Move phi3_v processor unit test to folder

tests/models/phi3_v/test_processor_phi3_v.js

@@ -0,0 +1,87 @@
+import { AutoProcessor, Phi3VProcessor } from "../../../src/transformers.js";
+
+import { load_cached_image } from "../../asset_cache.js";
+import { MAX_PROCESSOR_LOAD_TIME, MAX_TEST_EXECUTION_TIME } from "../../init.js";
+
+export default () => {
+  const model_id = "onnx-community/Phi-3.5-vision-instruct";
+
+  describe("Phi3VProcessor", () => {
+    /** @type {Phi3VProcessor} */
+    let processor;
+    let images = {};
+
+    beforeAll(async () => {
+      processor = await AutoProcessor.from_pretrained(model_id, {
+        // Use legacy to match python version
+        legacy: true,
+      });
+      images = {
+        white_image: await load_cached_image("white_image"),
+      };
+    }, MAX_PROCESSOR_LOAD_TIME);
+
+    const create_prompt = (text, images = []) => {
+      const placeholder = images.map((_, i) => `<|image_${i + 1}|>\n`).join("");
+      const messages = [{ role: "user", content: placeholder + text }];
+      const prompt = processor.tokenizer.apply_chat_template(messages, { tokenize: false, add_generation_prompt: true });
+      return prompt;
+    };
+
+    it(
+      "Text-only",
+      async () => {
+        const prompt = create_prompt("Hi there.");
+        const { input_ids, pixel_values } = await processor(prompt);
+        expect(input_ids.dims).toEqual([1, 11]);
+        expect(pixel_values).toBeUndefined();
+      },
+      MAX_TEST_EXECUTION_TIME,
+    );
+
+    it(
+      "Single image & text",
+      async () => {
+        const imgs = [images.white_image];
+        const prompt = create_prompt("Describe this image.", imgs);
+        const { input_ids, attention_mask, pixel_values, image_sizes } = await processor(prompt, imgs);
+        expect(input_ids.dims).toEqual([1, /* 773 */ 770]);
+        expect(attention_mask.dims).toEqual(input_ids.dims);
+        expect(pixel_values.dims).toEqual([1, 5, 3, 336, 336]);
+        expect(image_sizes.tolist()).toEqual([[672n, 672n]]);
+      },
+      MAX_TEST_EXECUTION_TIME,
+    );
+
+    it(
+      "Single image (num_crops=16) & text",
+      async () => {
+        const imgs = [images.white_image];
+        const prompt = create_prompt("Describe this image.", imgs);
+        const { input_ids, attention_mask, pixel_values, image_sizes } = await processor(prompt, imgs, { num_crops: 16 });
+        expect(input_ids.dims).toEqual([1, /* 2525 */ 2522]);
+        expect(attention_mask.dims).toEqual(input_ids.dims);
+        expect(pixel_values.dims).toEqual([1, 17, 3, 336, 336]);
+        expect(image_sizes.tolist()).toEqual([[1344n, 1344n]]);
+      },
+      MAX_TEST_EXECUTION_TIME,
+    );
+
+    it(
+      "Multiple images & text",
+      async () => {
+        const imgs = [images.white_image, images.white_image];
+        const prompt = create_prompt("Describe these images.", imgs);
+        const { input_ids, attention_mask, pixel_values, image_sizes } = await processor(prompt, imgs);
+        expect(input_ids.dims).toEqual([1, /* 1533 */ 1527]);
+        expect(attention_mask.dims).toEqual(input_ids.dims);
+        expect(pixel_values.dims).toEqual([2, 5, 3, 336, 336]);
+        expect(image_sizes.tolist()).toEqual([
+          [672n, 672n],
+          [672n, 672n],
+        ]);
+      },
+      MAX_TEST_EXECUTION_TIME,
+    );
+  });
+};