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592 lines
27 KiB
Python
592 lines
27 KiB
Python
# Copyright 2024 HuggingFace Inc.
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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 unittest
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import numpy as np
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from transformers import SmolVLMProcessor
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from transformers.image_utils import load_image
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from transformers.testing_utils import require_av, require_torch, require_vision
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from ...test_processing_common import ProcessorTesterMixin, url_to_local_path
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@require_torch
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@require_vision
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class SmolVLMProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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processor_class = SmolVLMProcessor
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videos_input_name = "pixel_values"
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# Tiny processor created with make_tiny_processor.py from "HuggingFaceTB/SmolVLM2-256M-Video-Instruct"
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tiny_model_id = "hf-internal-testing/tiny-processor-smolvlm"
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@classmethod
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def _setup_test_attributes(cls, processor):
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cls.image1 = load_image(
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url_to_local_path(
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"https://huggingface.co/datasets/hf-internal-testing/test-videos/resolve/main/statue_of_liberty_64x64.jpg"
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)
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)
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cls.image2 = load_image(
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url_to_local_path(
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"https://huggingface.co/datasets/hf-internal-testing/test-videos/resolve/main/chicago_64x64.jpg"
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)
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)
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cls.image3 = load_image(
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url_to_local_path(
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"https://huggingface.co/datasets/hf-internal-testing/test-videos/resolve/main/golden_gate_64x64.jpg"
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)
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)
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cls.bos_token = processor.tokenizer.bos_token
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cls.image_token = processor.image_token
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cls.video_token = processor.video_token
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cls.fake_image_token = processor.fake_image_token
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cls.global_img_token = processor.global_image_token
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cls.bos_token_id = processor.tokenizer.convert_tokens_to_ids(cls.bos_token)
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cls.image_token_id = processor.tokenizer.convert_tokens_to_ids(cls.image_token)
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cls.fake_image_token_id = processor.tokenizer.convert_tokens_to_ids(cls.fake_image_token)
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cls.global_img_tokens_id = processor.tokenizer(cls.global_img_token, add_special_tokens=False)["input_ids"]
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cls.padding_token_id = processor.tokenizer.pad_token_id
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cls.image_seq_len = processor.image_seq_len
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@classmethod
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def _setup_image_processor(cls):
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image_processor_class = cls._get_component_class_from_processor("image_processor")
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# size={"longest_edge": 1024} = 2×512 → 2×2 tile split + 1 global = 5 tiles for square images,
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# instead of the default 2048 which gives 4×4=17 tiles (too slow in splitting tests).
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# max_image_size stays at 512 so tile shapes in test_process_interleaved_images_prompts_* are correct.
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return image_processor_class.from_pretrained(cls.tiny_model_id, size={"longest_edge": 1024})
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@classmethod
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def _setup_video_processor(cls):
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video_processor_class = cls._get_component_class_from_processor("video_processor")
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# Image processor stays at max_image_size=512 (required by test_process_interleaved_images_prompts_*).
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# max_image_size=64 here only affects video frame tensor size in tests.
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return video_processor_class.from_pretrained(cls.tiny_model_id, max_image_size={"longest_edge": 64})
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@staticmethod
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def prepare_processor_dict():
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return {
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"image_seq_len": 2,
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"chat_template": "<|im_start|>{% for message in messages %}{{message['role'] | capitalize}}{% if message['content'][0]['type'] == 'image' %}{{':'}}{% else %}{{': '}}{% endif %}{% for line in message['content'] %}{% if line['type'] == 'text' %}{{line['text']}}{% elif line['type'] == 'image' %}{{ '<image>' }}{% endif %}{% endfor %}<end_of_utterance>\n{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}",
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}
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# Override as SmolVLM needs images/video to be an explicitly nested batch
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def prepare_image_inputs(self, batch_size: int | None = None):
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"""This function prepares a list of PIL images for testing"""
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images = super().prepare_image_inputs(batch_size)
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if isinstance(images, (list, tuple)):
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images = [[image] for image in images]
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return images
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def prepare_video_inputs(self, batch_size: int | None = None):
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"""This function prepares a list of numpy videos."""
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# 2 frames instead of 8: with 8 frames the expanded video token sequence exceeds the max_length
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# used in truncation tests, truncation cuts through video tokens, and _check_special_mm_tokens
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# raises a mismatch error.
