




"""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 					__magic_name__							( unittest.TestCase       ):






				'''simple docstring'''
				__UpperCamelCase    						=		MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING





				def      _lowerCAmelCase       ( self	,				_a	,				_a	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      hf_hub_download(
							    repo_id="""nateraw/video-demo"""	,				filename="""archery.mp4"""	,				repo_type="""dataset"""				)
							lowerCamelCase       =      VideoClassificationPipeline(model=_a	,				image_processor=_a	,				top_k=2				)
							lowerCamelCase       =      [
							    example_video_filepath,
							    """https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4""",
							]
							return video_classifier, examples





				def      _lowerCAmelCase       ( self	,				_a	,				_a				):





							"""simple docstring"""
							for example in examples:
										lowerCamelCase       =      video_classifier(_a				)

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





				@require_torch
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      """hf-internal-testing/tiny-random-VideoMAEForVideoClassification"""
							lowerCamelCase       =      VideoMAEFeatureExtractor(
							    size={"""shortest_edge""": 10}	,				crop_size={"""height""": 10, """width""": 10}				)
							lowerCamelCase       =      pipeline(
							    """video-classification"""	,				model=_a	,				feature_extractor=_a	,				frame_sampling_rate=4				)

							lowerCamelCase       =      hf_hub_download(repo_id="""nateraw/video-demo"""	,				filename="""archery.mp4"""	,				repo_type="""dataset"""				)
							lowerCamelCase       =      video_classifier(_a	,				top_k=2				)
							self.assertEqual(
							    nested_simplify(_a	,				decimals=4				)	,				[{"""score""": 0.5_199, """label""": """LABEL_0"""}, {"""score""": 0.4_801, """label""": """LABEL_1"""}]	,				)

							lowerCamelCase       =      video_classifier(
							    [
							        video_file_path,
							        video_file_path,
							    ]	,				top_k=2	,				)
							self.assertEqual(
							    nested_simplify(_a	,				decimals=4				)	,				[
							        [{"""score""": 0.5_199, """label""": """LABEL_0"""}, {"""score""": 0.4_801, """label""": """LABEL_1"""}],
							        [{"""score""": 0.5_199, """label""": """LABEL_0"""}, {"""score""": 0.4_801, """label""": """LABEL_1"""}],
							    ]	,				)





				@require_tf
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							pass









"""simple docstring"""




from functools import lru_cache



def 					a__				(       snake_case__       )		->      set:
			lowerCamelCase       =      2
			lowerCamelCase       =      set()
			while i * i <= n:
						if n % i:
									i += 1
						else:
									n //= i
									factors.add(snake_case__       )
			if n > 1:
						factors.add(snake_case__       )
			return factors



@lru_cache
def 					a__				(       snake_case__       )		->      int:
			return len(unique_prime_factors(snake_case__       )       )



def 					a__				(       snake_case__       )		->      bool:
			return len(set(snake_case__       )       ) in (0, 1)



def 					a__				(       snake_case__       )		->      list:
			lowerCamelCase       =      2

			while True:
						# Increment each value of a generated range
						lowerCamelCase       =      [base + i for i in range(snake_case__       )]

						# Run elements through out unique_prime_factors function
						# Append our target number to the end.
						lowerCamelCase       =      [upf_len(snake_case__       ) for x in group]
						checker.append(snake_case__       )

						# If all numbers in the list are equal, return the group variable.
						if equality(snake_case__       ):
									return group

						# Increment our base variable by 1
						base += 1



def 					a__				(       snake_case__ = 4       )		->      int:
			lowerCamelCase       =      run(snake_case__       )
			return results[0] if len(snake_case__       ) else None


if __name__ == "__main__":
					print(solution())



