


"""simple docstring"""


import os
from collections.abc import Iterator







def  __snake_case       (   UpperCamelCase__ = "."	)      ->		Iterator[str]:







		"""simple docstring"""





		for dir_path, dir_names, filenames in os.walk(UpperCamelCase__	):
				A					       = [d for d in dir_names if d != 'scripts' and d[0] not in '._']
				for filename in filenames:
						if filename == "__init__.py":
								continue
						if os.path.splitext(UpperCamelCase__	)[1] in (".py", ".ipynb"):
								yield os.path.join(UpperCamelCase__ ,			UpperCamelCase__	).lstrip('./'	)







def  __snake_case       (   UpperCamelCase__	)      ->		Union[str, Any]:







		"""simple docstring"""





		return f'{i * "  "}*' if i else "\n##"







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__	)      ->		str:







		"""simple docstring"""





		A					       = old_path.split(os.sep	)
		for i, new_part in enumerate(new_path.split(os.sep	)	):
				if (i + 1 > len(UpperCamelCase__	) or old_parts[i] != new_part) and new_part:
						print(f'{md_prefix(UpperCamelCase__	)} {new_part.replace("_" ,			" "	).title()}'	)
		return new_path







def  __snake_case       (   UpperCamelCase__ = "."	)      ->		None:







		"""simple docstring"""





		A					       = ''
		for filepath in sorted(good_file_paths(UpperCamelCase__	)	):
				A       , A					       = os.path.split(UpperCamelCase__	)
				if filepath != old_path:
						A					       = print_path(UpperCamelCase__ ,			UpperCamelCase__	)
				A					       = (filepath.count(os.sep	) + 1) if filepath else 0
				A					       = f'{filepath}/{filename}'.replace(' ' ,			'%20'	)
				A					       = os.path.splitext(filename.replace('_' ,			' '	).title()	)[0]
				print(f'{md_prefix(UpperCamelCase__	)} [{filename}]({url})'	)


if __name__ == "__main__":
	print_directory_md(".")









"""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			):
						lowerCAmelCase       =     MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING







						def __a						(							self   :    Dict			,			_lowercase   :    int			,			_lowercase   :    Any			,			_lowercase   :    int		):
								A					       = hf_hub_download(
								    repo_id='nateraw/video-demo'			,			filename='archery.mp4'			,			repo_type='dataset'		)
								A					       = VideoClassificationPipeline(model=_lowercase			,			image_processor=_lowercase			,			top_k=2		)
								A					       = [
								    example_video_filepath,
								    'https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4',
								]
								return video_classifier, examples







						def __a						(							self   :    Tuple			,			_lowercase   :    Union[str, Any]			,			_lowercase   :    List[Any]		):
								for example in examples:
										A					       = video_classifier(_lowercase		)

										self.assertEqual(
										    _lowercase			,			[
										        {'score': ANY(_lowercase		), 'label': ANY(_lowercase		)},
										        {'score': ANY(_lowercase		), 'label': ANY(_lowercase		)},
										    ]			,			)







						@require_torch
						def __a						(							self   :    str		):
								A					       = 'hf-internal-testing/tiny-random-VideoMAEForVideoClassification'
								A					       = VideoMAEFeatureExtractor(
								    size={'shortest_edge': 10}			,			crop_size={'height': 10, 'width': 10}		)
								A					       = pipeline(
								    'video-classification'			,			model=_lowercase			,			feature_extractor=_lowercase			,			frame_sampling_rate=4		)

								A					       = hf_hub_download(repo_id='nateraw/video-demo'			,			filename='archery.mp4'			,			repo_type='dataset'		)
								A					       = video_classifier(_lowercase			,			top_k=2		)
								self.assertEqual(
								    nested_simplify(_lowercase			,			decimals=4		)			,			[{'score': 0.5_1_9_9, 'label': 'LABEL_0'}, {'score': 0.4_8_0_1, 'label': 'LABEL_1'}]			,			)

								A					       = video_classifier(
								    [
								        video_file_path,
								        video_file_path,
								    ]			,			top_k=2			,			)
								self.assertEqual(
								    nested_simplify(_lowercase			,			decimals=4		)			,			[
								        [{'score': 0.5_1_9_9, 'label': 'LABEL_0'}, {'score': 0.4_8_0_1, 'label': 'LABEL_1'}],
								        [{'score': 0.5_1_9_9, 'label': 'LABEL_0'}, {'score': 0.4_8_0_1, 'label': 'LABEL_1'}],
								    ]			,			)







						@require_tf
						def __a						(							self   :    Dict		):
								pass