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video_input = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)] * 2
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if batch_size is None:
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return [[video_input]]
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return [[video_input]] * batch_size
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def get_split_image_expected_tokens(self, processor, image_rows, image_cols):
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text_split_images = []
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for n_h in range(image_rows):
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for n_w in range(image_cols):
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text_split_images += (
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[self.fake_image_token_id]
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+ processor.tokenizer(f"<row_{n_h + 1}_col_{n_w + 1}>", add_special_tokens=False)["input_ids"]
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+ [self.image_token_id] * self.image_seq_len
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)
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text_split_images += processor.tokenizer("\n", add_special_tokens=False)["input_ids"]
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text_split_images = text_split_images[:-1] # remove last newline
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# add double newline, as it gets its own token
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text_split_images += processor.tokenizer("\n\n", add_special_tokens=False)["input_ids"]
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text_split_images += (
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[self.fake_image_token_id]
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+ self.global_img_tokens_id
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+ [self.image_token_id] * self.image_seq_len
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+ [self.fake_image_token_id]
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)
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return text_split_images
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def test_process_interleaved_images_prompts_no_image_splitting(self):
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processor_components = self.prepare_components()
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processor_components["tokenizer"] = self.get_component("tokenizer", padding_side="left")
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processor_components["image_processor"] = self.get_component("image_processor", do_image_splitting=False)
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processor_kwargs = self.prepare_processor_dict()
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processor = self.processor_class(**processor_components, **processor_kwargs)
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# Test that a single image is processed correctly
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inputs = processor(images=self.image1)
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image1_expected_size = (512, 512)
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self.assertEqual(np.array(inputs["pixel_values"]).shape, (1, 1, 3, *image1_expected_size))
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self.assertEqual(np.array(inputs["pixel_attention_mask"]).shape, (1, 1, *image1_expected_size))
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# fmt: on
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# Test a single sample with image and text
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image_str = "<image>"
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text_str = "In this image, we see"
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text = image_str + text_str
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inputs = processor(text=text, images=self.image1)
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# fmt: off
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tokenized_sentence = processor.tokenizer(text_str, add_special_tokens=False)
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expected_input_ids = [[self.fake_image_token_id] + self.global_img_tokens_id + [self.image_token_id] * self.image_seq_len + [self.fake_image_token_id] + tokenized_sentence["input_ids"]]
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self.assertEqual(inputs["input_ids"], expected_input_ids)
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self.assertEqual(inputs["attention_mask"], [[1] * len(expected_input_ids[0])])
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self.assertEqual(np.array(inputs["pixel_values"]).shape, (1, 1, 3, *image1_expected_size))
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self.assertEqual(np.array(inputs["pixel_attention_mask"]).shape, (1, 1, *image1_expected_size))
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# fmt: on
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# Test that batch is correctly processed
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image_str = "<image>"
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text_str_1 = "In this image, we see"
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text_str_2 = "In this image, we see"
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text = [
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image_str + text_str_1,
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image_str + image_str + text_str_2,
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]
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images = [[self.image1], [self.image2, self.image3]]
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inputs = processor(text=text, images=images, padding=True)
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# fmt: off
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tokenized_sentence_1 = processor.tokenizer(text_str_1, add_special_tokens=False)
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tokenized_sentence_2 = processor.tokenizer(text_str_2, add_special_tokens=False)
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image_tokens = [self.fake_image_token_id] + self.global_img_tokens_id + [self.image_token_id] * self.image_seq_len + [self.fake_image_token_id]
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expected_input_ids_1 = image_tokens + tokenized_sentence_1["input_ids"]
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expected_input_ids_2 = 2 * image_tokens + tokenized_sentence_2["input_ids"]
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# Pad the first input to match the second input
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pad_len = len(expected_input_ids_2) - len(expected_input_ids_1)
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padded_expected_input_ids_1 = [self.padding_token_id] * pad_len + expected_input_ids_1
