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* load and save from video-processor folder * Update src/transformers/models/llava_onevision/processing_llava_onevision.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> --------- Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
304 lines
12 KiB
Python
304 lines
12 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 json
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import shutil
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import tempfile
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import unittest
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from transformers.testing_utils import require_torch, require_vision
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from transformers.utils import is_vision_available
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from ...test_processing_common import ProcessorTesterMixin
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if is_vision_available():
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from transformers import (
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AutoProcessor,
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LlavaOnevisionImageProcessor,
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LlavaOnevisionProcessor,
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LlavaOnevisionVideoProcessor,
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Qwen2TokenizerFast,
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)
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@require_vision
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class LlavaOnevisionProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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processor_class = LlavaOnevisionProcessor
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def setUp(self):
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self.tmpdirname = tempfile.mkdtemp()
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image_processor = LlavaOnevisionImageProcessor()
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video_processor = LlavaOnevisionVideoProcessor()
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tokenizer = Qwen2TokenizerFast.from_pretrained("Qwen/Qwen2-0.5B-Instruct")
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processor_kwargs = self.prepare_processor_dict()
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processor = LlavaOnevisionProcessor(
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video_processor=video_processor, image_processor=image_processor, tokenizer=tokenizer, **processor_kwargs
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)
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processor.save_pretrained(self.tmpdirname)
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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 prepare_processor_dict(self):
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return {"chat_template": "dummy_template", "num_image_tokens": 6, "vision_feature_select_strategy": "default"}
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def test_processor_to_json_string(self):
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processor = self.get_processor()
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obj = json.loads(processor.to_json_string())
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for key, value in self.prepare_processor_dict().items():
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# chat_tempalate are tested as a separate test because they are saved in separate files
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if key != "chat_template":
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self.assertEqual(obj[key], value)
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self.assertEqual(getattr(processor, key, None), value)
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# Copied from tests.models.llava.test_processor_llava.LlavaProcessorTest.test_chat_template_is_saved
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def test_chat_template_is_saved(self):
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processor_loaded = self.processor_class.from_pretrained(self.tmpdirname)
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processor_dict_loaded = json.loads(processor_loaded.to_json_string())
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# chat templates aren't serialized to json in processors
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self.assertFalse("chat_template" in processor_dict_loaded.keys())
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# they have to be saved as separate file and loaded back from that file
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# so we check if the same template is loaded
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processor_dict = self.prepare_processor_dict()
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self.assertTrue(processor_loaded.chat_template == processor_dict.get("chat_template", None))
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def tearDown(self):
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shutil.rmtree(self.tmpdirname)
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def test_chat_template(self):
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processor = AutoProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf")
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expected_prompt = "<|im_start|>user <image>\nWhat is shown in this image?<|im_end|><|im_start|>assistant\n"
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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": "image"},
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{"type": "text", "text": "What is shown in this image?"},
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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)
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self.assertEqual(expected_prompt, formatted_prompt)
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@require_torch
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@require_vision
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def test_image_processor_defaults_preserved_by_image_kwargs(self):
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# Rewrite as llava-next image processor return pixel values with an added dimesion for image patches
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if "image_processor" not in self.processor_class.attributes:
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self.skipTest(f"image_processor attribute not present in {self.processor_class}")
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image_processor = self.get_component("image_processor", size=(234, 234))
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video_processor = self.get_component("video_processor", size=(234, 234))
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tokenizer = self.get_component("tokenizer", max_length=117)
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processor = self.processor_class(
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tokenizer=tokenizer, image_processor=image_processor, video_processor=video_processor
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)
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self.skip_processor_without_typed_kwargs(processor)
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input_str = "lower newer"
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image_input = self.prepare_image_inputs()
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inputs = processor(text=input_str, images=image_input)
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# added dimension for image patches
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self.assertEqual(len(inputs["pixel_values"][0][0][0]), 234)
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@require_torch
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@require_vision
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def test_kwargs_overrides_default_image_processor_kwargs(self):
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if "image_processor" not in self.processor_class.attributes:
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self.skipTest(f"image_processor attribute not present in {self.processor_class}")
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image_processor = self.get_component("image_processor", crop_size=(234, 234))
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video_processor = self.get_component("video_processor", size=(234, 234))
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tokenizer = self.get_component("tokenizer", max_length=117)
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processor = self.processor_class(
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tokenizer=tokenizer, image_processor=image_processor, video_processor=video_processor
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)
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self.skip_processor_without_typed_kwargs(processor)
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input_str = "lower newer"
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image_input = self.prepare_image_inputs()
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inputs = processor(text=input_str, images=image_input, size=[224, 224])
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# added dimension for image patches
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self.assertEqual(len(inputs["pixel_values"][0][0][0]), 224)
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@require_torch
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@require_vision
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def test_unstructured_kwargs(self):
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image_processor = self.get_component("image_processor")
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video_processor = self.get_component("video_processor")
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tokenizer = self.get_component("tokenizer")
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processor = self.processor_class(
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tokenizer=tokenizer, image_processor=image_processor, video_processor=video_processor
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)
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self.skip_processor_without_typed_kwargs(processor)
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input_str = "lower newer"
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image_input = self.prepare_image_inputs()
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inputs = processor(
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text=input_str,
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images=image_input,
