checkpointing format that is deprecated (We will also silently ignore `gradient_checkpointing_kwargs` in case you passed it)."
                     "Please update to the new format on your modeling file. To use the new format, you need to completely remove the definition of the method `_set_gradient_checkpointing` in your model."
                 )
-                self.apply(partial(self._set_gradient_checkpointing, value=False))
+                self.apply(
+                    partial(self._set_gradient_checkpointing, value=False))
 
         if getattr(self, "_hf_peft_config_loaded", False):
             self.disable_input_require_grads()
@@ -2534,7 +2623,8 @@ def save_pretrained(
 
         hf_quantizer = getattr(self, "hf_quantizer", None)
         quantization_serializable = (
-            hf_quantizer is not None and isinstance(hf_quantizer, HfQuantizer) and hf_quantizer.is_serializable
+            hf_quantizer is not None and isinstance(
+                hf_quantizer, HfQuantizer) and hf_quantizer.is_serializable
         )
 
         if hf_quantizer is not None and not _hf_peft_config_loaded and not quantization_serializable:
@@ -2549,17 +2639,20 @@ def save_pretrained(
             )
             is_main_process = kwargs.pop("save_config")
         if safe_serialization and not is_safetensors_available():
-            raise ImportError("`safe_serialization` requires the `safetensors library: `pip install safetensors`.")
+            raise ImportError(
+                "`safe_serialization` requires the `safetensors library: `pip install safetensors`.")
 
         if os.path.isfile(save_directory):
-            logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
+            logger.error(
+                f"Provided path ({save_directory}) should be a directory, not a file")
             return
 
         os.makedirs(save_directory, exist_ok=True)
 
         if push_to_hub:
             commit_message = kwargs.pop("commit_message", None)
-            repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1])
+            repo_id = kwargs.pop(
+                "repo_id", save_directory.split(os.path.sep)[-1])
             repo_id = self._create_repo(repo_id, **kwargs)
             files_timestamps = self._get_files_timestamps(save_directory)
 
@@ -2592,7 +2685,8 @@ def save_pretrained(
                         UserWarning,
                     )
                     for param_name, param_value in misplaced_generation_parameters.items():
-                        setattr(model_to_save.generation_config, param_name, param_value)
+                        setattr(model_to_save.generation_config,
+                                param_name, param_value)
                         setattr(model_to_save.config, param_name, None)
 
                 model_to_save.config.save_pretrained(save_directory)
@@ -2684,7 +2778,8 @@ def save_pretrained(
                 else:
                     shared_ptrs = {}
             else:
-                shared_ptrs = {ptr: names for ptr, names in ptrs.items() if len(names) > 1}
+                shared_ptrs = {ptr: names for ptr,
+                               names in ptrs.items() if len(names) > 1}
 
             # Recursively descend to find tied weight keys
             _tied_weights_keys = _get_tied_weight_keys(self)
@@ -2696,13 +2791,15 @@ def save_pretrained(
                 if _tied_weights_keys is not None:
                     found = 0
                     for name in sorted(names):
-                        matches_pattern = any(re.search(pat, name) for pat in _tied_weights_keys)
+                        matches_pattern = any(re.search(pat, name)
+                                              for pat in _tied_weights_keys)
                         if matches_pattern and name in state_dict:
                             found += 1
                             if found < len(names):
                                 to_delete_names.add(name)
             # We are entering a place where the weights and the transformers configuration do NOT match.
-            shared_names, disjoint_names = _find_disjoint(shared_ptrs.values(), state_dict)
+            shared_names, disjoint_names = _find_disjoint(
+                shared_ptrs.values(), state_dict)
             # Those are actually tensor sharing but disjoint from each other, we can safely clone them
             # Reloaded won't have the same property, but it shouldn't matter in any meaningful way.
             for name in disjoint_names:
@@ -2713,7 +2810,8 @@ def save_pretrained(
             # the key back leading to random tensor. A proper warning will be shown
             # during reload (if applicable), but since the file is not necessarily compatible with
             # the config, better show a proper warning.
-            shared_names, identical_names = _find_identical(shared_names, state_dict)
+            shared_names, identical_names = _find_identical(
+                shared_names, state_dict)
             # delete tensors that have identical storage
             for inames in identical_names:
                 known = inames.intersection(to_delete_names)
@@ -2738,7 +2836,8 @@ def save_pretrained(
         else:
             weights_name = ADAPTER_SAFE_WEIGHTS_NAME if safe_serialization else ADAPTER_WEIGHTS_NAME
 
-        filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(".safetensors", "{suffix}.safetensors")
+        filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(
+            ".safetensors", "{suffix}.safetensors")
         state_dict_split = split_torch_state_dict_into_shards(
             state_dict, filename_pattern=filename_pattern, max_shard_size=max_shard_size
         )
@@ -2755,10 +2854,12 @@ def save_pretrained(
             full_filename = os.path.join(save_directory, filename)
             # If we have a shard file that is not going to be replaced, we delete it, but only from the main process
             # in distributed settings to avoid race conditions.
-            weights_no_suffix = weights_name.replace(".bin", "").replace(".safetensors", "")
+            weights_no_suffix = weights_name.replace(
+                ".bin", "").replace(".safetensors", "")
 
             # make sure that file to be deleted matches format of sharded file, e.g. pytorch_model-00001-of-00005
-            filename_no_suffix = filename.replace(".bin", "").replace(".safetensors", "")
+            filename_no_suffix = filename.replace(
+                ".bin", "").replace(".safetensors", "")
             reg = re.compile(r"(.*?)-\d{5}-of-\d{5}")
 
             if (
@@ -2772,9 +2873,11 @@ def save_pretrained(
         # Save the model
         filename_to_tensors = state_dict_split.filename_to_tensors.items()
         if module_map:
-            filename_to_tensors = logging.tqdm(filename_to_tensors, desc="Saving checkpoint shards")
+            filename_to_tensors = logging.tqdm(
+                filename_to_tensors, desc="Saving checkpoint shards")
         for shard_file, tensors in filename_to_tensors:
-            shard = {tensor: state_dict[tensor].contiguous() for tensor in tensors}
+            shard = {tensor: state_dict[tensor].contiguous()
+                     for tensor in tensors}
             # remake shard with onloaded parameters if necessary
             if module_map:
                 if accelerate_version < version.parse("0.31"):
@@ -2787,7 +2890,8 @@ def save_pretrained(
                 for module_name in shard:
                     module = module_map[module_name]
                     # update state dict with onloaded parameters
-                    shard_state_dict = get_state_dict_from_offload(module, module_name, shard_state_dict)
+                    shard_state_dict = get_state_dict_from_offload(
+                        module, module_name, shard_state_dict)
 
                 # assign shard to be the completed state dict
                 shard = shard_state_dict
@@ -2797,7 +2901,8 @@ def save_pretrained(
             if safe_serialization:
                 # At some point we will need to deal better with save_function (used for TPU and other distributed
                 # joyfulness), but for now this enough.
-                safe_save_file(shard, os.path.join(save_directory, shard_file), metadata={"format": "pt"})
+                safe_save_file(shard, os.path.join(
+                    save_directory, shard_file), metadata={"format": "pt"})
             else:
                 save_function(shard, os.path.join(save_directory, shard_file))
 
@@ -2806,7 +2911,8 @@ def save_pretrained(
             logger.info(f"Model weights saved in {path_to_weights}")
         else:
             save_index_file = SAFE_WEIGHTS_INDEX_NAME if safe_serialization else WEIGHTS_INDEX_NAME
-            save_index_file = os.path.join(save_directory, _add_variant(save_index_file, variant))
+            save_index_file = os.path.join(
+                save_directory, _add_variant(save_index_file, variant))
             # Save the index as well
             with open(save_index_file, "w", encoding="utf-8") as f:
                 content = json.dumps(index, indent=2, sort_keys=True) + "\n"
@@ -2862,16 +2968,19 @@ def get_memory_footprint(self, return_buffers=True):
                 are tensors that do not require gradients and not registered as parameters. E.g. mean and std in batch
                 norm layers. Please see: https://discuss.pytorch.org/t/what-pytorch-means-by-buffers/120266/2
         """
-        mem = sum([param.nelement() * param.element_size() for param in self.parameters()])
+        mem = sum([param.nelement() * param.element_size()
+                  for param in self.parameters()])
         if return_buffers:
-            mem_bufs = sum([buf.nelement() * buf.element_size() for buf in self.buffers()])
+            mem_bufs = sum([buf.nelement() * buf.element_size()
+                           for buf in self.buffers()])
             mem = mem + mem_bufs
         return mem
 
     @wraps(torch.nn.Module.cuda)
     def cuda(self, *args, **kwargs):
         if getattr(self, "quantization_method", None) == QuantizationMethod.HQQ:
-            raise ValueError("`.cuda` is not supported for HQQ-quantized models.")
+            raise ValueError(
+                "`.cuda` is not supported for HQQ-quantized models.")
         # Checks if the model has been loaded in 4-bit or 8-bit with BNB
         if getattr(self, "quantization_method", None) == QuantizationMethod.BITS_AND_BYTES:
             if getattr(self, "is_loaded_in_8bit", False):
@@ -2900,7 +3009,8 @@ def to(self, *args, **kwargs):
                     break
 
         if getattr(self, "quantization_method", None) == QuantizationMethod.HQQ:
-            raise ValueError("`.to` is not supported for HQQ-quantized models.")
+            raise ValueError(
+                "`.to` is not supported for HQQ-quantized models.")
         # Checks if the model has been loaded in 4-bit or 8-bit with BNB
         if getattr(self, "quantization_method", None) == QuantizationMethod.BITS_AND_BYTES:
             if dtype_present_in_args:
@@ -3263,7 +3373,8 @@ def from_pretrained(
             )
 
         if gguf_file is not None and not is_accelerate_available():
-            raise ValueError("accelerate is required when loading a GGUF file `pip install accelerate`.")
+            raise ValueError(
+                "accelerate is required when loading a GGUF file `pip install accelerate`.")
 
         if commit_hash is None:
             if not isinstance(config, PretrainedConfig):
@@ -3283,12 +3394,14 @@ def from_pretrained(
                     _raise_exceptions_for_missing_entries=False,
                     _raise_exceptions_for_connection_errors=False,
                 )
-                commit_hash = extract_commit_hash(resolved_config_file, commit_hash)
+                commit_hash = extract_commit_hash(
+                    resolved_config_file, commit_hash)
             else:
                 commit_hash = getattr(config, "_commit_hash", None)
 
         if is_peft_available():
-            _adapter_model_path = adapter_kwargs.pop("_adapter_model_path", None)
+            _adapter_model_path = adapter_kwargs.pop(
+                "_adapter_model_path", None)
 
             if _adapter_model_path is None:
                 _adapter_model_path = find_adapter_config_file(
@@ -3304,7 +3417,8 @@ def from_pretrained(
             if _adapter_model_path is not None and os.path.isfile(_adapter_model_path):
                 with open(_adapter_model_path, "r", encoding="utf-8") as f:
                     _adapter_model_path = pretrained_model_name_or_path
-                    pretrained_model_name_or_path = json.load(f)["base_model_name_or_path"]
+                    pretrained_model_name_or_path = json.load(
+                        f)["base_model_name_or_path"]
         else:
             _adapter_model_path = None
 
@@ -3331,7 +3445,8 @@ def from_pretrained(
             if low_cpu_mem_usage is None:
                 low_cpu_mem_usage = True
             elif not low_cpu_mem_usage:
-                raise ValueError("Passing along a `device_map` requires `low_cpu_mem_usage=True`")
+                raise ValueError(
+                    "Passing along a `device_map` requires `low_cpu_mem_usage=True`")
 
         if low_cpu_mem_usage:
             if is_deepspeed_zero3_enabled():
@@ -3352,8 +3467,10 @@ def from_pretrained(
                 )
 
             # preparing BitsAndBytesConfig from kwargs
-            config_dict = {k: v for k, v in kwargs.items() if k in inspect.signature(BitsAndBytesConfig).parameters}
-            config_dict = {**config_dict, "load_in_4bit": load_in_4bit, "load_in_8bit": load_in_8bit}
+            config_dict = {k: v for k, v in kwargs.items(
+            ) if k in inspect.signature(BitsAndBytesConfig).parameters}
+            config_dict = {
+                **config_dict, "load_in_4bit": load_in_4bit, "load_in_8bit": load_in_8bit}
             quantization_config, kwargs = BitsAndBytesConfig.from_dict(
                 config_dict=config_dict, return_unused_kwargs=True, **kwargs
             )
@@ -3364,7 +3481,8 @@ def from_pretrained(
 
         from_pt = not (from_tf | from_flax)
 
-        user_agent = {"file_type": "model", "framework": "pytorch", "from_auto_class": from_auto_class}
+        user_agent = {"file_type": "model", "framework": "pytorch",
+                      "from_auto_class": from_auto_class}
         if from_pipeline is not None:
             user_agent["using_pipeline"] = from_pipeline
 
@@ -3406,7 +3524,8 @@ def from_pretrained(
 
             model_kwargs = kwargs
 
-        pre_quantized = getattr(config, "quantization_config", None) is not None
+        pre_quantized = getattr(
+            config, "quantization_config", None) is not None
         if pre_quantized or quantization_config is not None:
             if pre_quantized:
                 config.quantization_config = AutoHfQuantizer.merge_quantization_configs(
@@ -3414,7 +3533,8 @@ def from_pretrained(
                 )
             else:
                 config.quantization_config = quantization_config
-            hf_quantizer = AutoHfQuantizer.from_config(config.quantization_config, pre_quantized=pre_quantized)
+            hf_quantizer = AutoHfQuantizer.from_config(
+                config.quantization_config, pre_quantized=pre_quantized)
         else:
             hf_quantizer = None
 
@@ -3431,7 +3551,8 @@ def from_pretrained(
             # Force-set to `True` for more mem efficiency
             if low_cpu_mem_usage is None:
                 low_cpu_mem_usage = True
-                logger.warning("`low_cpu_mem_usage` was None, now set to True since model is quantized.")
+                logger.warning(
+                    "`low_cpu_mem_usage` was None, now set to True since model is quantized.")
         is_quantized = hf_quantizer is not None
 
         # This variable will flag if we're loading a sharded checkpoint. In this case the archive file is just the
@@ -3455,55 +3576,70 @@ def from_pretrained(
             is_local = os.path.isdir(pretrained_model_name_or_path)
             if is_local:
                 if from_tf and os.path.isfile(
-                    os.path.join(pretrained_model_name_or_path, subfolder, TF_WEIGHTS_NAME + ".index")
+                    os.path.join(pretrained_model_name_or_path,
+                                 subfolder, TF_WEIGHTS_NAME + ".index")
                 ):
                     # Load from a TF 1.0 checkpoint in priority if from_tf
-                    archive_file = os.path.join(pretrained_model_name_or_path, subfolder, TF_WEIGHTS_NAME + ".index")
+                    archive_file = os.path.join(
+                        pretrained_model_name_or_path, subfolder, TF_WEIGHTS_NAME + ".index")
                 elif from_tf and os.path.isfile(
-                    os.path.join(pretrained_model_name_or_path, subfolder, TF2_WEIGHTS_NAME)
+                    os.path.join(pretrained_model_name_or_path,
+                                 subfolder, TF2_WEIGHTS_NAME)
                 ):
                     # Load from a TF 2.0 checkpoint in priority if from_tf
-                    archive_file = os.path.join(pretrained_model_name_or_path, subfolder, TF2_WEIGHTS_NAME)
+                    archive_file = os.path.join(
+                        pretrained_model_name_or_path, subfolder, TF2_WEIGHTS_NAME)
                 elif from_flax and os.path.isfile(
-                    os.path.join(pretrained_model_name_or_path, subfolder, FLAX_WEIGHTS_NAME)
+                    os.path.join(pretrained_model_name_or_path,
+                                 subfolder, FLAX_WEIGHTS_NAME)
                 ):
                     # Load from a Flax checkpoint in priority if from_flax
-                    archive_file = os.path.join(pretrained_model_name_or_path, subfolder, FLAX_WEIGHTS_NAME)
+                    archive_file = os.path.join(
+                        pretrained_model_name_or_path, subfolder, FLAX_WEIGHTS_NAME)
                 elif use_safetensors is not False and os.path.isfile(
-                    os.path.join(pretrained_model_name_or_path, subfolder, _add_variant(SAFE_WEIGHTS_NAME, variant))
+                    os.path.join(pretrained_model_name_or_path, subfolder,
+                                 _add_variant(SAFE_WEIGHTS_NAME, variant))
                 ):
                     # Load from a safetensors checkpoint
                     archive_file = os.path.join(
-                        pretrained_model_name_or_path, subfolder, _add_variant(SAFE_WEIGHTS_NAME, variant)
+                        pretrained_model_name_or_path, subfolder, _add_variant(
+                            SAFE_WEIGHTS_NAME, variant)
                     )
                 elif use_safetensors is not False and os.path.isfile(
                     os.path.join(
-                        pretrained_model_name_or_path, subfolder, _add_variant(SAFE_WEIGHTS_INDEX_NAME, variant)
+                        pretrained_model_name_or_path, subfolder, _add_variant(
+                            SAFE_WEIGHTS_INDEX_NAME, variant)
                     )
                 ):
                     # Load from a sharded safetensors checkpoint
                     archive_file = os.path.join(
-                        pretrained_model_name_or_path, subfolder, _add_variant(SAFE_WEIGHTS_INDEX_NAME, variant)
+                        pretrained_model_name_or_path, subfolder, _add_variant(
+                            SAFE_WEIGHTS_INDEX_NAME, variant)
                     )
                     is_sharded = True
                 elif not use_safetensors and os.path.isfile(
-                    os.path.join(pretrained_model_name_or_path, subfolder, _add_variant(WEIGHTS_NAME, variant))
+                    os.path.join(pretrained_model_name_or_path,
+                                 subfolder, _add_variant(WEIGHTS_NAME, variant))
                 ):
                     # Load from a PyTorch checkpoint
                     archive_file = os.path.join(
-                        pretrained_model_name_or_path, subfolder, _add_variant(WEIGHTS_NAME, variant)
+                        pretrained_model_name_or_path, subfolder, _add_variant(
+                            WEIGHTS_NAME, variant)
                     )
                 elif not use_safetensors and os.path.isfile(
-                    os.path.join(pretrained_model_name_or_path, subfolder, _add_variant(WEIGHTS_INDEX_NAME, variant))
+                    os.path.join(pretrained_model_name_or_path, subfolder,
+                                 _add_variant(WEIGHTS_INDEX_NAME, variant))
                 ):
                     # Load from a sharded PyTorch checkpoint
                     archive_file = os.path.join(
-                        pretrained_model_name_or_path, subfolder, _add_variant(WEIGHTS_INDEX_NAME, variant)
+                        pretrained_model_name_or_path, subfolder, _add_variant(
+                            WEIGHTS_INDEX_NAME, variant)
                     )
                     is_sharded = True
                 # At this stage we don't have a weight file so we will raise an error.
                 elif not use_safetensors and (
-                    os.path.isfile(os.path.join(pretrained_model_name_or_path, subfolder, TF_WEIGHTS_NAME + ".index"))
+                    os.path.isfile(os.path.join(
+                        pretrained_model_name_or_path, subfolder, TF_WEIGHTS_NAME + ".index"))
                     or os.path.isfile(os.path.join(pretrained_model_name_or_path, subfolder, TF2_WEIGHTS_NAME))
                 ):
                     raise EnvironmentError(
@@ -3512,7 +3648,8 @@ def from_pretrained(
                         " `from_tf=True` to load this model from those weights."
                     )
                 elif not use_safetensors and os.path.isfile(
-                    os.path.join(pretrained_model_name_or_path, subfolder, FLAX_WEIGHTS_NAME)
+                    os.path.join(pretrained_model_name_or_path,
+                                 subfolder, FLAX_WEIGHTS_NAME)
                 ):
                     raise EnvironmentError(
                         f"Error no file named {_add_variant(WEIGHTS_NAME, variant)} found in directory"
@@ -3539,11 +3676,13 @@ def from_pretrained(
                         f"We found a TensorFlow checkpoint at {pretrained_model_name_or_path + '.index'}, please set "
                         "from_tf to True to load from this checkpoint."
                     )
-                archive_file = os.path.join(subfolder, pretrained_model_name_or_path + ".index")
+                archive_file = os.path.join(
+                    subfolder, pretrained_model_name_or_path + ".index")
                 is_local = True
             elif is_remote_url(pretrained_model_name_or_path):
                 filename = pretrained_model_name_or_path
-                resolved_archive_file = download_url(pretrained_model_name_or_path)
+                resolved_archive_file = download_url(
+                    pretrained_model_name_or_path)
             else:
                 # set correct filename
                 if from_tf:
@@ -3571,7 +3710,8 @@ def from_pretrained(
                         "_raise_exceptions_for_missing_entries": False,
                         "_commit_hash": commit_hash,
                     }
-                    resolved_archive_file = cached_file(pretrained_model_name_or_path, filename, **cached_file_kwargs)
+                    resolved_archive_file = cached_file(
+                        pretrained_model_name_or_path, filename, **cached_file_kwargs)
 
                     # Since we set _raise_exceptions_for_missing_entries=False, we don't get an exception but a None
                     # result when internet is up, the repo and revision exist, but the file does not.
@@ -3641,7 +3781,8 @@ def from_pretrained(
                                     Thread(
                                         target=auto_conversion,
                                         args=(pretrained_model_name_or_path,),
-                                        kwargs={"ignore_errors_during_conversion": True, **cached_file_kwargs},
+                                        kwargs={
+                                            "ignore_errors_during_conversion": True, **cached_file_kwargs},
                                         name="Thread-autoconversion",
                                     ).start()
                         else:
@@ -3699,7 +3840,8 @@ def from_pretrained(
                 logger.info(f"loading weights file {archive_file}")
                 resolved_archive_file = archive_file
             else:
-                logger.info(f"loading weights file {filename} from cache at {resolved_archive_file}")
+                logger.info(
+                    f"loading weights file {filename} from cache at {resolved_archive_file}")
         elif gguf_file:
             from .modeling_gguf_pytorch_utils import load_gguf_checkpoint
 
@@ -3724,9 +3866,11 @@ def from_pretrained(
                     "_commit_hash": commit_hash,
                 }
 
-                gguf_path = cached_file(pretrained_model_name_or_path, gguf_file, **cached_file_kwargs)
+                gguf_path = cached_file(
+                    pretrained_model_name_or_path, gguf_file, **cached_file_kwargs)
 
-            state_dict = load_gguf_checkpoint(gguf_path, return_tensors=True)["tensors"]
+            state_dict = load_gguf_checkpoint(
+                gguf_path, return_tensors=True)["tensors"]
 
             resolved_archive_file = None
             is_sharded = False
@@ -3763,10 +3907,12 @@ def from_pretrained(
                 pass
             elif metadata.get("format") == "tf":
                 from_tf = True
-                logger.info("A TensorFlow safetensors file is being loaded in a PyTorch model.")
+                logger.info(
+                    "A TensorFlow safetensors file is being loaded in a PyTorch model.")
             elif metadata.get("format") == "flax":
                 from_flax = True
-                logger.info("A Flax safetensors file is being loaded in a PyTorch model.")
+                logger.info(
+                    "A Flax safetensors file is being loaded in a PyTorch model.")
             elif metadata.get("format") == "mlx":
                 # This is a mlx file, we assume weights are compatible with pt
                 pass
@@ -3795,15 +3941,18 @@ def from_pretrained(
                     if torch_dtype == "auto":
                         if hasattr(config, "torch_dtype") and config.torch_dtype is not None:
                             torch_dtype = config.torch_dtype
-                            logger.info(f"Will use torch_dtype={torch_dtype} as defined in model's config object")
+                            logger.info(
+                                f"Will use torch_dtype={torch_dtype} as defined in model's config object")
                         else:
                             if is_sharded and "dtype" in sharded_metadata:
                                 torch_dtype = sharded_metadata["dtype"]
                             elif not is_sharded:
                                 torch_dtype = get_state_dict_dtype(state_dict)
                             else:
-                                one_state_dict = load_state_dict(resolved_archive_file[0])
-                                torch_dtype = get_state_dict_dtype(one_state_dict)
+                                one_state_dict = load_state_dict(
+                                    resolved_archive_file[0])
+                                torch_dtype = get_state_dict_dtype(
+                                    one_state_dict)
                                 del one_state_dict  # free CPU memory
                             logger.info(
                                 "Since the `torch_dtype` attribute can't be found in model's config object, "
@@ -3819,7 +3968,8 @@ def from_pretrained(
 
             # Check if `_keep_in_fp32_modules` is not None
             use_keep_in_fp32_modules = (cls._keep_in_fp32_modules is not None) and (
-                (torch_dtype == torch.float16) or hasattr(hf_quantizer, "use_keep_in_fp32_modules")
+                (torch_dtype == torch.float16) or hasattr(
+                    hf_quantizer, "use_keep_in_fp32_modules")
             )
 
             if is_sharded:
@@ -3841,12 +3991,15 @@ def from_pretrained(
         if is_deepspeed_zero3_enabled() and not is_quantized:
             import deepspeed
 
-            logger.info("Detected DeepSpeed ZeRO-3: activating zero.init() for this model")
-            init_contexts = [deepspeed.zero.Init(config_dict_or_path=deepspeed_config())] + init_contexts
+            logger.info(
+                "Detected DeepSpeed ZeRO-3: activating zero.init() for this model")
+            init_contexts = [deepspeed.zero.Init(
+                config_dict_or_path=deepspeed_config())] + init_contexts
         elif low_cpu_mem_usage:
             init_contexts.append(init_empty_weights())
 
-        config = copy.deepcopy(config)  # We do not want to modify the config inplace in from_pretrained.
+        # We do not want to modify the config inplace in from_pretrained.
+        config = copy.deepcopy(config)
         config = cls._autoset_attn_implementation(
             config, use_flash_attention_2=use_flash_attention_2, torch_dtype=torch_dtype, device_map=device_map
         )
@@ -3881,7 +4034,8 @@ def from_pretrained(
             special_dtypes = {}
 
             if hf_quantizer is not None:
-                special_dtypes.update(hf_quantizer.get_special_dtypes_update(model, torch_dtype))
+                special_dtypes.update(
+                    hf_quantizer.get_special_dtypes_update(model, torch_dtype))
 
             special_dtypes.update(
                 {
@@ -3927,7 +4081,8 @@ def from_pretrained(
 
             # Make sure tied weights are tied before creating the device map.
             model.tie_weights()
-            device_map = infer_auto_device_map(model, dtype=target_dtype, **device_map_kwargs)
+            device_map = infer_auto_device_map(
+                model, dtype=target_dtype, **device_map_kwargs)
 
             if hf_quantizer is not None:
                 hf_quantizer.validate_environment(device_map=device_map)
@@ -3941,7 +4096,8 @@ def from_pretrained(
         if from_tf:
             if resolved_archive_file.endswith(".index"):
                 # Load from a TensorFlow 1.X checkpoint - provided by original authors
-                model = cls.load_tf_weights(model, config, resolved_archive_file[:-6])  # Remove the '.index'
+                model = cls.load_tf_weights(
+                    model, config, resolved_archive_file[:-6])  # Remove the '.index'
             else:
                 # Load from our TensorFlow 2.0 checkpoints
                 try:
@@ -3961,7 +4117,8 @@ def from_pretrained(
             try:
                 from .modeling_flax_pytorch_utils import load_flax_checkpoint_in_pytorch_model
 
-                model = load_flax_checkpoint_in_pytorch_model(model, resolved_archive_file)
+                model = load_flax_checkpoint_in_pytorch_model(
+                    model, resolved_archive_file)
             except ImportError:
                 logger.error(
                     "Loading a Flax model in PyTorch, requires both PyTorch and Flax to be installed. Please see"
@@ -4008,8 +4165,10 @@ def from_pretrained(
 
         # If it is a model with generation capabilities, attempt to load the generation config
         if model.can_generate() and generation_config is not None:
-            logger.info("The user-defined `generation_config` will be used to override the default generation config.")
-            model.generation_config = model.generation_config.from_dict(generation_config.to_dict())
+            logger.info(
+                "The user-defined `generation_config` will be used to override the default generation config.")
+            model.generation_config = model.generation_config.from_dict(
+                generation_config.to_dict())
         elif model.can_generate() and pretrained_model_name_or_path is not None:
             try:
                 model.generation_config = GenerationConfig.from_pretrained(
@@ -4111,7 +4270,8 @@ def _load_pretrained_model(
 
         if device_map is not None and "disk" in device_map.values():
             archive_file = (
-                resolved_archive_file[0] if isinstance(resolved_archive_file, (list, tuple)) else resolved_archive_file
+                resolved_archive_file[0] if isinstance(
+                    resolved_archive_file, (list, tuple)) else resolved_archive_file
             )
             is_safetensors = archive_file.endswith(".safetensors")
             if offload_folder is None and not is_safetensors:
@@ -4135,6 +4295,59 @@ def _load_pretrained_model(
         expected_keys = list(model_state_dict.keys())
         prefix = model.base_model_prefix
 
+        error_msgs = []
+        old_keys = []
+        new_keys = []
+        renamed_keys = {}
+        warning_msg = f"This model {type(model)}"
+
+        # Preserve the original_loaded_keys reference without modifying it
+        original_loaded_keys = loaded_keys  # Do not copy, just refer to the original
+
+        # Create a new list to hold the updated keys
+        updated_loaded_keys = []
+
+        # Single loop for processing keys
+        for key in loaded_keys:
+            new_key = key
+
+            if "gamma" in key:
+                new_key = key.replace("gamma", "weight")
+            elif "beta" in key:
+                new_key = key.replace("beta", "bias")
+            # to avoid logging parametrized weight norm renaming
+            if hasattr(nn.utils.parametrizations, "weight_norm"):
+                if "weight_g" in key:
+                    new_key = key.replace(
+                        "weight_g", "parametrizations.weight.original0")
+                if "weight_v" in key:
+                    new_key = key.replace(
+                        "weight_v", "parametrizations.weight.original1")
+            else:
+                if "parametrizations.weight.original0" in key:
+                    new_key = key.replace(
+                        "parametrizations.weight.original0", "weight_g")
+                if "parametrizations.weight.original1" in key:
+                    new_key = key.replace(
+                        "parametrizations.weight.original1", "weight_v")
+            if new_key != key:
+                old_keys.append(key)
+                new_keys.append(new_key)
+                renamed_keys[key] = new_key
+            # Add the new (or unchanged) key
+            updated_loaded_keys.append(new_key)
+
+        if renamed_keys:
+            warning_msg += 'contains parameters that have been renamed internally ("gamma" and "beta" in parameters or parametrized weight norm) (a few are listed below but more are present in the model):\n'
+            logger.warning(warning_msg)
+            for old_key, new_key in renamed_keys.items():
+                warning_msg += f"* {old_key} -> {new_key}\n"
+            warning_msg += "If you are using a model from the Hub, consider submitting a PR to adjust these weights and help future users."
+            logger.info(warning_msg)
+
+        # Now assign the updated list to loaded_keys
+        loaded_keys = updated_loaded_keys
+
         def _fix_key(key):
             if "beta" in key:
                 return key.replace("beta", "bias")
@@ -4159,7 +4372,8 @@ def _fix_key(key):
 
         if len(prefix) > 0:
             has_prefix_module = any(s.startswith(prefix) for s in loaded_keys)
-            expects_prefix_module = any(s.startswith(prefix) for s in expected_keys)
+            expects_prefix_module = any(s.startswith(prefix)
+                                        for s in expected_keys)
         else:
             has_prefix_module = False
             expects_prefix_module = False
@@ -4171,8 +4385,10 @@ def _fix_key(key):
 
         if remove_prefix_from_model:
             _prefix = f"{prefix}."
-            expected_keys_not_prefixed = [s for s in expected_keys if not s.startswith(_prefix)]
-            expected_keys = [s[len(_prefix) :] if s.startswith(_prefix) else s for s in expected_keys]
+            expected_keys_not_prefixed = [
+                s for s in expected_keys if not s.startswith(_prefix)]
+            expected_keys = [s[len(_prefix):] if s.startswith(
+                _prefix) else s for s in expected_keys]
         elif add_prefix_to_model:
             expected_keys = [".".join([prefix, s]) for s in expected_keys]
 
@@ -4183,7 +4399,8 @@ def _fix_key(key):
         # buffers
         model_buffers = {n for n, _ in model.named_buffers()}
         if remove_prefix_from_model:
-            model_buffers = {key[len(_prefix) :] if key.startswith(_prefix) else key for key in model_buffers}
+            model_buffers = {key[len(_prefix):] if key.startswith(
+                _prefix) else key for key in model_buffers}
         elif add_prefix_to_model:
             model_buffers = {".".join([prefix, key]) for key in model_buffers}
         unexpected_keys = sorted(unexpected_keys - model_buffers)
@@ -4196,31 +4413,37 @@ def _fix_key(key):
                 ptrs[id_tensor].append(name)
 
             # These are all the pointers of shared tensors.
-            tied_params = [names for _, names in ptrs.items() if len(names) > 1]
+            tied_params = [names for _,
+                           names in ptrs.items() if len(names) > 1]
         else:
             # id function doesn't work for meta tensor so we need this function
             tied_params = find_tied_parameters(model)
 
         for group in tied_params:
             if remove_prefix_from_model:
-                group = [key[len(_prefix) :] if key.startswith(_prefix) else key for key in group]
+                group = [key[len(_prefix):] if key.startswith(
+                    _prefix) else key for key in group]
             elif add_prefix_to_model:
                 group = [".".join([prefix, key]) for key in group]
             missing_in_group = [k for k in missing_keys if k in group]
             if len(missing_in_group) > 0 and len(missing_in_group) < len(group):
-                missing_keys = [k for k in missing_keys if k not in missing_in_group]
+                missing_keys = [
+                    k for k in missing_keys if k not in missing_in_group]
 
         # Some models may have keys that are not in the state by design, removing them before needlessly warning
         # the user.
         if cls._keys_to_ignore_on_load_missing is not None:
             for pat in cls._keys_to_ignore_on_load_missing:
-                missing_keys = [k for k in missing_keys if re.search(pat, k) is None]
+                missing_keys = [
+                    k for k in missing_keys if re.search(pat, k) is None]
 
         if cls._keys_to_ignore_on_load_unexpected is not None:
             for pat in cls._keys_to_ignore_on_load_unexpected:
-                unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]
+                unexpected_keys = [
+                    k for k in unexpected_keys if re.search(pat, k) is None]
         if hf_quantizer is not None:
-            missing_keys = hf_quantizer.update_missing_keys(model, missing_keys, prefix)
+            missing_keys = hf_quantizer.update_missing_keys(
+                model, missing_keys, prefix)
 
         # retrieve weights on meta device and put them back on CPU.
         # This is not ideal in terms of memory, but if we don't do that not, we can't initialize them in the next step
@@ -4256,7 +4479,8 @@ def _fix_key(key):
                     ):
                         set_module_tensor_to_device(model, key, "cpu", value)
                     else:
-                        hf_quantizer.create_quantized_param(model, value, key, "cpu", state_dict, unexpected_keys)
+                        hf_quantizer.create_quantized_param(
+                            model, value, key, "cpu", state_dict, unexpected_keys)
 
         # retrieve uninitialized modules and initialize before maybe overriding that with the pretrained weights.
         if _fast_init:
@@ -4264,10 +4488,11 @@ def _fix_key(key):
                 if remove_prefix_from_model:
                     _loaded_keys = [f"{prefix}.{k}" for k in loaded_keys]
                 elif add_prefix_to_model:
-                    _loaded_keys = [k[len(prefix) + 1 :] for k in loaded_keys]
+                    _loaded_keys = [k[len(prefix) + 1:] for k in loaded_keys]
                 else:
                     _loaded_keys = loaded_keys
-                not_initialized_submodules = set_initialized_submodules(model, _loaded_keys)
+                not_initialized_submodules = set_initialized_submodules(
+                    model, _loaded_keys)
                 # If we're about to tie the output embeds to the input embeds we don't need to init them
                 if hasattr(model.config, "tie_word_embeddings") and model.config.tie_word_embeddings:
                     output_embeddings = model.get_output_embeddings()
@@ -4314,7 +4539,8 @@ def _fix_key(key):
                     "properly saved?"
                 )
             if device_map is not None:
-                device_map = {k.replace(f"{cls.base_model_prefix}.", ""): v for k, v in device_map.items()}
+                device_map = {
+                    k.replace(f"{cls.base_model_prefix}.", ""): v for k, v in device_map.items()}
 
         def _find_mismatched_keys(
             state_dict,
@@ -4351,18 +4577,22 @@ def _find_mismatched_keys(
                             pass
                         else:
                             mismatched_keys.append(
-                                (checkpoint_key, state_dict[checkpoint_key].shape, model_state_dict[model_key].shape)
+                                (checkpoint_key, state_dict[checkpoint_key].shape,
+                                 model_state_dict[model_key].shape)
                             )
                             del state_dict[checkpoint_key]
             return mismatched_keys
 
         if resolved_archive_file is not None:
-            folder = os.path.sep.join(resolved_archive_file[0].split(os.path.sep)[:-1])
+            folder = os.path.sep.join(
+                resolved_archive_file[0].split(os.path.sep)[:-1])
         else:
             folder = None
         if device_map is not None and is_safetensors:
-            param_device_map = expand_device_map(device_map, original_loaded_keys, start_prefix)
-            str_dtype = str(dtype).replace("torch.", "") if dtype is not None else "float32"
+            param_device_map = expand_device_map(
+                device_map, original_loaded_keys, start_prefix)
+            str_dtype = str(dtype).replace(
+                "torch.", "") if dtype is not None else "float32"
             if sharded_metadata is None:
                 archive_file = (
                     resolved_archive_file[0]
@@ -4371,11 +4601,13 @@ def _find_mismatched_keys(
                 )
                 weight_map = {p: archive_file for p in original_loaded_keys}
             else:
-                weight_map = {p: os.path.join(folder, f) for p, f in sharded_metadata["weight_map"].items()}
+                weight_map = {p: os.path.join(
+                    folder, f) for p, f in sharded_metadata["weight_map"].items()}
             offload_index = {
-                p[len(start_prefix) :]: {"safetensors_file": f, "weight_name": p, "dtype": str_dtype}
+                p[len(start_prefix):]: {
+                    "safetensors_file": f, "weight_name": p, "dtype": str_dtype}
                 for p, f in weight_map.items()
-                if p.startswith(start_prefix) and param_device_map[p[len(start_prefix) :]] == "disk"
+                if p.startswith(start_prefix) and param_device_map[p[len(start_prefix):]] == "disk"
             }
         else:
             offload_index = None
@@ -4438,18 +4670,21 @@ def _find_mismatched_keys(
                 disk_only_shard_files = get_disk_only_shard_files(
                     device_map, sharded_metadata=sharded_metadata, start_prefix=start_prefix
                 )
-                disk_only_shard_files = [os.path.join(folder, f) for f in disk_only_shard_files]
+                disk_only_shard_files = [os.path.join(
+                    folder, f) for f in disk_only_shard_files]
             else:
                 disk_only_shard_files = []
 
             if len(resolved_archive_file) > 1:
-                resolved_archive_file = logging.tqdm(resolved_archive_file, desc="Loading checkpoint shards")
+                resolved_archive_file = logging.tqdm(
+                    resolved_archive_file, desc="Loading checkpoint shards")
             assign_to_params_buffers = None
             for shard_file in resolved_archive_file:
                 # Skip the load for shards that only contain disk-offloaded weights when using safetensors for the offload.
                 if shard_file in disk_only_shard_files:
                     continue
-                state_dict = load_state_dict(shard_file, is_quantized=is_quantized)
+                state_dict = load_state_dict(
+                    shard_file, is_quantized=is_quantized)
 