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self.assertEqual(
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inputs["input_ids"], [padded_expected_input_ids_1, expected_input_ids_2]
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)
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self.assertEqual(
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inputs["attention_mask"],
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[[0] * pad_len + [1] * len(expected_input_ids_1), [1] * len(expected_input_ids_2)]
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)
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self.assertEqual(np.array(inputs['pixel_values']).shape, (2, 2, 3, 512, 512))
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self.assertEqual(np.array(inputs['pixel_attention_mask']).shape, (2, 2, 512, 512))
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# fmt: on
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def test_process_interleaved_images_prompts_image_splitting(self):
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processor_components = self.prepare_components()
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processor_components["tokenizer"] = self.get_component("tokenizer", padding_side="left")
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processor_components["image_processor"] = self.get_component("image_processor", do_image_splitting=True)
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processor_kwargs = self.prepare_processor_dict()
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processor = self.processor_class(**processor_components, **processor_kwargs)
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# Test that a single image is processed correctly
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# size=1024=2×512 → 2×2 split + 1 global = 5 tiles total for square images
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inputs = processor(images=self.image1)
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self.assertEqual(np.array(inputs["pixel_values"]).shape, (1, 5, 3, 512, 512))
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self.assertEqual(np.array(inputs["pixel_attention_mask"]).shape, (1, 5, 512, 512))
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# fmt: on
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self.maxDiff = None
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# Test a single sample with image and text
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image_str = "<image>"
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text_str = "In this image, we see"
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text = image_str + text_str
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inputs = processor(text=text, images=self.image1)
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# fmt: off
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tokenized_sentence = processor.tokenizer(text_str, add_special_tokens=False)
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split_image1_tokens = self.get_split_image_expected_tokens(processor, 2, 2)
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expected_input_ids_1 = [split_image1_tokens + tokenized_sentence["input_ids"]]
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self.assertEqual(inputs["input_ids"], expected_input_ids_1)
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self.assertEqual(inputs["attention_mask"], [[1] * len(expected_input_ids_1[0])])
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self.assertEqual(np.array(inputs["pixel_values"]).shape, (1, 5, 3, 512, 512))
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self.assertEqual(np.array(inputs["pixel_attention_mask"]).shape, (1, 5, 512, 512))
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# fmt: on
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# Test that batch is correctly processed
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image_str = "<image>"
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text_str_1 = "In this image, we see"
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text_str_2 = "bla, bla"
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text = [
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image_str + text_str_1,
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text_str_2 + image_str + image_str,
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]
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images = [[self.image1], [self.image2, self.image3]]
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inputs = processor(text=text, images=images, padding=True)
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# fmt: off
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tokenized_sentence_1 = processor.tokenizer(text_str_1, add_special_tokens=False)
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tokenized_sentence_2 = processor.tokenizer(text_str_2, add_special_tokens=False)
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# 2×2 split per image = 5 tiles each; batch max = max(5, 10) = 10
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split_image1_tokens = self.get_split_image_expected_tokens(processor, 2, 2)
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split_image2_tokens = self.get_split_image_expected_tokens(processor, 2, 2)
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split_image3_tokens = self.get_split_image_expected_tokens(processor, 2, 2)
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expected_input_ids_1 = split_image1_tokens + tokenized_sentence_1["input_ids"]
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expected_input_ids_2 = tokenized_sentence_2["input_ids"] + split_image2_tokens + split_image3_tokens
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# Pad the first input to match the second input
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pad_len = len(expected_input_ids_2) - len(expected_input_ids_1)
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padded_expected_input_ids_1 = [self.padding_token_id] * pad_len + expected_input_ids_1
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self.assertEqual(
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inputs["input_ids"], [padded_expected_input_ids_1, expected_input_ids_2]
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)
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self.assertEqual(
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inputs["attention_mask"],
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[[0] * pad_len + [1] * len(expected_input_ids_1), [1] * len(expected_input_ids_2)]
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)
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self.assertEqual(np.array(inputs['pixel_values']).shape, (2, 10, 3, 512, 512))
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self.assertEqual(np.array(inputs['pixel_attention_mask']).shape, (2, 10, 512, 512))
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# fmt: on
