





"""simple docstring"""





import argparse
from collections import defaultdict

import yaml


lowerCAmelCase__     			=			'''docs/source/en/_toctree.yml'''


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	defaultdict(_SCREAMING_SNAKE_CASE    )
		for doc in model_doc:
				counts[doc["local"]] += 1
		UpperCamelCase   =	[key for key, value in counts.items() if value > 1]

		UpperCamelCase   =	[]
		for duplicate_key in duplicates:
				UpperCamelCase   =	list({doc["title"] for doc in model_doc if doc["local"] == duplicate_key}    )
				if len(_SCREAMING_SNAKE_CASE    ) > 1:
						raise ValueError(
						    F"{duplicate_key} is present several times in the documentation table of content at "
						    "`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the "
						    "others."    )
				# Only add this once
				new_doc.append({"local": duplicate_key, "title": titles[0]}    )

		# Add none duplicate-keys
		new_doc.extend([doc for doc in model_doc if counts[doc["local"]] == 1]    )

		# Sort
		return sorted(_SCREAMING_SNAKE_CASE  ,	key=lambda _SCREAMING_SNAKE_CASE    : s["title"].lower()    )


def       a__					(					_SCREAMING_SNAKE_CASE=False    ):




		"""simple docstring"""


		with open(_SCREAMING_SNAKE_CASE  ,	encoding="utf-8"    ) as f:
				UpperCamelCase   =	yaml.safe_load(f.read()    )

		# Get to the API doc
		UpperCamelCase   =	0
		while content[api_idx]["title"] != "API":
				api_idx += 1
		UpperCamelCase   =	content[api_idx]["sections"]

		# Then to the model doc
		UpperCamelCase   =	0
		while api_doc[model_idx]["title"] != "Models":
				model_idx += 1

		UpperCamelCase   =	api_doc[model_idx]["sections"]

		UpperCamelCase   =	[(idx, section) for idx, section in enumerate(_SCREAMING_SNAKE_CASE    ) if "sections" in section]
		UpperCamelCase   =	False
		for idx, modality_doc in modalities_docs:
				UpperCamelCase   =	modality_doc["sections"]
				UpperCamelCase   =	clean_model_doc_toc(_SCREAMING_SNAKE_CASE    )

				if old_modality_doc != new_modality_doc:
						UpperCamelCase   =	True
						if overwrite:
								UpperCamelCase   =	new_modality_doc

		if diff:
				if overwrite:
						UpperCamelCase   =	model_doc
						UpperCamelCase   =	api_doc
						with open(_SCREAMING_SNAKE_CASE  ,	"w"  ,	encoding="utf-8"    ) as f:
								f.write(yaml.dump(_SCREAMING_SNAKE_CASE  ,	allow_unicode=_SCREAMING_SNAKE_CASE    )    )
				else:
						raise ValueError(
						    "The model doc part of the table of content is not properly sorted, run `make style` to fix this."    )


if __name__ == "__main__":
						lowerCAmelCase__     			=			argparse.ArgumentParser()
						parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
						lowerCAmelCase__     			=			parser.parse_args()

						check_model_doc(args.fix_and_overwrite)













"""simple docstring"""





import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY





@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class        _lowerCamelCase      (       unittest.TestCase ):
						UpperCAmelCase_ =			MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a		) ->   str:
								UpperCamelCase   =	hf_hub_download(
								    repo_id="nateraw/video-demo"   ,							filename="archery.mp4"   ,							repo_type="dataset"		)
								UpperCamelCase   =	VideoClassificationPipeline(model=__a   ,							image_processor=__a   ,							top_k=2		)
								UpperCamelCase   =	[
								    example_video_filepath,
								    "https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4",
								]
								return video_classifier, examples


						def     snake_case_   (self   ,							__a   ,							__a		) ->   str:
								for example in examples:
										UpperCamelCase   =	video_classifier(__a		)

										self.assertEqual(
										    __a   ,							[
										        {"score": ANY(__a		), "label": ANY(__a		)},
										        {"score": ANY(__a		), "label": ANY(__a		)},
										    ]   ,							)


						@require_torch
						def     snake_case_   (self		) ->   int:
								UpperCamelCase   =	"hf-internal-testing/tiny-random-VideoMAEForVideoClassification"
								UpperCamelCase   =	VideoMAEFeatureExtractor(
								    size={"shortest_edge": 10}   ,							crop_size={"height": 10, "width": 10}		)
								UpperCamelCase   =	pipeline(
								    "video-classification"   ,							model=__a   ,							feature_extractor=__a   ,							frame_sampling_rate=4		)

								UpperCamelCase   =	hf_hub_download(repo_id="nateraw/video-demo"   ,							filename="archery.mp4"   ,							repo_type="dataset"		)
								UpperCamelCase   =	video_classifier(__a   ,							top_k=2		)
								self.assertEqual(
								    nested_simplify(__a   ,							decimals=4		)   ,							[{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}]   ,							)

								UpperCamelCase   =	video_classifier(
								    [
								        video_file_path,
								        video_file_path,
								    ]   ,							top_k=2   ,							)
								self.assertEqual(
								    nested_simplify(__a   ,							decimals=4		)   ,							[
								        [{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}],
								        [{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}],
								    ]   ,							)




						@require_tf
						def     snake_case_   (self		) ->   Optional[Any]:
								pass






