280 lines
11 KiB
Python
280 lines
11 KiB
Python
# Copyright 2025 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 inspect
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import shutil
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import tempfile
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import unittest
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import numpy as np
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from transformers import AutoProcessor
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from transformers.testing_utils import require_av, require_torch, require_vision
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from transformers.utils import is_torch_available, is_vision_available
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from ...test_processing_common import ProcessorTesterMixin, url_to_local_path
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if is_vision_available():
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from transformers import Glm4vProcessor
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if is_torch_available():
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import torch
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@require_vision
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@require_torch
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class Glm4vProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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processor_class = Glm4vProcessor
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@classmethod
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def setUpClass(cls):
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cls.tmpdirname = tempfile.mkdtemp()
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processor = Glm4vProcessor.from_pretrained(
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"THUDM/GLM-4.1V-9B-Thinking", patch_size=4, size={"shortest_edge": 12 * 12, "longest_edge": 18 * 18}
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)
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processor.save_pretrained(cls.tmpdirname)
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cls.image_token = processor.image_token
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def get_tokenizer(self, **kwargs):
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return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).tokenizer
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def get_image_processor(self, **kwargs):
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return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor
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def get_video_processor(self, **kwargs):
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return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).video_processor
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def get_processor(self, **kwargs):
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return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs)
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@classmethod
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def tearDownClass(cls):
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shutil.rmtree(cls.tmpdirname, ignore_errors=True)
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@require_torch
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@require_av
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def _test_apply_chat_template(
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self,
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modality: str,
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batch_size: int,
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return_tensors: str,
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input_name: str,
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processor_name: str,
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input_data: list[str],
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):
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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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if processor_name not in self.processor_class.attributes:
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self.skipTest(f"{processor_name} attribute not present in {self.processor_class}")
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batch_messages = [
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[
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{
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"role": "user",
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"content": [{"type": "text", "text": "Describe this."}],
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},
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]
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] * batch_size
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# Test that jinja can be applied
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formatted_prompt = processor.apply_chat_template(batch_messages, add_generation_prompt=True, tokenize=False)
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self.assertEqual(len(formatted_prompt), batch_size)
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# Test that tokenizing with template and directly with `self.tokenizer` gives same output
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formatted_prompt_tokenized = processor.apply_chat_template(
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batch_messages, add_generation_prompt=True, tokenize=True, return_tensors=return_tensors
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)
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add_special_tokens = True
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if processor.tokenizer.bos_token is not None and formatted_prompt[0].startswith(processor.tokenizer.bos_token):
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add_special_tokens = False
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tok_output = processor.tokenizer(
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formatted_prompt, return_tensors=return_tensors, add_special_tokens=add_special_tokens
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)
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expected_output = tok_output.input_ids
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self.assertListEqual(expected_output.tolist(), formatted_prompt_tokenized.tolist())
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# Test that kwargs passed to processor's `__call__` are actually used
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tokenized_prompt_100 = processor.apply_chat_template(
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batch_messages,
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add_generation_prompt=True,
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tokenize=True,
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padding="max_length",
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truncation=True,
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return_tensors=return_tensors,
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max_length=100,
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)
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self.assertEqual(len(tokenized_prompt_100[0]), 100)
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# Test that `return_dict=True` returns text related inputs in the dict
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out_dict_text = processor.apply_chat_template(
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batch_messages,
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add_generation_prompt=True,
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tokenize=True,
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return_dict=True,
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return_tensors=return_tensors,
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)
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self.assertTrue(all(key in out_dict_text for key in ["input_ids", "attention_mask"]))
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self.assertEqual(len(out_dict_text["input_ids"]), batch_size)
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self.assertEqual(len(out_dict_text["attention_mask"]), batch_size)
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# Test that with modality URLs and `return_dict=True`, we get modality inputs in the dict
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for idx, url in enumerate(input_data[:batch_size]):
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batch_messages[idx][0]["content"] = [batch_messages[idx][0]["content"][0], {"type": modality, "url": url}]
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out_dict = processor.apply_chat_template(
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batch_messages,
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add_generation_prompt=True,
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tokenize=True,
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return_dict=True,
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return_tensors=return_tensors,
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fps=2
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if isinstance(input_data[0], str)
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else None, # by default no more than 2 frames per second, otherwise too slow
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do_sample_frames=bool(isinstance(input_data[0], str)), # don't sample frames if decoded video is used
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)