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def test_add_special_tokens_processor(self):
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processor = self.get_processor()
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image_str = "<image>"
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text_str = "In this image, we see"
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text = text_str + image_str
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# fmt: off
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inputs = processor(text=text, images=self.image1, add_special_tokens=False)
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tokenized_sentence = processor.tokenizer(text_str, add_special_tokens=False)
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split_image1_tokens = self.get_split_image_expected_tokens(processor, 2, 2)
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expected_input_ids = [tokenized_sentence["input_ids"] + split_image1_tokens]
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self.assertEqual(inputs["input_ids"], expected_input_ids)
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inputs = processor(text=text, images=self.image1)
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expected_input_ids = [tokenized_sentence["input_ids"] + split_image1_tokens]
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self.assertEqual(inputs["input_ids"], expected_input_ids)
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# fmt: on
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@unittest.skip(reason="from @molbap @zucchini-nlp, passing non-nested images is error-prone and not recommended")
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def test_non_nested_images_with_batched_text(self):
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processor = self.get_processor()
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processor.image_processor.do_image_splitting = False
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image_str = "<image>"
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text_str_1 = "In this image, we see"
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text_str_2 = "In this image, we see"
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text = [
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image_str + text_str_1,
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image_str + image_str + text_str_2,
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]
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images = [[self.image1], [self.image2, self.image3]]
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inputs = processor(text=text, images=images, padding=True)
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self.assertEqual(np.array(inputs["pixel_values"]).shape, (2, 2, 3, 512, 512))
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self.assertEqual(np.array(inputs["pixel_attention_mask"]).shape, (2, 2, 512, 512))
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# Copied from tests.models.idefics2.test_processing_idefics2.Idefics2ProcessorTest.test_process_interleaved_images_prompts_image_error
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def test_process_interleaved_images_prompts_image_error(self):
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processor = self.get_processor()
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text = [
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"This is a test sentence.",
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"In this other sentence we try some good things",
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]
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images = [[self.image1], [self.image2]]
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with self.assertRaises(ValueError):
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processor(text=text, images=images, padding=True)
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images = [[self.image1], []]
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with self.assertRaises(ValueError):
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processor(text=text, images=images, padding=True)
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text = [
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"This is a test sentence.<image>",
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"In this other sentence we try some good things<image>",
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]
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images = [[self.image1], [self.image2, self.image3]]
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with self.assertRaises(ValueError):
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processor(text=text, images=images, padding=True)
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images = [[], [self.image2]]
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with self.assertRaises((ValueError, IndexError)):
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processor(text=text, images=images, padding=True)
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images = [self.image1, self.image2, self.image3]
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with self.assertRaises(ValueError):
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processor(text=text, images=images, padding=True)
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images = [self.image1]
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with self.assertRaises(ValueError):
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processor(text=text, images=images, padding=True)
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text = [
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"This is a test sentence.",
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"In this other sentence we try some good things<image>",
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]
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images = [[self.image1], []]
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with self.assertRaises(ValueError):
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processor(text=text, images=images, padding=True)
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images = [self.image1, self.image2]
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with self.assertRaises(ValueError):
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processor(text=text, images=images, padding=True)
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def test_apply_chat_template(self):
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# Message contains content which a mix of lists with images and image urls and string
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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 do these images show?"},
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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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"role": "assistant",