                 # Mistmatched keys contains tuples key/shape1/shape2 of weights in the checkpoint that have a shape not
                 # matching the weights in the model.
@@ -4466,7 +4701,8 @@ def _find_mismatched_keys(
                         for key, param in model_to_load.state_dict().items():
                             if param.device == torch.device("meta"):
                                 set_module_tensor_to_device(
-                                    model_to_load, key, "cpu", torch.empty(*param.size(), dtype=dtype)
+                                    model_to_load, key, "cpu", torch.empty(
+                                        *param.size(), dtype=dtype)
                                 )
                     else:
                         new_error_msgs, offload_index, state_dict_index = _load_state_dict_into_meta_model(
@@ -4507,17 +4743,21 @@ def _find_mismatched_keys(
                     if not is_safetensors:
                         for weight_name in offload_index:
                             shutil.move(
-                                os.path.join(offload_folder, f"{weight_name}.dat"),
-                                os.path.join(offload_folder, f"{prefix}.{weight_name}.dat"),
+                                os.path.join(offload_folder,
+                                             f"{weight_name}.dat"),
+                                os.path.join(offload_folder,
+                                             f"{prefix}.{weight_name}.dat"),
                             )
-                    offload_index = {f"{prefix}.{key}": value for key, value in offload_index.items()}
+                    offload_index = {
+                        f"{prefix}.{key}": value for key, value in offload_index.items()}
                 if not is_safetensors:
                     save_offload_index(offload_index, offload_folder)
                     offload_index = None
 
             if offload_state_dict:
                 # Load back temporarily offloaded state dict
-                load_offloaded_weights(model_to_load, state_dict_index, state_dict_folder)
+                load_offloaded_weights(
+                    model_to_load, state_dict_index, state_dict_folder)
                 shutil.rmtree(state_dict_folder)
 
         if len(error_msgs) > 0:
@@ -4526,7 +4766,8 @@ def _find_mismatched_keys(
                 error_msg += (
                     "\n\tYou may consider adding `ignore_mismatched_sizes=True` in the model `from_pretrained` method."
                 )
-            raise RuntimeError(f"Error(s) in loading state_dict for {model.__class__.__name__}:\n\t{error_msg}")
+            raise RuntimeError(
+                f"Error(s) in loading state_dict for {model.__class__.__name__}:\n\t{error_msg}")
 
         if len(unexpected_keys) > 0:
             archs = [] if model.config.architectures is None else model.config.architectures
@@ -4541,7 +4782,8 @@ def _find_mismatched_keys(
                 " (initializing a BertForSequenceClassification model from a BertForSequenceClassification model)."
             )
         else:
-            logger.info(f"All model checkpoint weights were used when initializing {model.__class__.__name__}.\n")
+            logger.info(
+                f"All model checkpoint weights were used when initializing {model.__class__.__name__}.\n")
         if len(missing_keys) > 0:
             logger.warning(
                 f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at"
@@ -4577,7 +4819,8 @@ def retrieve_modules_from_names(self, names, add_prefix=False, remove_prefix=Fal
         # torch.nn.ParameterList is a special case where two parameter keywords
         # are appended to the module name, *e.g.* bert.special_embeddings.0
         module_keys = module_keys.union(
-            {".".join(key.split(".")[:-2]) for key in names if len(key) > 0 and key[-1].isdigit()}
+            {".".join(key.split(".")[:-2])
+             for key in names if len(key) > 0 and key[-1].isdigit()}
         )
 
         retrieved_modules = []
@@ -4585,9 +4828,11 @@ def retrieve_modules_from_names(self, names, add_prefix=False, remove_prefix=Fal
         for name, module in self.named_modules():
             if remove_prefix:
                 _prefix = f"{self.base_model_prefix}."
-                name = name[len(_prefix) :] if name.startswith(_prefix) else name
+                name = name[len(_prefix):] if name.startswith(
+                    _prefix) else name
             elif add_prefix:
-                name = ".".join([self.base_model_prefix, name]) if len(name) > 0 else self.base_model_prefix
+                name = ".".join([self.base_model_prefix, name]) if len(
+                    name) > 0 else self.base_model_prefix
 
             if name in module_keys:
                 retrieved_modules.append(module)
@@ -4623,7 +4868,8 @@ def _load_pretrained_model_low_mem(
 
         _move_model_to_meta(model, loaded_state_dict_keys, start_prefix)
         state_dict = load_state_dict(resolved_archive_file)
-        expected_keys = loaded_state_dict_keys  # plug for missing expected_keys. TODO: replace with proper keys
+        # plug for missing expected_keys. TODO: replace with proper keys
+        expected_keys = loaded_state_dict_keys
         error_msgs = _load_state_dict_into_meta_model(
             model,
             state_dict,
@@ -4674,7 +4920,8 @@ def to_bettertransformer(self) -> "PreTrainedModel":
             [`PreTrainedModel`]: The model converted to BetterTransformer.
         """
         if not is_optimum_available():
-            raise ImportError("The package `optimum` is required to use Better Transformer.")
+            raise ImportError(
+                "The package `optimum` is required to use Better Transformer.")
 
         from optimum.version import __version__ as optimum_version
 
@@ -4696,7 +4943,8 @@ def reverse_bettertransformer(self):
             [`PreTrainedModel`]: The model converted back to the original modeling.
         """
         if not is_optimum_available():
-            raise ImportError("The package `optimum` is required to use Better Transformer.")
+            raise ImportError(
+                "The package `optimum` is required to use Better Transformer.")
 
         from optimum.version import __version__ as optimum_version
 
@@ -4732,7 +4980,8 @@ def warn_if_padding_and_no_attention_mask(self, input_ids, attention_mask):
             # If the pad token is equal to either BOS, EOS, or SEP, we do not know whether the user should use an
             # attention_mask or not. In this case, we should still show a warning because this is a rare case.
             if (
-                (self.config.bos_token_id is not None and self.config.bos_token_id == self.config.pad_token_id)
+                (self.config.bos_token_id is not None and self.config.bos_token_id ==
+                 self.config.pad_token_id)
                 or (self.config.eos_token_id is not None and self.config.eos_token_id == self.config.pad_token_id)
                 or (self.config.sep_token_id is not None and self.config.sep_token_id == self.config.pad_token_id)
             ):
@@ -4816,7 +5065,8 @@ def __init__(self, config: PretrainedConfig):
         super().__init__()
         self.dense_0 = nn.Linear(config.hidden_size * 2, config.hidden_size)
         self.activation = nn.Tanh()
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.LayerNorm = nn.LayerNorm(
+            config.hidden_size, eps=config.layer_norm_eps)
         self.dense_1 = nn.Linear(config.hidden_size, 1)
 
     def forward(
@@ -4853,9 +5103,13 @@ def forward(
         ), "One of start_states, start_positions should be not None"
         if start_positions is not None:
             slen, hsz = hidden_states.shape[-2:]
-            start_positions = start_positions[:, None, None].expand(-1, -1, hsz)  # shape (bsz, 1, hsz)
-            start_states = hidden_states.gather(-2, start_positions)  # shape (bsz, 1, hsz)
-            start_states = start_states.expand(-1, slen, -1)  # shape (bsz, slen, hsz)
+            # shape (bsz, 1, hsz)
+            start_positions = start_positions[:,
+                                              None, None].expand(-1, -1, hsz)
+            # shape (bsz, 1, hsz)
+            start_states = hidden_states.gather(-2, start_positions)
+            # shape (bsz, slen, hsz)
+            start_states = start_states.expand(-1, slen, -1)
 
         x = self.dense_0(torch.cat([hidden_states, start_states], dim=-1))
         x = self.activation(x)
@@ -4920,12 +5174,18 @@ def forward(
             start_states is not None or start_positions is not None
         ), "One of start_states, start_positions should be not None"
         if start_positions is not None:
-            start_positions = start_positions[:, None, None].expand(-1, -1, hsz)  # shape (bsz, 1, hsz)
-            start_states = hidden_states.gather(-2, start_positions).squeeze(-2)  # shape (bsz, hsz)
+            # shape (bsz, 1, hsz)
+            start_positions = start_positions[:,
+                                              None, None].expand(-1, -1, hsz)
+            # shape (bsz, hsz)
+            start_states = hidden_states.gather(-2,
+                                                start_positions).squeeze(-2)
 
         if cls_index is not None:
-            cls_index = cls_index[:, None, None].expand(-1, -1, hsz)  # shape (bsz, 1, hsz)
-            cls_token_state = hidden_states.gather(-2, cls_index).squeeze(-2)  # shape (bsz, hsz)
+            # shape (bsz, 1, hsz)
+            cls_index = cls_index[:, None, None].expand(-1, -1, hsz)
+            # shape (bsz, hsz)
+            cls_token_state = hidden_states.gather(-2, cls_index).squeeze(-2)
         else:
             cls_token_state = hidden_states[:, -1, :]  # shape (bsz, hsz)
 
@@ -5026,7 +5286,8 @@ def forward(
                     x.squeeze_(-1)
 
             # during training, compute the end logits based on the ground truth of the start position
-            end_logits = self.end_logits(hidden_states, start_positions=start_positions, p_mask=p_mask)
+            end_logits = self.end_logits(
+                hidden_states, start_positions=start_positions, p_mask=p_mask)
 
             loss_fct = CrossEntropyLoss()
             start_loss = loss_fct(start_logits, start_positions)
@@ -5035,7 +5296,8 @@ def forward(
 
             if cls_index is not None and is_impossible is not None:
                 # Predict answerability from the representation of CLS and START
-                cls_logits = self.answer_class(hidden_states, start_positions=start_positions, cls_index=cls_index)
+                cls_logits = self.answer_class(
+                    hidden_states, start_positions=start_positions, cls_index=cls_index)
                 loss_fct_cls = nn.BCEWithLogitsLoss()
                 cls_loss = loss_fct_cls(cls_logits, is_impossible)
 
@@ -5047,30 +5309,41 @@ def forward(
         else:
             # during inference, compute the end logits based on beam search
             bsz, slen, hsz = hidden_states.size()
-            start_log_probs = nn.functional.softmax(start_logits, dim=-1)  # shape (bsz, slen)
+            start_log_probs = nn.functional.softmax(
+                start_logits, dim=-1)  # shape (bsz, slen)
 
             start_top_log_probs, start_top_index = torch.topk(
                 start_log_probs, self.start_n_top, dim=-1
             )  # shape (bsz, start_n_top)
-            start_top_index_exp = start_top_index.unsqueeze(-1).expand(-1, -1, hsz)  # shape (bsz, start_n_top, hsz)
-            start_states = torch.gather(hidden_states, -2, start_top_index_exp)  # shape (bsz, start_n_top, hsz)
-            start_states = start_states.unsqueeze(1).expand(-1, slen, -1, -1)  # shape (bsz, slen, start_n_top, hsz)
+            # shape (bsz, start_n_top, hsz)
+            start_top_index_exp = start_top_index.unsqueeze(
+                -1).expand(-1, -1, hsz)
+            # shape (bsz, start_n_top, hsz)
+            start_states = torch.gather(hidden_states, -2, start_top_index_exp)
+            # shape (bsz, slen, start_n_top, hsz)
+            start_states = start_states.unsqueeze(1).expand(-1, slen, -1, -1)
 
             hidden_states_expanded = hidden_states.unsqueeze(2).expand_as(
                 start_states
             )  # shape (bsz, slen, start_n_top, hsz)
             p_mask = p_mask.unsqueeze(-1) if p_mask is not None else None
-            end_logits = self.end_logits(hidden_states_expanded, start_states=start_states, p_mask=p_mask)
-            end_log_probs = nn.functional.softmax(end_logits, dim=1)  # shape (bsz, slen, start_n_top)
+            end_logits = self.end_logits(
+                hidden_states_expanded, start_states=start_states, p_mask=p_mask)
+            end_log_probs = nn.functional.softmax(
+                end_logits, dim=1)  # shape (bsz, slen, start_n_top)
 
             end_top_log_probs, end_top_index = torch.topk(
                 end_log_probs, self.end_n_top, dim=1
             )  # shape (bsz, end_n_top, start_n_top)
-            end_top_log_probs = end_top_log_probs.view(-1, self.start_n_top * self.end_n_top)
-            end_top_index = end_top_index.view(-1, self.start_n_top * self.end_n_top)
+            end_top_log_probs = end_top_log_probs.view(
+                -1, self.start_n_top * self.end_n_top)
+            end_top_index = end_top_index.view(-1,
+                                               self.start_n_top * self.end_n_top)
 
-            start_states = torch.einsum("blh,bl->bh", hidden_states, start_log_probs)
-            cls_logits = self.answer_class(hidden_states, start_states=start_states, cls_index=cls_index)
+            start_states = torch.einsum(
+                "blh,bl->bh", hidden_states, start_log_probs)
+            cls_logits = self.answer_class(
+                hidden_states, start_states=start_states, cls_index=cls_index)
 
             if not return_dict:
                 return (start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits)
@@ -5129,7 +5402,8 @@ def __init__(self, config: PretrainedConfig):
             self.summary = nn.Linear(config.hidden_size, num_classes)
 
         activation_string = getattr(config, "summary_activation", None)
-        self.activation: Callable = get_activation(activation_string) if activation_string else Identity()
+        self.activation: Callable = get_activation(
+            activation_string) if activation_string else Identity()
 
         self.first_dropout = Identity()
         if hasattr(config, "summary_first_dropout") and config.summary_first_dropout > 0:
@@ -5169,9 +5443,11 @@ def forward(
                 )
             else:
                 cls_index = cls_index.unsqueeze(-1).unsqueeze(-1)
-                cls_index = cls_index.expand((-1,) * (cls_index.dim() - 1) + (hidden_states.size(-1),))
+                cls_index = cls_index.expand(
+                    (-1,) * (cls_index.dim() - 1) + (hidden_states.size(-1),))
             # shape of cls_index: (bsz, XX, 1, hidden_size) where XX are optional leading dim of hidden_states
-            output = hidden_states.gather(-2, cls_index).squeeze(-2)  # shape (bsz, XX, hidden_size)
+            # shape (bsz, XX, hidden_size)
+            output = hidden_states.gather(-2, cls_index).squeeze(-2)
         elif self.summary_type == "attn":
             raise NotImplementedError
 
@@ -5218,10 +5494,12 @@ def expand_device_map(device_map, param_names, start_prefix):
     Expand a device map to return the correspondance parameter name to device.
     """
     new_device_map = {}
-    param_names = [p[len(start_prefix) :] for p in param_names if p.startswith(start_prefix)]
+    param_names = [p[len(start_prefix):]
+                   for p in param_names if p.startswith(start_prefix)]
     for module, device in device_map.items():
         new_device_map.update(
-            {p: device for p in param_names if p == module or p.startswith(f"{module}.") or module == ""}
+            {p: device for p in param_names if p ==
+                module or p.startswith(f"{module}.") or module == ""}
         )
     return new_device_map
 
@@ -5232,7 +5510,7 @@ def get_disk_only_shard_files(device_map, sharded_metadata, start_prefix):
     """
 
     weight_map = {
-        p[len(start_prefix) :]: v for p, v in sharded_metadata["weight_map"].items() if p.startswith(start_prefix)
+        p[len(start_prefix):]: v for p, v in sharded_metadata["weight_map"].items() if p.startswith(start_prefix)
     }
     files_content = collections.defaultdict(list)
     for weight_name, filename in weight_map.items():

From 12893d3a8adb81a4ec639725bea52e87f283d4c2 Mon Sep 17 00:00:00 2001
From: "zly.idleness" <zly.idleness@gmail.com>
Date: Mon, 23 Sep 2024 19:20:41 +0800
Subject: [PATCH 2/4] update

---
 src/transformers/modeling_utils.py | 22 ----------------------
 1 file changed, 22 deletions(-)

diff --git a/src/transformers/modeling_utils.py b/src/transformers/modeling_utils.py
index fac3a45244b2..d9e8aee2cf65 100755
--- a/src/transformers/modeling_utils.py
+++ b/src/transformers/modeling_utils.py
@@ -4348,28 +4348,6 @@ def _load_pretrained_model(
         # Now assign the updated list to loaded_keys
         loaded_keys = updated_loaded_keys
 
-        def _fix_key(key):
-            if "beta" in key:
-                return key.replace("beta", "bias")
-            if "gamma" in key:
-                return key.replace("gamma", "weight")
-
-            # to avoid logging parametrized weight norm renaming
-            if hasattr(nn.utils.parametrizations, "weight_norm"):
-                if "weight_g" in key:
-                    return key.replace("weight_g", "parametrizations.weight.original0")
-                if "weight_v" in key:
-                    return key.replace("weight_v", "parametrizations.weight.original1")
-            else:
-                if "parametrizations.weight.original0" in key:
-                    return key.replace("parametrizations.weight.original0", "weight_g")
-                if "parametrizations.weight.original1" in key:
-                    return key.replace("parametrizations.weight.original1", "weight_v")
-            return key
-
-        original_loaded_keys = loaded_keys
-        loaded_keys = [_fix_key(key) for key in loaded_keys]
-
         if len(prefix) > 0:
             has_prefix_module = any(s.startswith(prefix) for s in loaded_keys)
             expects_prefix_module = any(s.startswith(prefix)

From 8e064de94f6a1d793bbe8c214650b456e7560961 Mon Sep 17 00:00:00 2001
From: "zly.idleness" <zly.idleness@gmail.com>
Date: Mon, 23 Sep 2024 19:24:07 +0800
Subject: [PATCH 3/4] make style

---
 examples/research_projects/lxmert/demo.ipynb  |  15 +-
 .../movement-pruning/Saving_PruneBERT.ipynb   |   8 +-
 .../research_projects/visual_bert/demo.ipynb  | 179 +++++++++---------
 3 files changed, 101 insertions(+), 101 deletions(-)