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input_name = getattr(self, input_name)
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self.assertTrue(input_name in out_dict)
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self.assertEqual(len(out_dict["input_ids"]), batch_size)
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self.assertEqual(len(out_dict["attention_mask"]), batch_size)
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if modality == "video":
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# qwen pixels don't scale with bs same way as other models, calculate expected video token count based on video_grid_thw
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expected_video_token_count = 0
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for thw in out_dict["video_grid_thw"]:
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expected_video_token_count += thw[0] * thw[1] * thw[2]
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mm_len = expected_video_token_count
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else:
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mm_len = batch_size * 4
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self.assertEqual(len(out_dict[input_name]), mm_len)
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return_tensor_to_type = {"pt": torch.Tensor, "np": np.ndarray, None: list}
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for k in out_dict:
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self.assertIsInstance(out_dict[k], return_tensor_to_type[return_tensors])
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@require_av
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def test_apply_chat_template_video_frame_sampling(self):
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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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signature = inspect.signature(processor.__call__)
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if "videos" not in {*signature.parameters.keys()} or (
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signature.parameters.get("videos") is not None
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and signature.parameters["videos"].annotation == inspect._empty
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):
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self.skipTest("Processor doesn't accept videos at input")
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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": "video"},
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{"type": "text", "text": "What is shown in this video?"},
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],
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},
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]
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]
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formatted_prompt = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
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self.assertEqual(len(formatted_prompt), 1)
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formatted_prompt_tokenized = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True)
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expected_output = processor.tokenizer(formatted_prompt, return_tensors=None).input_ids
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self.assertListEqual(expected_output, formatted_prompt_tokenized)
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out_dict = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True)
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self.assertListEqual(list(out_dict.keys()), ["input_ids", "attention_mask"])
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# Add video URL for return dict and load with `num_frames` arg
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messages[0][0]["content"][0] = {
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"type": "video",
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"url": url_to_local_path(
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"https://huggingface.co/datasets/raushan-testing-hf/videos-test/resolve/main/tiny_video.mp4"
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),
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}
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# Load with `video_fps` arg
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video_fps = 10
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out_dict_with_video = processor.apply_chat_template(
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messages,
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add_generation_prompt=True,
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tokenize=True,
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return_dict=True,
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video_fps=video_fps,
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)
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self.assertTrue(self.videos_input_name in out_dict_with_video)
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self.assertEqual(len(out_dict_with_video[self.videos_input_name]), 8)
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# Load the whole video
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out_dict_with_video = processor.apply_chat_template(
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messages,
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add_generation_prompt=True,
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tokenize=True,
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return_dict=True,
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do_sample_frames=False,
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)
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self.assertTrue(self.videos_input_name in out_dict_with_video)
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self.assertEqual(len(out_dict_with_video[self.videos_input_name]), 24)
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# Load video as a list of frames (i.e. images). NOTE: each frame should have same size
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# because we assume they come from one video
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messages[0][0]["content"][0] = {
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"type": "video",
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"url": [
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url_to_local_path(
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"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
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),
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url_to_local_path(
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"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
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),
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],
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}
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out_dict_with_video = processor.apply_chat_template(
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messages,
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add_generation_prompt=True,
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tokenize=True,
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return_dict=True,
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do_sample_frames=False,
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)
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self.assertTrue(self.videos_input_name in out_dict_with_video)
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self.assertEqual(len(out_dict_with_video[self.videos_input_name]), 4)
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# When the inputs are frame URLs/paths we expect that those are already
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# sampled and will raise an error is asked to sample again.
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with self.assertRaisesRegex(
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ValueError, "Sampling frames from a list of images is not supported! Set `do_sample_frames=False`"
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):
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out_dict_with_video = processor.apply_chat_template(
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messages,
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add_generation_prompt=True,
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tokenize=True,
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return_dict=True,
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do_sample_frames=True,
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)
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def test_model_input_names(self):
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processor = self.get_processor()
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text = self.prepare_text_inputs(modalities=["image", "video"])
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image_input = self.prepare_image_inputs()
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video_inputs = self.prepare_video_inputs()
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inputs_dict = {"text": text, "images": image_input, "videos": video_inputs}
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inputs = processor(**inputs_dict, return_tensors="pt", do_sample_frames=False)
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self.assertSetEqual(set(inputs.keys()), set(processor.model_input_names))
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