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"content": [
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{
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"type": "text",
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"text": "The first image shows the statue of Liberty in New York. The second image picture depicts Idefix, the dog of Obelix in Asterix and Obelix.",
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}
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],
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},
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{"role": "user", "content": [{"type": "text", "text": "And who is that?"}]},
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]
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processor = self.get_processor()
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# Make short sequence length to test that the fake tokens are added correctly
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rendered = processor.apply_chat_template(messages, add_generation_prompt=True)
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expected_rendered = (
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"<|im_start|>User: What do these images show?<image><image><end_of_utterance>\n"
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"Assistant: The first image shows the statue of Liberty in New York. The second image picture depicts Idefix, the dog of Obelix in Asterix and Obelix.<end_of_utterance>\n"
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"User: And who is that?<end_of_utterance>\n"
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"Assistant:"
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)
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self.assertEqual(rendered, expected_rendered)
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@require_av
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@require_torch
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def test_apply_chat_template_video_frame_sampling(self):
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# overridden because SmolVLM has special preprocessing for videos
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processor = self.get_processor()
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if processor.chat_template is None:
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self.skipTest("Processor has no chat template")
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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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{
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"type": "video",
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"url": url_to_local_path(
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"https://huggingface.co/datasets/hf-internal-testing/test-videos/resolve/main/tiny_video_320x240.mp4"
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),
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},
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{"type": "text", "text": "What is shown in this video?"},
|
||
],
|
||
},
|
||
]
|
||
]
|
||
|
||
num_frames = 3
|
||
out_dict_with_video = processor.apply_chat_template(
|
||
messages,
|
||
add_generation_prompt=True,
|
||
tokenize=True,
|
||
return_dict=True,
|
||
num_frames=num_frames,
|
||
return_tensors="pt",
|
||
)
|
||
self.assertTrue(self.videos_input_name in out_dict_with_video)
|
||
self.assertEqual(len(out_dict_with_video[self.videos_input_name]), 1)
|
||
# SmolVLM doesn't sample `num_frames` exactly, by uses other sampling method
|
||
self.assertEqual(len(out_dict_with_video[self.videos_input_name][0]), 1)
|
||
|
||
# Load with `fps` arg
|
||
fps = 10
|
||
out_dict_with_video = processor.apply_chat_template(
|
||
messages,
|
||
add_generation_prompt=True,
|
||
tokenize=True,
|
||
return_dict=True,
|
||
fps=fps,
|
||
return_tensors="pt",
|
||
)
|
||
self.assertTrue(self.videos_input_name in out_dict_with_video)
|
||
self.assertEqual(len(out_dict_with_video[self.videos_input_name]), 1)
|
||
# SmolVLM doesn't sample 1 frame per second exactly, by uses other sampling method
|
||
self.assertEqual(len(out_dict_with_video[self.videos_input_name][0]), 4)
|
||
|
||
# NOTE: the last assert checks are removed
|
||
# Loading video as a list of frames (i.e. images) is not supported in SmolVLM
|
||
|
||
@require_torch
|
||
@require_vision
|
||
def test_unstructured_kwargs_batched(self):
|
||
if "image_processor" not in self.processor_class.get_attributes():
|
||
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
|
||
image_processor = self.get_component("image_processor")
|
||
video_processor = self.get_component("video_processor")
|
||
tokenizer = self.get_component("tokenizer")
|
||
|
||
processor_kwargs = self.prepare_processor_dict()
|
||
processor = self.processor_class(
|
||
tokenizer=tokenizer, image_processor=image_processor, video_processor=video_processor, **processor_kwargs
|
||
)
|
||
self.skip_processor_without_typed_kwargs(processor)
|
||
|
||
input_str = self.prepare_text_inputs(batch_size=2, modalities="image")
|
||
image_input = self.prepare_image_inputs(batch_size=2)
|
||
inputs = processor(
|
||
text=input_str,
|
||
images=image_input,
|
||
return_tensors="pt",
|
||
padding="max_length",
|
||
max_length=76,
|
||
truncation=True,
|
||
max_image_size={"longest_edge": 300},
|
||
)
|
||
|
||
self.assertEqual(inputs["pixel_values"].shape[2], 3)
|
||
self.assertEqual(inputs["pixel_values"].shape[3], 300)
|
||
self.assertEqual(len(inputs["input_ids"][0]), 76)
|
||
|
||
@require_torch
|
||
@require_vision
|
||
def test_unstructured_kwargs_batched_video(self):
|
||
if "video_processor" not in self.processor_class.get_attributes():
|
||
self.skipTest(f"video_processor attribute not present in {self.processor_class}")
|
||
processor_components = self.prepare_components()
|
||
processor_kwargs = self.prepare_processor_dict()
|
||
processor = self.processor_class(**processor_components, **processor_kwargs)
|
||
self.skip_processor_without_typed_kwargs(processor)
|
||
|
||
input_str = self.prepare_text_inputs(batch_size=2, modalities="video")
|
||
video_input = self.prepare_video_inputs(batch_size=2)
|
||
inputs = processor(
|
||
text=input_str,
|
||
videos=video_input,
|
||
return_tensors="pt",
|
||
do_rescale=True,
|
||
rescale_factor=-1.0,
|
||
padding="max_length",
|
||
max_length=172,
|
||
)
|
||
|
||
self.assertLessEqual(inputs[self.videos_input_name][0].mean(), 0)
|
||
self.assertEqual(len(inputs["input_ids"][0]), 172)
|
||
|
||
@require_torch
|
||
@require_vision
|
||
def test_text_only_inference(self):
|
||
"""Test that the processor works correctly with text-only input."""
|
||
processor_components = self.prepare_components()
|
||
processor_components["tokenizer"] = self.get_component("tokenizer", padding_side="left")
|
||
processor_kwargs = self.prepare_processor_dict()
|
||
|
||
processor = self.processor_class(**processor_components, **processor_kwargs)
|
||
|
||
text = "This is a simple text without images."