diff --git a/examples/research_projects/lxmert/demo.ipynb b/examples/research_projects/lxmert/demo.ipynb
index e80865d0e2c8..576a4b7631cb 100644
--- a/examples/research_projects/lxmert/demo.ipynb
+++ b/examples/research_projects/lxmert/demo.ipynb
@@ -23,21 +23,18 @@
     }
    ],
    "source": [
-    "from IPython.display import clear_output, Image, display\n",
-    "import PIL.Image\n",
     "import io\n",
-    "import json\n",
-    "import torch\n",
+    "\n",
     "import numpy as np\n",
+    "import PIL.Image\n",
+    "from IPython.display import Image, display\n",
+    "from modeling_frcnn import GeneralizedRCNN\n",
     "from processing_image import Preprocess\n",
     "from visualizing_image import SingleImageViz\n",
-    "from modeling_frcnn import GeneralizedRCNN\n",
-    "from utils import Config\n",
+    "\n",
     "import utils\n",
     "from transformers import LxmertForQuestionAnswering, LxmertTokenizer\n",
-    "import wget\n",
-    "import pickle\n",
-    "import os\n",
+    "from utils import Config\n",
     "\n",
     "\n",
     "# URL = \"https://raw.githubusercontent.com/airsplay/py-bottom-up-attention/master/demo/data/images/input.jpg\",\n",
diff --git a/examples/research_projects/movement-pruning/Saving_PruneBERT.ipynb b/examples/research_projects/movement-pruning/Saving_PruneBERT.ipynb
index 019fc9c50e62..e159549a105c 100644
--- a/examples/research_projects/movement-pruning/Saving_PruneBERT.ipynb
+++ b/examples/research_projects/movement-pruning/Saving_PruneBERT.ipynb
@@ -31,19 +31,19 @@
    "source": [
     "# Includes\n",
     "\n",
-    "import h5py\n",
-    "import os\n",
     "import json\n",
+    "import os\n",
     "from collections import OrderedDict\n",
     "\n",
-    "from scipy import sparse\n",
+    "import h5py\n",
     "import numpy as np\n",
-    "\n",
     "import torch\n",
+    "from scipy import sparse\n",
     "from torch import nn\n",
     "\n",
     "from transformers import *\n",
     "\n",
+    "\n",
     "os.chdir(\"../../\")"
    ]
   },
diff --git a/examples/research_projects/visual_bert/demo.ipynb b/examples/research_projects/visual_bert/demo.ipynb
index 14a65ce3df33..9f61beea8e24 100644
--- a/examples/research_projects/visual_bert/demo.ipynb
+++ b/examples/research_projects/visual_bert/demo.ipynb
@@ -3,34 +3,47 @@
   {
    "cell_type": "code",
    "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# %pip install-r requirements.txt"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "**Note**: This demo is adapted from the LXMERT Demo present here: https://github.com/huggingface/transformers/tree/main/examples/research_projects/lxmert"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2021-08-11 04:32:30.532299: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n"
+     ]
+    }
+   ],
    "source": [
-    "from IPython.display import Image, display\n",
-    "import PIL.Image\n",
     "import io\n",
-    "import torch\n",
+    "\n",
     "import numpy as np\n",
+    "import PIL.Image\n",
+    "import torch\n",
+    "from IPython.display import Image, display\n",
+    "from modeling_frcnn import GeneralizedRCNN\n",
     "from processing_image import Preprocess\n",
     "from visualizing_image import SingleImageViz\n",
-    "from modeling_frcnn import GeneralizedRCNN\n",
-    "from utils import Config\n",
+    "\n",
     "import utils\n",
-    "from transformers import VisualBertForQuestionAnswering, BertTokenizerFast\n",
+    "from transformers import BertTokenizerFast, VisualBertForQuestionAnswering\n",
+    "from utils import Config\n",
+    "\n",
     "\n",
     "# URL = \"https://raw.githubusercontent.com/airsplay/py-bottom-up-attention/master/demo/data/images/input.jpg\"\n",
     "URL = \"https://vqa.cloudcv.org/media/test2014/COCO_test2014_000000262567.jpg\"\n",
@@ -45,49 +58,29 @@
     "    f = io.BytesIO()\n",
     "    PIL.Image.fromarray(a).save(f, fmt)\n",
     "    display(Image(data=f.getvalue()))"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "2021-08-11 04:32:30.532299: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n"
-     ]
-    }
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# load object, attribute, and answer labels\n",
     "\n",
     "objids = utils.get_data(OBJ_URL)\n",
     "attrids = utils.get_data(ATTR_URL)\n",
     "vqa_answers = utils.get_data(VQA_URL)"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "source": [
-    "# load models and model components\n",
-    "frcnn_cfg = Config.from_pretrained(\"unc-nlp/frcnn-vg-finetuned\")\n",
-    "\n",
-    "frcnn = GeneralizedRCNN.from_pretrained(\"unc-nlp/frcnn-vg-finetuned\", config=frcnn_cfg)\n",
-    "\n",
-    "image_preprocess = Preprocess(frcnn_cfg)\n",
-    "\n",
-    "bert_tokenizer = BertTokenizerFast.from_pretrained(\"bert-base-uncased\")\n",
-    "visualbert_vqa = VisualBertForQuestionAnswering.from_pretrained(\"uclanlp/visualbert-vqa\")"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "loading configuration file cache\n",
       "loading weights file https://cdn.huggingface.co/unc-nlp/frcnn-vg-finetuned/pytorch_model.bin from cache at /home/crocoder/.cache/torch/transformers/57f6df6abe353be2773f2700159c65615babf39ab5b48114d2b49267672ae10f.77b59256a4cf8343ae0f923246a81489fc8d82f98d082edc2d2037c977c0d9d0\n",
@@ -98,11 +91,42 @@
      ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "# load models and model components\n",
+    "frcnn_cfg = Config.from_pretrained(\"unc-nlp/frcnn-vg-finetuned\")\n",
+    "\n",
+    "frcnn = GeneralizedRCNN.from_pretrained(\"unc-nlp/frcnn-vg-finetuned\", config=frcnn_cfg)\n",
+    "\n",
+    "image_preprocess = Preprocess(frcnn_cfg)\n",
+    "\n",
+    "bert_tokenizer = BertTokenizerFast.from_pretrained(\"bert-base-uncased\")\n",
+    "visualbert_vqa = VisualBertForQuestionAnswering.from_pretrained(\"uclanlp/visualbert-vqa\")"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/crocoder/anaconda3/envs/transformers_env/lib/python3.8/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /pytorch/c10/core/TensorImpl.h:1156.)\n",
+      "  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# image viz\n",
     "frcnn_visualizer = SingleImageViz(URL, id2obj=objids, id2attr=attrids)\n",
@@ -126,32 +150,13 @@
     "    output_dict.pop(\"attr_probs\"),\n",
     ")\n",
     "showarray(frcnn_visualizer._get_buffer())"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "/home/crocoder/anaconda3/envs/transformers_env/lib/python3.8/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /pytorch/c10/core/TensorImpl.h:1156.)\n",
-      "  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n"
-     ]
-    },
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ],
-      "image/jpeg": 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pbxi+08eehzL+XIrRNrF/z8p/n8a4q8tfEa3FqJtQtmcv+7IQcHjr8v0qybPxV/0E7T/vgf8AxNZw+KXy/I0l8Mf66nUm0i/5+k/z+NMNpD/z9R/5/GuWNn4q/wCgnaf98D/4mmG08U/9BK0/74H/AMTWhmdSbOH/AJ+4/wDP40w2cJ/5fI/8/jXLGz8Uf9BK0/74H/xNRm08Tj/mI2v/AHwP/iaBnVNYwf8AP5H/AJ/GomsYP+f2P9P8a5g2nib/AKCNr/3wP/iaja08S/8AQRtf++B/8TSA6WaxhWMkXkZPoP8A9dMGnwFQTfRDI6cf41ysNzq1vr1vZX11HKsiFyEQDsfYelbxHApdTmX+8v8Awr82Wv7MgZgBfxEk4AAH+NFVoF/0uH/rov8AOiqOkx9OP/FU3X/XEf8AstddAelcfYHHiq7/AOuI/wDZa622PSurFfFH/DH8iEacXSrcdU4qux9BXOBQl/5GW0/65H/2atsCsWb/AJGW0/65H/2atsDis6e8vX/IuptH0/zFrJ8UD/im7z6L/wChCtisjxR/yLd59F/9CFaGZd0of8Siy/64R/8AoIq6Kp6T/wAgiy/64R/+girtABS0UuKBhijFFLQBwnxVtmvPDmn20eA0uoxqCegyj9a89XQdLkdtgYxopZ2KkEDIHA3c9R6V6P8AE+K7k8PWTWS5niv45ByBjCv6/hXKav4b1230q9mOlxRxi3cSASqQRjn+L+VdUW404+bYupjf8I7pwV5G4iCK4IBJIJx0z1z70TeHNPjEm0B2j2kj5gNpxg5z15HFVdQ8HWlm+oo+ryvHY3C20/l2fPJbaYx5nP3TnJGO2agPg9oLm5jv7wwqbs2UTxQeYHK7TvPzDauCvPJ56VHtvN/iOxiyeHLV9Yvbe1vC5hvvJlBiICb2IBU7vmwRg5xz69adpPh+0nvbb7US0JvPs7ooOSMEjncMdMVr2/gs3E2yW4EN7c3c0KRx224eZExA3sWGAT0IB56jvVrTPDUMtnDLqNuUQ6dLdxC3tk3RyCZl5+dd5wM8noQOABU80ea5pzytY56y8MR38Znt2u3gaXyo3W1LHcACS4ViFA3DnJz6VWutDtrGxSaaUi5dnQQqmRlW2nLZ4H4H+tbcfghpLRVG37TNDJdW9s8A27Vzwzh/ldgmcYYdMkVU1jwdJY2tnHETcXDwxTNGIVRIxIgcruL8kE44GCOc9qm8bWQOcm7sr6dpum/ZrH7VZ+cb25aHPmMDEo2jK4OCcsTzkcDis+LS1m1A2YwArMHck7VVc5b6AAmuvtvCkcdrBp39kxm5OnPfC9Mp3JOEMgULnbtwoXpnPOe1Yuh6DdQ6iv2iwsArAjzbuWQInfJ8lt3bHHrzQ5JpIFJp3K+pW+m2l9bm209Ht5LdHEU0jnJI6/KwOfxxUOuWdlb6mbe2tkh8pVWVUdiPMx82NxJ4OR17V1M3h2OPxZPC1pp8lkuwwTahJNsQYBJXyzuOc8Fh0AzVGDwgY/iDDCtmsmnjU1ULcBCWi80feHfI/OiUrpruCm00ygvhMP8AcnDFp0jT5cbo2Cnf14x5ice/WnNoun3Vjp0ccoSeSGYoBF/rdruRuOeMgADr07Cuk07wgYtfsLiJoby0S5a1nikgVAOCRgbjuU44Jwfl+6KybDwFe3ekG8jSZZ7aGRwptR5OFySPND/exk/dx2zTUoLYHUkzPt9I09rOaS+d+NPWdGhhGVzMF/vDcecc+vsKYPCjHTvtWy43eQbgEwN5ewDPL5xuwM4xjtnNbVr4NFlqGmx3LCe5YwCa2Nupi8tmVtpcvkkAjI247ZNVfEHg2azuZEaCE3LSORCqAeTHn5ATuxnHbHAxzngHNEHNsxNPtLIWN5e3Fqs4hMaLEzsFLMTydpB4CnuOtV9QsrWy1W4hhjPlERyorNnaHQPjPtux+Fdpp/g61S1s9Ol0pJJr6zknkumkIMUg37FChtpX5BnIJ+Y4IxXM6RpMsmp3Rn0WHUZNwRIpHZQGzgcIwJ4GMZo3g0CqNSuOu7bTF0y3u4Le2L+cFkihabYBjO19xzu6/dOOD6V3vhLStJn1PRd+nxxtNE0jqkkmMbDtHLH0z+VZFz4SsrNtSmXSIZ4oBbhbMzHyxM65kywYMQhDqOR1HXv0HhHwZInjeeSKGRNPjSKQfvFzEskJKr1ycZ2/hVRqtNtO1yG7pJnoP/COaF/z4j/v4/8AjTJfD+hpE7LZgMFJB8x+v51PeaPBaRLKkkhO8Dk9jUMZ/wBEl/H+VXKvV5W1Uf3v/MyqRXI/R/kxlroGjTW6vJZqXOcnzG9frVqPwvob9LFf+/j/AONNsz/oqfj/ADrStTWccTWSS5397Iw8V7GHovyKf/CJ6H/z4D/v4/8AjVXU/C2ippV462IDLA5B8x+u0+9dGOlVNW/5A99/17yf+gmn9Zrfzv72bWRzfhrwxo8/h60kksgzkNk+Y394+9a3/CJ6H/z4j/v4/wDjS+Ff+Ras/o3/AKEa2KFiay05397CyMX/AIRPRP8AnxH/AH8f/Gj/AIRPRP8AnxH/AH8f/Gtqin9Zrfzv72FkcjqXw28PanMJ5IriJlUJtil46k55B9aoH4TeG/W9/wC/o/8Aia77/lmfrTDUOtUerk/vHZHBn4T+G/W9/wC/o/8AiaafhP4c9b3/AL+j/wCJruzTan2s+7HZHC/8Kn8Oet5/39H/AMTWN4l+HOiaXp0c9s92HMyod0gPBB9vavUq5vxsP+JND/18r/Jq6cJOUq8Iyd1cUloVT8PtJ/5+L3/vtP8A4mj/AIV9pP8Az8Xv/faf/E11uKK4yjkv+FfaT/z8Xv8A32n/AMTSf8K+0n/n4vf++0/+JrraSgDkv+FfaT/z8Xv/AH2n/wATSf8ACv8ASv8An4vf++1/+JrraQ0Acl/wr/Sv+fi9/wC+1/8AiaT/AIV/pX/Pxe/99r/8TXWUUAcfJ4B0oH/j4vP++1/+JrW0nSLfRbR7a2eR0ZzITIQTkgDsB6VqSfeqI0AMNNNONMNADDTDTzTDSAytT/4/bD/rp/UVfNUNT/4/LD/rp/UVfNZQ+OXy/I0l8Mf66kZqM1I1RtWhAwjFMIp5phFAEZqNgKlNRsKQHN3vPjKx/wCuDf8As1bZFYl9/wAjlY/9cD/7NW6RkCjqcy/3l/4V+bEtx/pcP++v86Kdbj/Sof8AfX+dFUdJz9oyr4putxA/cjqf92uptp4v+eif99CvMtbsluvF13JLJ5cEFqjyMF3HkgAAZGTk+opttpdnIgle5ZIXl8qJjFyxwCSRu4AyPXrTxeI99Lsorr2Mue1z2KG5g4/fR/8AfQq5HdW//PeL/vsV4/BoEDeVHLMEnld41TYSAynHJzwPwNPh0S1maIpN+5dJG3shBBQZPGenT865PrS7r8SfaI9RluIP+EktG86PaIjzuGP4q2hd23/PxD/32K8NfRYGullikLQfZ2lUlcEkNtIIzx1B71fh0ewW2leffnyUkUquduWx/eGf/r0o4jlu/Pz8ip1NEey/a7b/AJ+If++xWV4nurdvDl2BPETheA4/vCvLz4eVbUymOTIjEv8Aq22bTz97PXFZuv6ZaWdnNGjF5Fxu+XAHI9+a0hiFOXLFp/eSppuyPb9Ku7YaRZA3EX+oT+Mf3RVz7Za/8/MP/fYrxSPSrFbT7OLcebFbJJ5u9sscKSCM4xz+lRWdpZGXbNFDtP8AFKz4H/fJpRrzlFyS2BTbV0e4/bLX/n5h/wC+xS/bLX/n5h/7+CvF4NOsRqb25s0eISMS0jOGVByejDsKqxadbXV4I0jEYkYhRknHoOv0FCryd7rRK41JnuX2y1/5+Yf++xS/bLX/AJ+Yf++xXicmhQRwCUkY8rzDweDkYXr6Mp/Gp77SLM3lw8O0FJgrIEwq5PGOefpgVP1rWyt+IvaanoXjW4gk0aERzRsftCnCsD2ar3iGeGbw9qMUUsckj27hUVgSTjoBXj2t6RafYIlj3C8kvWhXamFY8YH3vlHOeB3xU2o6HHZ2pmCSYD7DvjKc+2TyODXTHG80KcHZavvrsJTTZY1fVbyGK6lu7CVf7QnEz7IG4cbjgZPT5j61WPja4M8skmkPNvl89Ve2bEb4xkfN7Dg5HAqKfSbGaxsIXtw0l44Tzd7Ax5JUEDOOvJyDxXB2lwkU4aW2W5BGBGzMAT2+6Qf1rJVZScklt/m1+hSm3ex1mneMNRhvvNOmTSNb3LzRlomyzMxY7vx9MVbi8Y6hCsCDRHkjiga32OrgOjMWOcc9W7Y6CsSDTLM6pfu6wxJaWscs9vJI3lxyswUqSMtgZzgZOeKms1e0v7pfLhjR1jdBAzMhUgkFS2Tg9eeamnXlUfLFK4lUbdkag8aaosAjTQlDrG0UcpDl40bOVHbuRkgnnrVG88SaveSpI+lbSsSRDCt0RQo7dcAVY89v71IZ3/vVt+97L72V7w3/AITDXls/IGlRbxEYRcGJzIIz1XrjHJGcZwcZqtD4l1WFyW0O2lUoF2SRTYyP4uGByfrj2qyZ3/vVGbiT+9+lH73svxH7w0+L9ckupZ7nR7W4Z9oVZLeUCMKMALtYcYwMHPSqZ8UeJf7YTU2iLzrMJtpt2Ckg5xgduO1WmuZB/F+lRNdS/wB79KX73svxD3iR/G3iQXFvLFYQw+VKZmSO3k2yuRjLZY/kMAZ6VFD4z8RwWK2/2GN5Et5LZbh4JPMEbhgRw23I3Eg4z0zkcVE13MD9/wDQVG15P/f/AEFH73svxD3hzeLPEDJbNJYI91bmPF0YpA7hMbVYBtp4AGducd6p6h4i8RalAsdxHLvSRmWVY2DhTzsz3UHpnJHrjipGvZ/+en6CozfXH/PT9BRet2X4h7w+HxT4hg00Wgti8qRvDFdvE/mxRvncq4O3+JuSpI3HBFVtF8Q6no9/JcwaVHNIYDAwlE3UnmTKuCr4yMggAdADzTmv7j/np+gqtFeTrJKRJyTzwK3pRqulUbtol37iblctReINTt7m5ePSI/slyirJYsJ2i+Ugggl94ORnIbuR0OKgfWNbuL27v7ia5heZlZggKKAOAAOwAwAKkS+uCCTL+gp00rzaTOztkg4/lXM51I25kt/MG2tz6XikM/hSylJyTFGSfwqnGf8ARZPx/lT9HfzfAenv626H9aiT/j2k/H+VdX2GKp8MvR/kye0/49k/H+dadoe1ZVof9GT8f51p2ZrKOyJw/wDBh6L8jRHSquq/8ge+/wCveT/0E1aHSqurf8ge+/695P8A0E1RqUfCv/Is2f0b/wBCNbNY/hX/AJFmz+jf+hGtigBKKWigA/gP1phqT+A/WozQMaaaacRTaQCVzfjb/kCw/wDXyv8AJq6Wua8bf8gWH/r5X+TV1YL/AHiHr/mKWx0dFLRXKUNpD0p1NIoAQ02nUlADTSU41zHjjU9R0rRoJdLkZLiS5WP5Yw5IKscYIPoKBpXdjebqajNeVf8ACS+Lyqt9uyGUsp+zJggdSPl6DBpW8QeMEETS3jRxyEBXa2QA/T5eaB8vmeommGvNr3W/E1pHI39sK+25kgx9mjGduOenfPSmxax4snt1lGrW6s4Zo4miTdIF6kfJjsepGccUWDlPSDTDXmVr4g8UXcjKNVhRUUu7vEmFUd+EJ/KpjqnikSuG1i2WJY1kM5iXZtPT+DPPpjNKwcp2Wp/8flh/10/qKvNXlmo634mj1GC3lvVeZXGzZEhyTjBGF5zxipv7f8VsFIumIclVIt15I6gfL2qIxtKTLkvdSPSjUbGvP7zVfFNtGkovWeBo43Mv2ZQoLKDtztx3o+3+LJLee4huZHihKht1qoblS2cBTwMdc9x61dieU7w0w153JrniiOBJ5Lh1hf7sjW6hW+h281Laap4jvIzJ/atvCm8Rq0sajc57DCH8zx70WDlO8JppNeeprfiaS8FoLwecX2bTEgwc45+Wi41rXYpUSLV4Llm4/cwg4Ppyg/SlYLG9ff8AI5WOP+eB/wDZ63cHA+lcRA+qjxtb21/dRTTi1LqyAbQCCccAeprrvLvcD98mPp/9albUwjBfWG7r4V+bLcA/0qH/AH1/nRVRReRyK/mp8pB6f/Woq0jqVJvZnG6hBfSeMrn7LbpcRyWypJHIQFYcH1B64OQatW2m6zESq6VbtH5nmJGzgiNsYyPnz2HXPSr1v/yOFx/1xH8hXSRnDCniaUHPVbqP5HLGKd7nMwaR4h3wSfYkZoWZwTInzFjk5+ar2m6BrkRRXsU2RpNtzIhyzJjB+bpkCustm6VpwnisJUKclaw3CLVjzZtF19dZt4hpsQUwMggEi7SpyT/FnqM9e1aX/CPeImYltKhKGMRGPzVC7Qcjo+f1rrZD/wAVLaf9cj/7NW+tTCjTbenX+upU6cUlp0PChqstyoiGlyyyhdocAF9oHs+OAPToKz9a1N7q1uJHtJlZsZ+7jqP9quv0W3ggFnttjI89tNM0+4/KQHGAOmBgZzzk1U1ex06LQJFlkiEj2gmVwJTIX4IHA2bc/L/XtXSqUIu6Rx05ydn+n/BMm01i5nsYbdNOmeVoUjLoFLuoAwPvew7dqI9WCMxOlM4OMBiOMenz102m21qJ7CWyjjS2VhHuJcSKShIDg8Z4P3eKzNTgjtZ1giT5FUETE584H+IdgPQD8ean2NNKyX4lR5m7J/h/wTNOtzGSeQ2cxeYFWb5OATk4+b2xUUepvFIsi2lwGUhhynUf8Crq5mcxXFsc/YUsI5EX+EMQh3D3LEjPuRVXQYc3P2lWi82N1WNHkVTknluSM4Hp3IqlTgtEt/8Ahhpy5W7r7jHm1+4nimjaylCyyeYcbOPYfN06fkKRtdmaSZzYy5lcO33OCDnj5q34riWx164RvtDRvcEFLeYAMd3AOAQ3B6VTjgjbXzbXCoEadomCE7VJJGR7A8/hUKhSSso/1p/khJS8tr7f8E53WNdlazH+guH+0ecjkgGNz3XD+w65qxeaqbiI40142JyWXbyf++8flW14g0qzh0N5pI8PAqxPlj/ryU5PPYO3HT5Kuz6XZz3E1p5Jtlhu4rfztxJdWbBJzxnA3DGBWyo04xi0ur/QSlbVP8P+CcrLrVwmktFFZP5qIfKkbbmMkdR838wa5TTri80qW2vl0uJiqsqPLuw7d2BDD5hkDjGOO/NepQ6faXsKFrIwDzJIzCHb94FQtt5yc5ABx6jgVyiafZajZaOs9pHZQol9MIC0pjcpt6Y3Pt4ycZPDYx2zdGm76bg2/wCl/wAE5C01KVL+8kh022SKVfLmtwzskgznJLOWzkA8EdK0bfUpZLiWa5iEeVRESPG1VUYAHPpVqOz0BP7TvYIbe7EOnpOYYmnWFJjcLH8pfa5UqeRk8kgHgEZeuWtra6tCII/Jt57eCfywxby98aswBPJAJOM80KnCL5ktS1zN6P8AD/gmp/aUX91/0/xpP7Si/uv+n+NXvEUs8tv4jgus/ZbK+jjsFP3YhlwFT0BQZwOuAazfC97e2StdPeS2+jW8okuUB+W4b/nljo5YDGDkAZJrXS4J1HG919w86jF/df8AT/GtC1s5by2SeNkCPnAY88HFWPDcuNP0uASyQm9nuDHaxJmG54ACztngAj0bAOeOtc7pz/8AEviH1/maLxW6C1aV1GSXy/4JutpFz/fi/wC+j/hUbaPc/wDPSH/vo/4Vms+aiZqLw7fiHssR/wA/F/4D/wAE0m0W5P8Ay0h/76P+FRtod1/z0h/76P8AhWazVEWovDt+IvZ4j/n4v/Af+CaTaFdn/lpB/wB9H/CmHQbv/npB/wB9H/CswmmGlzQ7fiL2eI/5+L/wH/gmmdAu/wDnpB/30f8ACqkGiXMk06h4co2Dlj7+1UzUCffk+tdVFx9lU06Lr5i9nX6zX/gP/BNg+HrvPEkH/fR/wqwuh3X9mzQmSHcxyDuOO3tWDjPFXguzSZwP74/pXn1nGy06rr5ilTr9Zr/wH/gn0N4euFj8B2Fs4JkSAKSvIyDQkyi3cc9D/Ko/Ar+b8MbA+kLD9amjP+iv9D/Kum8eR6fiE6dfklea2f2fJ+YttcIIFBDVo2t7GvVX/KqVr/x7r/nvWnY9ayi4WWn4kUKeI9jG01svs/8ABLI1GHH3X/KquqahCdIvRtfmB+3+ya1R0qrqo/4k19/17yf+gmqvDt+Jr7PEfzr/AMB/4JkeF7+JPDdmpV+A3b/aNa/9ow/3ZPyql4U/5Fmz+jf+hmtqi8O34g6eIvpNf+A/8Epf2jD/AHZPy/8Ar0n9ow/3ZPyq7SUXh2/EXs8R/wA/F/4D/wAErC+iMDPtfAOOlRf2jD/dk/Kr/wDyzNRmi8e34lOnX6TX/gP/AASmdQi/uv8AlSfb4v7r/lVs0Urw7fiL2eI/5+L/AMB/4JT/ALQi/uyflXOeNL2N9HhAV/8Aj4XqPZq66ua8bf8AIFh/6+V/k1dWCcPrENOvf1B069tZr/wH/gmz9vi/uv8AlR9vi/uv+VW6SuW8O34h7PEf8/F/4D/wSp9vi/uv+VIb6L+6/wCVXDSGi8O34h7PEf8APxf+A/8ABKf26L+6/wCVJ9ui/uv+VWzSUXh2/Efs8R/z8X/gP/BKn22L+6/5VyXxB1JYNIsJ0Vt0N/HJyOuFY13GKxPE1vDcabGk8McqiYEB1DDODzzRePb8TSlTr86vNf8AgP8AwTzu71aw+y3UFvIzCJRFa/IRuVtu8+33T1/vUyW+0yOxligkQlzEy8SF+Dzuz8uevSvSW0XSv+gZZ/8Afhf8KjOjaX/0DbP/AL8L/hU3XYv953X3f8E8v1PULaeKUROWLXs0o+Uj5W24P6GpbW8tVjsrl5tslpGyeVtJMhyzLg4x1bBye3evQ59H0wQt/wAS2z/78L/hQuj6X5a/8S6z6f8APBf8KbS5boiFSXtXCXZP8TzPTZYbWdneaMM8LBGZCyxsTjDDHPGexHIq1cXlpdG4ga5RPMiiHnbGEe9PRQMhcHsO3QV6AdH0z/oG2f8A34X/AAph0jTP+gdaf9+F/wAKi50XPKdZull1a3lt2O2Py0RiME7FVQfxxmugutXsCl0sLnCxlrcbSPnk37x7Y39f9gV0eo6Vpy3liBp9qAZOcQrzyParp0jTf+gdaf8Aflf8KiMvekW7pJnAyajC7SAzEodOSBRg43gLx+YPtSXd3a3UV/GtwqeYYJEZlbDbI2UjgHnLd+Peu8Ok6b/0DrT/AL8r/hTDpOm9tPtf+/K/4VVyLnEanqVtcW1y1v8AZV+0BAUxL5gwQccnYMYxkdvSqdnLay2CW1xci3MVx524qzblIAIGAeflGM8c9a9AOk6cOmn2n/flf8KadK04f8w+1/78r/hRcLnB2+oxJr/9qOV2yXEjNFtyyq2eemD97pntU0mqW8L2rzTNqE8XmZnRihG4AKAWXJK4Y9OM8dK7M6Vp3/Pha/8Aflf8Kb/ZWnf8+Fr/AN+V/wAKLhc4yzubO58cacbMSgJYBG3tnkLjH3R09e9dyPuiucuLS2t/Gdl5FvFFmBvuIFz970rpOw+lL7RzL/eX/hX5sY3ANFDdD9KKuJ6NHZnN23PjCf8A64j+Qro1rnLb/kb5/wDriP5CujWtMR8a9I/kccOvqaFq/StWE9KxIGw1a1u3ArJFMY5/4qS0/wCuR/8AZq6BDXOsf+Kjtf8Arkf/AGauhjPFTT3l6/5FVNo+hxOn+BrpbRo49dmjj3nKLEQDx6b6q674JurPw3dKuuTGEAZhEZCnLDtvxXead/qG/wB4/wBKqeKf+RavPov/AKEK3qaSZxYR81CDfY5uz8E3t1p9lNL4guGYRKy7oySuVHQ76efh1IyIjayxRM7VMHC564+euu0n/kD2P/XvH/6CKu1B0WS2OHPw/uGtxbnXZDCDkRmE7QfpvxSN8OpGkEja05kGMMYOeOnO+u5paB2OKi8B3cO/yvEE8fmHL7YiNx9/n5qAfDYhgRq5BBzkW/8A9lXe0HPagLHlXi3wRPbaUrvrMkoluVLq0R+ZsN8x+bk9fzrXv/AFw1ntl12WRIwNitCSF+nz8Vs+OP8AkCQ5/wCflf5NW9ff8ecn4fzrrcV7Km+7f6E9Ti18B3Nx5MkuvTO6qNrNESV+h38V4/4u03UtP8WXMM2sXUslvJvilZmyuQOR83HQfkK+lYOIYj/sj+VeJfFy0+z+LY5wMC4hBz6kcf0rme7HZHmwk1G/urxrrVbqZ3AikaWRnLoGyFOTyAQDj1FSSWEs5Vprx5CqhFLgnCgYAHPQAYAqOzb/AEi6/wB//GrofikWkhssd9cRQRTapcyR2/8AqUdmIj/3QTx+FT291rNoJBba9fwiWQyyCOZ13uerHDck4HNR76XfQHLHsMiGoW8E0EOrXUcMxJljR2CyZ67gDz+NOt4/IgWLdu255xjvRu4ppagrREpamM1RlqaWoEOY1GTQTTCaQgJphpTSUCGmoE+/J9anNQp9+T611Uf4NX0X5kvdDgcEGrqfPpU/++P6VS+lXIjjSZv+ug/pXn1tl6oUuh738NH8z4Z2w/u+Yv8AKrsf/Hq/0/pWZ8KH8z4cqP7ssg/QVpR/8erD2P8AKun/AJdsVT4Zej/Iltf+PdfT/wCvWpY/erKtT+4StOxPz1lHZE4f+DD0X5GuvSquq/8AIGvv+veT/wBBNWl6VW1b/kD33/XvJ/6Cao1KXhT/AJFmz+jf+hmtmsbwp/yLNn9G/wDQzWzQAlBopKAF/wCWZplP/gP1qOgYhpKWkpAJXN+N/wDkCw/9fK/yaulrmvG//IFh/wCvlf5NXVgv94h6/wCZMtjo6KWkrlKENJmlNIaAEpKU0lACVk+If+PCP/rqP5GtbvWR4i/48Y/+ug/kaaLp/Gi21RmpGqNqkkr3H+pakX/Vr9BS3H+pamr/AKtfoKt/B8zmj/vT/wAK/NiGmGnmmVmdRl6l/wAflh/10/qKvGqOpf8AH5Y/9dP6irxrKHxS/roaS+GP9dRh71G1PI5phrQgafrTDTyaYaAGHFNOKccZppxQBzt9/wAjlY/9e5/9nreH3awb7/kc7H/rg3/s9bw4A+lLqcy/3l/4V+bGt90/SihuAfpRWsT0aOzPLdOvPEjpf6rctHEY7H7RGymPeeVC7k5YKQTgkDOODSPrnjaKKOQqh8xkQIuxnUv90MoO5c9sgZqFvEGlyW1/cfa5vPutMS1FuSnlo6iMEj5t3OzpgfU9r2p+L7K8DTQ3jRGeaN3jSC3XZh1YkOMOeR3x7mpk7vU5VBLp/X3klprnigS3aXd3ArQ2cs6eRJFJ8yY4O0tjr04NUp/GvjKxuBBNOscxAOzapIz0BA6H2PNXF8X6XZalFewy+dcpbTK0rxxIXYlSgKoxXjDc5yc1y+q39lJqv2qznOx8SbZGB2N1Kg55GfXB9fUpD5V2/r7zstV13xPp/m3MWsJPdWUiwXKeQqiMnP3TuO4AgjkL2wCKseHvFXi/XXaFNTnWbcAPKtFkRQf4nO8FV9wDXO6rrekSpqL2s7+bqtwksocpiIAsxC4bJ5Pfb0x71X0i80OxuzdXF7OXt5S0aIiYlXtk7/lPXONw+tJITS2aOs0PxL4w1O3uFh1OZZYS25ktFaBcDOWk38Zx/dNYU/jzxpqFlcxm5WWKOPzJFKr90Ec+/aoNI1TRIZor+ad0lhldzaoE8tgTkDcWzjsRtNUtE1TT7PVUe9kVrOVXhnVHGSjqVOMn3z+FU99yIQiopKJ0g8W/EOzRbcvsMSxLsAQkbx8gGDyT6CpZfF/xFinhhM6s8zFE8sxuCw6jKkgEdweneqsvjWykSwuQym5S+E8+XADIjsyD1z+8YfgKig8RaRYeRbxXUk8JlmaSSRkDKrxmPC4JHAJOeM+gpF8q7f195ePjH4iC6S3+0xlnjMiurxmMoM5beDtwMHnNXL7xV46gmtILe9DSPZrcTmRowiZZhneSFC8Lg55z15rFj8Q6TFEmni7kaA2ssLXBKeYGZ1cEDcRxsAxu9eRU6+KtKimWBLj9ybGOAyukMjB0cnO1iVOc9M/jxyByrt/X3lqPxb8R5GnUXEa+Q6pIZHjRVLAleWYDBAOD0PHqKrweO/Hs+rR6b9sEdy8wg2ugG1s45+hrN1PxHbXFnfRJd7pJJ7dkciOPKRxuvRDgdR07VDceILNfGbaxBIhiF2JwrMMkbs4ODQFl2/r7zX8T+LfEq6XDOmri+tDcGMl7dUKyKM9NzcEHg5zwcgVdsvGnijU7NlGukXnlySeQbZdmEBbBbdnJC5+6R71yur32jtplvplpdyGBrwzySuIyyjbtAA34OOeSRnPQVbsdS0W30GSIX8kF5OGE8ixRvleyKfMBAPBPHP0HOnM+Vai5VfY6WPxT4vbT1ZNcX7Z9kN2tuIF2+WATjduzu2jONuO2c1J4wk1Kfw5oer6tAsxngQhywPLKD7461y9l4mtrHQXiF80ty8D2yxtHGBGrAg/vMlyMHheBn6V6P4qgjv8A4N6SYnWSWGzt3Cqcn/VrWcrdXYcXGO8U/v8A0aPJINQszLNttI87uePr7VZ+3W3X7JH/AJ/CsS1hma5ucQyH5+cKeOtWxBcd4Jf++DUafzfijbnp/wDPtf8Ak3+Zofbrb/n0j/z+FL9utv8An0j/AM/hVD7Pcf8APCX/AL4NHkXHeCT/AL4NHu/zfig56f8Az7X/AJN/mXvt1v8A8+kf+fwo+3W3/PpH/n8Ko/Z7j/nhL/3waTyLj/nhL/3waPd/m/FBz0/+fa/8m/zL3262/wCfSP8Az+FSJJbXNtcEW6IUTIIH19qzvs1x/wA8JP8Avg1dsreYW12DDIMpxlTzwaNP5vxRE6kErqmvx7+pn/J/eNJiP+8acbW4H/LCT/vg0n2a4/54S/8AfBo93+f8Ub/WI/8APuP3P/MbiP8AvGkxH/eNP+zXH/PCX/vg0n2W4/54S/8AfBo93+f8UH1iP/PuP3P/ADGYi/vGoYxHvkyx61Y+y3H/ADwl/wC+DUEVvOZJQIZDg84U8V10Lexq+90XVfzESxEbr93H7n/mSrHGTwxqZONJm/3x/SmxQTqfmhkAx3U1KtvP/ZUw8mTO8YGw+1efOSvbm6oeJ5ZUYTUUm29vK3qe1/B1t/gKZf7tw4/8dFbEf/Hq30Nc98G5RB4UvYZz5ZFwSA/ynlfet5JEFs4LqDz3rs54+zepxVPhfo/yZNbf6hceh/nWlYZ8w1k280YhXMijr3rRsZ4RIcyoPqwrKM423Jw6/cw9F+RvL0qtqv8AyBr7/r3k/wDQTUiXVvj/AF8X/fYqtqtzbnR70CeP/j3k/jH901XPHujWxX8Kf8izZ/Rv/QzWzWD4VuYB4aswZowcNwWH941sfarf/nvF/wB9ijnj3HYlpDUf2q3/AOe8X/fYpPtVv/z3i/77FHPHuFib+A0ykFxCYmPnR4B67hURurf/AJ7xf99ijnj3AkNJUZuYP+e8X/fYpPtUH/PeL/vsUc8e6CxJXN+Nv+QND/18r/Jq3/tUH/PaP/vsVz3jVlfRIGVgwNyvIOezV1YGSeJhZ9f8yZbHTU2l4ppNc4xc02gmoJ7iO3jLucAUgJjSGqkF/FOuUYGpvNH94UDJc1j+Ij/ocf8A10H8jWoJFPQg1j+IHzar/wBdB/I00XT+NF9jUZpxNMJqSSC4/wBS1Iv+rX6CluP9U1NX/Vr9BVv4F6nLH/en/hX5saetNNOaoyazOszdS5vLH/rp/UVeNUNS/wCPyx/66f1FXiayh8Uv66Gkvhj/AF1GseKYaeaYeK0IGGmk89KeTUZoAQ/SmkUpFNoA52//AORysf8Ar3P/ALPW9/CKwb7/AJHKx/692/8AZ63uwHtS6nMv95f+FfmxG+6fpRQ2Ap+lFaxPRo7M8rsvC+lW7aik8vm3sWnLO0BjIWMsUIw+7lgG5GB16mtC5+H6WsZMiSxtHJGkzzQvHENxC5VyfmAJGeB6jNR2s2sz3E9oNMtDPLaLBLcZ/ePGu3aCd+3jaoyBk45zWhNZ6tdMJJtHtHuNwaSYv80hH94b9vPfABNedKniVJNve3bt+X49zBNale18GWX9pTWKWlzNM1rIY1uIjFluMFfnII96x5dD02O8FvEROBhTICwXd3xzyPfj6V1Pla8qLHb6bBbxLHIiJFJ93fjcwJcnPyjvjjpVW60bWr658+SxjWVgN7I6je394/N1PfGKqhCsp3qbW/H+u34g7dCDUPDeiCW/hgsPKfT5RH5nmuTMuWUlgTgHIBGMd6u6J4U0e6tkeTT45S9wI2Mtw6YXAPyYYbm9ue3HNWLi01+8u0gfTYFeYiWZkcBp2AbBY7sdz0xyc1q2eja/CoQaPbuiSmaJXlBETHHT5+eg4OelZKjWdFwW9+/l39df6sOVk0zmbLw9okum6iDp+ZYMMkrSuGA3quMZx0J7VSl8Mafc2V0IodskcLSqNzHdt5I6+gJ/Cu203QtfS2uVOjW85uGPmSSTYY8g44kA6jPSqs+geItEgOopp6EwEHEkiMpBOCCA2SDnFdbp1I+0stemvkvu1/O5z0ZKVOLuZX/CAWMRjg2q7loolbLAM7ZB/i4AKt+VNTwXo85je2O+Es4kdwylNq7icbjkYBx9O1dLaaL4qn03T2WyX92ROkglTcWPzc/N65P4mrH9geJleMw6TBCiMzeWkibWLDDZy56jjH5VzqniratX+X+XX8HubXickPBmjuBOm42nlNI0hDBhghSNu7rkr371afwRpNw8P2a2kaJbVZHaJXZ2JYgfLu6+vOOOtdGNA8Sh126RCsAjMfkCVdhUnJz8+euDnOeBTn0HxLI43aRAYvKERh81dpUHI/jzkeuaUqWJbuv0/wAt/PYLxOUk8D6RaidrsPEkTxqMK5Zg6lhwWGCMcj6/jBF4P0z+3F06WD/luImZXbpnBI5rq5PC3iJ4JYF0qKOOSRZNqTJwVBAxlz/eNEnhfxPJqDXwsAkxk8wFZo8Bs54+atYU69nzPdPto7K36iujhfEWg6N/YkV7a6eLZvtXlMqyuwI25B5J565/pWlbeFNF/s25lns90rQO8I81xtC/xdfXgfQ1q+KND1yPT7dp9Ht0t1uN3lLKNrsQc5w+e3YjFaw0TxhY2cixwyiPyygX7SuFB7gBuDWk6FZ4eKirO73fS60v/WmgXVzlbfwnoo0S4lms83RhWaP9642LvVeme+T17Y9a9Im0a3f4dJHBFtddOQock4IQetYVvoXjBNPa3EMrQyQiPDXK/KvB4+bjpj6E139haPHoVpZzrtkW1SJ1znBCgEUUqMuaftUnd6f09hSemh8n2V/dpcXWJSGL/N8o681c/tO8/wCex/75H+FQXNt9m13VbfGPLuWX8mNJsOa19jTf2V9wJssf2pe/89j/AN8j/Cl/tS8/57H/AL5H+FVtlL5Zpexp/wAq+4Lssf2pef8APc/98j/Cganef89z/wB8j/Cq+w1FdP8AZrd5du7bjjOO9Hsaf8q+4Ls0P7TvMcTH/vkf4Vbs9QuntrotKSVTI4HvXKf2q3UWrf8AfX/1qu2OrSeRdqLQnMefv4wAD7Ueypr7K+4malJWRo/2peH/AJbf+Oj/AAoOpXnUTH/vkf4VhxajLLII47NizdBu/wDrU+XUZoH2SWZyRkESAgj2IGDR7Kltyr7i7StzdDY/tS8/57f+Oj/CnwSX2qXcFlG3mSyuERcAZY8Dn8axzc3BhaZbZCgXccTqSB9Ovep9P1e90y9ttRhslkaCRZAolDe+GA5APSnCnSUk0kTWp1HTenQ3ZNLmfRrP7Hc2N5dT30sX2iF28tUSNWOdwGNvzEnHToSKzoNHvzM0kN9YSWbxPO14vmGNVRgGBGzfkFl429wenNJb+LxptnaSaRpElrHa3kkh33bPI3mR7GAYIuBtBwRyM9zzUc3ip5rm21GUa7JHFvjjL6yzTxv8pJV/LwoxxjHP4V0RkuVnN9Xl/L5/1qaFvp02oaJK1vJbTSxXoR7xSyxJF5e4sdwGBn1Ge3XiqOi7tRv5I5pl+zwJLNLJEvJjjQudue5C4GR3GRSt4+mc3kf9kqba8nWS4gaQlZUCBMN8v3iQG3jB3c4rJ0rVX0yWW/hsjJa7mgeCSQ5aORGUruCj+HIzjrg4rGdr/caexcqduXv+lzvNMtbS502G/thMtvcgkRyuGeNlYqQWAAboDnA647VdisLC50u6liS5jltog7SO4KMxIG0ADI6nHJ6Vy+j+KSYPstloNybK3j2RIJ9zKxLMWdtmDkn0HAA961LzxO39lx2CeHL1GjXcWjugyPIersBHyfbdwP10urNEPD+5dRNdbLTJNClvEguopVkSJC9wrq7HJbgIDgAevcU3TdEbUNhjZArSiIlmI2nBOTx0wD+RrBl8Q6g+k2kC6HKsUO9j+9yXdjy23bkcBR+FWdO8V6jp1pdR/wBgTSC6TapEvKHBGcBc/dZh261CkhxwrVO/L/wx0S+Gzx5jJGArs7Oxwiq20k8evAxmo7nw4ogud88UcKRBvPZmKEMDtxgE889uxq7NqfiG5unkl8HaoYZYBDJGscgJ+bcWB2cHdz0NQ3d9rb2lzFceDtV+xGFV2KrqyBAcHcUI/ibPHftV6EKl/d/r7ytpfhgx26x3TxRbJfLyznEhJyNpA6EYOeByOauz+G4nv7hImjhQ3LwwI7nLkHoOD6gZJH1qCx1XXLu0WSXwfqU0XmeZCIkkCrj5QudhyMKo7HjrV4ax4lzKD4W16NWmeZRAZI8bjnB+Q5H5UaCdJ3vYpp4a328cvmxBpY3kSIsd7BSwPbGflPU1DY6TaTefLP5nkwReYwRgC3zBQASDjlh2qyl74iSS0c+EdVYwRPHzFJ824uc/c/2/0qvZv4gtmlEnhLVZIpkMciCF1JGQeDsODkA9KV0X7FWegt9pdtbFBCXMM0aypvPzAHsfoQae+kadLYPcQC4iEcixkyuGD5z0wBgjGcc026k166Yn/hEdWRURY4VETnYB6/Jz39OT+FTXV3rMqwfZ/BuswmDHlqwZkHqceUCSe5zRcHRjZaGhF4cs7TVrArHcx/6fHEvnMMTLu++uAOOnr1HNel/2Laej/wDfVeWQalrn2+3dPCGrRqb2O5lMiu3IPb5BtHJ657c8V3H/AAlWq/8AQr3v5t/8RQ32IWFpyXvxu/68zWn0i1SCRwHyqkj5vauc8S2sf/CNWr5bJuB3/wB6rFx4p1Q28oPhi9AKEZy3p/u1zmveItQl8O20beH7qNRODvJOD97j7taYOpKONhroN4OgldQX9fM9FNhFj7z/AJ002UI/if8AOufbxXqg/wCZYvfzb/4moX8WamM58NXg9yT/APE1PtJ9w+pYf+Rf18y74k1Sw8M6NLqV2J3jVgiqnJZjnA6cdK8Ov/iXrk9+1zGsCx5wsBBK7fQ89feq/jrx3qHinUmhWOSHToGxHAGzuYdWbjk1xrXDd4WodSfcPqWH/kX9fM9Bh+LN0yZh09Fcdcvxn86WP4rap5wM9lC0XcIxDV5wJWV+Ijg08ztjJiIHrS9pPuH1LD/yI9/0vxVHf2Ud5bSbo3H3T1U9wa2ri5jvNMjlBOS4yM9ODXg3hnWDp90Y5GIt58Z/2T2NeoWl+REqZ43A0lUne1zSlg8OppqC/r5noot0I6t+dBt09W/Okhl3oGHcZqbPAp+0n3I+pYf+Rf18yrNAoiYgn86QQLsHXpUs/wDqmpF+4v0FU6k+TfqcyweH+stci+Ffm/MhMK+9MMS+pqdqjas/az7nV9Sw38i/r5mVqMYF3Y8nmT+oq8Yx71T1H/j8seP+Wn9RV81lCpPnlr2/I1ng8O4RXIuv5+pEUA7mmlR6mpG6UwnNa+1n3M/qWG/kX9fMZjDECkJ460v8RpvvRVd2n5IWCiowlFbKUvzEzTTnFKcfjSE1mdhzl9/yOVj/ANe7f+zVvgYUfSsC/wD+Rzsf+vdv/Zq3wflH0pdTlX+8v/CvzY1s4P0oob7p+lFaRPRo7M5HSv8AkYZP+uP+FdB0bFYGk/8AIxP/ANcf8K6Bxg5p1d4/4Y/+knLH4pepKpqZTVdDxUhkWMZd1UdMk4rNK+xdiaF/+J3bf9cz/WuqgbpXFxXcA1m3JnjwEPO8e9dRb39oMZuof+/gopQneWj37Py8iqkZWWnQ09NP7lv941B4o/5Fq8+i/wDoQpmnX9msRBuoB83eQf41F4mv7R/Dl2q3UDMQvAkBP3hXVUhLmej+44MFCX1eGnT/ADNjSf8AkDWP/XvH/wCgirlZelahZDSLINd24IgQEGQcfKPern9o2P8Az+W//f1f8ajkn2f3M6uWXYs0tVf7Rsf+f23/AO/q/wCNH9o2P/P7b/8Af1f8aXJPs/uYcsuxaFLVX+0bH/n9t/8Av6v+NH9o2P8Az+2//f1f8aOSfZ/cw5ZdjE8c/wDIEg/6+V/k1b99/wAeUn4fzrmPG19aSaNCsd1A5+0qcLID2at291GyazkAvLc9Okq+v1rscJexp6dX+hPLK70LsP8Ax7Rf7g/lStxg+lVYdRsRbxj7Zb/dH/LVfT60rajZEf8AH5b/APf1f8a5XCV9n9zHyy7HzV4mtPJ8d6+gGF+1uR/30azvI56V0ni8IfGWpToyssszcqcg8mskKAahpp6i23Kgt6d9mq8qr1qVYgRwKAM025qhq0ITTZSw4BXP/fQro/I46VQ1e3VtMmDDI4/mKAuYL7pLpCblRblsxbXHyjtgfw9hVpZAOUmxKYWUsZgxzzjLDFXIdJtDaxMYuSgP3j6fWp4dIszDcEw9F4+Y+/vWTpnU8a43dt33f9ehzFudt1IJHG51dN5bPJBHX+tJcr+6ghDIzRoSxDDA5JwD3/CtSW0skbb5ZLeikk1A1pD2tW/Fj/jVcutzNVvc5bf1uZ+5YtPKqwLyv8wB6KvT8yf0q7Zbo4JI3kh2lQVEZUsee+OcfWkNrCOtuw/E1Z0yOwjuWaaFmXYRgE9cj3pxjqjKtXapysr6W/r8zOs5JFsXWG4SKUyqfmcKSMHPJpxkt5JQI5hEn2qRgQQCBgYPtkjrQbSD/nn/AOPGoRZx5Pyjr6mqjTTjN37fmdCxLSUbbf18i20sZliYSr9o8l1DtKHKtnjLDjpnmm20wSzuftUyNKZUw5YOAdpwTjqBxUMVrCHO5OMf3jUiW0H9nyIY/wB4WGDuOMce9c0oW09DaeKfJGaWt3p9y1/zNfwZvZ9SVm3vlSSGznk9+9bw+6/1NV/htNoem3uoHWLOe4jdF8sQscggnOfmFdSdQ8JESY0u85Jx8x/+Lrfl0ucrq2g427/1+COdQ/IKmgb97W/9p8LQYjn0i/SQAEq2QeeRxv8ATFWEvPCVrctHcaNqCOhwyNkFT6Eb6hRClW/dRTXS2/e39fqexxn5RVfVf+QPff8AXvJ/6Ca5JPifoqgf6LqH/ftP/i6ZqnxH0o2F1bvZalHJJAcCSJR95cg/e6EEH6VoZHR+Ff8AkWbP6N/6Ga2a820H4jaRZaJbW8ltfF0DZKxpj7xP96tL/haGif8APrqH/ftP/i6AO2zSE1xB+KGi/wDPrqH/AH7T/wCLq3pfjvTdXuWgt4LtWVC5MiKBjIHZj60DOs3Dyz9aiL1nHWYPJb5JOvoP8a5m51GW8124hjvbmCNUDAJ9B2yPWolLlCx2hf3ppk965Dy5/wDoL3v/AHyf/iqryvKhw2rXw+qn/wCKqeeX8o7HY3Mv+jS/7h/lXK+JX/4pi0H/AE8D/wBmqq4naJsatdHKnsf/AIqsW4upZtMltXnkkMVwm0ufTfWuDnbHU3JWFJe6z0iWbAJrA8S6oNP8PajdZwY7d8H3IwP1NOm1VCDgNXCfES8nvNA+x27hPOkG8s2MqOcfmBSCx4+CSdxOd3J+pqGcYPQ/hWtHod2UGZIM/wC8f8Kf/YFyVw0kP/fR/wAKYHNyTtkALgZp29zC+/HtVy9iFnI9uVUzKwyQARgjNU5p5DEy+XEAfRBmkA9ZlCqCewrv/CmqvdwxrISSh27j/FXnCgkKOmfwrutDfyZoYgQQqjkdO1HUun8aPd7KTMSj2q+DWHp0gMMfPYVsI2etNmaFn/1TUi/6tfpRMf3TUi/6tfpTfwfM54/70/8ACvzY1qYae1MNZnWZmpf8flj/ANdP6irx6VR1H/j8sf8Arp/UVePWsofFL+uhrL4Y/wBdRhpn0p7fpTO9aGYz+LikPSlPDGk78HitKm69Ec2E+GX+KX5iU00pOaTtWZ1HO3//ACOdj/17t/7NW8PuisC//wCRzsf+vc/+zVvj7o57Uupyr/eX/hX5sa2Np+lFD/dP0orSJ6NHZnJaT/yMb/8AXH/Cuik6Gub0o/8AFRSf9cf8K0tb1T+ytNa68rzcMBt3bep9cGnUV3Ff3Y/+knNBNykl3ZeR6r6qwNmv/XQfyNcp/wAJpMI/N/sltpbaD53f/vmq1943eW3AbTWRVYMSZf8A7GtMMnGtFs6acHGabO2FjZDVYFMQ2lDn5j710NrpulsQDCuf99v8a82i8fxefBdnRsxY2DNz94kH/Zq4/jyUOGh0SUKeg88n/wBkqIVMQm7ye/cc3V016dz0yw0TTXjObYH5v77f41H4i0TTofD11JHbAOAuDvb+8PeuJsvihc2oMT6BIWznmcg/+gUa58UZbjQ7mJ9BeJGC5kNwSB8wP9yuidWs5O0n95x4aGJjSjGV7pa6no2l6Bpj6VZs1qCzQISd7ddo96uf8I9pP/PoP++2/wAa8wtfjG1lptsj6BMI0iVRI0xCnA/3Kt2/xjkul3QaKZB/s3Of/ZKl1ay3k/vN2qydm2vmeif8I7pP/PoP++2/xpf+Ed0n/n0H/fbf41wQ+LN4OvhuQ/8Abwf/AIinf8LbuO/hmX/wJP8A8RS9tW/mf3ivV7/j/wAE7v8A4R3Sf+fQf99t/jXM+OLO30fRoJ9PiEU73Kx5GWyNrHGDnuBWFcfGaSB0jPhe4d3DMFS4ycKMk/c6Ac/hWBq/xotdQlsRPoc8McF4krsJg5AUEHAwMnnPXtWdWvW5HaTv6ibrWvd/eReIRrEOj28k5kjlkuvKWFoAGPy5B6Z5z6VblfXPMNsY5fPIyIvs/wAxHqBtzWAPiPpVvBYI6T3Jiu5pHOGwqtHtVh0JOTkgEdOD3pJ/iLGsUEa2FtFA8UioWE5ikBZcqSfm6jPy9D9an6xW5Yrmel+rJ5qvd/ebt3f6hp+mrNNLJ9oM6262q24MjMVyABjOfaqlrJr2sTpFJKbMZJMbBVYAcksQOMAEmsyx8f6Bpwu4orW9JuJwwnPzPCDGASmewOVxnO09c1S034h2ttqDC4sZRA8ckTSI27AZSoYAgdyD9Kj2tZ3fPL72Pmq9394niEXdlNJNPMlzDLLvilgHyty6nqAQQQQQR/Sob6D7Dp8NzNqFuZZokmS1VZPM2t052bPf71VNU8TWV5YwabFG52l5WmIIG9nZtqjGSMbRk45z9as/2u0OkXVhqV7e3CPaolvaSRHED5Vgw3cLhc8r1B9DU1U5S5nJ39RSjKTuP1JBpKIJdStpLkpG5tolk3qHUOMkoF6EdGPWorW8vbnyxb29zL5r+XHsi3b34O0YHJ5HHvTZtZjl0K5sr7Ub3UGbyxaq6E/Z9p5ILfd+X5cLwc+1S+HfENlpGnXiOLv7SrCazYIDtkKPGe/HDhvqgrPkaju7+pPs5D4bnUbh4kgtrqR5siNUhyXx1xgc4qvdf2peCewhsbyW6UfPAkBLrgjOVAyK1r7xRo9xcXkFqLuC2msjBFJ5Y+Rmm85gQDnHJTI7YqrqniHTL7SbqwSa7ikaO0X7U8f+s8oMCGxk4+YY/wBxc47CT8/vYvZyK2li/vtkENtdOIgqzMkG7yh0y2On41ae11E3WpW9vHcSWtpI8U9ysHygKSMnGcdM1d/4STTNTvIRAdSjlGpLdKUiy8/yooOBn58qSB6ueatL4z0y4u4WttPLzwXs1xEJUlOQxDBgsbgE4HIYdB1xmlr5/eJ05NbHOC38uza6j07U5rULvNwsJEe3JGd3pkEZ9QaTTRaapNKgR7eKGMyzTyyEJGgIGTgEnkgYAJyRV+HxRo6yaYrR3ohttOuLZ0H3Q0nncY753rz9PSs7S9T0OC21G0mt7uNLyAR78g4KyK4zjnHy471XLo9X95Xs5CahBZafe/ZZppFJVZElA3xujAFWB64IPpn1q9aaTawxalqHm22rRWdmsoggaQKWaQL8/CthQSTtPYc1U1bVNAvJYQlrctHa20cCFjtL7RzgfUnr2/Kll1iygWJrPV9RikgKpbeUX/cr82SCWG3r90ZHXmqjG1nd/eKVN8pc8NaJpupXMdxd2WmCwvL1bRAZLverlQSkQUE5wQcvke9V9cl02y8IadYPp0TmO8vIkuN8m9Srpk/f25IwOQeOmDzU1r4gXzAr69q88k8haXypJAGUKcbiXGTnHGOMdav6HZx31vqNv5t4bOafc8DM21267mXdgnpyc9K1iouEry/HzNI0Jz+FEPhi4Nvp3h6PSopBHf6tJDfKvImA8rEcnqu1mODxyx7VgaLq+naB4hfULS3uJYLd5Qghl2vtKsoZW5xgHOfau1k0f+zbCWOxkubSOchZhFuRZFweGAbn8aLa3gtru3NneyQ/Z93liIlTGW67cNxnjOOtc0nC9ubt1N54KsqcXbq+3+YeH7qEa1HqZvtS83UdKLwTajK081tiRlGW6lTtbDAD73TitHxFGvmWzifzp2tFa4lKlS75b5iDzyu05PJ4J61EjLDqDXyahcLfMMNPk+YR/vbs1A9v+9l865mleVtxkaPJOfXmtbQ5X734mcsHWs/d/Ff5nQataQ3BvQbXypILOCZbnc2WJEYwR93BycYGeKtPpsd14kvWuYrZoZr8wq0jSh855CBAecEcsMVgb7maAW0moXc0anOxwSAfpu9Kt2yaixmaC9vlMhzKV3DefU/NzRFRk9JfiKOFrcik1p6r/MbdWkWn2EYFslzJM0qmZ2YeXtYqAoUgZ43HOeoqXxVapHpTXSqJpJbWBHfJAt8QJgY7luuTxjpznCLZajFHKkd1eIk2fMVQwD+ueeap31jeiyui1xc4eHY+VPzKBwDz0GBj6CtPYvv+Ivq8/wCmv8w8MS3lt4WtJLBiJ5L7y5Ng5ZcfKp9iS3HfFQ35a2167+wuVSK4fyTH1ChjjH4VFokF5a6ahtru5h3qQ3lAruGTwcHmniG7gCCK7uIxG5dNgI2scAkc8Hgc+1J0G+v4jWHm/wDh1/maWqahO8WlajD9rQ4kXzXuN07AEZO/aOMNgHHHPpW1pF40uuWt3tkjE2mEgzSb5XxLjc7YGTxwcdAK46Z9Ua7W7Oo3huVGFmJbeB/vZzT7G7vbPUZL25e4u5ZI9haQnceQepz6VMqF01d/eP6tP+mv8z1cXWbCVt3Rv8K5y3uv+KlumB6xD/2WqEOuSnw9dTNbvlZQMbuv3fasrStYmuNelMdnJI5j5RTzgY9q554Xb3n/AOBB9Xl/TR1Gu+Lbfw1Db+Yks91dNtht4VBdsdTz6cVjav49tE1G00+4tbmSK6AxPtAVWI5Xr1Hem6p58+qR6pJoU08tpCRboSwIckZOcegrnvEN5q3im1jt7fQ5tPa3YPCzpubefvHgCr+pf3n/AOBC+rT/AKa/zOtTUoIt0QV8fX/69c1qd7ELa5UZ5ulIH/fVQX93NZzQLLbPGjEIC5xk/lXP6tqIUyrxzKD1+v8AjVYPDcuYUld/eKeHkov/ADPSJ9WtSp3B8fX/AOvXn/jrUdHkgt/tlpcSgycbWxg4P+0KWXXFPp7fPXMeJZ4r+GAGVVKSZzuz2qfqf95/+BFfVp/01/mRpe+GkUD+z7of8D/+yqWLUPDn22BEsLkOXXB39Dn/AHq5uSJADi5QZ/z61JaQxnUrc/aUz5ifzHvVfU/7z/8AAifq0/6a/wAzotVvPDy6lKkthctJkZIfg8D/AGqzLu78PNbuIrC6WTsSTjr/AL1Qa1bxtrM3+kIDlcD8B71n3EZCyOXJ8vAwO5oeEt73M/8AwIiVGUVzPb1NqO58NrDGJNPui+wEkP3x/vVvaNf6LcTBbe0uFdRjLNnj/vquEDggEjJxXSeCVEms7DwCpOPXkUlho3vzP72FNe+j3rTJoZLWJ0RgCoxmtiI8cVhabGIoFTstbMJ4GKHhY/zS/wDAmZJE8ufKJPSgZ2DnjFDnMLUD7ij2pvCx5Pilv/MzCK/2lr+6vzYhFNNONNNYU48lZxTbVlu79WdS0Zmaj/x+WP8A10/qKvHpiqOoj/TLH/rp/UVeNbQ+KX9dDaXwx/rqRtzTT7089eaYeK0IIyPmNIenFKfvGkNaVN16I5cJ8Mv8UvzEpp9qd07U39azOs5y+/5HOx/69z/7NW+OMc9qwL7/AJHOx/692/8AZq3x90etLqcq/wB5f+FfmxrfdP0oofO059KK0iejR2ZxumnHiCT/AK4/4VL4lMcmmqk2PLM0YbPAxmq1i23XpT/0x/wpviEJc2HkOWCs4zg88Vo/jh6R/wDSTLDaV/mYeMpCLqNY8yt8oUDPyjHHHeq9xHvAX7LMzlSD/oy5HoQmSD3FST6VZoxAlm/76H+FR6i+g2EC2lvNcXV3jc+1l2j9K0o1OeoopHbDFKUkuXt2v+Q6NbO6tzbOsZMLhVUoFXftzyOgPPT14p1rqIeZra1hknlRXUvt2oHAOAT9eKytBFpqbPPqNnczorE+VBIqgL7Ajmuyv5/C6eEbjUNJmvWu48Rx2koVWDHpxt5HuK51iYSk7bozqVm3CSjt/wAHyOc1C3uWhgWS6aFkRmkiiOG25z168c1LNFpj6UZzErFYFCzyfNyMcAnvnOfzrKvtDV9TWRnlYGMEknvk+1TPotulm7bpQRjuPX6VUq8VNozw1S9Lm5dGlv5aLp9/ysdLBfQ2+nxGUmNVRRllIzx29fwqsJNIuJGu1RBLAd5kVNpPbn1HtVW10OzaCMmSYZUfxD0+lW10Gxx/rpv++l/wqViIpp9jRSalGSV7K2v/AA3f/gjX1GayZ4lzelIt6qBiRiW7dj8p+vFWIdatpi6IrmdApeEDLLkentxn0qM+HrBuPOmIPHLL/hWVqngW0jmxp16ySAZdWORu9AQKv6zBlyq9of8AA/Df+rGyk8F14gtYt6oBBeLI5GdgMDcnHbr+VUxpemK9+Z4IVSz8tUF0ZHWUMT+9Pl5OCAMYwPmHJ707fw9I0sCX9xc6dIAytcLysisMNtxgjIyCD1zW1aeFrWO6Q2viXUEMMZWJkXZsTOcA7+OTmuariIrmk9LEzqWSlyaGQukaNqVxJDZophhmWaWXawxbFSXPzAHCFeuATuFT6fb2Op2OiwzaUpS9mnRX3N/o6GT+HnHyg5O7OQPxqiLeKNNVmhnvJXuR9lF1O/LqSC5298lcZJ6Z471bg8PXcds1umt3UNuwKmCNyFIPUEZwfyrWV1CM29He3y3M1K7+Ehi06w8mC2bTVYy6dJdteZPyMoc4x93blQpyM5PXtT5tHto/D93LNZWUN3awwSkRmQv87KPnyNnIfPynIxViPws4sjZjXLlbZjkwhvkJ9SucVJJ4anktRav4gu2twuwRM5KBcg4xuxjIBx7Co51e9wtq3ylK9i0nTNR1uSDw/Gy6deCKOJpHPmAuQXPPQYwMY+8M5q3retW0HipbSfSIlj/0dXLgZQGNMg/QHH4Vz8Gm30Ot6jcWmp3Rvo5gGkViHdWySxOckZAz9auzaObi7uHlvJridZdjbYfNbaAAOrcDt+FTUqKE7N/1ocuIxEVJxa2/r9TY1fxBK1hqRvNLQmzvlt7dZFHAIk3BfQAKvA9R61L4c8QwXDWll5MVoXuCLmHySRPGdvdRjgZzuIAHNYMlpfXNssj6jd7bXcYHlUsEAPAVt3yngcDvSXlve21rPDb6rcGCRgblAxALED7wzyD61HtIv3SViIuoall4kuItOup5bRV0pd8cMAQYuHOcAL0wMglu3HcisnTLs6/LcaYunQrcTQMbciMD51w36hWH407TxqwsIILPWLyOBZWWSOORlRFODkgNjB+b9aw47LUG1GP+zJp0laRhBLHlDx1IIPGB+lWpx11B1o3s+m/9fkegW5srHWdIntbFFt7vUIYbXKjcqqSshz1zu2n8a5m1i0zV2s7s6aIBPcXEDxLI2CEiV1bk8H5ucYBwOBWVJHfWd1aWrXF/FFb/AOpkKOjJlgWdFJ45HbngV0E3hvV4/EtrpkcjWenfaZPsN0If3c3ytyhB/eFkVe5ySAaIu63Gq0Z200IY0s9Yl8OWM1lFGh01pmmVpdzBDMdhwW4Zl5wucnjsKwtaXTM2Z00QyyyhlljtVmMYYHjb5oDEkHkc8j3rt20S8s5tOsba8vUe5sbm4jsprJYmE0ecAQB2CucnpyfTmuU0qPUtc8QGHU7q5+2tBcRK1wCWimCuBF8x+Qk4AHHJHFWnZhKpFqxo3rSpDdaXNC8emRaLHOsTrhVlKId+OzeaSuevUVU8JRy6Xo2p38sV5Zw4iAu7eAmUBiSNvI+UgcncP4eucVFrulazZaLp+kzS3reVZvdXNptfFuQzEblzwcc8gYBFEMeoaZ4bk1hdTuIbm9mEEEiOQ5gjCAtuBzjJVQP9gjtSuthSqx6m5coujvqV+Le/t57nU0hD6fiNyhTIO4r3JOVAGSMcYqKw1i50C31uC6t2vrZNQa3F7HKqOko3YI6kggE4xjin3ml3+mHWNTGq3cNndJbrZXSAqLss6EKrB/mwm/Ppgg9ag8KLfy+A7w6ZpceoXX9rxDy3tlmITynydjZH1bqAc5HWrpScbruOVRTskNk8Q6w2mJfObo2TzNEsglGC6gEjGcjhhz05qpceJDqUTR3lo90zHOXIY11DadbTtZWOjywGxTXr5ULr5yYEcZCgH7/ooP3uPWi90SBLuxuotPWTU5NNneK0ubBLbzZ0kwu+3Viu4IWwv8RVeOeYajLVrsEqk5QUW9Fsefy6fcTwS6hZ288dtDKsUnzBiruGKjGc8hG9uPpWwniW6n1eERSPj5E+zu2S5z2966TSLOS803V49es4ra7F9ZNHZmEW8XneTcbEkUY2qeD25wCQCSOd8LzXcfxEBvbeKyuPMkTa6CMxTFHEfGAFw5X0Aq1U0sZ2crryNXUNWvrXVDbXVhc21ywBEMpZGIx1AIzU0t3qlvZC9uNKvUs8488hghOcY3bcdazvENrq9tpuhWcsM/8AakMt1MYNhMiQYQjI6gZWZvoSelamjHV10PUbi501LW2nsZGTU5FkxggERrlthLEbRxuBbPas2oXWhtQU40uVPSy7DI9fRrN7ldEuZreNgjzNcvsVj0BKgAE4PFaNxJZSQSA2zxFlI3LcMQPzzSXL2g8C6hBa6rZXFtb/AGZtgWVXaU7y5OUA3E8DnGEGT6517f25t5REWGVOAV56dDVOcV0KUJy+018kPg+yxQRoLsDrgu2AefWn3Fv5JXzZTHvGVJJAb6etcxNcym2QRqzAZ/h96hW+vmtjblZDHnIBB+U+1HNB9F95PLVX2n9y/wAjpmtkPP2n8c1EbFDz9p6+/wD9esGO4vgoK7sehU1Kl3dDkq2PTFTzQ7L7zRKp/O/uX+R2FvaoPCV7F5uVaYEv2H3aqeHdRg8N64t9LKjxrG6EFtudw9frVa21WVfBGoExkuLhcKRyRlO1Yi29tqtnqF07JbPHNAoefcAoZXyMAHOSo7Hp9aidSEbO23mTJSs7zf3L/I7y9+JE1xJ5VlLYRAnALnef51Xn8Y6tp96Yrm5sMgAmMxbTgj6/jXnQ0lv7QksnuLeG5SXylSQt87ZxwQCPxJApt9ZpFotvfG4X7TI8iNAyvu+UgcfLjjOTk+mKPrC5krLX1MHCS+2/uR6Bq2uWHiGCGMvAk4nSTKyZAI4xjtXNapZWst3JvvYo8Nxuxz+tY2mWVtLDZPefaA97cmCIxYAjxtG9gQcjLdBjoearW9jHceIRZ3rFIQXDMJAnIUnG5hgcjGTWlCtCOKjUt8N/6QPn5WuZ/cjYOk2bDi/h/If41TudFsyOdUgTHfA/xqnNZwW2qwxXFleQ2zqCFW4SVnzkZWQLtI/A9DVTVLP7Lql5aRBzHDO8aluSQGIGfypxq029F5/1qP8AeS05n9yGatpo0+6WEzh8qHztx6+/tUNko/tK2O8f61P5itnUfC+oSzBo/KYCaO3ABOWLdGHH3fmXJ/2hTR4cEtrpklvLbrcPHK7KWfM5SR8leMD5VGM7c+5qJ16aloyIN8quynq6j/hIJfnH30/kKgu2/d3CZ/iU/pWv/wAI/Hcy3MtzPDa7LFLmJh5hDEyqmXwrepGB6r71nyaHdC1klEsG8xef9ny3mGMfxDjHTJxnOOcVSrws1fqbqX7tx8/0M5cFR8w6V0vhZxb3TT8DYhwScVjabp8Elrd316lw0FvsURwkKzsxOOSDgYVj0PapbiwGn6xLApkkh2K6MRglGVWGffBFCqLn5bkwupI9b8LeMrjUoJESxa5eM8mNu35V08fiO8Vwv9i3G49tx/8Aia8g0PSreKWyuIWe3S6k8oxvcxzkggYPyAbTzjaea07PVLZZ9O1H7DfR25vfKYEb3XaVORgDrnp2IqVXjJpXM0mesx67fS/I2i3KKerEnA/8dp0mu368DQrpgOAwJ5/8drO0nxlpd/MbRLxWY/cLKy5+uQK6qO7tmiA+0RdP74rfmXJv1MYwl9af+FfmzFi1u+lmVG0W5jB6sSeP/HajfXb5ZGUaHcsASAQTz/47W011bZ/4+Iv++xTftVv/AM/EX/fYriTX1h6/ZX5s6VCXMcxeaxeyXNqzaPcIUfIUk/N09qtNrt//ANAO6/M//E1dvpY5byy8uRHxJztbOORWia0h8Utf6sazTUY6/wBXOeOu35/5gdz+Z/8AiaYddvv+gJc/mf8A4mugYUw1pqRZ9zEbV7wQiT+ybgsxwU5yP0qE65fd9EufzP8A8TW7/EaQ/rWlTdeiOXCJ8stftS/Mwv7bvv8AoC3P5n/4mk/tu+/6Atz+Z/8Aia28g0hOOazsdVn3OKn1O5m8X2RfTZoyIWGDn0b2rYk1u7jDZ0ifamcvk4wO/wB2or//AJHKxP8A07t/7NWtf/8AIMuf+uLf+gml1OVJ/WXr9lfmxtlefbrCO42bd4Py5zjBI/pRVfQv+QFb/Rv/AEI0VrDY9Kh8Jyts4XXJie0X+FVdZugI/vcBsntVa81GLTr67uJX2qkQH1zjgVy09/c6pcF5iUg/giz+prR/HD/DH/0kjCq9f5jr3VZr52jtTti6NL6/SoLa0RHOB1XknqadGoUAAAAdAKWQzgxpbBmlkcIqqMliegArOhNRqxk9rm1FKElJljSmk0+cNG3ynIIx1zV2HPzgnOT1xWf9g1dbtbYCJpSrMdk8TBAvXcwOFx33EVLHYa4000YRQYFV5HaSNUCt91txO0g+ucVj9TXNzaX/AK8jRStszfRm1PVI7aAxRSNEzAO/GFVmPb0BrMuNTj+yOMox4/j962dNutdttPaSXUBBZQaZLI0KvF8zuxUM6dTlW+ViPTB9cyz1P7T4YnW41CSHSYrZIHjQxuvnNkgiEgfPlWbduzgcHtWksPHmvZHLTlOMFG5f0a4/tGe2sofJEsi4UtJgcDPYH0pF1WIpuKIF/vGTioLzW9StNftEv9Y2m3nwsazK8KIUPzomP3YwRhfes3XpftcFhfpq0kmlb2t441tVRrfGCQE3YY8gk5BPftS+rw7Iv2k+52DyyW2lC+TTIymwSGSS5DMqE4D+WMMFJ4DEYPFUItU1DVMx209lCY8Es88UROfeQjJ+lZmrXrWtqmqW+rNKL/TxbiGW3SNwiBI1+UM3H7s88fd6mq0Gm6xZw6dqOnNayvLaNNILr7PIuQ7htqOCCAqqTgEjPan9Wj2QlOfc247LWNRkubZkLyxP5MvnTJHsc5AUbsAscHCjk4rM0+SawvpBcXDzRKpVoGIUqc+uM1Lqz39/IILbU0Z55U1CSS4kjjBZxgsGIAwD0HXmq0M2vP4xvr/iHbdObh5vLiUCQnIy2FyQW4HXtWU8JFxastRzqTlFK4+/1e1axjSKFAquMASdOD7VZm1mNrlUVAqkdpf/AK1Lq0t3/wAI9bNbXSzNHYPbP/q1VYhMQSDgcEo3OckvgdcU2LV7nUjdXGqarE+lWk8U0CLEknlpuwEVONgIIBXgHGcHFdUqadOMLLS/42J5533HjUk/vf8AkSrU0jQ2FteM6+XcM6oBKc5XGc8f7Qqje6zq9vqtrFfatFe3KymW0nKK2EeMGM8g4XJQhegwcVsXl5qunDTf7T1SWe7i81RPLbLgM2MssrAliMYBI9OmKz9hHXRA5z7nHhbW71O6kkkYEnosg/wqb7JY/wDPaT/v6P8ACuoh1W5tPFztFqLTSXWmruuJAnmS/vCwyR/ENowc5wo9K1NYW0uNQS48uGaSSCF5iACWfYu7J9cg5981VRJyv/WxMqjbOSvfDL6fbLcXSSJGWCn/AEhGKMRkBgOVJGSA2DxTLHw8NSB+yOHbdtEbXcSO59FVsFj9Aa9D1UWhTVZlmtphqF0skIVlLY3M25h/CRnHOOpqvocDWepLdJJp0SxyBJHlaEsoBBJXOSfYpnpUciI52cEvhppdPN8JEjt8uoaa9ijLFQCwCsQSRkdB3qlJ4ftX0Q33244IzsIB/ix1r1LTmTYY7mSyfSvMlYiYx+dz3A+/uOB049a3I1T/AIVE0QUZAORgY5npqKE5M8ftfBUFzDBuv3USqCAEGemcCrF38N7dBF5N9cMJI8gFRndk5/pXvsBWTwpYtydlvGeTyDtA4pbogT6XIc4+UZzz2rRxShfzOec5czV+i/M+fofhkLyyuGS7uHuLdwrARg8HHB96hg+F8txEsqXMxhb/AJa+UNoHrmvp1Nscz/3pSTlT1wMc/lTNqGBrcAZAGR/D+VTZG1z5wm+Et3JO5tzeNH/CTCDkY60aP8MoLxpEe9nYqSNsaqCSO3519KFtkeBnAHqa5JbK1t9UNxFEFklJLNk89fypxinJIyxFRwpSkuiPGE+EOqXE0iL9pZ4yA6+V93PSqFj8MLy+a7aNphFatiR9g9/8DX07G48yZ9oHzdeecVyfh9l8/wAQkqBmbkH3L1tSpxdKo2tkvzNeZ3R4vZ/DOO9gaeDUWkiVghdduAxGcflV+L4TyszI1zcBwGO4Acbc5yPwNes39lZaf4eiS0tIoA9wGbYgAJwatTTbL2KTCgmBznHXO6vPm2n80dFZWowkurf6Hkdl8KrW70prmWS8UhwgmR12591K/TvTp/hJLpBEst+WiLgFgoFd/Yata2ujFJriKJjPnaxA7CuQ8W+LlvruPTYZA8YnU706HntWskrGEptRfo/yZzV54V02O7ZZNZWM9lfbnp9aYfDulKg3awijsSy/N9KyvET3UuoSICzqpGNxzjgetVruOU21p93cE7/QVi4q8f66GuFm3h7vsjTl8P6cchNehx7lf8adLp0TRuR4htiQpO0KvPt96udaKX+Nhj2qaMhUO0rnHpWnJF7oXPLua8GmIYEY69bpnPyFVyOfrTjpkGc/8JHbD2Kr/jWKZdqAsAR7Cq84DruRuD2o5Idg55dzp4tOt88+IrVv+Ar/APFVZi0u0Y8a9asO4wP/AIquMgfa2D39KtENGVlQ7SOpH9aXs4dkNVJ9zuYtCim8NXsVvq8UkrTja6qDt+7xjPP/ANeuFvHu7e31DTyGkdriNjLjH+rDr0x33evGK7Hwq6totwwOB9q/9lWuO1O7c6pe7scTuAcf7Rp+yh2Ic5PdksXjGa2vZ5/sk6NJcicCG48vPAGxyFyy8dOOprLvNb+12Jgkt5FlWaSWKRZBhQ5BIZdvPTggjrVOdvmznk1WJLE1Cw9OLukQlY29J14wC1gntGuXguPNt2Em3ax28MMHcMqDgYPXnmobm6jXUSLu3uJOSZVEoQtkcY+U7cZ75z7VQsG/4mNsP+mqfzFXtVXzPEFwufT/ANBFbYWjCWMjDa43flbHvrkc11aBrSYWVqhVYVmHmHktkuUIzk/3en50zU9UXUNTmureCWATOZGR5BJhiSTghRxz0x+NaWlxWUPyyxK+04Ykcisa7QRanKiDChjjArNUoJ3SHqtbnU3XiS7s5oAbFv3dmYsEkZfjbJ07bE4/2etYNlr80V3pQ8gsbRHixu/1m9mOenGN/wClbOujdexqT/yyH8zWAiRpqdsB185P5iieHpRk0l/X9MzpLmgmXp9faK+NtcWkjRG0W0kRZAjH5xIGBKnHIHGDTbrxTcvp5sna+Vkh8hRHdlIiuMDdHt5OOOCAfSqOsf8AIxS/76fyFUr1c3Mhz6fyqvq9Nq9uptyp0+bz/Qt6dq/2aGe2uYHuLacLuVHCMGU5BDYPqRyD1q1Ffy6pqk00ltJuYrsRG+VI1GNvTJ4CjOR0PXPGREEA5Na+hf8AH24H/PM/zFCpR5uawQ+Jam5aR/arWO2tNPulsxOJZGL73kYAgAMFAAGT2z+Va+r3F2dMkeO3vIhbqZFkmZpGDYHJYjtgVn6FqJt7YJJKscUY6k4AqHXvFCX1hLZWm7bJw8zcAjuAKXsad07EK+5T8G3k0/ivTo5n3RtLhhgDIwa+ioNNsSBmAc/7R/xr538EWw/4SaynbOxWJU+pwa+iLa4DxqQea19nDk2W5zqpP601d/CvzZJLpFkDkQDH+8f8ai/sqy/54D/vo/41o7g8XuKZiuNQj9Yat9lfmzpVSfNuUV020jdXWHDKcg7j1/OrBqRqibiuhRS2RTbe5GTxUbVI3eoiRTAZ/FSGlP3uaQnpV1N16I5cJ8Mv8UvzG/hScdxSmkbmoOo52/8A+Rysf+vc/wDs9a1//wAgy5/64t/6CayL7P8AwmVj/wBe5/8AZq17/wD5Blz/ANcX/wDQTS6nKv8AeZf4V+bKmhf8gK3x6N/6EaKNC/5AUAx2b/0I0VcNj0MP8J4dez6helJvtAaSZNzeZtUDDY44AHQVWji1XzHUMoKAFiSoAB6HPTFWoLi38lDJGW8pShyoYZLZBwTz171Obi3uYrg7GRBHGpKqAeD6A4xUqV2teyN6NOm3dPXyfl/mUlh1LyJpGnRGiZVKttGcgnP6D659qltL3UtJ1Cy1CZVlSCdJDHkAnvg45XIzg057q3lSWNomVTs2Ecn5VIGefenNcQTOzCDMkzLuDjheQTjnn9KEkne/4A403pF/j6/8D8xYbyw06e5iSe+ktLy3aCV3hjWSE7lZcAOQ2CgzkrkE9Kmuddgm02exgaYI9vDbW7yIuW2OXZnAPGSxwBnHFV7pYTJPDb26l2lJO5OFAz7+/tTYpbWJY0eNZZY2LARRjaenBJPtWnMuXR/gQ4JT5b/1f/LX8DUn1axlW9mYXn22705LRkWNTEhXYNwbOSDs6Y4z3rL054E0+80y+a4FvO0c6zQxKzo6bgBsLDOQ7d+OKvTaFc3F2ip5nllfmZUAAP581Nc+GFS0DyT7RGuDhMliT1PPvVytzfF+DMKdnG7t95lajPb6lq8t5LLLBG8yARBFZlhAx1z94ADjGDzyKta/La3hhTS7m5+zQExxW0lssYiXudwdtzE8kkDP0wBauLCwtWhDq7MYlORGDke/PWp3ks4ZDG8DhlG0jyl6+vWs5TVtHv5M0jGDbUpWMrWS+pagHtsxWsUSQQpLgEIowM4zyep56k1tW99ZQW+lyk3BvbC0kgCBF8uRmaQgk5yAN/oc9OOpriazyvySHAIP7peffr/nFOle1jLRtHIGAA/1a8d89aamk9/wYKML3uOXULPzUM6kMtlFBHK1sk/lup+Y7HO0gjjJ5HpTtR1ex1Oa9jlkuILeSWOaJ4okdlKxhGVlyowcZBBGPTniCQw3EZWOCYqFwxW3U7ffOeKzLb7P5klttkeRiCNsQLZGe2fek6nuu7/BhKMdEnfQtXGt20nh220O4E3kQox82NRuWXzHZTjPzKVfBBxg8j3paReWkVpd2t48/wBnu1UeZFGCyMjAg7SwBHUYyOvtSXtuJbgxpbzrMWGIfI+bGPTOf0qJzAsiQvHJD5ahSGiAbOMkkZ9Sa0ck4xfN+DIdk3+BtwTW2q+J4HTzUSJYUt0KA5SNAAWOeD8oPGeSa7i/TTbqK6ykzG7nFxL5jHCkbunPP3j6cVyPh02CaxbyMJwu3adsCk8IR03V2n2jSnJDG7wDwBap/wDF1UJUmvenZ/4ZfoTUTUmo6r1K3hqz0LVPH1rbItxKLeE27pMnlrwGIIZXJPWvVD4Q0AShfsA+YEnM8n6fNXi+hXdpB4m1OdJbqKRSdhihUMOf94YrsU1qaaKSZNS1ciEAu20fKCcdPM55xTfsL61P/JZf5GVpPWx26+DvD5ZkNgABjgzyZ/nSDwjoDRsx08blJwPOfI/8erhf+EicPuGp6xk9TsH/AMXQPEDDIGp6wEPbYOf/ACJRbD/8/P8AyWX+QuWXb8Ud23hHw+Iw62Izxk+dJx/49UfiS1ttM8HXNpZoIoBjC7i3Vwep571xqatdyWzzLd641uh5cQ5VT7nfVDU9SvNS0ua2trjWrucgbYlh3Z+YZ4Dn+VFsP/z9/wDJZf5Byy7Hp1owHhm1jJAX7JEQv4LTpn3vpwJBKuMD04WvObLUrwW8FqJNcedIlV4BASwwBxjdnj6VfsJbvVLjy4L7Uo2jYBlmTaUJ9PmPp7U5OhyfxOvaX+RhOEuZu3RfmekmUtalw43gEFyOR60skwCpIrAAsNxA5NciuhayxZRq9/s6Yw2P50v9haw2V/ta/wAg+jf41HNhv+fv/ksv8jo9nPt+KOrupTHbSN7VhYBuICWx7fnWXd6JrAi2vrF8Nx7hv/iqqXGi6nCEjfU7vcRkOQcj9aqDw7mrVP8AyWX+RhioS9hO/byO72RRW8itP8zbuMetcro5ijl8Qfvek64yOvLVn6jpOrWtv+91m8jDHGXyP/Zq5O4ikiFwsersTKQXbOMnn3560vrGHp0ppTu3b7Mu9+qOhUptrT8Udp4p1uwt9Jhi+0JvVw23OOxrgdZ8ePeWbGzUosaiInPJ9cfnWHfWoDEyamkpz/FgkfrVT7BA1jKP7SgPzg71AwOnvXk1K8G/muj/AMjrr039Xp+r/NFK6leQ7y7ep3HFQRTq19bgsc+YvT6ip5dPtpX3y6tEgA+6cf41Z0/SrWS6ieLUrVwrr2yev+9XQ60Nv0f+Ry1IS9m/R/kytqkiJqs2TjJHX6Cpbu2SW3tnV+QvHOc9K0NR0SCTUpZP7QiUnGVYD0HvRJpluY4Qb+KPauASOD096ydaF4/5Pt6FYKD+rf8AbqMDywrYLEH1I4qGRIMksoDdiORXSJpVsw41WBvbA/xqq3hu1USu2oR5AJAA4/nWirw/pP8AyKdORjggRLmQYxwMVUkIOcMPqK6u28J2t7ZpL/aSqT1XA45+tQ3Pg23h5/tSMD3A/wAaHXhf/gP/ACF7OVjkF+/1GK04ZBsxlT6CtAeHLNTzqkOPoP8AGnx6HZwtn+1rfPuB/wDFUe3p/wBJ/wCQezkb/hSNBpEyk7d11n2+6tcDrYCapeAsAPPk6f7xr03w9pludCnKajEw+05yAPRfeuJ1DSLFr+6Y6zbAiZ/vY4O4/wC1Ve2glf8AR/5E8krnJSABRgk59RioM8mujl0ewkGDrlt9SB/8VUX/AAj9jjJ1y2A+g/8AiqX1in/Sf+QezkY9h/yErbH/AD1T+YrU1M+Xrdwcc/L1/wB0Voab4ctPtkLpq8EhV1bAA9f96na1pdqNTmk/tSAsSAU4yOB71rgq8Hj6dvyf+Qp05KLMia+YsHi2pIOvvUcsy3RVyhWQDDY6H3rVTQrAj/kNWx/Af/FVIuiWKjB1m2/DH/xVZfWIf0n/AJFezkT6yM6nHwf9SP5mqBtJG1C2cgbfNXp9a6y50W2uNViLanEuYwApA55PvSXOj2lleQo+qxbi6kIQB3+tXWrx9o1+j/yIw1P92mzktWgUa3KQBncv8hWVfw4ndvpXY6nptm2pyyHU4A2QdhxnoPesu/0y1NvJL/aUO/j5MDPX60lVXK/Xs/8AI7XGPsfn+hFPpE9tp6Xz2tsLdkDrvuI1kZd23IjJ3kZ7gYq7caDq+jTP9o01YnSTyJFimSRkc9FYISRnBxnr2zVyS80ybQvsF1dNcTm3CQpLaxj7M2/duWbdvx975MY5ro9aurHRfF2tXVo80t3NqayyRzKojTypd5AIOWywHUDAyOetedGtV5lG13r3117309TnSOFvNM1O0eCK5sYh50hiTFzGyq4xlWIJCMM8hsEVo6d4ble4nTVLVI4xZS3MLQTpKsu0HGGXKkZBBwa2WuNPuL23Ekpu7EXX2iS0Gl20AB2sFJMePMI3fxYB9Oa2odWFpLZTWscjS2lvcRq5sooVZ3wVPlodoA79T9aUqtZxSUdfn8hcpzFlpOrLqIjjg8h4UVmLXCRiIMDtBJICsf7pwfatuH/hIYrae4NzcwwwyPFIXuthDqMlcFgScHoOT2qW1ura3a7RYpYorqVLljJaxXRjkAYMoEh+ZTuyGyGHTnkmjq2rG5smikMrSm9luDI8aoGVlRRwpwD8p4HFaxqVW0nGyuu/z6mfIudvy8ja0y5vrmze6uNfvreASLEpV2cliCem4cADk+44NNurnWrS8ltZNVvTJE5jIE7nJBxxzWNo+rWP2RrC+eUJ54nR4ArEnBDKQSOoxz2x0NbK60s2qPqyRB5jcNJ5TDKDuOQQcg/y60k2q07ptW0/TX77lWNCaz1eO6s4Dr12TNE8kreY+Itm7ePvc42mqN3dahbJDNDrF7PbzglHd2RgQcEEbjg9O56irR1623WGNP8AKWKKWGYIzcrJvB27mPZs89/asq9vIJIYLS0ErQ2+475VCszMck4BOBwB1PSpozqqcee/X9f+BbyKaNW+i1iC3iuIL28eE20cz5uvmGQMkLnO0E4zj8ajuU1U6g1vaahcMFjibMt4EJLoGwNxGeScAUkmr2PlLKi3P2pbH7JsZV2ZKbS2c56E4GPfNKNY09p55pIX3skKxu1vHLgIgVl2ucDJA+bk8dKqFXEWva9r9P8Ag9O4NIrRf29L5rC7uUETmNzLdeX8w6qNzDJ9hzUt3/accMU0F9eMn2ZJ5Xe4ICliRgHI9OB160ahqun6pJcib7TChu5rmEpGrEiTGVYbhg/KOQT1NMvNYt73SbewlSVRbwjynUD/AFncEZ5UjHPUfpWzqYiTi3G3ddlZeev4W/PGkopO3d/mNsprye2murrWLyC3iZY9yMzsWbOABuHZSTzUF9dapYX01rJqNyzRsV3LM2G9COe9Nsbq1FjPY3vnLFJIkqvCoZlZQwxgkcEMe/YUs+pxyaw2prGDIs6ukEi5TaOgJyDngduatSqqpLTTp26W1++/bQ00sR6tbXlhq+kT3OrXHmTo6y4LEwbeo68n5uRxzkVpM11baw1mdRubmBrUyAyEjcGg3jK5PTPr2rI1fV7PULjSbU2YtniaR5pY97kKx52hn5PfnHIHPWtWS5srvWYprKS4bFoY2E0SpjZBsBGGbOdue2PeopSrc0faX+1fa2+mxk0vaO3ZfqdLof8AyArf6N/6EaKTQv8AkB2/0b/0I0V2w2OvD/CeR29vCbG6JhjyJePlHotMEEP/ADxj/wC+RXX+FPCJ1m1fzroRRSzH7oy3AH+FdXf+CdF0iwjkjheaUyhS8rZ4we1Y4WLc36/oVhpxVSz7nltppkl/J5dnYmd/SKLd/Kuq074YateMpuraCyiPUyAbsfSvYoIIbaMRwRJEg6Ki4FSitFBGTrPojhrf4WaIqol0XljXny0AQMf9ojk/TOK6Oy8K+H7CMJb6LYKB3aBWb8yM1r0tUZOTZQi0bSyvOm2fX/ngv+FZ/iLSNMTQLpk060VgFwRCoP3h7VtLKqIe59BWb4ilVvD90O+F/wDQhVT+JmND+HE5fUNJtPLiey0qznu/sttlGt0fEZD7mwR6hct29qfJpGl/b79o9NillW8KvHFYpcYjwMcEjaCd3zD06jvrnw1Za19knuZbhWWzjQCNgBgDPcH1p48A6V/z8Xv/AH2v/wATXPT95P1f5nRLQ5b+yLKTS7j7PpcdvEnmt5s1nGwcBjgeZ1VscYHU/WjXtL09I3a0sLVhlPtLGFd0bbRtA44U+o6nOewrqx4A0r/n4vf++1/+Jp3/AAr/AEn/AJ+L3/vtf/ia05dSbnD2mkx3UGktaWULeRfM10RGvyL8mGf/AGcBuvHX1rC1Pw8lzqi3NhpokWR3WONYuJADnAx1wPTnmvRtQ8CaXHeWKie8w8mDl19R/s1bufh9pPlY8+8OTj76/wDxNYVZKMJt9CqmkEzyHXNKuJ5NHig0CFbpISX0qGJ9wUOx+YbvM+bPTOQOlUPEduqX1mJLK2tJfsib7OJP+PcjI2Nkli2AG+Y5G4A9K0/FnhGLQr57UvMU3gxsSPmU59vwrAtdKjexnlSWZXjVmULjk7o19P8AaNdkoNUoyfn+g0uZ6HqFiuhZtC9rYQyXFuNT+WJVKxIEEij0H+u4/wBkVFpmo2N3ptvdrFatp0kMz3lyIxiKQF8At/AQAmF4znvmvKm8P3ks7rjLKQrbnQfMf4eep9utNh0S4MkKgMGlzs+72JB+nQ9aw+ZXI+x6Kt3Z2b314bS3GiNYJLFfJCuXuDt3L5nUtvLLszwBnHetu71WytrDVJJLYQ6UpgEFysSgSxNIo3Kw++MEEnnB446V5bJok0TJLDI/ltbrIxJTd7kL1x74pZNLu4oPOaaQJgE/dyAehI6gH3p/MXI10PVG1PTRrWn20mnXQjn1GKG3mexSOCRCTwHDHzQeDnnp71zV54u0ybw/a6iYvLLXUtuPKhVchVjYA4643detckdLvlaJRO7GVxGux0Ybj2JHQ/WlTS75pdnnvgEBsSR8E9v97g8dfahadQ9m30PRNP8AENtPZadqESznTYbScXUoUbImBk3K/PDMCuB3yMVzH9svqEZgtNHvr2R4hOtuYG/ex7gMgKQxXPdT29qwXs5W1eSxivJgBM0YZscAE8nj0FVdTg8qwW4ivpp4ZCU+ZApDDGQRz6g00wdNnqA1eK6e9sjZ3EtwLG2Emm2f+sT7vygncfkwMggnnnpkWLbxvYaT4i1Lzo7mQwGBmEaglcJkrnPLD7p9wa8xtdOaSCAf2hKtxJAZkiCAjaATjd64B7VY0+R4LS8lt9TuUeNA0gaBdpOcAA7vU+lGyIlSb0PW/wDhduioTjTtWcH/AGAP61H/AMLt0hWLDStVOexx/jXkceqaxOC0dzIwDqh4Xq2cfyqxbS6vLfNBPNKuPOT5dufMRC2Pzx+dPnZqqSfQ9Nn+NOkTEFtH1Q46DI4/Wlj+JNrrX76PTruJYhtw7cn9fevJ5brV0ZlldnxCZVKOhG0fxZGQQMHgVeD6xayiCK+Vy0av8jx8AqrHODwBnqeuM1UJ2kjDE0OajJJdDsdX+KdnqUIX7DcxEdCxyf51ysnie3lYkQyHPqo/xrmLu91G3cpcyYfAOPlYEHoQRwR7itCQ3VrFOI78tNalVuI/KAC54+U98Hg8CpbvuaKO9kSzX8V1KcxPkDptqtMxuNInCqY1DjAA+lWLFp7uHzJb54wZViQLCHJY+vTA/P6VbtfNuZXtJNSmR1LbiturIAoyTncPQ9qwnG7v5o2qXnRhHzf6GI+m3F3IoBUrj6H+da+meGjFPDKxdXVwQNowefXNO05bh4GlLMzhyAQAOwq7D4nvtPljtWnnYSOFIZsjk1vc5Kq9x+j/ACY68a3t9RlF0kr5xwuD2HvTLmLz4kaGOQJjOCmePzqteeIJY9YmDP0I6jOOBW5/wmD6XYh1udrSgHb1z/nNZveP9dB4P/dv+3Uc7b3ltG5jNvIXHBBUf41LdahbtbELbzx/KeAo5/Wur0TxFBrYkY2yROozvJ5Jqa7gN/ZyeXvQBGLMe/HatCkzhbHWVt0QIkuRwcDr+tW7vXo5Y8CGTP8AtIK6TTtBWWxhYSMrMDnnrya0I9PWOBhcBTj7uBSY0eXXN3EWD7JM55+UAUz7VDId5jkPoAK7m/0uIqSq4OeKypIVt4fLTjBwfrQBp+ErmGTw/cKIyo+04Ix/srXnGoyWv9r38cm9X+0PtYDj7x616loTLHoUrdzcgfoteVajdeTrl8+wMwuJME9vmNUSymbaSSdY40ZixwuB1rrNb8L6fpvhO1nJuv7VDHzsrmJgTwBzwQKytL8W3en6lBcAkLGeQp5xXW6r8RL/AF6NrO11CTyCPmhn43+2aBI86tZ3S8tnJbaJFzj2PSrOpTrNq87qGAOOv0Feq6HrV/e6RFpV7otlPbbgSXADZz98EDkii88L6TZzvfsvmPNIq7WGQnH/ANYVeDa+vUwl8LPIoElmbEMMjn0Vc1baCe3YC4t5Ez2ZSK+orGKztF8m3hijC8jYoGRWH400VNaslKKDNGcof6VmB5pFcWkOsRm5heSMxAEBc45PvTPEPkXl3bSWxnwsikB0Axz7Gusjt3tLjDIUJUbWP3SeeKoX/iJpJVtZ4dkokCup6deCKuq7TZOHTdNHmeszNDrcu5T95fr0FZt3dl3cBTg4rr9Zjil1SVhGAcjn8BWNf2i+VI468VHtPd+Z3Kh+5evX9C3ezousIsqsUNuFO0e5rp9M1a1vrhUCzK3/AC0GM8DuOayb5dlwp/uwh+Ovetzw/KtsiXqNuYHEi+3rWWFf7qJglaojvbTxTothAsNrZ3MaD+7EuT9TnmpT4003/nhd/wDfsf41v2l5BfWy3FvIHjYdQensaW4iE8DxEkB1KkjtWpCOUk8faISUH2jf6BF/+KqWPxtpckeFjuXA4I2KR/Osqbw5qFnfIygyQgk70PseoqPTrPVYtR3QxyhvMJLEYGM96t/B8zlT/wBpf+Ffmy/9v0C/vo2SyuIZ8nDpGF7Hrg1R0vxPH4c1WeGaO4kspZW+ZUHytn613qk8Z696R1SVGjcAqwwRXGn/ALQ/8K/NnRa8jIk8a6YeRBd4P/TMf41U/wCErsYWaRorkiU5XCD9efetXSdUR559LkkBmtzhSe4qeI/vZ/8Ae/xqqv8AEh8/yKXU55/GWnl8+Tdf98D/ABph8Yaf/wA8br/vgf410T/fFMJ5opWvL1/yLlsvQw28UWS263BiudjHAGwZ/n7VCfF+n/8APG6/74H+NdBnk0mfWumpuvRHFhPhl/il+Zz58X6f18m6/wC+B/jTT4usP+eN1/3wP8a6HOBwaYcc1B1nJJqMWqeK7SaFJFRYmU71xzhj/Wuiv/8AkGXP/XF//QTVlz8pqtf/APIMuf8Ari3/AKCaXU5V/vMv8K/NlXQ/+QFb49G/9CNFJoX/ACA7f6N/6EaKuGx6GH+Eg+Hv/HhD/wBdZP5V0viX/kGx/wDXZf5Gua+Hv/HhD/11k/lXS+Jf+QbH/wBdl/kajCfE/X9EZYb+N8zbFOpBVO51jTLKbybrUbSCUDOyWdVbH0JrVJvYzLwpcVmf8JHof/QZ07/wKT/Gl/4SPQ/+gzp3/gUn+NHLLsBZEJPK884xVLxFGqeHrsgc4X/0IUsXiPQwvOs6d1/5+k/xqtq2r6NqGlz2sWt6YryYwWukxwQfX2qpxlzPQyofw4mjpH/Htbf9e0f/AKCK1RXAQanPBII4/FOiqiIFXM6dAAB2q2ur3hPHizQ//AiP/CuejCST06s6JvVeh2wpwrjl1G8PXxhoI/7eY/8ACnf2vNHz/wAJfoLH2njP9K25JdjO50t7pz37RFJWjaMkggZ9P8Kgm0W8WMFtSm6+/wDjWEPEF9/D4s0FR7zx/wCFRXGs3siAv4v0NhnoLiMD+Vc2Kw96UnbW3mX7eSjyp/gih8TNHW00WG8kuTdSJKFw46DBPqa8nsNTGn2rSKmXfei8cZ3Rnn8AenNd3478SxNowtH8SaVesZAfKtGVyODySBXn2j3BS1b/AE+1T5zwzgdhXZVpJUYWT3ffyNadabev5IsQ6laxRNCgdI/M81GaCOVgSACPm+g5H5VJHdMNLuriVW3tIwgkIxkvw/6Dt0zU32s/9BOz/wC+1pDdn/oJ2f8A32tcvI+35m6m/wCrFH+07PzQ6rP9pS0+z7So2nKkE5znoemKfNqVs6zyKspmuEWN0OAqgFSSDnJztHYYzRHdY1KU/b7XO0fMXGD0q6JpnXK3tsw7EMP8KFTb6P8AH/IUZyf9IhbW7GN4fLicIl5HPgQom1FzleDljyOT19qzLfUrUQCK5Ew2T+chjAO7gZByRjoOefpWpI05HN3B+Y/wqo5l/wCe8X5im6cv5X+P+RfM3/SKX9rRprb3yxFo2mZ9p4JUk8fXBqO+vrI2cdrF5xgRmkZ3jG4scDG3OMfKO/c1bJkz/r4vzFVb0ubSTMqHp396Xs5fyv8AH/IG3Z/8D/MtaZrKQWccYklaby2RUMS7UBB6PndjnOMAZqv9ujGmy2yIfMMpeUnoQBhR+rfnV3RWf7VajzUxt6Z/2aspu3an+8Q/e79OtV7KXJez38/8jCdSSlbyXbqzG0vV4rCWZpYmYNH8m3HDghlP5irL+ILdpbZhDIBHbSJJ05laMpke3C/rUR3Y/wBbH+dRSuUGfMQmpVKb+y/x/wAjdycVv+QLrdtFZxxGJyy20sXAGMsxI/Dmr9hrNq8jXCLIGkt1gmVolYKVVACuThvu9CBWBK+8nJzXQ+HWZdLuTHIqNuPLdP4a2hh5XV4v8Thr4qUacmraHP6nq0d1cpsHmIiBFLQpFgZJxtXgdavXGuWdwt08MMouL1lM4cAKnO44OctlgOoGKyggLBsU1X2ueoz1pewl/K/x/wAi/bSOm0/VreziaNJbmICXeJIkG6VcfdYbuB+JHJ60RX8ax3zrGY3mO1AOiKTkj9APzqHT55J4lCTDaOMtxiurtUvEUA6haAn+9tP9KynSmn8L6d/8jWpUapQku77GJpd20Ni7CJziQ/MPoKa+oQzXcINrlzIo3Ee9aWoveopLXtpJnqFxn+Vc6gkS7idJkz5gPXpzVvDN/Zf4mU68lBrTZ9F5lnVBCNQmzbxs3HJ6nge1YOpzNKIwECrHkYByO3+FaWpyub6bzJ4ycjJXvxWTNMHXaTnPaiOGkrPlf4mVKtJ0Ypvouxd8PawLS+AlyIzgcGvV4tRhGnyhDuDRONy9vlOK8NfYCcI1dN4RuL66uza205UEHKSHqO9XKEo6tWGmetaJGJtOtZAD91gf++jVm6t90XlkkbsjNXNDs/s+kRQuQXTOcdOSTTruHdKoHY5qBnLRHz5Li2kx5sJ6ewrA1u3MF2uPuyrkfUVp3twlt40kYHAlHI/Co/EaGURlcfu2Bz9aQxNJ/wCQFP7XIP6LXkmrbn1m9UAkm4k4A/2jXtnh+zFxpF0mOfPz+gqt8PvCWg6h4k1Jr5DNeRTOTHNwo+Y8gd6pEs4fw58Ltf12ZGa1NvbjBZ5DjIPTFd/D+z8nkK02tskpOdscHb65617NZadaWMKx28e1R6sT/Oro6UmwPKYfhh/YkAew1S5ZEGWW5w2cemMYrlvE8ptNsUkyZZwdufY17T4jiuH0O7+ycT+UduBk18qa/wDb5vEk9vIsrS5HBBJ6CrwWuPp/11CXwM9cttZc3AKP/Dj61tw3V06Fi4ZeoBFcFpnh/XNH06KXVIhEpIGCfmH1rduteh0ywLu4bjjnk1i20y0k0S65qdvBaskmNxwpH1zg/mK4XUX83V7VyytJtQSYOcn/APViofEd1Lc6iMscFAf1NVrNT9phyc/Ov86utL32isLTtST8h+pqf7RlPuP5Cs69H+iyfh/MVq6kP+JhKfp/IVl32RZycen86lfC/U7F/Bfr+hf1VjHcbuP9QmPzNGkzs4mSLO1BvA9vT8qr6xKDJ5X8Xlqw9xzUOiTtBdSuBkeXk/mM1GF/hRORL3zrdH1e/wBO8uayuV6fvIm5BHY+46j8K7ew8c2cqhL+J7aTuy/Mh/wryOFrm3voltgTCi70z0KHGQfoeR+NaMmqOHYDBAOAQOtbtGCZ7PHqVndxZt7qKTP91qmQnHLDGPWvGNK1NX1SFSuCSeQMdjRceIHS6mQT3C4kYcSEd6pr3Pmc0X/tT/wr82e1b1B5ZR+NYeq+KbDTFdEk8+4GcRp2Pua850nWTPqsCNLM+SeGkJ7GqV7qkaX9woGMSt/OuRL/AGl/4V+bOm/vGkNTuo9UGoCRhLv3NtHUelemaZfR39ubmM8SYYj0rxk6oW3YzgDJPPSu68IzT/aYTGSLdoPnz/EcDFVV+OHz/IpHaMfnppNITk0n1pUt5+v6I0nsvQO5pD+tGaac9a6am69EcWE+GX+KX5gfWkOaOaac+pzUHUI/Q1XvsnTbn/ri/wDKp3PHpVa+4025548l/wCRqepyr/eZf4V+bK2hf8gO3+jf+hGik0P/AJAdufZv/QjRWkNj0MP8JF8Pf+PCH/rrJ/Kul8Tf8g2P/rsv8jXNfD0f6BD/ANdZP5V0viX/AJBsf/XZf5GownxP1/RGWG/jfM2xXm/im3s5fEeqyzqHlWCFFDQq4AIPTJ4Jx1xx2r0gc9K4DXNJ1bVPGV/b2FoJUeCJnJZVIK/Uj+9Xbh20ptdv1RkznrvQdOa8nkkVYIzN5aBFJGcegIwOR+dRL4Zs96xSgJNI7RxqMkEg45OeOeO9dYPC3iTe7zaZC4Z/M2tKmFb1Hz01dA16MjOnpJIrFlcyoSpPUj5qftbvd/iI5B9AsY7aGTaWklXeEAOAMkcnPtVy28PaYUto5LUM9wGO/e2U5IGOcdu9bJ8MeIpLePGnDEa7FxKmT1P973pE8OeLlt8vp9vAig7JZZk3Jnrj5gPzzSVVqWrJhZxVjmINN0yO8PmwRlMf8tHbA9zg5q9PpWmx3ipBYxSrIq7QXk2kn+7yDj61UvRb6Owe9hjusfJhJA+9s5/hbr249Kz5/E+szXCS2OlxReWNqea2QvpgZHrnr1rGniLRacups6UnsjVvtJ05buURW6pEh2/fbHHU8n1pdSstBTUrtI3RpI7gJJDEpAj3vtGMHHBOMcVxdxaazeHN3LI+f4Q4A/Q1qXF/qc9zJcDT7SKWWdZ5mi48xlOQDljx9MZ75NWsVe92P2DJrtdO/wBIMRMFvDL5BmaEuWk5+6u/7oAzk4PPSobPwtNc35iu70HEssL4U7EZdu09eQxYDtUEMmoxmcSWNvPFNJ5pjlPyhxnBGGB7nvg981d059cma+i8pXa6YTyOWUMGDZ45wATj8hWVTExUG5N29X/mVKm0tjIfw5ZxWbzXF00KxQxzSKsO5hvYgKBuGTjB5x1qtHoyW+p/2eZBIGkQJJgjKuFKnHbhhxV/VrnVLoXkk1vCv2woHCEAKF6BeeOg9arRLqGoyG7MSq42ICjAY2IqjqeuAK2qVf3UWm92NRfNsXdQ0/TntLyS1tPINncrDnzGbzFbfgtk/e+Ttgc9Kj0a0sLqQW9xYI0YBee5Mjhok9Rg7ePcHJOKtXcmpXa7XsLdFaUTTCM485/Vvm9zwMDk0sD3kFi9mdItJYnk8xt0jgk9gSsgyB2zWf1h81+Yfs3bYz9LtbG6+0xTaeixwwO8l55j7lbB2DGdvJwMYyfWtHTLO2bTYWaLJOe59TVaKa7FsdLbSrR1jy+8u4Ysf4jtcAkDgZHH51o2EclvYxxyDa4zkZz3NZzxFRJck3cujBXd0JJY2o6RA/8AAjVWSzgH/LP9TV92461UlJyeay+tYj+d/ezqUIdim1rD2T9TVO+t4ls5CF7DufWr7HnnNUr8k2cnPp/Ol9arvRzf3spwhZ6F3RYI/tVq2znb6n+7VlYIw2pfL13Z/Wq2i/8AHza8/wAP/stWVJDaj7Z/rWjxFb2fxvfuzlqwjzvTov8A0ox5I4Uz8owPc1mTupfAHFW7uXZGe5qjGm7LvnFVTxFfdzf3snE8t7JEW0tz0FdT4ahSWwulkXKbiev+7XNjqWOMDpXUeEZY1WaCVWYuC/HTHFdEMTWcleT+9nm4pJUZdNDlEDzfKicL3p7Wkg5YAj2rqIm0fy9gtZBwe/8A9ekH9lnzCLWXgDPzdf1qfrVf+d/ezo5UUNOls/L8tkbeP73H8q6NG09rcyGNTj0Y8frWWn9lqwdbaXkdc/8A16fHcaf9ilDxyxrvAJJ+nvXPUxNe/wDEe66s3qqPsKenV/oSXT2LKdigfVj/AI1lLbpJLAfLYKZQM9jzU01zo1uC0aTS56jd/wDXqO11vSY7iJfInVPMU5ZuBz161v8AWa/87+9nPUS5Xp0ZW1rS7iO+lZUUxggABuegrEkimBwUIIPHFejM+jXzNP58bc/d3+2PWnW/hmzv7xYFBeQ8jJOQPzpLF1rfG/vZyUq9KNOMZbpLo/8AI8zKPnJBq3ol1Jp2sxXUZIKMD9fUV3+s+GbHS4i15HsA6EkjJ/OsWG38PMEczIjDsZOf/Qq2lWlPDPnbfvLf0ZosRSvdfk/8j13StTjubKK4jx5brk+xqW7uFDRuCCrdTXA6Tqmn2StFBeoInGCPMBGfXrVqfV4FREW+UbTkfMP8a4nOJoq8H3+5/wCRianqMNxqouAcyeYB9BmtK5vY50YZycis25TRri4EzzRK4bcSr4yf++qT/iV4IF2o/wCB/wD2VTzo09tT8/uf+R3PhKSEaVdOxx/pHU/RaxbvUEs/EUur6U8SXcBIljznzFzg5p/h5LZNLmFvPvjM2Sd2ecD3rFa10ldSnk89RL5jbvn75570e0QlWpNu7f3P/INc+JviJb8XdhdPHE6geV1VT3r0v4c/EdPE6pplxFL9uij3PKej+pryu40/R3ODKm3OcB+//fVWtCu7Dw7qH2yxuEjlIw3zdR+Jo9pETqw6X+5/5H0bc4NnN/1zb+VcR4j02zj0G3vEt4xcSTqGk28kDd3qtpfjuLU7doFv7fzShBQlcnip9YkupdAthN/qPOBVscE/N7fWtMJJRxkJdjKeJpqLTv8Ac/8AI6u8s454yHRWQDoRXz38SNKm07xI58tUt5OYwh4/Lsa93aTVj1Q/98//AFq4jxtaWt/at/abIsqnKEnB/pWbtYunjKSe7+5/5Hluqrm+T/rkP5mmWo/0mH/fH866G/tNLNwvmyqGCDHzdufeolttJRgyzICpyPn/APr06rXtGa0cXSVJLX7n/kY+pf8AH/L+H8hWRqDYs5Pw/nXS3dnaz3Dyi+iUN2OPT61lapp9uthKwv4mIxwMeo96SkuX5m6xlL2XLd79pdvQz9eYpfQuvURL+PJpmmKHupiPuiEsPzFa+rabbTTxM+owxnyl+Vse/vUWmabbQmYrqUMh27cDHAyPessJL93FGX1mn7RLW/o/8ilJct5cYC4BjFVTubqKuDTLZbGFTrEHzMzZ456e9RjTbX/oMw/p/jXTzI4/rdLz+5/5E+iA/wBsW/ynqe3+yar3wP2+54P+tbt7mr+jWFumrQMurxOQT8oxzwfemXNhA17cH+2IgTKxxxxz9avmXJ8zmWKpfWW9fhXR935CaBu/ty24PVu3+yaqagx/tO66/wCuft7mtjRLGGPWLdhqschBb5Rjn5T71VvrCBtQuSdYiUmVjg445PvXIn/tDf8AdX5s6PrdLm6/c/8AIyWldbebG45UD9a9g8Kp5el2ZIwfJXt7V5tYaLb3VxFD/asUheRQFAHOOfWvW7SNYQFXoowKdWS54er/ACLjiqTu9fuf+RqZoz71ErkD7po3n+7SpPWXr/kaSxlKy327P/Ik7UhPJ60zccfdNG5v7proqSV16I48Li6SjK9/il0ff0HcnvzSE03ef7tJuPoajmR0/XKXn9z/AMgb7pHeq1//AMg25/64t/I1YJJHSq9//wAg254/5Yt/I0XuzOlUVSvKUduVdGur7oq6F/yBLf6N/wChGijQ/wDkCW/0b/0I0VpDY9PD/CO+HEDzafbbR1kk5rq/FFkItKiZ3/5bqP0Ncf8ACy8aex8iNsSQTODgc4K5/rXW+K42XSo5Z2wPOX5pGwOh9aWEXvP1/RGOHdq9vM6IS2sXES7yP7oz+tc5aXEreO9RKAJm3X37JVXVfiJ4X0gmP7a19OP+WVopf/x7p+teeXPxH1WbxBd3mkWcdkZYwmZ8Oyj5ecdM8V24eUVGp/hf5oy9nOVtD21o2KGSZzsHJaRsKP6VzOqfELwvpBaJtQF3Ov8AyxtPnOfQ46V45qV/qesvu1fVLi6/2Gc7B+HSq8ccMK4jRQK4nV7HRHDr7TPSvEvj7WNLnWy0m1t4w8Yk8+YbmXJIwB07elcBqWo6prDl9X1a6uc/8s95RP8AvlcCtrxdLs1eLP8Az7r/ADauYkYFuTU1ZPmY8JCKpRduhpsIYNBt1ijUDzDgAfWqQmJ7VZmb/iQ2/wD10P8AWs4N71x0Xo/VnbIsl89aA3NQB8mpFatSSYN7Vc0vUbW1vn8+XZ+7I+6Tzx6CqAPPWtbSLmG00HxBOFuluiYYhLBcCPCsG4+6TjI5GeRgcUp041ISUtrGNeTUdOpgX13DJAAjZO4Hoaj0e9gjtXDPj94f4T6CtzxJYaeda1C/1N71kn1P7JGlo6qUwqlnOQc/eGF4zzyKq2HhKztby20i9ubp72+v5rSGaBgI4iknlBmUglssDkAjA55rvlyujGPRN/oYqpPmvoN/tC2/56f+On/Cj+0Lb/np/wCOn/CqOqWen6fpGmMhvJL68thcMxlURx/vHXAXbk5C+ox7543NFht/K8Pac9rbyRaskzXU0kQaRT5jxja55TaEDcEZzzmsOWHmX7Wp5GOl7CNSlkL/AClQAdp9qsnULY/x/wDjp/wrndLvjaayJXdQmMMXt0uNoOMkRv8AKx+tdPqd3Z2PiK2uoFENldWiOZm06GXfxgusDHYpLKRgHjnHWly0/MUalRdis19bn/lp/wCOmq73UJ6P+ho8TLaw+JnMMMkNjKsMyquFLRsituAGQu7Jbb2zjtW7N4ItIA/m3d0fIuJpJtrAZtFEpRxx1Jgbnp8y8UuSHmX7aouxzhuI/wC8fyNVb2RZLV1Ukk4wMe9araDYi1a1E95/aS6YNR83evk4KCTy9uM/cP3t33uMd6fe+F7OWS+0uzubtNQsJIEmmmcGKUySJGdqgArhnGMk5AJ4oUIX6jdapboUdJuI4Z7dpHwFXng+lTfaoc33z/6zO3g89a3bDSdPvdKk0jTJb2IPr1rayS3bq2cR3A3jAGM8/Kc4wPmOeII/DOkTX0K/bZI4pBc+dDHqFvdSqI4mkVwY+ACQRtI4x1Oci+WHLbzMJzm5NvsvzOPlRXbnJA/WoZVJwiA49au69aW1tY6bf6c90kF7G5MVxKHZGRyp+YBQQeD0Fbc0UAt5NGFrbiBdAW/Fz5Q87zzCJi3mfexk7NuduO2eacVBdwqym+xyOx2lUbcItb/h+aK1ndpm25jIHBPcVieG9UFlqK/aZwkLjEkjWMV4V+iSkD8cg13dve6Tpuua9ZNEtk1xcQSWby6fFdiOIq5PyufkDb42+XJGMY4rWDipJo48RBzpSjLZnNWVncXEDyRRlgHxnIHvV6HTLxXJaE7SMH5h/jVX7Pe6dd6jp9xMRcW908cvlNhdynBwBjjI9KuNcSrlGmkwcYO48Uv3fZ/ga/vPIYumXYJUwnj/AGhzRdaXef2RMPs+5wwIG4c9Pejz5/PIM8nI4+c09rif+y5h50m4MP4j7VhUdK+z3XY3rKr9Xp6rd9/Iwx4f1KSMMbcLnryP8a3PDnge+vJlWaILDuw5bkgeop/heG+1vUEsYbktMDkh3PSvovQtBg03T44XjRnA5JGefqa3Xs+z/AwftF1X4nn1p8N9EtLq3uFmB2r+8XBwW9elbF5o9rZXf2+1lXIwpUKRkd+a75rW3xxBF/3wKq3VmkkLhIIskf3RRel2f4E2qd0efeMra31LwpdCJVklWMtGpXvj3rwoaLfMRm1A/wCBD/GvbPFusf2Vod1p7222Vo2Ak6HnPSvGYJ7lhk3Ex/4Ga2lKmsK9/iXbszejCo5dPxLFvot2o5gx+I/xqy2l3h/5Y/8Ajw/xpIpbgDHnS/8AfRqQSzn/AJbyf99GvP5qXZ/ejujGr0a+5lc6Ref88P8Ax4f40g0e85/cf+PD/GrBlnxzNL/32aBJPj/XS4/3jSvR7P8AAq1buvuZ1Hhizlt9FmSRNrG4zjPbC1zN3pl3/aVy6xcGZiDkep966zwyXbRJyzMT9o4JOT0WuUv5pxqN1iaQASt/EfU1bdKy0f4GUVV5nqvuYo066PWH/wAeH+NPXSpjyYf1FVBcXG7ieT/vs08XVwvJnf8A77NRej2f4FtVu6/E0bXR3W5jkaEZDDnI9a39Q1rWotOWwhO6BJQ6g444Pr9a5i2vpvtESmV8bhn5j60mtX0qzOomkGGHRj6VtgXS+u0+VO/yMa0ari72+5nf6j458QXdusSqsYxhimATXB6iNVvpiZjJJnuzgn+dVpr+fHE0nP8AtGqwubhjnz5f++zWbnTff8C406kdrfczZ1OwuXukKxkjYO49TWYylGKMMEHBrZmlk/tiBTIxUpyNxx3rKuR/pUv++f51rXjG7lHuY4WUuVRl2/VkDciqGoj/AEKT8P51oHGKo6gB9jk9eP51nH4Pmdr/AIT9f0DXh/pUX/XFf61T00stxJjoYzkflWzf21retHJ9vhTEYXGQf61HYaZbLcN/xMYWyhGBj29648PWjCMU/wAn/kZJbHN3HyR28Z42xDj6k1BketbsmlWrsCdUg4AHAH+NR/2Raf8AQUh/T/Guj61T8/uf+RyeyZX8Pkf25bc92/8AQTUF44XVLrPTzn/ma3dF0u1i1aB11KF2BPyjHPyn3qtfaVaNf3DHVIVJlYkHHHP1rT61T5Ou/Z/5HKqT+st/3V+bDw9/yH7X6t/6Cap6irNql2Auf3z9P941raFYxRa3bFdVikxuwgxz8p96c9jENVnc6pFkzOSvHHJ461yrEQ+sN/3V0fd+RrytSJvBVqZPEVqrj/VK0rA/gBXrMPUntXK6BaQQyC4V43kK7d4HJFdRCeT71pKpGdSHL59H29C4qxfU/LS5qND8lLVUt5+v6Iuey9B+flFJmg8DrSZxXVU3Xojiwnwy/wAUvzHdcU09aQ57Uhz6VB1BnDYNVr//AJB1z/1yf+RqxuBFV78j+zroEc+S/wDI0CexV0P/AJAlv9G/9CNFJoZ/4kluCOzf+hGiqhsb4f4TzPR/Ed14fkuJLKeWC4MnDooIwQB3qLUPEF1qcxl1G/vbok8ByAo/AVd1NiHvOeki/wAlqh5xZRk1z4WV5fP9DTDx/eadyOPUrOEfu4XH/AR/jUK6rF9tkfbJgr6D2qyZfeq6yf6W5z/D/hXXh/hqW/lf5omV7r1JP7XhJ+7J+Q/xpDq8P92T/vkf407zD60bznk1yNo0SZteK9etrnVInjSYAQKPmUep96wDqkJ/hk/If411Xjlsa1Dz/wAuy/8AoTVzW/j71VU+JmWFT9jG3YtS6xbnRbePZLkSEngY7+9URqkP92T8h/jWxM3/ABTtqc/8tT/7NWcG965qVrP1Z0yv3IhqkI/gk/IVINVt/wC5L+Q/xqVXx/FTw+e9ak69yIatAP4JfyH+NSw38zxXEMLlbe4Ks6FRklM457dT09akDcda6TRD+4T/AHW/nROXLRqS7L9Uc+Ivyr1OObxTrME09yl2pluJVlcvBG48wA4dQykKw7EYNTaLf67BYSRwXqBWld90ih3RmADMrFSyE9ypGadet+4Xn+IVcDAdDmuuppQhJdW/wsUqa53co3FlqF3HbpPPEy28XkxDptTcWxwOeWPX1q3bS63Zae1lb3kKwNuAygZk3DDbGK7kyODtIzUm/wBaN4Jrl5mackTOshqNpqP7k2O6OExgSW0bqyk5+YMhDHJ6tk8AdAKv/adba9e6kuLOWV0EeJreORAo6BUZCqgdsAVXjYf2nLz/AAj+lW94z14o5mTCEXczb601HUbuS7vLlJZ5MbnJPYYHbgAAAAdAKtzah4gl87zNRVhPaLZScD5oVxhfu+w56nnnk09nz3qEuPWi7L5IkTXetf2Z/Z/2yL7P5flfcXf5ec7N+3dtzztzj2qvqmoa3caWbee9jMS7MlUVXfZwm5woZ8dtxOKtM3HWqd+3+hSc+n86FKVxunGxoxat4i1M28UuoRAGZbvKRJGTMqtiQlVBLfMck8njOcCrxg8QTTPcC50+ORFkUNFbxxkh1Ktu2xjcSCRk5NYkDt9ni5H3B29qv2TE2110+5/jVxqK1pK5z1qLtzRdtunmVLjw/q1xaW1rJc2zQ2wYQrnG3ccnnbk8+tSyWPiN9K/ss39r9k2eX90bym7ds8zZv2buduce1QhuelIVJzxR7WK+z+Jbw039r8P+COsdN1yxunkhfSvnREZXtIpFwowDtaMgN/tYyTkkkmtTTIfEkepXNwb2xluJ2815Z4UlYMMYKlkJX8MdvSsUR4OQtXtDH+nysFOfLP8AStqNSEpJNficWOo1KdGU1LZdgg0i+Cyy3E8UkzyGRnLklicZJJHWp59KuXAYSRe/zH/CudQuFdSGw4446HtUmX+zhCrcNR7Sn2/Ev2FZfa/A3F0y5bHzxbhyPmP+FSPpdy2nShXiyzddx9vaufgRwxyrAgd6uwR+ZpMylXUFx29xXPUqU77dV1N61Ct7CneXV9PNeZLp+n6xpOsRX+n3MUUq4BIY8juOle++H/GiX0CQ3FnNHOqgHBDBjjrnivGtF8O2U8iu8mou56iCED9SDXrvhXT9PtQI7TcJR9/fMXf8fSuiM4Pp+JjUpTivi/D/AIJ1I1m3wNySA+mB/jTTq8BJwJf++R/jVmVLcmMSOpfPy5PJNIzqpaMEFh1welJuK6fiZKNT+b8P+CeafEq0Gr2SvAuHRTkvxxXl1toFwka5eL/vo/4V6j8UtTA04WUEqvLgmQJztGOhryuzidrWP92x684961lOH1V6faXXyZ00KVa/xdOxeXR5x/HH9Mn/AAqQaTN/ej/76P8AhVfyn/55n8qPJl4PlNj6Vw89Lt+J2ezr/wA34f8ABJzpE3ZovzP+FJ/ZE/TfF/30f8KgMUv/ADzP5UnkTHpE35Uc9Lt+InSr/wA34f8ABOu0C0e30maNypJnyMH2FczfaRPJe3DB4gGkYjn3+ldL4ailGiT5Q/8AHx6ey1yeoW0/9oXJ8o481v5mrc6Vlp+JnGlX5n734f8ABGnRrj+/F/30f8KYdGuefni/76P+FRG2uD/yyNQtaz94zUc9Lt+Jp7Kv/N+H/BL8OkXIuY23xYDA/ePr9KTVtIuZbpyHiALDqx9PpVS3tpxcxHZxvH86ZrdtN9qd9vG4d/atsFOm8bTstfUyq0q3K7y/D/glr+xbn/npF/30f8Kcui3AP34v++j/AIVRFrPnBUfnUv2SZe3X3rL2lLt+P/ANPZV/5vw/4JsS4/tmDn+D/Gsu5/4+ZsdnP86m0+KRb6PIHU9/Y1DdRyG6lwB989/etJ1FKnzeb/JGdKlKE+Tsl+bISOD61Sv/APjyk/D+Yq2UkA7fnVO/Vvsch4xx/OpjJcnzOtxfsnp1/Qqsv7iP/dH8qk01T9qb/cP9KljtjJbR/MB8o/lUthaFLhjvz8h/pUxkuYiMXoYrLmoiKvGz/wBs/lUbWo7sfyoUkZuLJtBH/E7t/q3/AKCap6iv/Eyuv+uz/wAzWpoduF1q3O49W/8AQTVS/t1/tG6JJ5lb+Zrbm/dr1/Q4VF/W5f4V+bDw4P8AioLT6t/6CasSQltXuMf893/9CNO8PQKNeteT1b/0E1pW9sG1G5Ocnzn/AJmuam74h/4V+bHUjaVzqdDbbGo6YFdXA3y+9cppqlAtdLbt2611shGmh+Q0oPpTFOYyfenA81z0t5+v6I1nsvQk/gpM0nRBSZOK6qm69EcWE+GX+KX5jskj6U08UdBkUmetZnUHSq1//wAg65/65P8AyNWP51Wvz/xLbr/rk38jTEyvoRzolv04Df8AoRopuh/8ga3Ps3/oRoqobG+H+E4a8MIup45ULBmB/QVXZLNQCIW6/wCe9O1EH7fKcen8hVZ2bbg56151Gnea1f3no0aMXJPXXzLPlWZ6QH/P41WjjthqMo8o7dgwPyp6M3YGoUJOoS9fuj+ldGFh7tXV/C+vmjKdGN4779y8I7PPMJ/z+NLstBx5B/z+NRjJ7Uq5yRiuPk8395r7CPd/eb/idIxqkYuVDv5C4I9MmsYJZ5/1JrovEgsZ9QjeaZ1YQqPlHbJ9qyBDpn/PxL+X/wBat6mHvJtS/wDJjzcPVhClGMozuuyY+WOD+yoMx5j3nA9OtVDHaD/ljWvImnjSYQbiQJvODj6+1U/L0zP/AB9S/l/9auelh20/e6vqdEq9NfZn9zKfl2qn/U04LbdoTVkppeMfaZPy/wDrUuzTNo/0mT8v/rVr9Wf83/kxPt6X8s/uZAotwf8AVVt6YqGFfKXbwev1rN26WMf6TJ+X/wBatjSRZbRsmcrtOCR7/SpqUHGhVfN9nv5owxFam4pKM9+qZzk1tbTIEMRAznP+TQLOMjHmS/8AfR/xq4P7Nx/x8yfl/wDWpynTgf8Aj5l/75/+tW0VWhHljPT1X+Ru6tBu7jP7pGa1rFn78v5n/Gnx2kR/5aS/mf8AGrrrpvU3Mv5f/Wpq/wBnZyLqX8v/AK1O9f8AnX3r/IOeh/JP7pGclpbnUZUBcOFyW3Hnp71bFjF/z0f8z/jRF/ZY1KVvtMpfYOCPp7VfB07acTyfl/8AWqZSr/zr71/kKnOg0/dnu+kjPNlFnBkf9f8AGl+xxN/y1f8AM/41dJ0/HNzJ+X/1qcBp4H/HzJ+X/wBap5q/86+9f5GnPQ/kqfdIofYYs/6x/wAz/jVPVbONdNmIdyQB3PqPetoHT8/8fUn5f/WqnrC2I0qcrcSFuMce49qalXvrNfev8gcqFtIz+6RFZ2KvZQEPJzGvc+n1q5BZBY5QHfkY6n/GpbD7CLG3zcSA+Wv8vpVtfsW19szEY59v0pc1e/xr71/kRVnQ5NIz6dJd0Zg08f8APVx/wI/4002Kg4M7f99H/GtLOn5BFw/5f/WpxGnnnzpP++f/AK1RzV/5196/yNueh/LU+6RmCxXbkTSEexP+NWLLTt0p8u4kU7T3P+NXl+wAcSyfl/8AWqzZ/ZDMdkjfdPb/AOtWlGVf2ivNfev8jmx1SisNNqM726qVuhhDT1I/18n/AH0f8aZ/Zqk/62T/AL6P+NbGLLb/AK1/y/8ArU0NZAYM8n/fP/1qy5sR/OvvX+R1c1D+Wp90jFuLCNE5kkPPqf8AGpjpzeSUS4dMnOTn/Grd79jEK7JpC27uP/rVYY2OOZpP++f/AK1JSr3+Naen+RtJ4dUoS5Z7vpLpbpcpR6ZKE/e6pOF/uLnn9auQPeQQ+VDq9zDH2SLIA/WpF+wsP9fJ+X/1qbixHPnyfl/9aupYzFraUfuj/kZSqYeW8J/+Ay/zFilvoJvPh1S587GPNZju/PPAqvJPqhlZhrV1luSdx5/Wp82P/PeT8v8A61MBsQf9fJ+X/wBam8bjH9uP3R/yBVMKv+Xc/wDwGX+ZjXdpctDMzahMxKknOeePrUNhYztZRkX0i5zwM+v1rbuzYG1m2zPny24x7fSq+m/Yv7OiLTOGAPGPc+1dCxGJeEk+aN+ZdI9n/dF9YoKovcnt2l/mUjYTg838v5H/ABqRrG4CZ/tGXH4/41oMbA8md/y/+tSl9PC4+0P+P/6q5PrGJ/mj90f/AJE0+s0f5J/+Ay/zMoWExB/0+X9f8aQWMx/5fpR+f+NaJfTz/wAvT/h/+qmeZpwz/pT/AJf/AFqpV8T/ADR+6P8A8iT9aofyT+6X+Zf0fSrp9MlZNVnUCXG0ZweB71z15Zzi6nDX0pxIwyc88/Wu10GSyOjzbLlmHn+nsPauWvp9J+23Aa/YN5jZH4/StniMRyrWP3R/+RMViKKk3yy+5/5mV9jl/wCfyT/P41DJbS5x9rk/z+NaZutFUHOoH/P4VCbrQc5Oon/P4UlXxPeP3R/+RG8VQ/kn90v8ylFbSmZB9qf7w5/yakvLFzO6vcu+CDyPb61Yiu9BNxHt1Fi24YHqc/SpLy+0JLyRZdQZXGMjHTj6VP1nFKqrSW3aPf0I+s0L3cZfc/8AMr4GelB4GMUh1Tw6D/yE2H/AT/hTX1bw3/0FH/75P/xNYfVp+X3mrzKl/LL/AMBZPYn/AE2P8f5Gq1yx+0y8/wAZ/nUtjqXh976JYdQleQ5wNh9D7VDc6r4cW5lD6hMHDkEBDwc/Suj6vP2KWm/fyOb+0aXtW7S2/lfcgc8VRvyPsUn4fzq4+qeHWGEv5yx+6PLPX8qp319of2KRPtc3n8fLsOOv09KcaElDpv3N3mFJ0npLe3wvsOgcC2j9do/lVi0Obhv901BDqPhwW0aveXIYIM4jPXH0qxZ32gyTsILq4Z9pyGQ9PypRw8lK+n3mUcxpNqNpf+AszWbioHcCrTXnh0/8vt1/3wf/AImmG58OH/l8uf8Avk//ABNNYeXdfeYPMqf8sv8AwFkuiNnWbfju3/oJqrft/wATC5/66t/M1atNR0CzuUnjupyyZwGU46Y/u1h3WppNfTuqEo0jMDnqCauUHGCXmY0q6qYmU0mlypaprqzb8PNnXbb6t/6Ca1LMk6lc5HHnvj8zWL4akMmu2+AAAW7/AOya6C0jA1K4zx+9f+Zrmpq2If8AhX5surNSlodJZjp71uW/QVj2YG0VsQ/dHuK6mJGkh/ck+9OByKijP7o/WnA9eawpby9f0RrPZehPn9360zPGaU42Aj1poP511VN16I4cJ8Mv8UvzHE8+9NzwBjFBoJqDqE6VXvj/AMS+6/65N/I1Pn2qvf8A/IPuf+uTfyNAnsV9D/5Atv8ARv8A0I0U3Qv+QPb/AI/+hGiqhsb4f4TyLXL9zrFx5VzmP5cbWyPuiqEc7XV1bw3ErtG0qgjPWuttrKxNtpCRhJHuYriWZZbOM7iqSAHzCSwwVGABg9eDxXNS6FNa29vKt20moeULsW6Q5VYwC+S+eu0ZxtxjvU4dqMotraxpJRhPdme8UIhmnF1II0k2KDGMkkE/3unFaWkeHp9Wt754DcvNbWyzpELclpi0sce1cHOP3mc+2Md6r3Wl3bR3FukeyWJkluIlhbCZwvBJPdwDkDk8VuaVdQWFz4jsr6++xS3WntZ+f9nkw0oniJDBdxAKq2cAcZwCcA9ntYNO369/+GMpSjYxrTRtTn1G4sIdL1GS7iQ+bB9mYyIPUryR9aZBp19dNMtnYXlwYF3SiKFnMYyR82BxyD1rrJb7Q57m4huJYpZNPsre2inu1uVhmZThyRFiTI+VUzjheccVdfU7DxJr0sGn3jwf8TpL2GQW82bgFRwu3JEgIYjfgfMfm9c5VFayIc+lzjYbK4u9ai0pUYXzyiDypMKVfOCGz0x3z0q1qukHS4baYXltdW1wXEdxb52lkxuX5lU5GQenQgjNXH1OCx+Keo6nLCz28eoTs8kQJby2dlLAdDw2feqt2bE6bpug2eoQXAjnuruW6WGZYlLxoqqAy7ycR8nbjLDnAzRzoXOu4tnpK6jpz3NtqNnJNFDJcPZ/OJBGmSzZ2bOAM43ZxVg6BKmnG6N3Zectst49pu/fLA2MSEbduMEHGc4OcYo0OWOw0G5SXxBM2n3FpOJNKiSdWklKEKW48vCtsbduJwMYqebU9Ne3m1NLrN/daKmnJYm3cOGWJYS5bGzZtjLDBzkgY70lUtcfMzHgQ3KBoE81TIsIKIGBkbO1eB944OB1ODV7TNFm1C4nhdHgaKO4IDQZLSRJvaPHHzfdz3G4cU3wVeW2krqRvVZQYY7uxAiZt13EcxdOn32GenNbt14l059TtZrS5kEk+m3rSP5Lg/b54yrqMe4jGenvQqmgXkc8dOmgubm1vLa5guoIvMaE23zryACwOCq89eeo45q7qXhfWdK1WLT57G4NxMoMKpA3735QTtBUE43AHA4NQ2+t2MukRJLO73SeHprWQeW+fN+2NKq5x02EHPQDjOeK0m1zSBql5ePNayw61pkUG2eK5AtpEEQYSeXtbafLYZjZvfjIJzqzuK/c5m8T7NcSw3K+TPG22SJ1CMh9CuOKta94auNLtJrg3lrLLBcLBdQ27EtbSMCQjggAdCPlyARjioPEmpfbtR+2RpZukEUMKS2iTiN9mMD98d5wBjJ7AVteIb7THg8RtY3Qlm1e+S4MTRSqbUeYXbzCRgtuYAbNwwCfam6qZXM2YVroDz6O+p3Os2dhbrP9nH2pJcu+3d8vlxtxjucU7QtIk1iJI7fUrMXspfy7R/MErhRn5SEKdj95hV7wvfvpV4BL4litbGO93XNjHFcML5BjOE2bGDDIw+33xTvCeo2+lzSXf9vNaabNI/2rSohOJJk5wgKjYcg4yzDGScUuaN/IV3fQwNOmhN0DcyJt2tksTjODjOOeuKtuixyS3IMbIkPmLGjnY/zBc8nPU/pWXa2rlYW8lX3kqgZG/eHPfB4544qwFvmbzREvl+SUEXltsK5JK9c9QTnOeKKc6ajaVt+39bGkFp1ND7QqzWrQuYxPsO1XIOCeR69qnhnlB3Pqr7c4z5p4Pp1rGeOaO8jubjKLFKqBFQgDbj5een86WGTFwqJEFRXLPl884xxxx7V0wlh3B3ste3TTy239OhnPmUtG/wCvmdVDOuzP9sSeo/ef/ZU69eNtPYf22ZC38PmgjqP9qsBJI5pI3Ikwm5QfMJPbvVhmjKOE8xCxz8jlecVpfC33/qy8tr3M7yel3/XzN+MpHaRga4PMVAPLE3IOOn3uMU2VpBIAmtPsOPmWXgj865hYijiQBS2TnJJyD61tw6a0lvCF2BSg2gk8ZH0rFVKbpyWl7u2mu6t07XuEmk1eT6E7TOp2jV5T6kSnAH51XknnjBC6pcED+ITHH86bLpcsSnBi3EEZ3HH8qgWwkaNkXywSMElif6VftKfJZqN7duvT7Pyf5lXj/M/6+ZYW4uyONVmP/bU/41Nb6leW16gOozspUk/vT/jWaNJuYVl8uWM71IAJPyn16fWqD/aRqIilCK/klfvcfXpV4Z4flTl8Xa3r5fMzrNOLTbt/Xmdyut7gv+luvfHmf/X6Uq622zL3TBu48wf41wCXErR7cr/q9mS3fdnPT8KsLLKXLo3DDDhZOQfbj/GoSwrVmvz8/L+kW35v+vmdZqOrtLAoW8bJYceZz/Oqk2qTnJ+2PwcH970/WuVnMrTEiXBGPvEk/wAqZPcPICEG3e258NnP044HJ9awSo++rLy+7S2nffyNnL3I6vr/AFudMupTMcfbJfXiakF9cPtAvJiXzjEprk1mnSOUDq4wDnO0Z+lLDeyQIRubf1B34X8Rj+tXQlRXKqiXW+nmtPPS/wB/kZya1s3/AF8z0bTbqN9NiaSdWc5yWkGfvGp2uIf+esf/AH8FebQXNysKqk6/TGe/0pxurwNzMqjuSuP6V5zjLyOlPD21lL7l/md9cXERgkAkQ5U/xj0qrazRC0jDSAEZ4Le9cUbq6IyLhPyH+FHn3xXIkLfRP/rV0JVPq7VlbmX5MX+zX+KX3L/M6PUJnNrKsDtu3gjaeaxXW8fDM1wcerEVJpf264uVjMqgk/xD/wCtW7/Z94V2GaLceQe38q4Ks+SXvWPRw1CnVg/ZuWl+3T5lfQmK2kiyHBD/AMZ5/WtNmT+9H+YqG0051ZxcyKc8qY6nNlAD96Q/lVqpTa3OOVGunpF/18zpvDk0EejTK00Kt9ozgsB2WvLNYs3fXL8xpuU3DkEcjG416LpWjvcafJJE6qgl2neec4HpXL32m3K39wFkhAEjD9fpVSnBJWZEKVRyaaOTOnzf88T+VH9nzZH7quiawuh1li/z+FN+wXeM+bFj/PtU+1Xc1+ry7GNZWEi31uxjwBIpJP1qzq9jJLq07ogZSRzkegrTh0+7aVMSw8sP5/Sn3enXa3Lq0sPGP5fSsnUj7RO/T9SfYSvaxzn9mzZ/1Y/MUv8AZs391f8AvoVtNY3QGTLF+X/1qjNnc9fNj/L/AOtWqqLuDoyXQg0exki1WB22BQTklh6Gq95YSNfXBGzBlYg7h61sabZTvqEQkdGXJyAPY+1QXVnci6m2ugXe2OOgz9K2517Ja9f0MfZP2r06fqZUdhIJFJaPgg/eovLFyZHDJjjvV37LdZ++n5f/AFqR7W4OVLpz/n0pKa5Pmb+zl7Nq3UorprGNW82PoOKuaXZmK7YtIn+rI6+4qysaLGoZMkAZOanso4mnIK/wnipjPUSpNNHPGwbuy0n2E92FaWFpp20cxm6Znmx/2x+VH2MD+P8ASrxxSZXFO5PIW/DSiLxDabmAUFsk8fwmuot5IxfXP7xMGVjnI9TXOeGIludehWQbky3H/ATXTR2EH2uYGPgSN3PTJqI29u/8K/NnI/jZ0Fg0ci/I6sR1wc1swgcZNc3oaBLi7RRgK4wPzro0HSt2Wi9Gf3DH3pVPpTIj/o7fWnKeKwo7z9f0RpPZehPn90tM4pSf3Y+tNrqqbr0Rw4T4Zf4pfmOHBHNJ7UZxjNIc9Kg6gycVXvm/4l9zn/nk38jVjPrVW+40+5/65P8AyNAnsV9D/wCQNbf8C/8AQjRSaGf+JRb/AEP/AKEaKqGxvh/hPGLnVr6wvLNfJhzZwyRxhsnIk3Zzg9fmOPwptvrl61p9mFtbGYWxthdbT5vlf3Ou3pxnGccZq5qWj3F5eGaJ4gpAGGYg/wAqrRaJdW7l3khIIxwx/wAKwwlalJwjJ6nViYcmMlBPS5XuPEl7PC0f2a1Weby1nuEU+ZOEIKhvmx1VScAZIGc1siwu9Stb/UZbaLfNOZZCMABmIYgZOcZNZtn4Yu5L2PdJAVBycMf8K762sC2iXkCFQVdcZPsldOGq0ZRm+bZX/FHA+Z7nJ3kt1dWxhOm2cbMVMssaAPLtGBnnA99oGTyafp2oatpK6gumg2aXybJEglZQq7s4HzfUZOTgn1rXOkz8/PH+f/1qjbSbgdHi/wC+j/hWH1ij/MiLye5DLFLo80kUuh2FwZogCZy2QpPQbHAHTr1rKsY7iwv1uo7G3dlDAJKoZPmBHTPPWuy8S2E8moRlXj/1Kjk+5rJj0u4Dhi8X/fR/wodeitHIfvaDJYbs2CPHpFlDC0bwqiAYG7OWyWJ3e5NZNudSSF7ZNKspHi3xx3DqPMRWJyB82D1OCQSM8EcV2k1rIdHhQMmQ5PXjvWLa2c73FyFeMENjk/WsqWIpNO8urLk5JqxmWk2sWgsQmlWD/YlkCGRFO/f/AH/m+bHb0wKhtbXV7UWDrpdo32OUyoZNp35IOG+bkcfqa6Y2xhAw0bN7tVeVbtiSDF+LH/CtPb0P5kTzzOOjjv8ATr6OUWluxAKtHKFZXBGCCM+h/wAK6iz8N61rkEdzDoNoIRD5cUcciqsYz1GXyTkn7xPWqi6XdvM88rQmQ9DuJwPbivRPBX2iz0bbOwJ+Ypg5zzSqV6SpTlF6pX/IyqVJxWnU4bVtL1JoYrGTw3p0ckcYRZI5DuAHfAk25Pckd6lGi65e2iM+h2SI0iGeZCgaTbxz8/HvtAz1Oa6Ai5a9e4d0LPnOea3A08OhRZ8suzH2HerVai7+8bxlOzPHdYtJdPvGka2iEkUocK2CCeuMA9KyJbqRZI9trAuN3yKv3iwwSef/AKwrtPF2j3F3rLSxyxqjxq6oxxz0PasKHQriQGRHhD9CzMfl+nFCq0XBy5trDjKaizJguLi0jSPyYneJi0bN1iY+mDjt3zzVmzkvo7eELFEsMEvmbpOASeMHnkcn8zWpHoU8DEg28h7b2OB+GKZPol/cfM00LlSDwxwP04rNV6L3kiY1Ki2Zly3ZVmhWHewn855HPVz7ela2kvqWpX5lECGBN0r4AAXGeRk81HPoFyuZpWh2s/3VYn+ldnoehzR208QlXdPJHGAp4C7gx7egrodWgqUZcy1v+guebe5q+GvBmtSWHmQWKeW7FlMcigEH/ebNbv8AwhPiAnmxJGc486P/AOKrvNKvLayso4Vjkwoxwo/xrQ/tm3/uS/8AfI/xqPb0LfEhe0qXueYN4L18NuNkfp50f+Nd3brJbaPaQSjbJHbojLnOCFAIq7PrEGOFk/75H+NY93qcbZwH/Kj6xRW0kJucrX6GRqsuc81zRuGjmO01r6hL5hOM/jWFJC5fOV/OpeKpfzIFFmpFPuGc15p9rN946vJs5GXRfYKMf0rvRvjhc5HCnv7VwGi6dPH4hBcxlpFkbg+uT6V1YPEUnUspdH+Ry42L9hP0MiOFSmTn8KniiCnncp7c1qxaRKcAmMAdwx/wqcaTKDkmP8T/APWrk+s0v5kdqRnK8oODK7ADocGs653t95nbJrfbTZoyGJj98Hqahm0S4J+/D+Z/wqPrFLmb5ux1VE3h4esv0Ob8sqOBg/WkDzZ+8T+Fbp0O4VcK8P4sf8KRtFugMB4QMep/wqvrFH+ZHI0YLPNgkuRjnoKYskwGfNP4jIrbOhXTJgvD0/vH/CkHh+5C5d4cD/aP+FDxFH+ZCszHmUJDFMsahpMgkDgY9qqsWbPJI+tdB/Y1yqSmRofLYcqGPy46EcVBHoVyybw0Iz0yx/wrqjiKX1ST5vtL8mKz5iPw8calGGzjJrr2K+euM9KwNK0S6hvUbzIevZj/AIVumzn80IXjyR614uKq05TTUj6DKZqMJJ9n+Q9mRXP86hZ0BJPWpP7Mue8sX5mmtpc//PWP/vr/AOtWCqU/5jZ1YnUeG3U6JNgn/j4/otcrfzKNQuf+urA/ma6vw3Yyx6LMGkQn7Rng+y1y1/pczX9ywkj5lbufU10SqU+RXZyxqL2kik8y44qIzjrnrVg6TL/z0i/M/wCFINIl7vH/AN9f/WqVVpdynUXcZbzr9pi4HLjv71JqUo+3SjGOR/IU+HS5I545C0ZCsCcN/wDWqvqTj7fLx6fyFKMoyq+7rp+oua7IXmHv+VMM2On8qaTkdBn1qNic9MV0JIG2XdOmJ1KEY9f5Gqt3K32uf/fb+dS6Z/yEYfx/kaq3h/0uf/ro3866LfuV6/oc137Z+i/MjEjbh6UOx3daaOoxSSffNC/hv1/Q3/5dv1GSOw71PpzE3THP8B/mKqyEgVPpmftLf7h/mKIbmH2iiSaaT2oOfWm80zFgelMc4UmlINMYElVz1NUkZzdkdH4Ojxq9u2OpP/oJrqkX/TZ/+ujfzrA8KR7dWtgPf/0E10iD/TJiP+erfzrNf7y/8K/NnH9ol0YZur3/AK6D+tb8fOAemawtIGLq9/66f1Nbafdya3Zoi7HzAw96cCKZGcwN9aBWNHefr+iLnsvQsZ/cr9aaPrR/yyX603POa6qm69EcWE+GX+KX5js570ZyKb0oyc1B1C7sj3qtfn/QLnH/ADyb+Rqfdg1Bf4+wXPp5TfyNAmVtDP8AxKLcfX/0I0Umh/8AIJtv+Bf+hGiqhsb4f4TxLVV/0z/gIqtZ/wCvb/dNXdUH+lE/7Iqnaf69v900YLeB2Yv/AJGE/wDEzW8E6e19rq4O1UxlvSvSb6zWG11CNBjLqTz7JXM/CSz+06xKD0DCvZbTSrS4169gkVfL2qSCM/wrXdh4/u5f4f1R5KfvfJnjMltJkgIx+gzVaS2mx/qZP++TXpGsaeuk6lLBJbP5LHMUoXhh6exriNX1bUba4zb22FBI2bCeK4xXG+LEddUiIRj+4Xt7msFJXJwBzXS+K9Xn+3xxmzkbMKnKqcHk+1Y0MN/dcjTLnB6EJQM0pGYeHbbc2P3h/wDZq597h90yxDJJ9cZ9q6a40HXL3QbaC2smSQSEkSsFwOef5VQ0/wAGancSTxSXEETRNh+S3PPTj2rChs/V/maVN16IwoZZ1DPdiNBj5UUgn8aqT+ZK3ySPg9Fr0ey+HPmY3STzDu20Io/XNdTpXgzTdJIkEIln9W5C10pNmLaOA0DwPc3CpdapI0EOAwiH3mHv6V3Ng0e+FEXbAFIRfYVd1JJJFWBSQ8jiNQPfr+maYlp5mqNDEp2xpsUD2rPEq2Gqen6oxqu9vU5+W2Vry4ubhAsKfvPr6CpZbh7rRbd24/eEge3NV9Thug/2BUldI2+Z9pO403V55tK8JxyrbSyT7yscaoT8xzyfaqVSGuq+9f5nVB6P0OJ8aSQzXsSxnMttAQ2O2TwP51y8lzPARskCqR09fwq6bDUp7eWaW0umllmyxMTdh9Peq72V4SWSynkPQYiY0KpDklqunVf5jXwsqiTewMuGJPfpVqKcyttkcYU4VDwo/Clj0i+MR3WNwGPP+pbj9K1tP06aa2PnaMWkQj70bAsPyrKNSHdfev8AMziK0aizRlKsu4D5cZFeq/Dm2gm1KeLA3Q7ZkHUdNv8A7NXmmsaNbQ20Ulpa3lvOWCvCcsvPcHA/Ku18D2Wr6F4b1vUisn2sNHGFwc7CRkDjn3rrlUh7GOq3fVeXmNtXZ7vBt8oFSCPUGlkk2isnw7m38P20chwQCRuPODzz+JNXZnUxFtwJ7DNY+1h3X3r/ADIKt3cZBrn76fg81Y1G7aIfLG0n+7WJcSPIu4I/PbBqXVh3X3r/ADKVjPvX3q2Oo/lWJICWyK1pUm35ET/98msjXVurOwla3t5Gc8AhCdoI61n7SHdfev8AMu6KupT+TpF22eRGa4nw0D/bMWc/6puv0roLpL648KTO1vOZGUKQYzknPpWL4etLuPWo1kglUmJsAofSuvB1Ie0eq2fVdvU5cb/AnbsJCY8Bt56+nWnrtZyCx570R6dd7Qfsswx3Mbf4VNHa3SMT9luGb3iOBXP7WHdfev8AM61YbuQk4GT1O7tSOFfndj29KsfZZwQ7Wsq9slDg002dyAf9Cnye4jNZqpDneq6dV/mdlRpYeHrL9DKkYqx5wuO9RlnIyTwegrSm0+6UFhaTt6fuz+tQC0v9xAs52OO8R/wqvaQ/mX3r/M5LopuONzZK/wAKg8sf8KhYFwHkIHoo7CtObTLuVjLHazktjKbDlPYcciqj6dej/lzuePWJv8KPaQ7r71/mJtFF8bSEyAfWnoizwhOPNQfJ/tDuKsPp15sLNaTqMZ/1Z/wpsVneBVeO0uD3DCMnmupVIfVXqviXVdn5kfaIdOcC+jPQbq6DzBtJ6Y96y/7PvF1CNls5wGIY/um4OPpWp9kucFTbSbjzjYa8fEyg5LVfee7lU1yyXk/yGGYHim+aM9P1p/2O6xj7JP8A9+zTGtLodLSf/v01ZKUO5o6i7nYeFZAdDnx/z8+vstcjqMg/tO66f65/5mt7wpqOp6ffRWkdkfJkdnYvC2Qdvr+Ap974n1yO/uEXTUKrIwB+zv0z9a35oOK1/r7zm57Tdrfecn5vzdaQuPeukHinXs/8gxP/AAHf/GnHxRr3/QMj/wDAd/8AGlzU/wCb+vvD2r8vvOZVx70oYZ710o8Ua/8A9AyP/wAB3/xpR4n1/wD6Bsf/AIDv/jRzU/5v6+8PaP8ApnMkgnHNNLAds11H/CTa9/0Do/8AwHf/ABpD4n17/oGR/wDgO/8AjRzU/wCb+vvB1H/TMLS2H9ow8ev8jVS7JN5Px/y0b+ddbZ+ItcmvI45dOjVGzkiBxjj60ybxJr6TyImnRFVYgH7O/TP1rfmh7Fe91/T1MOdus/Tv5nI8hhx3pJThjxXWf8JL4gyM6dF1/wCfd/8AGiTxNrwYj+zY/wDwHf8AxqVOHJ8S3/rqb875H69zi2O7gZFW9M3faWH+wf6V0h8Ta+B/yDY//AZ/8adB4h1q4kKTaeiKBkEQMOfzpwnC6s/6+8yUve/4JxBBB6U09O1dkfFHiD/oFx/+Az/400+KfEP/AEC4/wDwGf8Axo54fzL8P8zJyOMPWlhXfcqPSuvPinxF/wBAuP8A8Bn/AMatw+IvEAkOdMjxgf8ALu/+NaRnDuvvX+ZhVlpYh8MJjVID9f5GugVf9Ml9PMb+dWNE8Qau99EJrONE5yTCw7H3rUHiK8+0SjyrfhyPuH1+tZxcXXbT6L82YL4jG0ri7vh6yD+ZrZU96raRrVzHf6iwSLLy5OVPqfetxdeusD93Dzz90/410M0RBGcW7fWlBGc1fTW7kwE7Ic5/un/Gga3c4+5D/wB8n/GsaVry9f8AIufT0Kv/ACxX600H8q0v7ZuPKU7Iuv8AdP8AjTP7auf+ecX/AHyf8a6p7r0Rx4W3LK380vzKFJnjg1ojWbk5+SH/AL5P+NJ/bVxn7kX/AHyf8azOkzzyDVe9/wCQfc/9cm/ka2P7ZuP+ecX/AHyf8agu9cuUs528uHiNj90+n1oEzF0P/kE2/wCP/oRoq/p+u3M2iozJD8yMDhT6n3oqobG+H+E8K1Qf6R/wEVStP9e3+7Whqi5n/wCAis+1H+kMP9k0YLeB2Yv/AJGE/wDEz0/4Hwh7+8kPRWH8hXpGk3yP4w1KR22q7bVP935VxXPfC3ww+k+HXnIJvLoF2UdhjgflSs7QahqJwVZJF+o4SvRoOykn/L+qPHWsn6M9O8pHjCTBXB9QCDWddaFpdxnz7QjPR14qhoniGC5iMNzKqkD+I8N/9euiidSA0bHaR3rnlBMz2MKXwvaTziUSkEDaAQCKtRaOkOFfy2x6Ej+taYLKPl457Ck3EHmpUUNtmJPplnLO6OrhQM4DEVi+H4beLU9YWOJcCfAzz3auhuGL3khI7dq53RSRqesEf89/6tWFDZ+r/M0qdPRG8RvYIeAemKrywNH74p5lyMEVci/0mAFlIdeD7+9dBkczIVGvW5I+WKN5D9cGq81y9jDNcHiaUcewNbNxppXU/PZf3YTB9/aud1eTz7ltxG0HH6VSScZJrp/kTJXcfU5+bUruOQvJPtTBdjtHT8q5zxL4kv5/D9tcQzmMNMQvyqePm9R7U3xTqQeU2kR4AHmY/QVi64hfwdp2MZ+0N1/4HXIsPSSdor7jqglZ+hmDxNrbW5xeYAfk7F/wpYta1aM7Ybsqp6/Ip5/EVkxyeWjxvH8rDjHY+tX4YBJKq+Y+O+xc/wA6I4el7OS5V06AkuVmlDruqMT5t84UDP3E/wAK27C81u9VJPtqW8D/APLZkU8egGOTWLBp5muWjWFUjYDLTOMgZHv/AJzXUyWzW6IuxVjVeowB/wDqqI4Wjb4F9yM4pEes3cq2MaxTsQrgb3A3OeeTxx9BivU/h9cQXPh++m1CZHT7RtPm4UYGfSvIdU4sk2MrKXGPbg1ayVXa7M5HUZyAcV0zoUlRgnFbvovIpQTbPW9e8e6NoqbY4muHx8qoeK4TUvi1qrEfZbC2gQ8DcSxH61yUtyxLqflHGc81kXL7t3zHruJ9q53Qo/yL7kV7OKOhuPiV4jZzi4gXnoIRVM/ETxJnm5iP/bIVzbEM2M8fyqMEDOMmp+r0f5F9yDlR1sXxL1lWxNFA4/3cVpwfEaO5QR3cckRPcYYfyrz0rnPHtShGLn5eaX1aj/IvuQJLseh6x4hkk0SQ2N4rNkABVGR+GKxtC1PUZdcieaYlhG2CVA9fauehQ55OK2dEU/26mBwYmIz9K6sHh6PtH7q2fTyObGpKhNrsWTruoYA+1nPbCLj+VA1vUg4/0zKnr8i8fpWZjYp3kbfQVFGw2sM4A5zXN9Wo/wAi+5HTZdjaXXL2bMbzbl6jIH+FPj1+/cMTOeB2Vf8ACsiBV3/f4xk8c0xWjBYqG6Y61CoUuZrlXToddRL6vT06y/Q1pdevihxcMreyrz+lUz4h1QDAuiT3+Rfy6VRyxPO7GeCRUUgw3HfoKv6tR/kX3I5Womp/wkGqMOLk5HfYv+FMPiDWkP8Ax/cejIh/pWW7ngbWAHqMU0ox28bfrxS+rUf5V9wrJ9DVl8Rak8ZVpVIIwcKBUcOv6lFGkcU6qgzzsBI/Os3AAf5wR6CmgKQA3TrmupUKX1VrlXxLp5MXKuY0U8Q6sLpVN6Wy39xf8K0W1m/K+b9oO8cA7R/hXMrtN2m3ONwrVDYt3+teVXoUk1aK+49fLYxtLTo/yLzeINTA5uj/AN8L/hTD4i1MD/j6P/fC/wCFZjfWonPWpWHpfyr7iZRiuh0ugeINSl161je5JUlsjYv90+1UtR8SammqXaLdEATOB8i/3j7VW8Nn/iorT6t/6Caz9VP/ABN7z/ru/wD6Ea1WHpctuVfcYNR5tjRHiXVcf8fZ/wC+F/wpw8S6mcf6Uf8Avhf8KwckUoaj6tS/lX3B7vY3x4k1P/n7P/fC/wCFPHiPU8f8fR/74X/CueEhpwkNL6rS/lX3FJx7HQDxHqWP+Pon/gC/4UHxHqX/AD9H/vhf8KwhLR5lL6tS/lX3Fe52Om03XtRl1GJHuSVJPG1fQ+1RXXiDUkuplW6ICuw+4vr9KzdGkzqsPrk/yNQXj/6bcf8AXRv51q8PT9ilyrd9PIxSj7V6dP1NVfEOpsw/0ojn+4v+FJL4h1MOf9KOP9xf8Kx0b51xnrRO37xqlYalyP3Vv2Onlh7N6dTUPiPVev2o4/3F/wAKsWOuajPOyyXJZdhIGxf8K58tV3Sj/pTf7h/pRDD0k0+VfcZRUeZaEp8Rat/z9n/vhf8ACmnxHqv/AD9n/vhf8KzCaYTQsPS/lX3GbjHsan/CR6rkZvCOf7i/4VrprmpkKftJ6c/Iv+FcknzTIPfJroLRQV59a1jhqVvhX3I5qijzbHT2OrX7JzcE8/3R/hWrAzMS55LZJrBsE6dhmt6Djt2xVxpwh8KSJSSItLJ+13vr5n9TW2hz19Kw9L/4/L3/AK6dfxNbUbc5zVspF2M/6M3+9Qp4psR/0Z/rQvOMVhR3n6/oi57L0LJ/1Ax603PpQT/o6/U00nmuqpuvRHFhPhl/il+YoIFLTc0eg/KoOoUNkY71Wvv+PG5/65N/I1YzVa+P+gXH/XNv5GgT2Kuk/wDIDi/3W/maKTSj/wASOL/db+ZoqobG+H+E8m1Ejzj/ALtVdMiWW/JJ4UdPXmrGpDMx+gqvYEQXQkJ7c08D8UDrxf8Av8/8TPpPwpdohiQHDKR8p4ptzZW1/wCItTinGFJXJXqPlSqWi6l9usIZ4NKkmBUFJEJx+YFU01G7/t++R9PuzIwDlQM8YX2FenSg7TTXT9UeZGjNPW33r/Mk1Xw3Lp0263m82PqARhq6DQdSP2JI7nqvAccj8fSseTWZZ1W3l068Eij5cjkiqkOsfZLgt/Z90pP3gV4P4VgoNaXH9Vn3X3o9EQgx9cj1pjkbCa519da3kAWzuGBXPyjipF16eUYXTbk+23/61JQdtA+qzet195cP/H0/0rnNLcRajrJIz/pGMfi1aB1S4W4b/iVXecf3aw7LU1N/qOzTLveZsycd8msKVJpPVbsueHldarZdTrbODz8Ng4Na4SG1TLYJx0rmoNbuYIQq6XdAf7n/ANalbW7luX0u7Pttrf2ZH1WfdfejRv5mkjYjC8HFeY63dvDAxQZlZiFHp711eoeImt4y02n3QB4GR1rlLy/sZrYSyWU53Mcj8/eqjTevoRLDSTWq37nnOoRFGJY5YnLH1NTanEJfBdgGXOLhv/Z63b6TQ2P7ywuB/wAC/wDsqx9YvbG50mOys0kiWKTeN/Pr7n1rH2bVzpjQkk9vvRyiQxqfugGui0axWRt7sAOw9apWenNNJvyXVeoArfsRst9vkFhuIHFOMHyP5AqE+Vr9V/mJCj/2tJGwK5QA8fStOKNkbAB2dCr/ANKz4pSl/JlWY7QME8jpipvtMoHzIwbJJGamMJELDz/pr/ML2BZ1WN1wFbIwetZtxaQ52q8g7nn/AOtVqSc4AHBBLHJzVSS4G1skA7s59q0U6sVaL/IpYeXVL71/mUJrRARgv+NV5LRVXkt19e1W3/eAkyZz3qEdcb8t6Cj2tf8Am/FB9Xl2X3r/ADKbW6Ko5OSM8Ui265Oc8VcMJ6B0Bx0IpwVVGN4P8qXtq/8AN+KF9Wl2X3r/ADKJhXvmpFt42zgt781YVY1GcqcnvSkLwN6jvxS9tX/m/FD+rS7L71/mRLbJjqxI6Ctfw7Cv9pltzZWNsZ/Af1rOIXAAcetPTGMqcnHHtVKtW6v8UZ18HKpTcFZX81/majeHryT+OH8XOP5U0eGrzH+tt+vQOf8ACsraTnD4HelEe7+In6Cs3FdvxMlhMX/z8X3L/wCSNU6FcQDc7Q88cOf8KYvh67DDdJAR6Bj/AIVSjT96XJA4wM96g8ts/wCtHvWSiud6dup11MLi/q9NKa3l0Xl/eNUaBe7ifOg5P94/4UHw9ctwXgJHcMf8KyjsX7xQkcUEgqcOgyOwrXlXb8Tk+qYz+dfcv/kjR/4R68B/10BA7Fj/AIVCfDV85JM1uf8AgZ/wrN8lADiRen5U1Yk4/eA47etHKu34i+qYz+dfcv8A5I05fDd4sZbzIMKCT8x5/SmxeH7uaBXWW3CnsWPr9KzWjySxlHToaRIQwB3j6V0qK+rPT7S6+TF9Vxd7c6v6L/5I018MXyz7hLbcc/eP+FWDot6sbRb7fcxyDuOP5ViJGqXAbzVzjoau5U2zY29a8yvCN17vbqepl+GxcVK9RbS6Lt/iL/8Awjd6UH7y3zj++f8ACo38N35/5aW3/fR/wqSytRJZRNhWwME5xzT2ggX7xjH1cVqqUP5fxOJ0Mf8A8/F9y/8AkiTQvD1/Dr1pIz2+0Fs4Y5+6fas7UvDmovqt24ltcGZyMuf7x9q0tMS2OtWeyWLdvbADAn7prnNThT+17398n+vf/wBCPvRyR/l/ESw2MejqK/ov/kix/wAI1qX/AD2tf++z/hSf8IzqX/Pa1/77P+FZvkp/z3j/AC/+vR5C/wDPdPy/+vRyL+X8R/VcZ/z8X3L/AOSNL/hGdS/57Wv/AH0f8KP+EZ1P/nva/wDfR/wrN8hP+e6flR5Cf890/KjlX8v4h9Vxn/Pxfcv/AJI0v+EZ1P8A5723/fZ/wpP+EZ1P/n4tv++z/hWd5Ef/AD3SjyE/57p+VHLH+X8Q+qYz/n4vuX/yRvaT4d1GHU4ZHngKgnIDH0PtUN34a1F7ydluIAGkYj5z6/Sq2jQKNXgP2hTyePwNQXsC/brg/aAP3rfzNaOMfZrTr38jNYXGe0a9or27Lv8A4i5H4Z1LzEzcQYyP4z/hS3HhjUWnYi4gA/3z6fSsxYUDhvtAODmorghp2IbIqHyRjqvxNZUMXGnZ1Fv2X/yRp/8ACLaj3uIP++z/AIVc03w5fW9yzvNCQUI4c+3tXO4960NHAF4//XM/zFQnC+34kU6eI51eov8AwH/gk3/CKX//AD2t/wDvs/4Uf8Ipf/8APa3/AO+z/hWTgUhAANLmh2/EzdLE/wDPxf8AgP8AwTftfC96kmTLb8f7Z/wrdtPD92oBMkOM/wB4/wCFcTaA7xXSWQOwDNaXh2/Ew9niP+fi/wDAf+CddaaROi8vF+BP+FacenygffT86wLLiMHua042wOKV4dvxKVPE/wDPxf8AgP8AwR+lWshvL8ArxJjr7mthLWQd1/Oue0tsXl5/v/1Na8bfKaV4dvxH7PEdJr/wH/gmqkDi2YZXOfWkWBx3X86ij4tH+v8AhTV6GsqTheXu9e/oXOnibL94tv5f+CW3GyFQT3qPoaYTkg0ua0lLmY6FJ0otN3bbf3j80hOaT3pT9Kk1Aniq18f9BuB28pv5GrGflxVa+P8AoFz/ANcm/kaAZV0nH9ixeu1v5mio9KONFiI9G/maKqGx0Yf4Tyy6u3WQABenpUAunbghfyqO8b98P90VEjc0sLTjeLsdeKxdf6zKPM7XNiw8R6jpjZtpdq9052n8K6zw9410eXWXm1tZ7YSxhN8R3KDx14zjivOi1RnpXbheVRqJq/u/qjhni68rXm/6+R9N2uhaFrlotxp181ynUNFKrY/IZFVbrw1HbN+885/fcAf5c18522o3unSeZZ3UsD+sbEVuW3xO8WWQ2/2q86/3ZxuFc/JQeysH1vFfzv8Ar5Hut1pVjPcDe8ocIOMgf0qAaJZq37uSfPoGH+FYEPxH0+PWIbDWlFvI0atHcqPlySRg+nSu3W5D26zwmOWIjKyRkEGh0Y25rCWNxEdOd/18ira+HUnuSGE4GOSXAwPyrI0/wvBNqmrR+dIAk+Mhh6t7e1b8d5cPIX80xqRyTycVhaXqTR6rqypkK8/Lt16tWVClBp6dWOrjMRdPney/rY1H0DTraPMlxMAByzOOf0rFum0yMlYJJnP94uMfyqxcSPI5Z3Ln1NZM1qsrMU4bP4Vv7Gn2M/r+J/nf9fIq3Fok7szTO4/hGelU7mzQWSrluG/xqzNDJCQCcZ6EVOMzWimVeQfzq4UoWlZdP8jOeNxDcbze/wDn5HG6lAIkLDP41zrzSOMIoyzYHH511muESllXhRxVHSbKJSZZAMBSFGefrXO6ML7HTHHYj+d/18iaytYVtFYS8kc8cVFZIDGEViSSxIxwAAK0pUUPuf5Y0X8aq6cALdyUfJJC4HJ9a0VKCg1bsarGV2m+d/18ivGgOpyKeEVNxz6cUXDJvIBJHrmi6tYZpC5Lozjlcjp2rNltIlZsyMR0HvWHso9ifruJX2n/AF8ieR/m4IZR69qpyXJ3EKR+Peq7xRjcodj71CYkHckdhnqaTprsH17E/wAz+/8A4BO92/GAv5Un2mTrgD8DVfZFn7pJ74NXrDTWuZ49kMskRPz7OuPWl7OPYPr2J/mf9fIZGbmRZHWMBIxlmI6Vo6bp4v7a4uXuEit4QBuYfeY9AKu3WkWmm/bUuHl8huIxkbhg9TVSGyt7qBpLVZktYQTywOffpR7OPYX17E/zP+vkQXdpKl5HbWq+dJ5YaTaM7T159OKpPK0LbXT5h/eGK1rDQ7q5g3kBY3J2/vOvsTjGasx+GLaYYkEkbno6kPk/pRyR7C+vYn+Z/wBfI577R8v3VB7DFBuXjwAVJPoOlWNR0GexnAaK48sk7W2g5/Ks42vP3vx/xo9nHsP69if5n/XyLLXj4OCue4pPtj8cjFVvLj4CgsB601EUseMNR7OPYf17E/zP+vkW2uZWGQmc+oxUIUB8Ek+yjAFQopOck/TNNKqO5qox5dkZVcRVq253cfKw3jn8aUFAhL5Yk/dHSmGBtoOANx4GaQoi/KTubuR2p6mV32HPIrf8swB7dqZhRzk49KUxxKMnJOeMGo9iHoxBo1Bt9iQsdmS2PrULORxn9aTBUndz6UE8HPFdSv8AVX/iX5Mm75hY2Au1NaBf/RHPv6/Ss6MBp1BBAPrV/wAtRZvjON3+FeXX3R6uXylaWnSX5FJyrZ+UH61Xbbn7q/lVhkAGc1AwA6VaOaUp9jT8L4/4SS0+UDlu3+yap6oR/a97/wBd5P8A0I1f8L/8jHafVv8A0E1S1T/kL3v/AF3f/wBCNX0M7yvsU8ik3U7NFILy7DSaM07NIGzQF5dhM0U7NGeaAvLsX9EP/E4t/wAf/QTVa+P+n3H/AF1b+Zq1oh/4m9v+P/oJqtfH/T7n/rq38zWr/hL1/QzvL2j06fqVuaKdmkzWRpeXYMGtDRh/pb/9cz/MVQzxWho5/wBLf/rmf5iqje5UHLmWhnc01umKdupN2TihXuZScrbFq0X5veulsV6GuetogxHWt20sUYDJf8DVXZlr2OhteAPSr8bZFYUOnQN1aT8x/hVtNKt8Z3y/99D/AApaju+xd0w/6Ve/74/ma2UIzj1rlrHTIJLi5UvJhXwMEe/tWpHotscfPN/30P8ACnqF32OkjP8Aojn/AGqaprOh0i3/ALNlj3y7WYE/MM9vaoV0G0x/rJ/++h/hWFK95+v6I0m3ZadDaHX+dPzxWKNAtf8AnpP/AN9D/Cnf8I/adPMn/wC+h/hW2pF32NntRnpWOvh+0PHmT5/3h/hQfD9p/wA9J/8Avof4U9Qu+xrk1Wvj/oNx/wBcm/lVA+H7T/npP/30P8KY2g2vTzJ/++h/hS1Fr2JdKI/saIf7LfzNFTRQJa2ohQkqoOM9aKuOx1UNInjF6f36/wC6KjjbJ/CpbuGSSUMi5G0dxUcdvKpyU/UUYWSvFXN8TRqPEykou1+wzNGeKd9nm/ufqKd9nlx9z9RXZhY354vS8eunVHFKhV091/cV26VWkFXmtpSPun8xURspD/CfzFL6lP8Amj/4Eh+yqfyP7ma/jZS2uQAdTbL/ADau/wDg/wCJhGZvDt6/7uT95bMx6N3X8eK4/U7H+1r1Lp5ljZYxGAFJ6En+tLYaS1jcpcw3mJYzuVtp4rWnhZxeso2f95EujUa+B/cz364j2TMF7dvSuV05SdQ1Rl7Tf1aqFv4yvJIVWVrcyAYJ2Nz+tZ9n4mktby9dzB+9kzyjHufT61lTws6d02t31Qp0Kr2i9l0Z1z/vUynXuKrTnyISEGXPHSsBvFOWDLJACD2jeo38Sb9372BSe4jfj6Vt7CXdfejP6tW/lf3GjBM5nLzEFcHqOgplx5n2fCOWBP3u2KyDrFuGDSSxSEDABR8flT59btrq2WM3SQ88hImqoUJK+q27omeGraPle/Zla4hWU7Q3Q4/3j6U026tcsUXAVSfaka5smAH9oAYx0ieg3NoeP7TI57Qt/hWf1afdfejVU5r7L+5/5EszBzHG+1Qo3Nu7+lZ1oXWBnWQHLHqavG8stzsL5MvjcfJfmqNra29xaMsl2FG49Izmh0JKDu106o2jGfK/df3MglJZ2wOVHPPSov7OurjCxR4GMhm4BrYhstPhYsJkc5z80bVNMUm4N+qp/dWJqxdF/wA0f/AkQ4VP5H9z/wAjFXw1KRunmCgc5Wp49I05JYw0m92OApPU1dFtabdpvS31V/8AGhYbKNo2SaFNhyCIWzU+wf8ANH/wJC9nU/kf3P8AyJbTw1BZ3HnPG4kRsbXHGPp6Ulrd29vfNHpy/ZXVs55KuT1FTXl2bsENqrDIABEZyMUy1+zQKFW8UkHLP5R3Mfc9afsH/NH/AMCQezqfyP7n/kYustcXmqXXlqGMJ6N90e59q6VfDs1r4etrV5DIb2Tc0qDG0kdB7cVSRLGPYjyxSqrb9rxMQzerDufrW1a+I5oYXSO8hwZd4/ct8oxjA9qPq/8Aej/4Ehezq/yP7n/kVtUgh0vR00u3Ym8jhAhiH/LVmH3vzrl9N1X+y5zaX2551z5jhxtT6cc1saisV9qsd+b/AMqSPGwJG2B9Kxbrwxp0s5mOoyDJyRsJoeH/AL0f/AkHs6v8j+5/5Gj/AGzBqLiAyzmLPMm8At7dKp3/AIajG6S3eRUb5iXbdj+VXbCz0qz6eRKezSROSv05q2Tb7Cgu1IbqGjY/zpewf80f/AkV7Op/I/uf+Rx76O6LmNxJg8gHn8Kglt54k5hKp1zjmuznjsZ0CtJArgcOkDA1V+xQJjGo8D1iJo9g/wCaP/gSF7Op/I/uf+RxRDo3KsPrTCwD8fdHt1ruGsrOQYku42U9jAaqtoOk7TtuQrdmEbZFHsH/ADR/8CQ/Z1P5H9z/AMjkHZ2bcwKjtSDOeFy1dZ/wj9gG3DUnzjHMRNMfw9YsuDqTAZzxEeaPYP8Amj/4Eh8lT+R/c/8AI5YqC3LnPsOKaUOeoOPwzXT/APCNad/0E3/79Gkbw1pxx/xM3/79Gj2D/mj/AOBIXs6n8j+5/wCRy7A9wfqOlISAM4z9a6n/AIRnT+2pyY9PKNWE8K6G8Q83V5lfuBCf8K0naGHcbpu6ejT6MSo1G/hf3P8AyONWTE6M3A9avGZTZyEHgH/CumPhPw/kf8Tmfj/pif8ACkPhrRgfIXVJjE3LP5RyD9Me1eZVi5NWX5f5noYPmpqXMmtJdH29DjHlHY1Azgmu5Pg7QT/zGZ/+/J/wpv8Awhmg/wDQauP+/P8A9atVSn/L+X+ZxPEQfX8znvC7j/hI7Tr1b/0E1T1Vx/a97/13k/8AQjXcaZ4a0LTdRiu11edzHn5TERnII9Pesy90LQp764lOp3ALyM2PL6ZOf7tN05pfD/X3k+2hfc4/d7Ubq6n/AIR7Qf8AoKXH/fv/AOxo/wCEd0H/AKClx/37/wDsaXJP+V/18x+1h3OVzRkDiuq/4R7Qf+gpcf8Afv8A+xo/4R3Qf+gpcf8Afv8A+xo5J/yv+vmL2sO5yu7NJu9xXV/8I7oP/QUuP+/f/wBjR/wjug/9BS4/79//AGNHJP8Alf8AXzD2sO5iaG//ABOLf6t/6CarX7f8TC56f61v5murs9H0Kyu47hdSnYpngx9eMf3azbqz0B7uZzfXOWdicL7/AO7RUlyU0mnv28hQfNUbT6fqc8WpN5rc+w+H/wDn/uv++f8A7Gj7B4f/AOf+5/75/wDsaw9suz+5m3K+6+8ww5rS0Zibx8/88z/MVa+w+Hv+f65/75/+xq3p9toUVwzRXk5YoRyv0/2aqNVN7P7hwjaS1X3nMbjTo+Wrof7H0X/n7ufyH/xNSR6RooP/AB93P5f/AGNCrL+V/czOVOTKVkgytdHaDEeajt7HRkI/0uf8v/rVqxro6pj7XL/3yf8ACn7Zfyv7mL2UhIT/APXq1GwHH401H0cf8vcv/fJ/wqZZtIDZ+1Sf98n/AAo9sv5X9zH7ORBprf6TdZ/v/wCNbMZ9KzNKWya4uz5z7S+VOOo59q2IxZAD98/5f/Wpusl0f3MFSky3Cc2T/wC9/hTVPFJ51ulu0cchOTnkGo1lUH71TRu+Z23f+Q5xlorFkHpT+pquJk/vfpT/AD0z979K3syOWXYlzgZ96UnqPWofPjx979KQzJ/e/SiwckuxLnBFRs1M85P736UwzJu60WYcsuwO3yke1FRPIpU80VUTeimk7n//2Q=="
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# test_questions_for_url1 = [\n",
     "#     \"Where is this scene?\",\n",
@@ -170,13 +175,30 @@
     "# Very important that the boxes are normalized\n",
     "# normalized_boxes = output_dict.get(\"normalized_boxes\")\n",
     "features = output_dict.get(\"roi_features\")"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question: ['Where is the cat?']\n",
+      "prediction from VisualBert VQA: outside\n",
+      "Question: ['What is near the disk?']\n",
+      "prediction from VisualBert VQA: nothing\n",
+      "Question: ['What is the color of the table?']\n",
+      "prediction from VisualBert VQA: brown\n",
+      "Question: ['What is the color of the cat?']\n",
+      "prediction from VisualBert VQA: gray\n",
+      "Question: ['What is the shape of the monitor?']\n",
+      "prediction from VisualBert VQA: square\n"
+     ]
+    }
+   ],
    "source": [
     "for test_question in test_questions_for_url2:\n",
     "    test_question = [test_question]\n",
@@ -204,32 +226,16 @@
     "    pred_vqa = output_vqa[\"logits\"].argmax(-1)\n",
     "    print(\"Question:\", test_question)\n",
     "    print(\"prediction from VisualBert VQA:\", vqa_answers[pred_vqa])"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Question: ['Where is the cat?']\n",
-      "prediction from VisualBert VQA: outside\n",
-      "Question: ['What is near the disk?']\n",
-      "prediction from VisualBert VQA: nothing\n",
-      "Question: ['What is the color of the table?']\n",
-      "prediction from VisualBert VQA: brown\n",
-      "Question: ['What is the color of the cat?']\n",
-      "prediction from VisualBert VQA: gray\n",
-      "Question: ['What is the shape of the monitor?']\n",
-      "prediction from VisualBert VQA: square\n"
-     ]
-    }
-   ],
-   "metadata": {}
+   ]
   }
  ],
  "metadata": {
+  "interpreter": {
+   "hash": "f237d186bbb22b392353378fb98a8d08e33f23f14150c8880e3780871939e71d"
+  },
   "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.8.0 64-bit ('transformers_env': conda)"
+   "display_name": "Python 3.8.0 64-bit ('transformers_env': conda)",
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -242,9 +248,6 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.8.0"
-  },
-  "interpreter": {
-   "hash": "f237d186bbb22b392353378fb98a8d08e33f23f14150c8880e3780871939e71d"
   }
  },
  "nbformat": 4,

