mirror of
https://github.com/huggingface/transformers.git
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9c500015c5
* pipeline generation defaults * add max_new_tokens=20 in test pipelines * pop all kwargs that are used to parameterize generation config * add class attr that tell us whether a pipeline calls generate * tmp commit * pt text gen pipeline tests passing * remove failing tf tests * fix text gen pipeline mixin test corner case * update text_to_audio pipeline tests * trigger tests * a few more tests * skips * some more audio tests * not slow * broken * lower severity of generation mode errors * fix all asr pipeline tests * nit * skip * image to text pipeline tests * text2test pipeline * last pipelines * fix flaky * PR comments * handle generate attrs more carefully in models that cant generate * same as above
389 lines
15 KiB
Python
389 lines
15 KiB
Python
# Copyright 2024 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import base64
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import unittest
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from transformers import MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING, is_vision_available
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from transformers.pipelines import ImageTextToTextPipeline, pipeline
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from transformers.testing_utils import (
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is_pipeline_test,
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require_torch,
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require_vision,
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slow,
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)
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from .test_pipelines_common import ANY
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if is_vision_available():
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from PIL import Image
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else:
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class Image:
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@staticmethod
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def open(*args, **kwargs):
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pass
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@is_pipeline_test
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@require_vision
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class ImageTextToTextPipelineTests(unittest.TestCase):
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model_mapping = MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING
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def get_test_pipeline(self, model, tokenizer, processor, image_processor, torch_dtype="float32"):
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pipe = ImageTextToTextPipeline(model=model, processor=processor, torch_dtype=torch_dtype, max_new_tokens=10)
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image_token = getattr(processor.tokenizer, "image_token", "")
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examples = [
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{
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"images": Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
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"text": f"{image_token}This is a ",
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},
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{
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"images": "./tests/fixtures/tests_samples/COCO/000000039769.png",
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"text": f"{image_token}Here I see a ",
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},
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]
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return pipe, examples
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def run_pipeline_test(self, pipe, examples):
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outputs = pipe(examples[0].get("images"), text=examples[0].get("text"))
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self.assertEqual(
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outputs,
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[
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{"input_text": ANY(str), "generated_text": ANY(str)},
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],
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)
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@require_torch
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def test_small_model_pt_token_text_only(self):
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pipe = pipeline("image-text-to-text", model="llava-hf/llava-interleave-qwen-0.5b-hf")
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text = "What is the capital of France? Assistant:"
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outputs = pipe(text=text)
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self.assertEqual(
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outputs,
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[
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{
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"input_text": "What is the capital of France? Assistant:",
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"generated_text": "What is the capital of France? Assistant: The capital of France is Paris.",
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}
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],
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)
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messages = [
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[
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "Write a poem on Hugging Face, the company"},
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],
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},
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],
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[
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "What is the capital of France?"},
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],
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},
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],
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]
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outputs = pipe(text=messages)
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self.assertEqual(
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outputs,
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[
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[
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{
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"input_text": [
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{
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"role": "user",
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"content": [{"type": "text", "text": "Write a poem on Hugging Face, the company"}],
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}
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],
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"generated_text": [
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{
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"role": "user",
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"content": [{"type": "text", "text": "Write a poem on Hugging Face, the company"}],
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},
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{
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"role": "assistant",
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"content": "Hugging Face, a company of minds\nWith tools and services that make our lives easier\nFrom",
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},
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],
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}
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],
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[
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{
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"input_text": [
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{"role": "user", "content": [{"type": "text", "text": "What is the capital of France?"}]}
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],
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"generated_text": [
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{"role": "user", "content": [{"type": "text", "text": "What is the capital of France?"}]},
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{"role": "assistant", "content": "Paris"},
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],
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}
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],
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],
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)
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@require_torch
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def test_small_model_pt_token(self):
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pipe = pipeline("image-text-to-text", model="llava-hf/llava-interleave-qwen-0.5b-hf")
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image = "./tests/fixtures/tests_samples/COCO/000000039769.png"
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text = "<image> What this is? Assistant: This is"
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outputs = pipe(image, text=text)
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self.assertEqual(
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outputs,
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[
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{
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"input_text": "<image> What this is? Assistant: This is",
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"generated_text": "<image> What this is? Assistant: This is a photo of two cats lying on a pink blanket. The cats are sleeping and appear to be comfortable",
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}
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],
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)
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outputs = pipe([image, image], text=[text, text])
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self.assertEqual(
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outputs,
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[
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{
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"input_text": "<image> What this is? Assistant: This is",
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"generated_text": "<image> What this is? Assistant: This is a photo of two cats lying on a pink blanket. The cats are sleeping and appear to be comfortable",
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},
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{
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"input_text": "<image> What this is? Assistant: This is",
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"generated_text": "<image> What this is? Assistant: This is a photo of two cats lying on a pink blanket. The cats are sleeping and appear to be comfortable",
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},
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],
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)
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@require_torch
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def test_consistent_batching_behaviour(self):
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pipe = pipeline("image-text-to-text", model="microsoft/kosmos-2-patch14-224")
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image = "./tests/fixtures/tests_samples/COCO/000000039769.png"
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prompt = "a photo of"
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outputs = pipe([image, image], text=[prompt, prompt], max_new_tokens=10)
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outputs_batched = pipe([image, image], text=[prompt, prompt], batch_size=2, max_new_tokens=10)
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self.assertEqual(outputs, outputs_batched)
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@slow
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@require_torch
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def test_model_pt_chat_template(self):