|
||
inputs = processor(text=text)
|
||
|
||
tokenized_sentence = processor.tokenizer(text, add_special_tokens=False)
|
||
expected_input_ids = [tokenized_sentence["input_ids"]]
|
||
|
||
self.assertEqual(inputs["input_ids"], expected_input_ids)
|
||
self.assertEqual(inputs["attention_mask"], [[1] * len(expected_input_ids[0])])
|
||
self.assertTrue("pixel_values" not in inputs)
|
||
self.assertTrue("pixel_attention_mask" not in inputs)
|
||
|
||
# Test batch of texts without image tokens
|
||
texts = ["First text.", "Second piece of text."]
|
||
batch_inputs = processor(text=texts, padding=True)
|
||
|
||
tokenized_1 = processor.tokenizer(texts[0], add_special_tokens=False)
|
||
tokenized_2 = processor.tokenizer(texts[1], add_special_tokens=False)
|
||
|
||
expected_1 = tokenized_1["input_ids"]
|
||
expected_2 = tokenized_2["input_ids"]
|
||
|
||
# Pad the shorter sequence
|
||
pad_len = len(expected_2) - len(expected_1)
|
||
if pad_len > 0:
|
||
padded_expected_1 = [self.padding_token_id] * pad_len + expected_1
|
||
expected_attention_1 = [0] * pad_len + [1] * len(expected_1)
|
||
self.assertEqual(batch_inputs["input_ids"], [padded_expected_1, expected_2])
|
||
self.assertEqual(batch_inputs["attention_mask"], [expected_attention_1, [1] * len(expected_2)])
|
||
else:
|
||
pad_len = -pad_len
|
||
padded_expected_2 = [self.padding_token_id] * pad_len + expected_2
|
||
expected_attention_2 = [0] * pad_len + [1] * len(expected_2)
|
||
self.assertEqual(batch_inputs["input_ids"], [expected_1, padded_expected_2])
|
||
self.assertEqual(batch_inputs["attention_mask"], [[1] * len(expected_1), expected_attention_2])
|
||
|
||
@require_torch
|
||
@require_vision
|
||
def test_missing_images_error(self):
|
||
"""Test that appropriate error is raised when images are referenced but not provided."""
|
||
processor = self.get_processor()
|
||
|
||
# Test single text with image token but no image
|
||
text = "Let me show you this image: <image> What do you think?"
|
||
with self.assertRaises(ValueError) as context:
|
||
processor(text=text)
|
||
self.assertTrue("tokens in the text but no images/videos were passed" in str(context.exception))
|
||
|
||
# Test batch with image tokens but no images
|
||
texts = [
|
||
"First text with <image> token.",
|
||
"Second text <image> with token.",
|
||
]
|
||
with self.assertRaises(ValueError) as context:
|
||
processor(text=texts)
|
||
self.assertTrue("tokens in the text but no images/videos were passed" in str(context.exception))
|
||
|
||
# Test with None as Images
|
||
with self.assertRaises(ValueError) as context:
|
||
processor(text=text, images=None)
|
||
self.assertTrue("tokens in the text but no images/videos were passed" in str(context.exception))
|
||
|
||
with self.assertRaises(ValueError) as context:
|
||
processor(text=texts, images=None)
|
||
self.assertTrue("tokens in the text but no images/videos were passed" in str(context.exception))
|
||
|
||
def test_special_mm_token_truncation(self):
|
||
"""Tests that special vision tokens do not get truncated when `truncation=True` is set."""
|
||
|
||
processor = self.get_processor()
|
||
|
||
input_str = self.prepare_text_inputs(batch_size=2, modalities="image")
|
||
image_input = self.prepare_image_inputs(batch_size=2)
|
||
_ = processor(
|
||
text=input_str,
|
||
images=image_input,
|
||
return_tensors="pt",
|
||
truncation=None,
|
||
padding=True,
|
||
)
|
||
|
||
with self.assertRaises(ValueError):
|
||
_ = processor(
|
||
text=input_str,
|
||
images=image_input,
|
||
return_tensors="pt",
|
||
truncation=True,
|
||
padding=True,
|
||
max_length=20,
|
||
)
|
||
|
||
@unittest.skip(
|
||
"SmolVLM cannot accept list of decoded video frames, because it needs to know video fps and duration"
|
||
)
|
||
def test_apply_chat_template_decoded_video_0(self):
|
||
pass
|