From 28efda0d872d8eaf2e6d8fdaa789e191e8645989 Mon Sep 17 00:00:00 2001
From: "zly.idleness" <zly.idleness@gmail.com>
Date: Mon, 23 Sep 2024 19:29:56 +0800
Subject: [PATCH 4/4] make style

---
 src/transformers/modeling_utils.py | 641 ++++++++++-------------------
 1 file changed, 215 insertions(+), 426 deletions(-)

diff --git a/src/transformers/modeling_utils.py b/src/transformers/modeling_utils.py
index d9e8aee2cf65..fdfd52506a50 100755
--- a/src/transformers/modeling_utils.py
+++ b/src/transformers/modeling_utils.py
@@ -122,8 +122,7 @@
         set_module_tensor_to_device,
     )
 
-    accelerate_version = version.parse(
-        importlib.metadata.version("accelerate"))
+    accelerate_version = version.parse(importlib.metadata.version("accelerate"))
     if accelerate_version >= version.parse("0.31"):
         from accelerate.utils.modeling import get_state_dict_from_offload
 
@@ -159,8 +158,7 @@ def is_local_dist_rank_0():
     import smdistributed.modelparallel.torch as smp
     from smdistributed.modelparallel import __version__ as SMP_VERSION
 
-    IS_SAGEMAKER_MP_POST_1_10 = version.parse(
-        SMP_VERSION) >= version.parse("1.10")
+    IS_SAGEMAKER_MP_POST_1_10 = version.parse(SMP_VERSION) >= version.parse("1.10")
 else:
     IS_SAGEMAKER_MP_POST_1_10 = False
 
@@ -221,8 +219,7 @@ def get_parameter_device(parameter: Union[nn.Module, GenerationMixin, "ModuleUti
         # For nn.DataParallel compatibility in PyTorch 1.5
 
         def find_tensor_attributes(module: nn.Module) -> List[Tuple[str, Tensor]]:
-            tuples = [(k, v)
-                      for k, v in module.__dict__.items() if torch.is_tensor(v)]
+            tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
             return tuples
 
         gen = parameter._named_members(get_members_fn=find_tensor_attributes)
@@ -240,8 +237,7 @@ def get_first_parameter_dtype(parameter: Union[nn.Module, GenerationMixin, "Modu
         # For nn.DataParallel compatibility in PyTorch > 1.5
 
         def find_tensor_attributes(module: nn.Module) -> List[Tuple[str, Tensor]]:
-            tuples = [(k, v)
-                      for k, v in module.__dict__.items() if torch.is_tensor(v)]
+            tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
             return tuples
 
         gen = parameter._named_members(get_members_fn=find_tensor_attributes)
@@ -276,8 +272,7 @@ def get_parameter_dtype(parameter: Union[nn.Module, GenerationMixin, "ModuleUtil
 
     # For nn.DataParallel compatibility in PyTorch > 1.5
     def find_tensor_attributes(module: nn.Module) -> List[Tuple[str, Tensor]]:
-        tuples = [(k, v)
-                  for k, v in module.__dict__.items() if torch.is_tensor(v)]
+        tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
         return tuples
 
     gen = parameter._named_members(get_members_fn=find_tensor_attributes)
@@ -446,8 +441,7 @@ def shard_checkpoint(
     weight_map = {}
     shards = {}
     for idx, shard in enumerate(sharded_state_dicts):
-        shard_file = weights_name.replace(
-            ".bin", f"-{idx+1:05d}-of-{len(sharded_state_dicts):05d}.bin")
+        shard_file = weights_name.replace(".bin", f"-{idx+1:05d}-of-{len(sharded_state_dicts):05d}.bin")
         shard_file = shard_file.replace(
             ".safetensors", f"-{idx + 1:05d}-of-{len(sharded_state_dicts):05d}.safetensors"
         )
@@ -493,11 +487,9 @@ def load_sharded_checkpoint(model, folder, strict=True, prefer_safe=True):
 
     if not index_present and not (safe_index_present and is_safetensors_available()):
         filenames = (
-            (WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_INDEX_NAME) if is_safetensors_available(
-            ) else (WEIGHTS_INDEX_NAME,)
+            (WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_INDEX_NAME) if is_safetensors_available() else (WEIGHTS_INDEX_NAME,)
         )
-        raise ValueError(
-            f"Can't find a checkpoint index ({' or '.join(filenames)}) in {folder}.")
+        raise ValueError(f"Can't find a checkpoint index ({' or '.join(filenames)}) in {folder}.")
 
     load_safe = False
     if safe_index_present:
@@ -533,10 +525,8 @@ def load_sharded_checkpoint(model, folder, strict=True, prefer_safe=True):
             error_message += f"\nMissing key(s): {str_unexpected_keys}."
         raise RuntimeError(error_message)
 