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return_tensors="pt",
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size={"height": 214, "width": 214},
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padding="max_length",
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max_length=76,
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)
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# added dimension for image patches
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self.assertEqual(inputs["pixel_values"].shape[3], 214)
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self.assertEqual(len(inputs["input_ids"][0]), 76)
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@require_torch
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@require_vision
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def test_unstructured_kwargs_batched(self):
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image_processor = self.get_component("image_processor")
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video_processor = self.get_component("video_processor")
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tokenizer = self.get_component("tokenizer")
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processor = self.processor_class(
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tokenizer=tokenizer, image_processor=image_processor, video_processor=video_processor
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)
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self.skip_processor_without_typed_kwargs(processor)
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input_str = ["lower newer", "upper older longer string"]
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image_input = self.prepare_image_inputs() * 2
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inputs = processor(
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text=input_str,
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images=image_input,
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return_tensors="pt",
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size={"height": 214, "width": 214},
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padding="longest",
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max_length=76,
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)
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self.assertEqual(inputs["pixel_values"].shape[3], 214)
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self.assertEqual(len(inputs["input_ids"][0]), 4)
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@require_torch
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@require_vision
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def test_structured_kwargs_nested(self):
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image_processor = self.get_component("image_processor")
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video_processor = self.get_component("video_processor")
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tokenizer = self.get_component("tokenizer")
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processor = self.processor_class(
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tokenizer=tokenizer, image_processor=image_processor, video_processor=video_processor
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)
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self.skip_processor_without_typed_kwargs(processor)
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input_str = "lower newer"
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image_input = self.prepare_image_inputs()
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# Define the kwargs for each modality
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all_kwargs = {
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"common_kwargs": {"return_tensors": "pt"},
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"images_kwargs": {"size": {"height": 214, "width": 214}},
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"text_kwargs": {"padding": "max_length", "max_length": 76},
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}
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inputs = processor(text=input_str, images=image_input, **all_kwargs)
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self.skip_processor_without_typed_kwargs(processor)
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self.assertEqual(inputs["pixel_values"].shape[3], 214)
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self.assertEqual(len(inputs["input_ids"][0]), 76)
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@require_torch
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@require_vision
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def test_structured_kwargs_nested_from_dict(self):
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image_processor = self.get_component("image_processor")
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video_processor = self.get_component("video_processor")
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tokenizer = self.get_component("tokenizer")
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processor = self.processor_class(
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tokenizer=tokenizer, image_processor=image_processor, video_processor=video_processor
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)
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self.skip_processor_without_typed_kwargs(processor)
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input_str = "lower newer"
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image_input = self.prepare_image_inputs()
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# Define the kwargs for each modality
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all_kwargs = {
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"common_kwargs": {"return_tensors": "pt"},
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"images_kwargs": {"size": {"height": 214, "width": 214}},
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"text_kwargs": {"padding": "max_length", "max_length": 76},
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}
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inputs = processor(text=input_str, images=image_input, **all_kwargs)
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self.assertEqual(inputs["pixel_values"].shape[3], 214)
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self.assertEqual(len(inputs["input_ids"][0]), 76)
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@require_torch
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@require_vision
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def test_doubly_passed_kwargs(self):
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image_processor = self.get_component("image_processor")
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video_processor = self.get_component("video_processor")
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tokenizer = self.get_component("tokenizer")
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processor = self.processor_class(
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tokenizer=tokenizer, image_processor=image_processor, video_processor=video_processor
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)
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self.skip_processor_without_typed_kwargs(processor)
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input_str = ["lower newer"]
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image_input = self.prepare_image_inputs()
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with self.assertRaises(ValueError):
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_ = processor(
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text=input_str,
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images=image_input,
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images_kwargs={"size": {"height": 222, "width": 222}},
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size={"height": 214, "width": 214},
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)
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@require_vision
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@require_torch
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def test_kwargs_overrides_default_tokenizer_kwargs(self):
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image_processor = self.get_component("image_processor")
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video_processor = self.get_component("video_processor")
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tokenizer = self.get_component("tokenizer", max_length=117)
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processor = self.processor_class(
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tokenizer=tokenizer, image_processor=image_processor, video_processor=video_processor
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)
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self.skip_processor_without_typed_kwargs(processor)
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input_str = "lower newer"
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image_input = self.prepare_image_inputs()
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inputs = processor(text=input_str, images=image_input, return_tensors="pt", max_length=112)
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self.assertEqual(len(inputs["input_ids"][0]), 2)
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@require_vision
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@require_torch
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def test_tokenizer_defaults_preserved_by_kwargs(self):
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image_processor = self.get_component("image_processor")
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video_processor = self.get_component("video_processor")
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tokenizer = self.get_component("tokenizer", max_length=117)
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processor = self.processor_class(
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tokenizer=tokenizer, image_processor=image_processor, video_processor=video_processor
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)
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self.skip_processor_without_typed_kwargs(processor)
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input_str = "lower newer"
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image_input = self.prepare_image_inputs()
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inputs = processor(text=input_str, images=image_input, return_tensors="pt")
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self.assertEqual(len(inputs["input_ids"][0]), 2)
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