





def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						Dict:






				"""simple docstring"""


				snake_case_						:    List[str]				=  [False] * len(_UpperCamelCase )
				snake_case_						:    Optional[int]				=  []
				queue.append(_UpperCamelCase )
				snake_case_						:    List[Any]				=  True

				while queue:
								snake_case_						:    int				=  queue.pop(0 )
								for ind in range(len(graph[u] ) ):
												if visited[ind] is False and graph[u][ind] > 0:
																queue.append(_UpperCamelCase )
																snake_case_						:    str				=  True
																snake_case_						:    Optional[int]				=  u

				return visited[t]






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						Dict:






				"""simple docstring"""


				snake_case_						:    Optional[int]				=  [-1] * (len(_UpperCamelCase ))
				snake_case_						:    Dict				=  0
				while bfs(_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ):
								snake_case_						:    Optional[Any]				=  float('''Inf''' )
								snake_case_						:    str				=  sink

								while s != source:
												# Find the minimum value in select path
												snake_case_						:    Optional[Any]				=  min(_UpperCamelCase ,				graph[parent[s]][s] )
												snake_case_						:    Dict				=  parent[s]

								max_flow += path_flow
								snake_case_						:    List[str]				=  sink

								while v != source:
												snake_case_						:    int				=  parent[v]
												graph[u][v] -= path_flow
												graph[v][u] += path_flow
												snake_case_						:    Dict				=  parent[v]
				return max_flow


lowerCAmelCase_    =				[
    [0, 1_6, 1_3, 0, 0, 0],
    [0, 0, 1_0, 1_2, 0, 0],
    [0, 4, 0, 0, 1_4, 0],
    [0, 0, 9, 0, 0, 2_0],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

lowerCAmelCase_  ,					lowerCAmelCase_    =				0, 5





print(ford_fulkerson(graph, source, sink))







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_     :     Any       			=      MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__    )     ->   Dict:


							'''simple docstring'''



							snake_case_						:    Any				=  hf_hub_download(
							    repo_id='''nateraw/video-demo'''					,							filename='''archery.mp4'''					,							repo_type='''dataset'''    )
							snake_case_						:    List[Any]				=  VideoClassificationPipeline(model=__magic_name__					,							image_processor=__magic_name__					,							top_k=2    )
							snake_case_						:    str				=  [
							    example_video_filepath,
							    '''https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4''',
							]
							return video_classifier, examples






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__    )     ->   Any:


							'''simple docstring'''



							for example in examples:
											snake_case_						:    Union[str, Any]				=  video_classifier(__magic_name__    )

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






			@require_torch
			def 			lowerCamelCase  (self    )     ->   str:


							'''simple docstring'''



							snake_case_						:    Any				=  '''hf-internal-testing/tiny-random-VideoMAEForVideoClassification'''
							snake_case_						:    str				=  VideoMAEFeatureExtractor(
							    size={'''shortest_edge''': 10}					,							crop_size={'''height''': 10, '''width''': 10}    )
							snake_case_						:    int				=  pipeline(
							    '''video-classification'''					,							model=__magic_name__					,							feature_extractor=__magic_name__					,							frame_sampling_rate=4    )

							snake_case_						:    List[str]				=  hf_hub_download(repo_id='''nateraw/video-demo'''					,							filename='''archery.mp4'''					,							repo_type='''dataset'''    )
							snake_case_						:    Union[str, Any]				=  video_classifier(__magic_name__					,							top_k=2    )
							self.assertEqual(
							    nested_simplify(__magic_name__					,							decimals=4    )					,							[{'''score''': 0.5_199, '''label''': '''LABEL_0'''}, {'''score''': 0.4_801, '''label''': '''LABEL_1'''}]					,							)

							snake_case_						:    int				=  video_classifier(
							    [
							        video_file_path,
							        video_file_path,
							    ]					,							top_k=2					,							)
							self.assertEqual(
							    nested_simplify(__magic_name__					,							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    )     ->   Optional[int]:


							'''simple docstring'''



							pass