-    weights_only_kwarg = {
-        "weights_only": True} if is_torch_greater_or_equal_than_1_13 else {}
-    loader = safe_load_file if load_safe else partial(
-        torch.load, map_location="cpu", **weights_only_kwarg)
+    weights_only_kwarg = {"weights_only": True} if is_torch_greater_or_equal_than_1_13 else {}
+    loader = safe_load_file if load_safe else partial(torch.load, map_location="cpu", **weights_only_kwarg)
 
     for shard_file in shard_files:
         state_dict = loader(os.path.join(folder, shard_file))
@@ -566,8 +556,7 @@ def load_state_dict(checkpoint_file: Union[str, os.PathLike], is_quantized: bool
         return safe_load_file(checkpoint_file)
     try:
         if (
-            (is_deepspeed_zero3_enabled() and torch.distributed.is_initialized()
-             and torch.distributed.get_rank() > 0)
+            (is_deepspeed_zero3_enabled() and torch.distributed.is_initialized() and torch.distributed.get_rank() > 0)
             or (is_fsdp_enabled() and not is_local_dist_rank_0())
         ) and not is_quantized:
             map_location = "meta"
@@ -582,8 +571,7 @@ def load_state_dict(checkpoint_file: Union[str, os.PathLike], is_quantized: bool
             and is_zipfile(checkpoint_file)
         ):
             extra_args = {"mmap": True}
-        weights_only_kwarg = {
-            "weights_only": True} if is_torch_greater_or_equal_than_1_13 else {}
+        weights_only_kwarg = {"weights_only": True} if is_torch_greater_or_equal_than_1_13 else {}
         return torch.load(
             checkpoint_file,
             map_location=map_location,
@@ -619,8 +607,7 @@ def set_initialized_submodules(model, state_dict_keys):
     """
     not_initialized_submodules = {}
     for module_name, module in model.named_modules():
-        loaded_keys = {k.replace(f"{module_name}.", "")
-                       for k in state_dict_keys if k.startswith(f"{module_name}.")}
+        loaded_keys = {k.replace(f"{module_name}.", "") for k in state_dict_keys if k.startswith(f"{module_name}.")}
         if loaded_keys.issuperset(module.state_dict()):
             module._is_hf_initialized = True
         else:
@@ -640,17 +627,14 @@ def _end_ptr(tensor: torch.Tensor) -> int:
 def _get_tied_weight_keys(module: nn.Module, prefix=""):
     tied_weight_keys = []
     if getattr(module, "_tied_weights_keys", None) is not None:
-        names = [
-            f"{prefix}.{k}" if prefix else k for k in module._tied_weights_keys]
+        names = [f"{prefix}.{k}" if prefix else k for k in module._tied_weights_keys]
         tied_weight_keys.extend(names)
     if getattr(module, "_dynamic_tied_weights_keys", None) is not None:
-        names = [
-            f"{prefix}.{k}" if prefix else k for k in module._dynamic_tied_weights_keys]
+        names = [f"{prefix}.{k}" if prefix else k for k in module._dynamic_tied_weights_keys]
         tied_weight_keys.extend(names)
     for name, submodule in module.named_children():
         local_prefix = f"{prefix}.{name}" if prefix else name
-        tied_weight_keys.extend(_get_tied_weight_keys(
-            submodule, prefix=local_prefix))
+        tied_weight_keys.extend(_get_tied_weight_keys(submodule, prefix=local_prefix))
     return tied_weight_keys
 
 
@@ -746,8 +730,7 @@ def _load_state_dict_into_model(model_to_load, state_dict, start_prefix, assign_
     # PyTorch's `_load_from_state_dict` does not copy parameters in a module's descendants
     # so we need to apply the function recursively.
     def load(module: nn.Module, state_dict, prefix="", assign_to_params_buffers=False):
-        local_metadata = {} if metadata is None else metadata.get(
-            prefix[:-1], {})
+        local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
         local_metadata["assign_to_params_buffers"] = assign_to_params_buffers
 
         args = (state_dict, prefix, local_metadata, True, [], [], error_msgs)
@@ -759,10 +742,8 @@ def load(module: nn.Module, state_dict, prefix="", assign_to_params_buffers=Fals
 
                 # In sharded models, each shard has only part of the full state_dict, so only gather
                 # parameters that are in the current state_dict.
-                named_parameters = dict(module.named_parameters(
-                    prefix=prefix[:-1], recurse=False))
-                params_to_gather = [named_parameters[k]
-                                    for k in state_dict.keys() if k in named_parameters]
+                named_parameters = dict(module.named_parameters(prefix=prefix[:-1], recurse=False))
+                params_to_gather = [named_parameters[k] for k in state_dict.keys() if k in named_parameters]
                 if len(params_to_gather) > 0:
                     # because zero3 puts placeholders in model params, this context
                     # manager gathers (unpartitions) the params of the current layer, then loads from
@@ -775,11 +756,9 @@ def load(module: nn.Module, state_dict, prefix="", assign_to_params_buffers=Fals
 
         for name, child in module._modules.items():
             if child is not None:
-                load(child, state_dict, prefix + name +
-                     ".", assign_to_params_buffers)
+                load(child, state_dict, prefix + name + ".", assign_to_params_buffers)
 
-    load(model_to_load, state_dict, prefix=start_prefix,
-         assign_to_params_buffers=assign_to_params_buffers)
+    load(model_to_load, state_dict, prefix=start_prefix, assign_to_params_buffers=assign_to_params_buffers)
     # Delete `state_dict` so it could be collected by GC earlier. Note that `state_dict` is a copy of the argument, so
     # it's safe to delete it.
     del state_dict
@@ -822,8 +801,7 @@ def _move_model_to_meta(model, loaded_state_dict_keys, start_prefix):
     # dematerialize param storage for keys that are going to be replaced by state_dict, by
     # putting those on the meta device
     for k in loaded_state_dict_keys:
-        submodule, param_name = find_submodule_and_param_name(
-            model, k, start_prefix)
+        submodule, param_name = find_submodule_and_param_name(model, k, start_prefix)
         if submodule is not None:
             # selectively switch to the meta device only those params/buffers that will
             # be next replaced from state_dict. This a complex way to do p.to_("meta")
@@ -892,18 +870,14 @@ def _load_state_dict_into_meta_model(
         # To reproduce `_load_state_dict_into_model` behaviour, we need to manually rename parametrized weigth norm, if necessary.
         if hasattr(nn.utils.parametrizations, "weight_norm"):
             if "weight_g" in key:
-                new_key = key.replace(
-                    "weight_g", "parametrizations.weight.original0")
+                new_key = key.replace("weight_g", "parametrizations.weight.original0")
             if "weight_v" in key:
-                new_key = key.replace(
-                    "weight_v", "parametrizations.weight.original1")
+                new_key = key.replace("weight_v", "parametrizations.weight.original1")
         else:
             if "parametrizations.weight.original0" in key:
-                new_key = key.replace(
-                    "parametrizations.weight.original0", "weight_g")
+                new_key = key.replace("parametrizations.weight.original0", "weight_g")
             if "parametrizations.weight.original1" in key:
-                new_key = key.replace(
-                    "parametrizations.weight.original1", "weight_v")
+                new_key = key.replace("parametrizations.weight.original1", "weight_v")
         if new_key:
             old_keys.append(key)
             new_keys.append(new_key)
@@ -924,7 +898,7 @@ def _load_state_dict_into_meta_model(
             continue
 
         if param_name.startswith(start_prefix):
-            param_name = param_name[len(start_prefix):]
+            param_name = param_name[len(start_prefix) :]
 
         module_name = param_name
         set_module_kwargs = {}
@@ -981,11 +955,9 @@ def _load_state_dict_into_meta_model(
 
         if param_device == "disk":
             if not is_safetensors:
-                offload_index = offload_weight(
-                    param, param_name, offload_folder, offload_index)
+                offload_index = offload_weight(param, param_name, offload_folder, offload_index)
         elif param_device == "cpu" and state_dict_index is not None:
-            state_dict_index = offload_weight(
-                param, param_name, state_dict_folder, state_dict_index)
+            state_dict_index = offload_weight(param, param_name, state_dict_folder, state_dict_index)
         elif (
             not is_quantized
             or (not hf_quantizer.requires_parameters_quantization)
@@ -999,11 +971,9 @@ def _load_state_dict_into_meta_model(
                 param_device = "cpu" if is_local_dist_rank_0() else "meta"
 
             # For backward compatibility with older versions of `accelerate` and for non-quantized params
-            set_module_tensor_to_device(
-                model, param_name, param_device, **set_module_kwargs)
+            set_module_tensor_to_device(model, param_name, param_device, **set_module_kwargs)
         else:
-            hf_quantizer.create_quantized_param(
-                model, param, param_name, param_device, state_dict, unexpected_keys)
+            hf_quantizer.create_quantized_param(model, param, param_name, param_device, state_dict, unexpected_keys)
             # For quantized modules with FSDP/DeepSpeed Stage 3, we need to quantize the parameter on the GPU
             # and then cast it to CPU to avoid excessive memory usage on each GPU
             # in comparison to the sharded model across GPUs.
@@ -1039,8 +1009,7 @@ def _hook_rss_memory_pre_forward(module, *args, **kwargs):
         try:
             import psutil
         except ImportError:
-            raise ImportError(
-                "You need to install psutil (pip install psutil) to use memory tracing.")
+            raise ImportError("You need to install psutil (pip install psutil) to use memory tracing.")
 
         process = psutil.Process(os.getpid())
         mem = process.memory_info()
@@ -1052,15 +1021,13 @@ def _hook_rss_memory_post_forward(module, *args, **kwargs):
         try:
             import psutil
         except ImportError:
-            raise ImportError(
-                "You need to install psutil (pip install psutil) to use memory tracing.")
+            raise ImportError("You need to install psutil (pip install psutil) to use memory tracing.")
 
         process = psutil.Process(os.getpid())
         mem = process.memory_info()
         module.mem_rss_post_forward = mem.rss
         mem_rss_diff = module.mem_rss_post_forward - module.mem_rss_pre_forward
-        module.mem_rss_diff = mem_rss_diff + \
-            (module.mem_rss_diff if hasattr(module, "mem_rss_diff") else 0)
+        module.mem_rss_diff = mem_rss_diff + (module.mem_rss_diff if hasattr(module, "mem_rss_diff") else 0)
         return None
 
     def add_memory_hooks(self):
@@ -1118,10 +1085,8 @@ def invert_attention_mask(self, encoder_attention_mask: Tensor) -> Tensor:
         # /transformer/transformer_layers.py#L270
         # encoder_extended_attention_mask = (encoder_extended_attention_mask ==
         # encoder_extended_attention_mask.transpose(-1, -2))
-        encoder_extended_attention_mask = encoder_extended_attention_mask.to(
-            dtype=self.dtype)  # fp16 compatibility
-        encoder_extended_attention_mask = (
-            1.0 - encoder_extended_attention_mask) * torch.finfo(self.dtype).min
+        encoder_extended_attention_mask = encoder_extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility
+        encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * torch.finfo(self.dtype).min
 
         return encoder_extended_attention_mask
 
@@ -1135,8 +1100,7 @@ def create_extended_attention_mask_for_decoder(input_shape, attention_mask, devi
             device = attention_mask.device
         batch_size, seq_length = input_shape
         seq_ids = torch.arange(seq_length, device=device)
-        causal_mask = seq_ids[None, None, :].repeat(
-            batch_size, seq_length, 1) <= seq_ids[None, :, None]
+        causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]
         # in case past_key_values are used we need to add a prefix ones mask to the causal mask
         # causal and attention masks must have same type with pytorch version < 1.3
         causal_mask = causal_mask.to(attention_mask.dtype)
@@ -1145,15 +1109,13 @@ def create_extended_attention_mask_for_decoder(input_shape, attention_mask, devi
             prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1]
             causal_mask = torch.cat(
                 [
-                    torch.ones((batch_size, seq_length, prefix_seq_len),
-                               device=device, dtype=causal_mask.dtype),
+                    torch.ones((batch_size, seq_length, prefix_seq_len), device=device, dtype=causal_mask.dtype),
                     causal_mask,
                 ],
                 axis=-1,
             )
 
-        extended_attention_mask = causal_mask[:, None,
-                                              :, :] * attention_mask[:, None, None, :]
+        extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
         return extended_attention_mask
 
     def get_extended_attention_mask(
@@ -1204,10 +1166,8 @@ def get_extended_attention_mask(
         # positions we want to attend and the dtype's smallest value for masked positions.
         # Since we are adding it to the raw scores before the softmax, this is
         # effectively the same as removing these entirely.
-        extended_attention_mask = extended_attention_mask.to(
-            dtype=dtype)  # fp16 compatibility
-        extended_attention_mask = (
-            1.0 - extended_attention_mask) * torch.finfo(dtype).min
+        extended_attention_mask = extended_attention_mask.to(dtype=dtype)  # fp16 compatibility
+        extended_attention_mask = (1.0 - extended_attention_mask) * torch.finfo(dtype).min
         return extended_attention_mask
 
     def get_head_mask(
@@ -1229,8 +1189,7 @@ def get_head_mask(
             `[None]` for each layer.
         """
         if head_mask is not None:
-            head_mask = self._convert_head_mask_to_5d(
-                head_mask, num_hidden_layers)
+            head_mask = self._convert_head_mask_to_5d(head_mask, num_hidden_layers)
             if is_attention_chunked is True:
                 head_mask = head_mask.unsqueeze(-1)
         else:
@@ -1241,14 +1200,12 @@ def get_head_mask(
     def _convert_head_mask_to_5d(self, head_mask, num_hidden_layers):
         """-> [num_hidden_layers x batch x num_heads x seq_length x seq_length]"""
         if head_mask.dim() == 1:
-            head_mask = head_mask.unsqueeze(0).unsqueeze(
-                0).unsqueeze(-1).unsqueeze(-1)
+            head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
             head_mask = head_mask.expand(num_hidden_layers, -1, -1, -1, -1)
         elif head_mask.dim() == 2:
             # We can specify head_mask for each layer
             head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)
-        assert head_mask.dim(
-        ) == 5, f"head_mask.dim != 5, instead {head_mask.dim()}"
+        assert head_mask.dim() == 5, f"head_mask.dim != 5, instead {head_mask.dim()}"
         # switch to float if need + fp16 compatibility
         head_mask = head_mask.to(dtype=self.dtype)
         return head_mask
@@ -1453,13 +1410,11 @@ def __init__(self, config: PretrainedConfig, *inputs, **kwargs):
 
         self.name_or_path = config.name_or_path
         self.warnings_issued = {}
-        self.generation_config = GenerationConfig.from_model_config(
-            config) if self.can_generate() else None
+        self.generation_config = GenerationConfig.from_model_config(config) if self.can_generate() else None
         # Overwrite the class attribute to make it an instance attribute, so models like
         # `InstructBlipForConditionalGeneration` can dynamically update it without modifying the class attribute
         # when a different component (e.g. language_model) is used.
-        self._keep_in_fp32_modules = copy.copy(
-            self.__class__._keep_in_fp32_modules)
+        self._keep_in_fp32_modules = copy.copy(self.__class__._keep_in_fp32_modules)
 
     def post_init(self):
         """
@@ -1477,8 +1432,7 @@ def dequantize(self):
         hf_quantizer = getattr(self, "hf_quantizer", None)
 
         if hf_quantizer is None:
-            raise ValueError(
-                "You need to first quantize your model in order to dequantize it")
+            raise ValueError("You need to first quantize your model in order to dequantize it")
 
         return hf_quantizer.dequantize(self)
 
@@ -1547,8 +1501,7 @@ def _from_config(cls, config, **kwargs):
         else:
             attn_implementation = None
 
-        config._attn_implementation = kwargs.pop(
-            "attn_implementation", attn_implementation)
+        config._attn_implementation = kwargs.pop("attn_implementation", attn_implementation)
         config = cls._autoset_attn_implementation(
             config,
             use_flash_attention_2=use_flash_attention_2,
@@ -1559,8 +1512,7 @@ def _from_config(cls, config, **kwargs):
         if is_deepspeed_zero3_enabled():
             import deepspeed
 
-            logger.info(
-                "Detected DeepSpeed ZeRO-3: activating zero.init() for this model")
+            logger.info("Detected DeepSpeed ZeRO-3: activating zero.init() for this model")
             # this immediately partitions the model across all gpus, to avoid the overhead in time
             # and memory copying it on CPU or each GPU first
             with deepspeed.zero.Init(config_dict_or_path=deepspeed_config()):
@@ -1670,8 +1622,7 @@ def _set_default_torch_dtype(cls, dtype: torch.dtype) -> torch.dtype:
                 f"Can't instantiate {cls.__name__} model under dtype={dtype} since it is not a floating point dtype"
             )
 
-        logger.info(
-            f"Instantiating {cls.__name__} model under default dtype {dtype}.")
+        logger.info(f"Instantiating {cls.__name__} model under default dtype {dtype}.")
         dtype_orig = torch.get_default_dtype()
         torch.set_default_dtype(dtype)
         return dtype_orig
@@ -1723,27 +1674,23 @@ def _check_and_enable_flash_attn_2(
             install_message = "Please refer to the documentation of https://huggingface.co/docs/transformers/perf_infer_gpu_one#flashattention-2 to install Flash Attention 2."
 
             if importlib.util.find_spec("flash_attn") is None:
-                raise ImportError(
-                    f"{preface} the package flash_attn seems to be not installed. {install_message}")
+                raise ImportError(f"{preface} the package flash_attn seems to be not installed. {install_message}")
 
-            flash_attention_version = version.parse(
-                importlib.metadata.version("flash_attn"))
+            flash_attention_version = version.parse(importlib.metadata.version("flash_attn"))
             if torch.version.cuda:
                 if flash_attention_version < version.parse("2.1.0"):
                     raise ImportError(
                         f"{preface} you need flash_attn package version to be greater or equal than 2.1.0. Detected version {flash_attention_version}. {install_message}"
                     )
                 else:
-                    raise ImportError(
-                        f"{preface} Flash Attention 2 is not available. {install_message}")
+                    raise ImportError(f"{preface} Flash Attention 2 is not available. {install_message}")
             elif torch.version.hip:
                 if flash_attention_version < version.parse("2.0.4"):
                     raise ImportError(
                         f"{preface} you need flash_attn package version to be greater or equal than 2.0.4. Make sure to have that version installed - detected version {flash_attention_version}. {install_message}"
                     )
                 else:
-                    raise ImportError(
-                        f"{preface} Flash Attention 2 is not available. {install_message}")
+                    raise ImportError(f"{preface} Flash Attention 2 is not available. {install_message}")
 
         _is_bettertransformer = getattr(cls, "use_bettertransformer", False)
 
@@ -1830,8 +1777,7 @@ def enable_input_require_grads(self):
         def make_inputs_require_grads(module, input, output):
             output.requires_grad_(True)
 
-        self._require_grads_hook = self.get_input_embeddings(
-        ).register_forward_hook(make_inputs_require_grads)
+        self._require_grads_hook = self.get_input_embeddings().register_forward_hook(make_inputs_require_grads)
 
     def disable_input_require_grads(self):
         """
@@ -1902,8 +1848,7 @@ def tie_weights(self):
         if getattr(self.config, "tie_word_embeddings", True):
             output_embeddings = self.get_output_embeddings()
             if output_embeddings is not None:
-                self._tie_or_clone_weights(
-                    output_embeddings, self.get_input_embeddings())
+                self._tie_or_clone_weights(output_embeddings, self.get_input_embeddings())
 
         if getattr(self.config, "is_encoder_decoder", False) and getattr(self.config, "tie_encoder_decoder", False):
             if hasattr(self, self.base_model_prefix):
@@ -1948,12 +1893,10 @@ def tie_encoder_to_decoder_recursively(
             if hasattr(decoder_pointer, "weight"):
                 assert hasattr(encoder_pointer, "weight")
                 encoder_pointer.weight = decoder_pointer.weight
-                tied_weights.append(
-                    f"{base_encoder_name}{total_encoder_name}.weight")
+                tied_weights.append(f"{base_encoder_name}{total_encoder_name}.weight")
                 if hasattr(decoder_pointer, "bias"):
                     assert hasattr(encoder_pointer, "bias")
-                    tied_weights.append(
-                        f"{base_encoder_name}{total_encoder_name}.bias")
+                    tied_weights.append(f"{base_encoder_name}{total_encoder_name}.bias")
                     encoder_pointer.bias = decoder_pointer.bias
                 return
 
@@ -1964,8 +1907,7 @@ def tie_encoder_to_decoder_recursively(
                     len(encoder_modules) > 0
                 ), f"Encoder module {encoder_pointer} does not match decoder module {decoder_pointer}"
 
-                all_encoder_weights = {
-                    module_name + "/" + sub_name for sub_name in encoder_modules.keys()}
+                all_encoder_weights = {module_name + "/" + sub_name for sub_name in encoder_modules.keys()}
                 encoder_layer_pos = 0
                 for name, module in decoder_modules.items():
                     if name.isdigit():
@@ -1998,8 +1940,7 @@ def tie_encoder_to_decoder_recursively(
                         total_encoder_name=f"{total_encoder_name}.{encoder_name}",
                         total_decoder_name=f"{total_decoder_name}.{decoder_name}",
                     )
-                    all_encoder_weights.remove(
-                        module_name + "/" + encoder_name)
+                    all_encoder_weights.remove(module_name + "/" + encoder_name)
 
                 uninitialized_encoder_weights += list(all_encoder_weights)
 
@@ -2017,8 +1958,7 @@ def tie_encoder_to_decoder_recursively(
     def _tie_or_clone_weights(self, output_embeddings, input_embeddings):
         """Tie or clone module weights depending of whether we are using TorchScript or not"""
         if self.config.torchscript:
-            output_embeddings.weight = nn.Parameter(
-                input_embeddings.weight.clone())
+            output_embeddings.weight = nn.Parameter(input_embeddings.weight.clone())
         else:
             output_embeddings.weight = input_embeddings.weight
 
@@ -2027,8 +1967,7 @@ def _tie_or_clone_weights(self, output_embeddings, input_embeddings):
                 output_embeddings.bias.data,
                 (
                     0,
-                    output_embeddings.weight.shape[0] -
-                    output_embeddings.bias.shape[0],
+                    output_embeddings.weight.shape[0] - output_embeddings.bias.shape[0],
                 ),
                 "constant",
                 0,
@@ -2061,8 +2000,7 @@ def _get_no_split_modules(self, device_map: str):
                             "class needs to implement the `_no_split_modules` attribute."
                         )
                     else:
-                        _no_split_modules = _no_split_modules | set(
-                            module._no_split_modules)
+                        _no_split_modules = _no_split_modules | set(module._no_split_modules)
                 modules_to_check += list(module.children())
         return list(_no_split_modules)
 
@@ -2091,14 +2029,12 @@ def resize_token_embeddings(
         Return:
             `torch.nn.Embedding`: Pointer to the input tokens Embeddings Module of the model.
         """
-        model_embeds = self._resize_token_embeddings(
-            new_num_tokens, pad_to_multiple_of)
+        model_embeds = self._resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
         if new_num_tokens is None and pad_to_multiple_of is None:
             return model_embeds
 
         # Since we are basically resuing the same old embeddings with new weight values, gathering is required
-        is_quantized = hasattr(
-            self, "hf_quantizer") and self.hf_quantizer is not None
+        is_quantized = hasattr(self, "hf_quantizer") and self.hf_quantizer is not None
         if is_deepspeed_zero3_enabled() and not is_quantized:
             import deepspeed
 
@@ -2118,16 +2054,14 @@ def resize_token_embeddings(
 
     def _resize_token_embeddings(self, new_num_tokens, pad_to_multiple_of=None):
         old_embeddings = self.get_input_embeddings()
-        new_embeddings = self._get_resized_embeddings(
-            old_embeddings, new_num_tokens, pad_to_multiple_of)
+        new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens, pad_to_multiple_of)
         if hasattr(old_embeddings, "_hf_hook"):
             hook = old_embeddings._hf_hook
             add_hook_to_module(new_embeddings, hook)
         old_embeddings_requires_grad = old_embeddings.weight.requires_grad
         new_embeddings.requires_grad_(old_embeddings_requires_grad)
         self.set_input_embeddings(new_embeddings)
-        is_quantized = hasattr(
-            self, "hf_quantizer") and self.hf_quantizer is not None
+        is_quantized = hasattr(self, "hf_quantizer") and self.hf_quantizer is not None
 
         # Update new_num_tokens with the actual size of new_embeddings
         if pad_to_multiple_of is not None:
@@ -2143,11 +2077,9 @@ def _resize_token_embeddings(self, new_num_tokens, pad_to_multiple_of=None):
         if self.get_output_embeddings() is not None and not self.config.tie_word_embeddings:
             old_lm_head = self.get_output_embeddings()
             if isinstance(old_lm_head, torch.nn.Embedding):
-                new_lm_head = self._get_resized_embeddings(
-                    old_lm_head, new_num_tokens)
+                new_lm_head = self._get_resized_embeddings(old_lm_head, new_num_tokens)
             else:
-                new_lm_head = self._get_resized_lm_head(
-                    old_lm_head, new_num_tokens)
+                new_lm_head = self._get_resized_lm_head(old_lm_head, new_num_tokens)
             if hasattr(old_lm_head, "_hf_hook"):
                 hook = old_lm_head._hf_hook
                 add_hook_to_module(new_lm_head, hook)
@@ -2198,8 +2130,7 @@ def _get_resized_embeddings(
                 )
             if new_num_tokens is None:
                 new_num_tokens = old_embeddings.weight.shape[0]
-            new_num_tokens = ((new_num_tokens + pad_to_multiple_of - 1) //
-                              pad_to_multiple_of) * pad_to_multiple_of
+            new_num_tokens = ((new_num_tokens + pad_to_multiple_of - 1) // pad_to_multiple_of) * pad_to_multiple_of
         else:
             logger.info(
                 "You are resizing the embedding layer without providing a `pad_to_multiple_of` parameter. This means that the new embedding"
@@ -2211,8 +2142,7 @@ def _get_resized_embeddings(
         if new_num_tokens is None:
             return old_embeddings
 
-        is_quantized = hasattr(
-            self, "hf_quantizer") and self.hf_quantizer is not None
+        is_quantized = hasattr(self, "hf_quantizer") and self.hf_quantizer is not None
         if is_deepspeed_zero3_enabled() and not is_quantized:
             import deepspeed
 
@@ -2257,11 +2187,9 @@ def _get_resized_embeddings(
 
             params = [old_embeddings.weight, new_embeddings.weight]
             with deepspeed.zero.GatheredParameters(params, modifier_rank=0):
-                new_embeddings.weight.data[:n,
-                                           :] = old_embeddings.weight.data[:n, :]
+                new_embeddings.weight.data[:n, :] = old_embeddings.weight.data[:n, :]
         else:
-            new_embeddings.weight.data[:n,
-                                       :] = old_embeddings.weight.data[:n, :]
+            new_embeddings.weight.data[:n, :] = old_embeddings.weight.data[:n, :]
 
         # Replace weights in old_embeddings and return to maintain the same embedding type.
         # This ensures correct functionality when a Custom Embedding class is passed as input.
@@ -2312,8 +2240,7 @@ def _get_resized_lm_head(
         if new_num_tokens is None:
             return old_lm_head
 
-        is_quantized = hasattr(
-            self, "hf_quantizer") and self.hf_quantizer is not None
+        is_quantized = hasattr(self, "hf_quantizer") and self.hf_quantizer is not None
         if is_deepspeed_zero3_enabled() and not is_quantized:
             import deepspeed
 
@@ -2337,8 +2264,7 @@ def _get_resized_lm_head(
             )
 
         # Build new lm head
-        new_lm_head_shape = (old_lm_head_dim, new_num_tokens) if not transposed else (
-            new_num_tokens, old_lm_head_dim)
+        new_lm_head_shape = (old_lm_head_dim, new_num_tokens) if not transposed else (new_num_tokens, old_lm_head_dim)
         has_new_lm_head_bias = old_lm_head.bias is not None
 
         # When using DeepSpeed ZeRO-3, we shouldn't create new embeddings with DeepSpeed init
@@ -2360,8 +2286,7 @@ def _get_resized_lm_head(
         if is_deepspeed_zero3_enabled() and not is_quantized:
             import deepspeed
 
-            params = [old_lm_head.weight, old_lm_head.bias,
-                      new_lm_head.weight, new_lm_head.bias]
+            params = [old_lm_head.weight, old_lm_head.bias, new_lm_head.weight, new_lm_head.bias]
             with deepspeed.zero.GatheredParameters(params, modifier_rank=0):
                 self._copy_lm_head_original_to_resized(
                     new_lm_head, old_lm_head, num_tokens_to_copy, transposed, has_new_lm_head_bias
@@ -2378,11 +2303,9 @@ def _copy_lm_head_original_to_resized(
     ):
         # Copy old lm head weights to new lm head
         if not transposed:
-            new_lm_head.weight.data[:num_tokens_to_copy,
-                                    :] = old_lm_head.weight.data[:num_tokens_to_copy, :]
+            new_lm_head.weight.data[:num_tokens_to_copy, :] = old_lm_head.weight.data[:num_tokens_to_copy, :]
         else:
-            new_lm_head.weight.data[:,
-                                    :num_tokens_to_copy] = old_lm_head.weight.data[:, :num_tokens_to_copy]
+            new_lm_head.weight.data[:, :num_tokens_to_copy] = old_lm_head.weight.data[:, :num_tokens_to_copy]
 
         # Copy bias weights to new lm head
         if has_new_lm_head_bias:
@@ -2429,8 +2352,7 @@ def prune_heads(self, heads_to_prune: Dict[int, List[int]]):
         """
         # save new sets of pruned heads as union of previously stored pruned heads and newly pruned heads
         for layer, heads in heads_to_prune.items():
-            union_heads = set(
-                self.config.pruned_heads.get(layer, [])) | set(heads)
+            union_heads = set(self.config.pruned_heads.get(layer, [])) | set(heads)
             # Unfortunately we have to store it as list for JSON
             self.config.pruned_heads[layer] = list(union_heads)
 
@@ -2451,23 +2373,19 @@ def gradient_checkpointing_enable(self, gradient_checkpointing_kwargs=None):
                 Additional keyword arguments passed along to the `torch.utils.checkpoint.checkpoint` function.
         """
         if not self.supports_gradient_checkpointing:
-            raise ValueError(
-                f"{self.__class__.__name__} does not support gradient checkpointing.")
+            raise ValueError(f"{self.__class__.__name__} does not support gradient checkpointing.")
 
         if gradient_checkpointing_kwargs is None:
             gradient_checkpointing_kwargs = {"use_reentrant": True}
 
-        gradient_checkpointing_func = functools.partial(
-            checkpoint, **gradient_checkpointing_kwargs)
+        gradient_checkpointing_func = functools.partial(checkpoint, **gradient_checkpointing_kwargs)
 
         # For old GC format (transformers < 4.35.0) for models that live on the Hub
         # we will fall back to the overwritten `_set_gradient_checkpointing` method
-        _is_using_old_format = "value" in inspect.signature(
-            self._set_gradient_checkpointing).parameters
+        _is_using_old_format = "value" in inspect.signature(self._set_gradient_checkpointing).parameters
 
         if not _is_using_old_format:
-            self._set_gradient_checkpointing(
-                enable=True, gradient_checkpointing_func=gradient_checkpointing_func)
+            self._set_gradient_checkpointing(enable=True, gradient_checkpointing_func=gradient_checkpointing_func)
         else:
             self.apply(partial(self._set_gradient_checkpointing, value=True))
             logger.warning(
@@ -2514,8 +2432,7 @@ def gradient_checkpointing_disable(self):
         if self.supports_gradient_checkpointing:
             # For old GC format (transformers < 4.35.0) for models that live on the Hub
             # we will fall back to the overwritten `_set_gradient_checkpointing` methid
-            _is_using_old_format = "value" in inspect.signature(
-                self._set_gradient_checkpointing).parameters
+            _is_using_old_format = "value" in inspect.signature(self._set_gradient_checkpointing).parameters
             if not _is_using_old_format:
                 self._set_gradient_checkpointing(enable=False)
             else:
@@ -2523,8 +2440,7 @@ def gradient_checkpointing_disable(self):
                     "You are using an old version of the checkpointing format that is deprecated (We will also silently ignore `gradient_checkpointing_kwargs` in case you passed it)."
                     "Please update to the new format on your modeling file. To use the new format, you need to completely remove the definition of the method `_set_gradient_checkpointing` in your model."
                 )
-                self.apply(
-                    partial(self._set_gradient_checkpointing, value=False))
+                self.apply(partial(self._set_gradient_checkpointing, value=False))
 
         if getattr(self, "_hf_peft_config_loaded", False):
             self.disable_input_require_grads()
@@ -2623,8 +2539,7 @@ def save_pretrained(
 
         hf_quantizer = getattr(self, "hf_quantizer", None)
         quantization_serializable = (
-            hf_quantizer is not None and isinstance(
-                hf_quantizer, HfQuantizer) and hf_quantizer.is_serializable
+            hf_quantizer is not None and isinstance(hf_quantizer, HfQuantizer) and hf_quantizer.is_serializable
         )
 
         if hf_quantizer is not None and not _hf_peft_config_loaded and not quantization_serializable:
@@ -2639,20 +2554,17 @@ def save_pretrained(
             )
             is_main_process = kwargs.pop("save_config")
         if safe_serialization and not is_safetensors_available():
-            raise ImportError(
-                "`safe_serialization` requires the `safetensors library: `pip install safetensors`.")
+            raise ImportError("`safe_serialization` requires the `safetensors library: `pip install safetensors`.")
 
         if os.path.isfile(save_directory):
-            logger.error(
-                f"Provided path ({save_directory}) should be a directory, not a file")
+            logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
             return
 
         os.makedirs(save_directory, exist_ok=True)
 
         if push_to_hub:
             commit_message = kwargs.pop("commit_message", None)
-            repo_id = kwargs.pop(
-                "repo_id", save_directory.split(os.path.sep)[-1])
+            repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1])
             repo_id = self._create_repo(repo_id, **kwargs)
             files_timestamps = self._get_files_timestamps(save_directory)
 
@@ -2685,8 +2597,7 @@ def save_pretrained(
                         UserWarning,
                     )
                     for param_name, param_value in misplaced_generation_parameters.items():
-                        setattr(model_to_save.generation_config,
-                                param_name, param_value)
+                        setattr(model_to_save.generation_config, param_name, param_value)
                         setattr(model_to_save.config, param_name, None)
 
                 model_to_save.config.save_pretrained(save_directory)
@@ -2778,8 +2689,7 @@ def save_pretrained(
                 else:
                     shared_ptrs = {}
             else:
-                shared_ptrs = {ptr: names for ptr,
-                               names in ptrs.items() if len(names) > 1}
+                shared_ptrs = {ptr: names for ptr, names in ptrs.items() if len(names) > 1}
 
             # Recursively descend to find tied weight keys
             _tied_weights_keys = _get_tied_weight_keys(self)
@@ -2791,15 +2701,13 @@ def save_pretrained(
                 if _tied_weights_keys is not None:
                     found = 0
                     for name in sorted(names):
-                        matches_pattern = any(re.search(pat, name)
-                                              for pat in _tied_weights_keys)
+                        matches_pattern = any(re.search(pat, name) for pat in _tied_weights_keys)
                         if matches_pattern and name in state_dict:
                             found += 1
                             if found < len(names):
                                 to_delete_names.add(name)
             # We are entering a place where the weights and the transformers configuration do NOT match.
-            shared_names, disjoint_names = _find_disjoint(
-                shared_ptrs.values(), state_dict)
+            shared_names, disjoint_names = _find_disjoint(shared_ptrs.values(), state_dict)
             # Those are actually tensor sharing but disjoint from each other, we can safely clone them
             # Reloaded won't have the same property, but it shouldn't matter in any meaningful way.
             for name in disjoint_names:
@@ -2810,8 +2718,7 @@ def save_pretrained(
             # the key back leading to random tensor. A proper warning will be shown
             # during reload (if applicable), but since the file is not necessarily compatible with
             # the config, better show a proper warning.
-            shared_names, identical_names = _find_identical(
-                shared_names, state_dict)
+            shared_names, identical_names = _find_identical(shared_names, state_dict)
             # delete tensors that have identical storage
             for inames in identical_names:
                 known = inames.intersection(to_delete_names)
@@ -2836,8 +2743,7 @@ def save_pretrained(
         else:
             weights_name = ADAPTER_SAFE_WEIGHTS_NAME if safe_serialization else ADAPTER_WEIGHTS_NAME
 
-        filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(
-            ".safetensors", "{suffix}.safetensors")
+        filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(".safetensors", "{suffix}.safetensors")
         state_dict_split = split_torch_state_dict_into_shards(
             state_dict, filename_pattern=filename_pattern, max_shard_size=max_shard_size
         )
@@ -2854,12 +2760,10 @@ def save_pretrained(
             full_filename = os.path.join(save_directory, filename)
             # If we have a shard file that is not going to be replaced, we delete it, but only from the main process
             # in distributed settings to avoid race conditions.
-            weights_no_suffix = weights_name.replace(
-                ".bin", "").replace(".safetensors", "")
+            weights_no_suffix = weights_name.replace(".bin", "").replace(".safetensors", "")
 
             # make sure that file to be deleted matches format of sharded file, e.g. pytorch_model-00001-of-00005
-            filename_no_suffix = filename.replace(
-                ".bin", "").replace(".safetensors", "")
+            filename_no_suffix = filename.replace(".bin", "").replace(".safetensors", "")
             reg = re.compile(r"(.*?)-\d{5}-of-\d{5}")
 
             if (
@@ -2873,11 +2777,9 @@ def save_pretrained(
         # Save the model
         filename_to_tensors = state_dict_split.filename_to_tensors.items()
         if module_map:
-            filename_to_tensors = logging.tqdm(
-                filename_to_tensors, desc="Saving checkpoint shards")
+            filename_to_tensors = logging.tqdm(filename_to_tensors, desc="Saving checkpoint shards")
         for shard_file, tensors in filename_to_tensors:
-            shard = {tensor: state_dict[tensor].contiguous()
-                     for tensor in tensors}
+            shard = {tensor: state_dict[tensor].contiguous() for tensor in tensors}
             # remake shard with onloaded parameters if necessary
             if module_map:
                 if accelerate_version < version.parse("0.31"):
@@ -2890,8 +2792,7 @@ def save_pretrained(
                 for module_name in shard:
                     module = module_map[module_name]
                     # update state dict with onloaded parameters
-                    shard_state_dict = get_state_dict_from_offload(
-                        module, module_name, shard_state_dict)
+                    shard_state_dict = get_state_dict_from_offload(module, module_name, shard_state_dict)
 
                 # assign shard to be the completed state dict
                 shard = shard_state_dict
@@ -2901,8 +2802,7 @@ def save_pretrained(
             if safe_serialization:
                 # At some point we will need to deal better with save_function (used for TPU and other distributed
                 # joyfulness), but for now this enough.
-                safe_save_file(shard, os.path.join(
-                    save_directory, shard_file), metadata={"format": "pt"})
+                safe_save_file(shard, os.path.join(save_directory, shard_file), metadata={"format": "pt"})
             else:
                 save_function(shard, os.path.join(save_directory, shard_file))
 
@@ -2911,8 +2811,7 @@ def save_pretrained(
             logger.info(f"Model weights saved in {path_to_weights}")
         else:
             save_index_file = SAFE_WEIGHTS_INDEX_NAME if safe_serialization else WEIGHTS_INDEX_NAME
-            save_index_file = os.path.join(
-                save_directory, _add_variant(save_index_file, variant))
+            save_index_file = os.path.join(save_directory, _add_variant(save_index_file, variant))
             # Save the index as well
             with open(save_index_file, "w", encoding="utf-8") as f:
                 content = json.dumps(index, indent=2, sort_keys=True) + "\n"
@@ -2968,19 +2867,16 @@ def get_memory_footprint(self, return_buffers=True):
                 are tensors that do not require gradients and not registered as parameters. E.g. mean and std in batch
                 norm layers. Please see: https://discuss.pytorch.org/t/what-pytorch-means-by-buffers/120266/2
         """
-        mem = sum([param.nelement() * param.element_size()
-                  for param in self.parameters()])
+        mem = sum([param.nelement() * param.element_size() for param in self.parameters()])
         if return_buffers:
-            mem_bufs = sum([buf.nelement() * buf.element_size()
-                           for buf in self.buffers()])
+            mem_bufs = sum([buf.nelement() * buf.element_size() for buf in self.buffers()])
             mem = mem + mem_bufs
         return mem
 
     @wraps(torch.nn.Module.cuda)
     def cuda(self, *args, **kwargs):
         if getattr(self, "quantization_method", None) == QuantizationMethod.HQQ:
-            raise ValueError(
-                "`.cuda` is not supported for HQQ-quantized models.")
+            raise ValueError("`.cuda` is not supported for HQQ-quantized models.")
         # Checks if the model has been loaded in 4-bit or 8-bit with BNB
         if getattr(self, "quantization_method", None) == QuantizationMethod.BITS_AND_BYTES:
             if getattr(self, "is_loaded_in_8bit", False):
@@ -3009,8 +2905,7 @@ def to(self, *args, **kwargs):
                     break
 
         if getattr(self, "quantization_method", None) == QuantizationMethod.HQQ:
-            raise ValueError(
-                "`.to` is not supported for HQQ-quantized models.")
+            raise ValueError("`.to` is not supported for HQQ-quantized models.")
         # Checks if the model has been loaded in 4-bit or 8-bit with BNB
         if getattr(self, "quantization_method", None) == QuantizationMethod.BITS_AND_BYTES:
             if dtype_present_in_args:
@@ -3373,8 +3268,7 @@ def from_pretrained(
             )
 
         if gguf_file is not None and not is_accelerate_available():
-            raise ValueError(
-                "accelerate is required when loading a GGUF file `pip install accelerate`.")
+            raise ValueError("accelerate is required when loading a GGUF file `pip install accelerate`.")
 