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pipe = pipeline("image-text-to-text", model="llava-hf/llava-interleave-qwen-0.5b-hf")
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image_ny = "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
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image_chicago = "https://cdn.britannica.com/59/94459-050-DBA42467/Skyline-Chicago.jpg"
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "What’s the difference between these two images?"},
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{"type": "image"},
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{"type": "image"},
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],
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}
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]
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outputs = pipe([image_ny, image_chicago], text=messages, return_full_text=True, max_new_tokens=10)
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self.assertEqual(
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outputs,
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[
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{
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"input_text": [
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "What’s the difference between these two images?"},
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{
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"type": "image",
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"image": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg",
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},
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{
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"type": "image",
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"image": "https://cdn.britannica.com/59/94459-050-DBA42467/Skyline-Chicago.jpg",
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},
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],
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}
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],
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"generated_text": [
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "What’s the difference between these two images?"},
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{
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"type": "image",
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"image": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg",
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},
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{
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"type": "image",
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"image": "https://cdn.britannica.com/59/94459-050-DBA42467/Skyline-Chicago.jpg",
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},
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],
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},
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{
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"role": "assistant",
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"content": "The first image shows a statue of Liberty in the",
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},
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],
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}
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],
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)
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@slow
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@require_torch
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def test_model_pt_chat_template_continue_final_message(self):
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pipe = pipeline("image-text-to-text", model="llava-hf/llava-interleave-qwen-0.5b-hf")
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "image",
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"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
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},
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{"type": "text", "text": "Describe this image."},
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],
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},
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{
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"role": "assistant",
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"content": [
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{"type": "text", "text": "There is a dog and"},
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],
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},
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]
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outputs = pipe(text=messages, max_new_tokens=10)
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self.assertEqual(
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outputs,
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[
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{
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"input_text": [
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{
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"role": "user",
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"content": [
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{
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"type": "image",
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"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
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},
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{"type": "text", "text": "Describe this image."},
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],
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},
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{"role": "assistant", "content": [{"type": "text", "text": "There is a dog and"}]},
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],
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"generated_text": [
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{
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"role": "user",
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"content": [
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{
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"type": "image",
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"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
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},
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{"type": "text", "text": "Describe this image."},
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],
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},
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{
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"role": "assistant",
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"content": [
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{
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"type": "text",
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"text": "There is a dog and a person in the image. The dog is sitting",
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}
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],
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},
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],
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}
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],
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)
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@slow
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@require_torch
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def test_model_pt_chat_template_new_text(self):
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pipe = pipeline("image-text-to-text", model="llava-hf/llava-interleave-qwen-0.5b-hf")
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "image",
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"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
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},
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{"type": "text", "text": "Describe this image."},
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],
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}
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]
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outputs = pipe(text=messages, return_full_text=False, max_new_tokens=10)
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self.assertEqual(
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outputs,
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[
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{
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"input_text": [
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{
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"role": "user",
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"content": [
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{
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"type": "image",
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"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
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},
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{"type": "text", "text": "Describe this image."},
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],
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}
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],
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"generated_text": "In the image, a woman is sitting on the",
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}
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],
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)
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@slow
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@require_torch
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def test_model_pt_chat_template_image_url(self):
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pipe = pipeline("image-text-to-text", model="llava-hf/llava-interleave-qwen-0.5b-hf")
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "image_url",
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"image_url": {
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"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
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},
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},
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{"type": "text", "text": "Describe this image in one sentence."},
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],
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}
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]
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outputs = pipe(text=messages, return_full_text=False, max_new_tokens=10)[0]["generated_text"]
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self.assertEqual(outputs, "A statue of liberty in the foreground of a city")
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@slow
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@require_torch
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def test_model_pt_chat_template_image_url_base64(self):
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with open("./tests/fixtures/tests_samples/COCO/000000039769.png", "rb") as image_file:
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base64_image = base64.b64encode(image_file.read()).decode("utf-8")
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pipe = pipeline("image-text-to-text", model="llava-hf/llava-onevision-qwen2-0.5b-ov-hf")
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "image_url",
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"image_url": {"url": f"data:image/jpeg;base64,{base64_image}"},
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},
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{"type": "text", "text": "Describe this image in one sentence."},
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],
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}
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]
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outputs = pipe(text=messages, return_full_text=False, max_new_tokens=10)[0]["generated_text"]
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self.assertEqual(outputs, "Two cats are sleeping on a pink blanket, with")
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