





"""simple docstring"""





def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		if not isinstance(_lowerCamelCase  ,	_lowerCamelCase    ):
				UpperCamelCase   =	F"Input value of [number={number}] must be an integer"
				raise TypeError(_lowerCamelCase    )

		if number < 1:
				UpperCamelCase   =	F"Input value of [number={number}] must be > 0"
				raise ValueError(_lowerCamelCase    )

		UpperCamelCase   =	1

		for i in range(1  ,	_lowerCamelCase    ):
				current_number *= 4 * i - 2
				current_number //= i + 1

		return current_number


if __name__ == "__main__":
						import doctest

						doctest.testmod()







"""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