         if commit_hash is None:
             if not isinstance(config, PretrainedConfig):
@@ -3394,14 +3288,12 @@ def from_pretrained(
                     _raise_exceptions_for_missing_entries=False,
                     _raise_exceptions_for_connection_errors=False,
                 )
-                commit_hash = extract_commit_hash(
-                    resolved_config_file, commit_hash)
+                commit_hash = extract_commit_hash(resolved_config_file, commit_hash)
             else:
                 commit_hash = getattr(config, "_commit_hash", None)
 
         if is_peft_available():
-            _adapter_model_path = adapter_kwargs.pop(
-                "_adapter_model_path", None)
+            _adapter_model_path = adapter_kwargs.pop("_adapter_model_path", None)
 
             if _adapter_model_path is None:
                 _adapter_model_path = find_adapter_config_file(
@@ -3417,8 +3309,7 @@ def from_pretrained(
             if _adapter_model_path is not None and os.path.isfile(_adapter_model_path):
                 with open(_adapter_model_path, "r", encoding="utf-8") as f:
                     _adapter_model_path = pretrained_model_name_or_path
-                    pretrained_model_name_or_path = json.load(
-                        f)["base_model_name_or_path"]
+                    pretrained_model_name_or_path = json.load(f)["base_model_name_or_path"]
         else:
             _adapter_model_path = None
 
@@ -3445,8 +3336,7 @@ def from_pretrained(
             if low_cpu_mem_usage is None:
                 low_cpu_mem_usage = True
             elif not low_cpu_mem_usage:
-                raise ValueError(
-                    "Passing along a `device_map` requires `low_cpu_mem_usage=True`")
+                raise ValueError("Passing along a `device_map` requires `low_cpu_mem_usage=True`")
 
         if low_cpu_mem_usage:
             if is_deepspeed_zero3_enabled():
@@ -3467,10 +3357,8 @@ def from_pretrained(
                 )
 
             # preparing BitsAndBytesConfig from kwargs
-            config_dict = {k: v for k, v in kwargs.items(
-            ) if k in inspect.signature(BitsAndBytesConfig).parameters}
-            config_dict = {
-                **config_dict, "load_in_4bit": load_in_4bit, "load_in_8bit": load_in_8bit}
+            config_dict = {k: v for k, v in kwargs.items() if k in inspect.signature(BitsAndBytesConfig).parameters}
+            config_dict = {**config_dict, "load_in_4bit": load_in_4bit, "load_in_8bit": load_in_8bit}
             quantization_config, kwargs = BitsAndBytesConfig.from_dict(
                 config_dict=config_dict, return_unused_kwargs=True, **kwargs
             )
@@ -3481,8 +3369,7 @@ def from_pretrained(
 
         from_pt = not (from_tf | from_flax)
 
-        user_agent = {"file_type": "model", "framework": "pytorch",
-                      "from_auto_class": from_auto_class}
+        user_agent = {"file_type": "model", "framework": "pytorch", "from_auto_class": from_auto_class}
         if from_pipeline is not None:
             user_agent["using_pipeline"] = from_pipeline
 
@@ -3524,8 +3411,7 @@ def from_pretrained(
 
             model_kwargs = kwargs
 
-        pre_quantized = getattr(
-            config, "quantization_config", None) is not None
+        pre_quantized = getattr(config, "quantization_config", None) is not None
         if pre_quantized or quantization_config is not None:
             if pre_quantized:
                 config.quantization_config = AutoHfQuantizer.merge_quantization_configs(
@@ -3533,8 +3419,7 @@ def from_pretrained(
                 )
             else:
                 config.quantization_config = quantization_config
-            hf_quantizer = AutoHfQuantizer.from_config(
-                config.quantization_config, pre_quantized=pre_quantized)
+            hf_quantizer = AutoHfQuantizer.from_config(config.quantization_config, pre_quantized=pre_quantized)
         else:
             hf_quantizer = None
 
@@ -3551,8 +3436,7 @@ def from_pretrained(
             # Force-set to `True` for more mem efficiency
             if low_cpu_mem_usage is None:
                 low_cpu_mem_usage = True
-                logger.warning(
-                    "`low_cpu_mem_usage` was None, now set to True since model is quantized.")
+                logger.warning("`low_cpu_mem_usage` was None, now set to True since model is quantized.")
         is_quantized = hf_quantizer is not None
 
         # This variable will flag if we're loading a sharded checkpoint. In this case the archive file is just the
@@ -3576,70 +3460,55 @@ def from_pretrained(
             is_local = os.path.isdir(pretrained_model_name_or_path)
             if is_local:
                 if from_tf and os.path.isfile(
-                    os.path.join(pretrained_model_name_or_path,
-                                 subfolder, TF_WEIGHTS_NAME + ".index")
+                    os.path.join(pretrained_model_name_or_path, subfolder, TF_WEIGHTS_NAME + ".index")
                 ):
                     # Load from a TF 1.0 checkpoint in priority if from_tf
-                    archive_file = os.path.join(
-                        pretrained_model_name_or_path, subfolder, TF_WEIGHTS_NAME + ".index")
+                    archive_file = os.path.join(pretrained_model_name_or_path, subfolder, TF_WEIGHTS_NAME + ".index")
                 elif from_tf and os.path.isfile(
-                    os.path.join(pretrained_model_name_or_path,
-                                 subfolder, TF2_WEIGHTS_NAME)
+                    os.path.join(pretrained_model_name_or_path, subfolder, TF2_WEIGHTS_NAME)
                 ):
                     # Load from a TF 2.0 checkpoint in priority if from_tf
-                    archive_file = os.path.join(
-                        pretrained_model_name_or_path, subfolder, TF2_WEIGHTS_NAME)
+                    archive_file = os.path.join(pretrained_model_name_or_path, subfolder, TF2_WEIGHTS_NAME)
                 elif from_flax and os.path.isfile(
-                    os.path.join(pretrained_model_name_or_path,
-                                 subfolder, FLAX_WEIGHTS_NAME)
+                    os.path.join(pretrained_model_name_or_path, subfolder, FLAX_WEIGHTS_NAME)
                 ):
                     # Load from a Flax checkpoint in priority if from_flax
-                    archive_file = os.path.join(
-                        pretrained_model_name_or_path, subfolder, FLAX_WEIGHTS_NAME)
+                    archive_file = os.path.join(pretrained_model_name_or_path, subfolder, FLAX_WEIGHTS_NAME)
                 elif use_safetensors is not False and os.path.isfile(
-                    os.path.join(pretrained_model_name_or_path, subfolder,
-                                 _add_variant(SAFE_WEIGHTS_NAME, variant))
+                    os.path.join(pretrained_model_name_or_path, subfolder, _add_variant(SAFE_WEIGHTS_NAME, variant))
                 ):
                     # Load from a safetensors checkpoint
                     archive_file = os.path.join(
-                        pretrained_model_name_or_path, subfolder, _add_variant(
-                            SAFE_WEIGHTS_NAME, variant)
+                        pretrained_model_name_or_path, subfolder, _add_variant(SAFE_WEIGHTS_NAME, variant)
                     )
                 elif use_safetensors is not False and os.path.isfile(
                     os.path.join(
-                        pretrained_model_name_or_path, subfolder, _add_variant(
-                            SAFE_WEIGHTS_INDEX_NAME, variant)
+                        pretrained_model_name_or_path, subfolder, _add_variant(SAFE_WEIGHTS_INDEX_NAME, variant)
                     )
                 ):
                     # Load from a sharded safetensors checkpoint
                     archive_file = os.path.join(
-                        pretrained_model_name_or_path, subfolder, _add_variant(
-                            SAFE_WEIGHTS_INDEX_NAME, variant)
+                        pretrained_model_name_or_path, subfolder, _add_variant(SAFE_WEIGHTS_INDEX_NAME, variant)
                     )
                     is_sharded = True
                 elif not use_safetensors and os.path.isfile(
-                    os.path.join(pretrained_model_name_or_path,
-                                 subfolder, _add_variant(WEIGHTS_NAME, variant))
+                    os.path.join(pretrained_model_name_or_path, subfolder, _add_variant(WEIGHTS_NAME, variant))
                 ):
                     # Load from a PyTorch checkpoint
                     archive_file = os.path.join(
-                        pretrained_model_name_or_path, subfolder, _add_variant(
-                            WEIGHTS_NAME, variant)
+                        pretrained_model_name_or_path, subfolder, _add_variant(WEIGHTS_NAME, variant)
                     )
                 elif not use_safetensors and os.path.isfile(
-                    os.path.join(pretrained_model_name_or_path, subfolder,
-                                 _add_variant(WEIGHTS_INDEX_NAME, variant))
+                    os.path.join(pretrained_model_name_or_path, subfolder, _add_variant(WEIGHTS_INDEX_NAME, variant))
                 ):
                     # Load from a sharded PyTorch checkpoint
                     archive_file = os.path.join(
-                        pretrained_model_name_or_path, subfolder, _add_variant(
-                            WEIGHTS_INDEX_NAME, variant)
+                        pretrained_model_name_or_path, subfolder, _add_variant(WEIGHTS_INDEX_NAME, variant)
                     )
                     is_sharded = True
                 # At this stage we don't have a weight file so we will raise an error.
                 elif not use_safetensors and (
-                    os.path.isfile(os.path.join(
-                        pretrained_model_name_or_path, subfolder, TF_WEIGHTS_NAME + ".index"))
+                    os.path.isfile(os.path.join(pretrained_model_name_or_path, subfolder, TF_WEIGHTS_NAME + ".index"))
                     or os.path.isfile(os.path.join(pretrained_model_name_or_path, subfolder, TF2_WEIGHTS_NAME))
                 ):
                     raise EnvironmentError(
@@ -3648,8 +3517,7 @@ def from_pretrained(
                         " `from_tf=True` to load this model from those weights."
                     )
                 elif not use_safetensors and os.path.isfile(
-                    os.path.join(pretrained_model_name_or_path,
-                                 subfolder, FLAX_WEIGHTS_NAME)
+                    os.path.join(pretrained_model_name_or_path, subfolder, FLAX_WEIGHTS_NAME)
                 ):
                     raise EnvironmentError(
                         f"Error no file named {_add_variant(WEIGHTS_NAME, variant)} found in directory"
@@ -3676,13 +3544,11 @@ def from_pretrained(
                         f"We found a TensorFlow checkpoint at {pretrained_model_name_or_path + '.index'}, please set "
                         "from_tf to True to load from this checkpoint."
                     )
-                archive_file = os.path.join(
-                    subfolder, pretrained_model_name_or_path + ".index")
+                archive_file = os.path.join(subfolder, pretrained_model_name_or_path + ".index")
                 is_local = True
             elif is_remote_url(pretrained_model_name_or_path):
                 filename = pretrained_model_name_or_path
-                resolved_archive_file = download_url(
-                    pretrained_model_name_or_path)
+                resolved_archive_file = download_url(pretrained_model_name_or_path)
             else:
                 # set correct filename
                 if from_tf:
@@ -3710,8 +3576,7 @@ def from_pretrained(
                         "_raise_exceptions_for_missing_entries": False,
                         "_commit_hash": commit_hash,
                     }
-                    resolved_archive_file = cached_file(
-                        pretrained_model_name_or_path, filename, **cached_file_kwargs)
+                    resolved_archive_file = cached_file(pretrained_model_name_or_path, filename, **cached_file_kwargs)
 
                     # Since we set _raise_exceptions_for_missing_entries=False, we don't get an exception but a None
                     # result when internet is up, the repo and revision exist, but the file does not.
@@ -3781,8 +3646,7 @@ def from_pretrained(
                                     Thread(
                                         target=auto_conversion,
                                         args=(pretrained_model_name_or_path,),
-                                        kwargs={
-                                            "ignore_errors_during_conversion": True, **cached_file_kwargs},
+                                        kwargs={"ignore_errors_during_conversion": True, **cached_file_kwargs},
                                         name="Thread-autoconversion",
                                     ).start()
                         else:
@@ -3840,8 +3704,7 @@ def from_pretrained(
                 logger.info(f"loading weights file {archive_file}")
                 resolved_archive_file = archive_file
             else:
-                logger.info(
-                    f"loading weights file {filename} from cache at {resolved_archive_file}")
+                logger.info(f"loading weights file {filename} from cache at {resolved_archive_file}")
         elif gguf_file:
             from .modeling_gguf_pytorch_utils import load_gguf_checkpoint
 
@@ -3866,11 +3729,9 @@ def from_pretrained(
                     "_commit_hash": commit_hash,
                 }
 
-                gguf_path = cached_file(
-                    pretrained_model_name_or_path, gguf_file, **cached_file_kwargs)
+                gguf_path = cached_file(pretrained_model_name_or_path, gguf_file, **cached_file_kwargs)
 
-            state_dict = load_gguf_checkpoint(
-                gguf_path, return_tensors=True)["tensors"]
+            state_dict = load_gguf_checkpoint(gguf_path, return_tensors=True)["tensors"]
 
             resolved_archive_file = None
             is_sharded = False
@@ -3907,12 +3768,10 @@ def from_pretrained(
                 pass
             elif metadata.get("format") == "tf":
                 from_tf = True
-                logger.info(
-                    "A TensorFlow safetensors file is being loaded in a PyTorch model.")
+                logger.info("A TensorFlow safetensors file is being loaded in a PyTorch model.")
             elif metadata.get("format") == "flax":
                 from_flax = True
-                logger.info(
-                    "A Flax safetensors file is being loaded in a PyTorch model.")
+                logger.info("A Flax safetensors file is being loaded in a PyTorch model.")
             elif metadata.get("format") == "mlx":
                 # This is a mlx file, we assume weights are compatible with pt
                 pass
@@ -3941,18 +3800,15 @@ def from_pretrained(
                     if torch_dtype == "auto":
                         if hasattr(config, "torch_dtype") and config.torch_dtype is not None:
                             torch_dtype = config.torch_dtype
-                            logger.info(
-                                f"Will use torch_dtype={torch_dtype} as defined in model's config object")
+                            logger.info(f"Will use torch_dtype={torch_dtype} as defined in model's config object")
                         else:
                             if is_sharded and "dtype" in sharded_metadata:
                                 torch_dtype = sharded_metadata["dtype"]
                             elif not is_sharded:
                                 torch_dtype = get_state_dict_dtype(state_dict)
                             else:
-                                one_state_dict = load_state_dict(
-                                    resolved_archive_file[0])
-                                torch_dtype = get_state_dict_dtype(
-                                    one_state_dict)
+                                one_state_dict = load_state_dict(resolved_archive_file[0])
+                                torch_dtype = get_state_dict_dtype(one_state_dict)
                                 del one_state_dict  # free CPU memory
                             logger.info(
                                 "Since the `torch_dtype` attribute can't be found in model's config object, "
@@ -3968,8 +3824,7 @@ def from_pretrained(
 
             # Check if `_keep_in_fp32_modules` is not None
             use_keep_in_fp32_modules = (cls._keep_in_fp32_modules is not None) and (
-                (torch_dtype == torch.float16) or hasattr(
-                    hf_quantizer, "use_keep_in_fp32_modules")
+                (torch_dtype == torch.float16) or hasattr(hf_quantizer, "use_keep_in_fp32_modules")
             )
 
             if is_sharded:
@@ -3991,10 +3846,8 @@ def from_pretrained(
         if is_deepspeed_zero3_enabled() and not is_quantized:
             import deepspeed
 
-            logger.info(
-                "Detected DeepSpeed ZeRO-3: activating zero.init() for this model")
-            init_contexts = [deepspeed.zero.Init(
-                config_dict_or_path=deepspeed_config())] + init_contexts
+            logger.info("Detected DeepSpeed ZeRO-3: activating zero.init() for this model")
+            init_contexts = [deepspeed.zero.Init(config_dict_or_path=deepspeed_config())] + init_contexts
         elif low_cpu_mem_usage:
             init_contexts.append(init_empty_weights())
 
@@ -4034,8 +3887,7 @@ def from_pretrained(
             special_dtypes = {}
 
             if hf_quantizer is not None:
-                special_dtypes.update(
-                    hf_quantizer.get_special_dtypes_update(model, torch_dtype))
+                special_dtypes.update(hf_quantizer.get_special_dtypes_update(model, torch_dtype))
 
             special_dtypes.update(
                 {
@@ -4081,8 +3933,7 @@ def from_pretrained(
 
             # Make sure tied weights are tied before creating the device map.
             model.tie_weights()
-            device_map = infer_auto_device_map(
-                model, dtype=target_dtype, **device_map_kwargs)
+            device_map = infer_auto_device_map(model, dtype=target_dtype, **device_map_kwargs)
 
             if hf_quantizer is not None:
                 hf_quantizer.validate_environment(device_map=device_map)
@@ -4096,8 +3947,7 @@ def from_pretrained(
         if from_tf:
             if resolved_archive_file.endswith(".index"):
                 # Load from a TensorFlow 1.X checkpoint - provided by original authors
-                model = cls.load_tf_weights(
-                    model, config, resolved_archive_file[:-6])  # Remove the '.index'
+                model = cls.load_tf_weights(model, config, resolved_archive_file[:-6])  # Remove the '.index'
             else:
                 # Load from our TensorFlow 2.0 checkpoints
                 try:
@@ -4117,8 +3967,7 @@ def from_pretrained(
             try:
                 from .modeling_flax_pytorch_utils import load_flax_checkpoint_in_pytorch_model
 
-                model = load_flax_checkpoint_in_pytorch_model(
-                    model, resolved_archive_file)
+                model = load_flax_checkpoint_in_pytorch_model(model, resolved_archive_file)
             except ImportError:
                 logger.error(
                     "Loading a Flax model in PyTorch, requires both PyTorch and Flax to be installed. Please see"
@@ -4165,10 +4014,8 @@ def from_pretrained(
 
         # If it is a model with generation capabilities, attempt to load the generation config
         if model.can_generate() and generation_config is not None:
-            logger.info(
-                "The user-defined `generation_config` will be used to override the default generation config.")
-            model.generation_config = model.generation_config.from_dict(
-                generation_config.to_dict())
+            logger.info("The user-defined `generation_config` will be used to override the default generation config.")
+            model.generation_config = model.generation_config.from_dict(generation_config.to_dict())
         elif model.can_generate() and pretrained_model_name_or_path is not None:
             try:
                 model.generation_config = GenerationConfig.from_pretrained(
@@ -4270,8 +4117,7 @@ def _load_pretrained_model(
 
         if device_map is not None and "disk" in device_map.values():
             archive_file = (
-                resolved_archive_file[0] if isinstance(
-                    resolved_archive_file, (list, tuple)) else resolved_archive_file
+                resolved_archive_file[0] if isinstance(resolved_archive_file, (list, tuple)) else resolved_archive_file
             )
             is_safetensors = archive_file.endswith(".safetensors")
             if offload_folder is None and not is_safetensors:
@@ -4318,18 +4164,14 @@ def _load_pretrained_model(
             # to avoid logging parametrized weight norm renaming
             if hasattr(nn.utils.parametrizations, "weight_norm"):
                 if "weight_g" in key:
-                    new_key = key.replace(
-                        "weight_g", "parametrizations.weight.original0")
+                    new_key = key.replace("weight_g", "parametrizations.weight.original0")
                 if "weight_v" in key:
-                    new_key = key.replace(
-                        "weight_v", "parametrizations.weight.original1")
+                    new_key = key.replace("weight_v", "parametrizations.weight.original1")
             else:
                 if "parametrizations.weight.original0" in key:
-                    new_key = key.replace(
-                        "parametrizations.weight.original0", "weight_g")
+                    new_key = key.replace("parametrizations.weight.original0", "weight_g")
                 if "parametrizations.weight.original1" in key:
-                    new_key = key.replace(
-                        "parametrizations.weight.original1", "weight_v")
+                    new_key = key.replace("parametrizations.weight.original1", "weight_v")
             if new_key != key:
                 old_keys.append(key)
                 new_keys.append(new_key)
@@ -4350,8 +4192,7 @@ def _load_pretrained_model(
 
         if len(prefix) > 0:
             has_prefix_module = any(s.startswith(prefix) for s in loaded_keys)
-            expects_prefix_module = any(s.startswith(prefix)
-                                        for s in expected_keys)
+            expects_prefix_module = any(s.startswith(prefix) for s in expected_keys)
         else:
             has_prefix_module = False
             expects_prefix_module = False
@@ -4363,10 +4204,8 @@ def _load_pretrained_model(
 
         if remove_prefix_from_model:
             _prefix = f"{prefix}."
-            expected_keys_not_prefixed = [
-                s for s in expected_keys if not s.startswith(_prefix)]
-            expected_keys = [s[len(_prefix):] if s.startswith(
-                _prefix) else s for s in expected_keys]
+            expected_keys_not_prefixed = [s for s in expected_keys if not s.startswith(_prefix)]
+            expected_keys = [s[len(_prefix) :] if s.startswith(_prefix) else s for s in expected_keys]
         elif add_prefix_to_model:
             expected_keys = [".".join([prefix, s]) for s in expected_keys]
 
@@ -4377,8 +4216,7 @@ def _load_pretrained_model(
         # buffers
         model_buffers = {n for n, _ in model.named_buffers()}
         if remove_prefix_from_model:
-            model_buffers = {key[len(_prefix):] if key.startswith(
-                _prefix) else key for key in model_buffers}
+            model_buffers = {key[len(_prefix) :] if key.startswith(_prefix) else key for key in model_buffers}
         elif add_prefix_to_model:
             model_buffers = {".".join([prefix, key]) for key in model_buffers}
         unexpected_keys = sorted(unexpected_keys - model_buffers)
@@ -4391,37 +4229,31 @@ def _load_pretrained_model(
                 ptrs[id_tensor].append(name)
 
             # These are all the pointers of shared tensors.
-            tied_params = [names for _,
-                           names in ptrs.items() if len(names) > 1]
+            tied_params = [names for _, names in ptrs.items() if len(names) > 1]
         else:
             # id function doesn't work for meta tensor so we need this function
             tied_params = find_tied_parameters(model)
 
         for group in tied_params:
             if remove_prefix_from_model:
-                group = [key[len(_prefix):] if key.startswith(
-                    _prefix) else key for key in group]
+                group = [key[len(_prefix) :] if key.startswith(_prefix) else key for key in group]
             elif add_prefix_to_model:
                 group = [".".join([prefix, key]) for key in group]
             missing_in_group = [k for k in missing_keys if k in group]
             if len(missing_in_group) > 0 and len(missing_in_group) < len(group):
-                missing_keys = [
-                    k for k in missing_keys if k not in missing_in_group]
+                missing_keys = [k for k in missing_keys if k not in missing_in_group]
 
         # Some models may have keys that are not in the state by design, removing them before needlessly warning
         # the user.
         if cls._keys_to_ignore_on_load_missing is not None:
             for pat in cls._keys_to_ignore_on_load_missing:
-                missing_keys = [
-                    k for k in missing_keys if re.search(pat, k) is None]
+                missing_keys = [k for k in missing_keys if re.search(pat, k) is None]
 
         if cls._keys_to_ignore_on_load_unexpected is not None:
             for pat in cls._keys_to_ignore_on_load_unexpected:
-                unexpected_keys = [
-                    k for k in unexpected_keys if re.search(pat, k) is None]
+                unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]
         if hf_quantizer is not None:
-            missing_keys = hf_quantizer.update_missing_keys(
-                model, missing_keys, prefix)
+            missing_keys = hf_quantizer.update_missing_keys(model, missing_keys, prefix)
 
         # retrieve weights on meta device and put them back on CPU.
         # This is not ideal in terms of memory, but if we don't do that not, we can't initialize them in the next step
@@ -4457,8 +4289,7 @@ def _load_pretrained_model(
                     ):
                         set_module_tensor_to_device(model, key, "cpu", value)
                     else:
-                        hf_quantizer.create_quantized_param(
-                            model, value, key, "cpu", state_dict, unexpected_keys)
+                        hf_quantizer.create_quantized_param(model, value, key, "cpu", state_dict, unexpected_keys)
 
         # retrieve uninitialized modules and initialize before maybe overriding that with the pretrained weights.
         if _fast_init:
@@ -4466,11 +4297,10 @@ def _load_pretrained_model(
                 if remove_prefix_from_model:
                     _loaded_keys = [f"{prefix}.{k}" for k in loaded_keys]
                 elif add_prefix_to_model:
-                    _loaded_keys = [k[len(prefix) + 1:] for k in loaded_keys]
+                    _loaded_keys = [k[len(prefix) + 1 :] for k in loaded_keys]
                 else:
                     _loaded_keys = loaded_keys
-                not_initialized_submodules = set_initialized_submodules(
-                    model, _loaded_keys)
+                not_initialized_submodules = set_initialized_submodules(model, _loaded_keys)
                 # If we're about to tie the output embeds to the input embeds we don't need to init them
                 if hasattr(model.config, "tie_word_embeddings") and model.config.tie_word_embeddings:
                     output_embeddings = model.get_output_embeddings()
@@ -4517,8 +4347,7 @@ def _load_pretrained_model(
                     "properly saved?"
                 )
             if device_map is not None:
-                device_map = {
-                    k.replace(f"{cls.base_model_prefix}.", ""): v for k, v in device_map.items()}
+                device_map = {k.replace(f"{cls.base_model_prefix}.", ""): v for k, v in device_map.items()}
 
         def _find_mismatched_keys(
             state_dict,
@@ -4555,22 +4384,18 @@ def _find_mismatched_keys(
                             pass
                         else:
                             mismatched_keys.append(
-                                (checkpoint_key, state_dict[checkpoint_key].shape,
-                                 model_state_dict[model_key].shape)
+                                (checkpoint_key, state_dict[checkpoint_key].shape, model_state_dict[model_key].shape)
                             )
                             del state_dict[checkpoint_key]
             return mismatched_keys
 
         if resolved_archive_file is not None:
-            folder = os.path.sep.join(
-                resolved_archive_file[0].split(os.path.sep)[:-1])
+            folder = os.path.sep.join(resolved_archive_file[0].split(os.path.sep)[:-1])
         else:
             folder = None
         if device_map is not None and is_safetensors:
-            param_device_map = expand_device_map(
-                device_map, original_loaded_keys, start_prefix)
-            str_dtype = str(dtype).replace(
-                "torch.", "") if dtype is not None else "float32"
+            param_device_map = expand_device_map(device_map, original_loaded_keys, start_prefix)
+            str_dtype = str(dtype).replace("torch.", "") if dtype is not None else "float32"
             if sharded_metadata is None:
                 archive_file = (
                     resolved_archive_file[0]
@@ -4579,13 +4404,11 @@ def _find_mismatched_keys(
                 )
                 weight_map = {p: archive_file for p in original_loaded_keys}
             else:
-                weight_map = {p: os.path.join(
-                    folder, f) for p, f in sharded_metadata["weight_map"].items()}
+                weight_map = {p: os.path.join(folder, f) for p, f in sharded_metadata["weight_map"].items()}
             offload_index = {
-                p[len(start_prefix):]: {
-                    "safetensors_file": f, "weight_name": p, "dtype": str_dtype}
+                p[len(start_prefix) :]: {"safetensors_file": f, "weight_name": p, "dtype": str_dtype}
                 for p, f in weight_map.items()
-                if p.startswith(start_prefix) and param_device_map[p[len(start_prefix):]] == "disk"
+                if p.startswith(start_prefix) and param_device_map[p[len(start_prefix) :]] == "disk"
             }
         else:
             offload_index = None
@@ -4648,21 +4471,18 @@ def _find_mismatched_keys(
                 disk_only_shard_files = get_disk_only_shard_files(
                     device_map, sharded_metadata=sharded_metadata, start_prefix=start_prefix
                 )
-                disk_only_shard_files = [os.path.join(
-                    folder, f) for f in disk_only_shard_files]
+                disk_only_shard_files = [os.path.join(folder, f) for f in disk_only_shard_files]
             else:
                 disk_only_shard_files = []
 
             if len(resolved_archive_file) > 1:
-                resolved_archive_file = logging.tqdm(
-                    resolved_archive_file, desc="Loading checkpoint shards")
+                resolved_archive_file = logging.tqdm(resolved_archive_file, desc="Loading checkpoint shards")
             assign_to_params_buffers = None
             for shard_file in resolved_archive_file:
                 # Skip the load for shards that only contain disk-offloaded weights when using safetensors for the offload.
                 if shard_file in disk_only_shard_files:
                     continue
-                state_dict = load_state_dict(
-                    shard_file, is_quantized=is_quantized)
+                state_dict = load_state_dict(shard_file, is_quantized=is_quantized)
 
                 # Mistmatched keys contains tuples key/shape1/shape2 of weights in the checkpoint that have a shape not
                 # matching the weights in the model.
@@ -4679,8 +4499,7 @@ def _find_mismatched_keys(
                         for key, param in model_to_load.state_dict().items():
                             if param.device == torch.device("meta"):
                                 set_module_tensor_to_device(
-                                    model_to_load, key, "cpu", torch.empty(
-                                        *param.size(), dtype=dtype)
+                                    model_to_load, key, "cpu", torch.empty(*param.size(), dtype=dtype)
                                 )
                     else:
                         new_error_msgs, offload_index, state_dict_index = _load_state_dict_into_meta_model(
@@ -4721,21 +4540,17 @@ def _find_mismatched_keys(
                     if not is_safetensors:
                         for weight_name in offload_index:
                             shutil.move(
-                                os.path.join(offload_folder,
-                                             f"{weight_name}.dat"),
-                                os.path.join(offload_folder,
-                                             f"{prefix}.{weight_name}.dat"),
+                                os.path.join(offload_folder, f"{weight_name}.dat"),
+                                os.path.join(offload_folder, f"{prefix}.{weight_name}.dat"),
                             )
-                    offload_index = {
-                        f"{prefix}.{key}": value for key, value in offload_index.items()}
+                    offload_index = {f"{prefix}.{key}": value for key, value in offload_index.items()}
                 if not is_safetensors:
                     save_offload_index(offload_index, offload_folder)
                     offload_index = None
 
             if offload_state_dict:
                 # Load back temporarily offloaded state dict
-                load_offloaded_weights(
-                    model_to_load, state_dict_index, state_dict_folder)
+                load_offloaded_weights(model_to_load, state_dict_index, state_dict_folder)
                 shutil.rmtree(state_dict_folder)
 
         if len(error_msgs) > 0:
@@ -4744,8 +4559,7 @@ def _find_mismatched_keys(
                 error_msg += (
                     "\n\tYou may consider adding `ignore_mismatched_sizes=True` in the model `from_pretrained` method."
                 )
-            raise RuntimeError(
-                f"Error(s) in loading state_dict for {model.__class__.__name__}:\n\t{error_msg}")
+            raise RuntimeError(f"Error(s) in loading state_dict for {model.__class__.__name__}:\n\t{error_msg}")
 
         if len(unexpected_keys) > 0:
             archs = [] if model.config.architectures is None else model.config.architectures
@@ -4760,8 +4574,7 @@ def _find_mismatched_keys(
                 " (initializing a BertForSequenceClassification model from a BertForSequenceClassification model)."
             )
         else:
-            logger.info(
-                f"All model checkpoint weights were used when initializing {model.__class__.__name__}.\n")
+            logger.info(f"All model checkpoint weights were used when initializing {model.__class__.__name__}.\n")
         if len(missing_keys) > 0:
             logger.warning(
                 f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at"
@@ -4797,8 +4610,7 @@ def retrieve_modules_from_names(self, names, add_prefix=False, remove_prefix=Fal
         # torch.nn.ParameterList is a special case where two parameter keywords
         # are appended to the module name, *e.g.* bert.special_embeddings.0
         module_keys = module_keys.union(
-            {".".join(key.split(".")[:-2])
-             for key in names if len(key) > 0 and key[-1].isdigit()}
+            {".".join(key.split(".")[:-2]) for key in names if len(key) > 0 and key[-1].isdigit()}
         )
 
         retrieved_modules = []
@@ -4806,11 +4618,9 @@ def retrieve_modules_from_names(self, names, add_prefix=False, remove_prefix=Fal
         for name, module in self.named_modules():
             if remove_prefix:
                 _prefix = f"{self.base_model_prefix}."
-                name = name[len(_prefix):] if name.startswith(
-                    _prefix) else name
+                name = name[len(_prefix) :] if name.startswith(_prefix) else name
             elif add_prefix:
-                name = ".".join([self.base_model_prefix, name]) if len(
-                    name) > 0 else self.base_model_prefix
+                name = ".".join([self.base_model_prefix, name]) if len(name) > 0 else self.base_model_prefix
 
             if name in module_keys:
                 retrieved_modules.append(module)
@@ -4898,8 +4708,7 @@ def to_bettertransformer(self) -> "PreTrainedModel":
             [`PreTrainedModel`]: The model converted to BetterTransformer.
         """
         if not is_optimum_available():
-            raise ImportError(
-                "The package `optimum` is required to use Better Transformer.")
+            raise ImportError("The package `optimum` is required to use Better Transformer.")
 
         from optimum.version import __version__ as optimum_version
 
@@ -4921,8 +4730,7 @@ def reverse_bettertransformer(self):
             [`PreTrainedModel`]: The model converted back to the original modeling.
         """
         if not is_optimum_available():
-            raise ImportError(
-                "The package `optimum` is required to use Better Transformer.")
+            raise ImportError("The package `optimum` is required to use Better Transformer.")
 
         from optimum.version import __version__ as optimum_version
 
@@ -4958,8 +4766,7 @@ def warn_if_padding_and_no_attention_mask(self, input_ids, attention_mask):
             # If the pad token is equal to either BOS, EOS, or SEP, we do not know whether the user should use an
             # attention_mask or not. In this case, we should still show a warning because this is a rare case.
             if (
-                (self.config.bos_token_id is not None and self.config.bos_token_id ==
-                 self.config.pad_token_id)
+                (self.config.bos_token_id is not None and self.config.bos_token_id == self.config.pad_token_id)
                 or (self.config.eos_token_id is not None and self.config.eos_token_id == self.config.pad_token_id)
                 or (self.config.sep_token_id is not None and self.config.sep_token_id == self.config.pad_token_id)
             ):
@@ -5043,8 +4850,7 @@ def __init__(self, config: PretrainedConfig):
         super().__init__()
         self.dense_0 = nn.Linear(config.hidden_size * 2, config.hidden_size)
         self.activation = nn.Tanh()
-        self.LayerNorm = nn.LayerNorm(
-            config.hidden_size, eps=config.layer_norm_eps)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
         self.dense_1 = nn.Linear(config.hidden_size, 1)
 
     def forward(
@@ -5082,8 +4888,7 @@ def forward(
         if start_positions is not None:
             slen, hsz = hidden_states.shape[-2:]
             # shape (bsz, 1, hsz)
-            start_positions = start_positions[:,
-                                              None, None].expand(-1, -1, hsz)
+            start_positions = start_positions[:, None, None].expand(-1, -1, hsz)
             # shape (bsz, 1, hsz)
             start_states = hidden_states.gather(-2, start_positions)
             # shape (bsz, slen, hsz)
@@ -5153,11 +4958,9 @@ def forward(
         ), "One of start_states, start_positions should be not None"
         if start_positions is not None:
             # shape (bsz, 1, hsz)
-            start_positions = start_positions[:,
-                                              None, None].expand(-1, -1, hsz)
+            start_positions = start_positions[:, None, None].expand(-1, -1, hsz)
             # shape (bsz, hsz)
-            start_states = hidden_states.gather(-2,
-                                                start_positions).squeeze(-2)
+            start_states = hidden_states.gather(-2, start_positions).squeeze(-2)
 
         if cls_index is not None:
             # shape (bsz, 1, hsz)
@@ -5264,8 +5067,7 @@ def forward(
                     x.squeeze_(-1)
 
             # during training, compute the end logits based on the ground truth of the start position
-            end_logits = self.end_logits(
-                hidden_states, start_positions=start_positions, p_mask=p_mask)
+            end_logits = self.end_logits(hidden_states, start_positions=start_positions, p_mask=p_mask)
 
             loss_fct = CrossEntropyLoss()
             start_loss = loss_fct(start_logits, start_positions)
@@ -5274,8 +5076,7 @@ def forward(
 
             if cls_index is not None and is_impossible is not None:
                 # Predict answerability from the representation of CLS and START
-                cls_logits = self.answer_class(
-                    hidden_states, start_positions=start_positions, cls_index=cls_index)
+                cls_logits = self.answer_class(hidden_states, start_positions=start_positions, cls_index=cls_index)
                 loss_fct_cls = nn.BCEWithLogitsLoss()
                 cls_loss = loss_fct_cls(cls_logits, is_impossible)
 
@@ -5287,15 +5088,13 @@ def forward(
         else:
             # during inference, compute the end logits based on beam search
             bsz, slen, hsz = hidden_states.size()
-            start_log_probs = nn.functional.softmax(
-                start_logits, dim=-1)  # shape (bsz, slen)
+            start_log_probs = nn.functional.softmax(start_logits, dim=-1)  # shape (bsz, slen)
 
             start_top_log_probs, start_top_index = torch.topk(
                 start_log_probs, self.start_n_top, dim=-1
             )  # shape (bsz, start_n_top)
             # shape (bsz, start_n_top, hsz)
-            start_top_index_exp = start_top_index.unsqueeze(
-                -1).expand(-1, -1, hsz)
+            start_top_index_exp = start_top_index.unsqueeze(-1).expand(-1, -1, hsz)
             # shape (bsz, start_n_top, hsz)
             start_states = torch.gather(hidden_states, -2, start_top_index_exp)
             # shape (bsz, slen, start_n_top, hsz)
@@ -5305,23 +5104,17 @@ def forward(
                 start_states
             )  # shape (bsz, slen, start_n_top, hsz)
             p_mask = p_mask.unsqueeze(-1) if p_mask is not None else None
-            end_logits = self.end_logits(
-                hidden_states_expanded, start_states=start_states, p_mask=p_mask)
-            end_log_probs = nn.functional.softmax(
-                end_logits, dim=1)  # shape (bsz, slen, start_n_top)
+            end_logits = self.end_logits(hidden_states_expanded, start_states=start_states, p_mask=p_mask)
+            end_log_probs = nn.functional.softmax(end_logits, dim=1)  # shape (bsz, slen, start_n_top)
 
             end_top_log_probs, end_top_index = torch.topk(
                 end_log_probs, self.end_n_top, dim=1
             )  # shape (bsz, end_n_top, start_n_top)
-            end_top_log_probs = end_top_log_probs.view(
-                -1, self.start_n_top * self.end_n_top)
-            end_top_index = end_top_index.view(-1,
-                                               self.start_n_top * self.end_n_top)
+            end_top_log_probs = end_top_log_probs.view(-1, self.start_n_top * self.end_n_top)
+            end_top_index = end_top_index.view(-1, self.start_n_top * self.end_n_top)
 
-            start_states = torch.einsum(
-                "blh,bl->bh", hidden_states, start_log_probs)
-            cls_logits = self.answer_class(
-                hidden_states, start_states=start_states, cls_index=cls_index)
+            start_states = torch.einsum("blh,bl->bh", hidden_states, start_log_probs)
+            cls_logits = self.answer_class(hidden_states, start_states=start_states, cls_index=cls_index)
 
             if not return_dict:
                 return (start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits)
@@ -5380,8 +5173,7 @@ def __init__(self, config: PretrainedConfig):
             self.summary = nn.Linear(config.hidden_size, num_classes)
 
         activation_string = getattr(config, "summary_activation", None)
-        self.activation: Callable = get_activation(
-            activation_string) if activation_string else Identity()
+        self.activation: Callable = get_activation(activation_string) if activation_string else Identity()
 
         self.first_dropout = Identity()
         if hasattr(config, "summary_first_dropout") and config.summary_first_dropout > 0:
@@ -5421,8 +5213,7 @@ def forward(
                 )
             else:
                 cls_index = cls_index.unsqueeze(-1).unsqueeze(-1)
-                cls_index = cls_index.expand(
-                    (-1,) * (cls_index.dim() - 1) + (hidden_states.size(-1),))
+                cls_index = cls_index.expand((-1,) * (cls_index.dim() - 1) + (hidden_states.size(-1),))
             # shape of cls_index: (bsz, XX, 1, hidden_size) where XX are optional leading dim of hidden_states
             # shape (bsz, XX, hidden_size)
             output = hidden_states.gather(-2, cls_index).squeeze(-2)
@@ -5472,12 +5263,10 @@ def expand_device_map(device_map, param_names, start_prefix):
     Expand a device map to return the correspondance parameter name to device.
     """
     new_device_map = {}
-    param_names = [p[len(start_prefix):]
-                   for p in param_names if p.startswith(start_prefix)]
+    param_names = [p[len(start_prefix) :] for p in param_names if p.startswith(start_prefix)]
     for module, device in device_map.items():
         new_device_map.update(
-            {p: device for p in param_names if p ==
-                module or p.startswith(f"{module}.") or module == ""}
+            {p: device for p in param_names if p == module or p.startswith(f"{module}.") or module == ""}
         )
     return new_device_map
 
@@ -5488,7 +5277,7 @@ def get_disk_only_shard_files(device_map, sharded_metadata, start_prefix):
     """
 
     weight_map = {
-        p[len(start_prefix):]: v for p, v in sharded_metadata["weight_map"].items() if p.startswith(start_prefix)
+        p[len(start_prefix) :]: v for p, v in sharded_metadata["weight_map"].items() if p.startswith(start_prefix)
     }
     files_content = collections.defaultdict(list)
     for weight_name, filename in weight_map.items():