
import sys


def       A       (					lowercase )   ->  str:
		'''simple docstring'''

		UpperCamelCase		=     len(lowercase )
		UpperCamelCase		=     [[0 for x in range(lowercase )] for x in range(lowercase )]
		UpperCamelCase		=     [[0 for x in range(lowercase )] for x in range(lowercase )]

		for chain_length in range(2    ,       lowercase ):
				for a in range(1    ,       n - chain_length + 1 ):
						UpperCamelCase		=     a + chain_length - 1

						UpperCamelCase		=     sys.maxsize
						for c in range(lowercase    ,       lowercase ):
								UpperCamelCase		=     (
								    matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
								)
								if cost < matrix[a][b]:
										UpperCamelCase		=     cost
										UpperCamelCase		=     c
		return matrix, sol


def       A       (					lowercase    ,       lowercase    ,       lowercase )   ->  List[str]:
		'''simple docstring'''

		if i == j:
				print('A' + str(lowercase )    ,       end=' ' )
		else:
				print('('    ,       end=' ' )
				print_optiomal_solution(lowercase    ,       lowercase    ,       optimal_solution[i][j] )
				print_optiomal_solution(lowercase    ,       optimal_solution[i][j] + 1    ,       lowercase )
				print(')'    ,       end=' ' )


def       A       (					)   ->  Any:
		'''simple docstring'''

		UpperCamelCase		=     [30, 35, 15, 5, 10, 20, 25]
		UpperCamelCase		=     len(lowercase )
		# Size of matrix created from above array will be
		# 30*35 35*15 15*5 5*10 10*20 20*25
		UpperCamelCase	,  UpperCamelCase		=     matrix_chain_order(lowercase )

		print('No. of Operation required: ' + str(matrix[1][n - 1] ) )
		print_optiomal_solution(lowercase    ,       1    ,       n - 1 )


if __name__ == "__main__":
					main()


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 				lowercase					(		unittest.TestCase   ):
		__lowercase       :       Any	     =				MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_       )  ->					List[Any]:
				"""simple docstring"""
				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 __UpperCamelCase ( self				,      A_				,      A_       )  ->					Optional[int]:
				"""simple docstring"""
				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 __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				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 __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				pass
