
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 ):
				_lowercase    	=MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING




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




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Optional[Any]:
							for example in examples:
										lowerCAmelCase_      			=			video_classifier(_UpperCamelCase  )

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




				@require_torch
				def   __a		(      self  )       ->   List[str]:
							lowerCAmelCase_      			=			"hf-internal-testing/tiny-random-VideoMAEForVideoClassification"
							lowerCAmelCase_      			=			VideoMAEFeatureExtractor(
							    size={"shortest_edge": 10}       ,       crop_size={"height": 10, "width": 10}  )
							lowerCAmelCase_      			=			pipeline(
							    "video-classification"       ,       model=_UpperCamelCase       ,       feature_extractor=_UpperCamelCase       ,       frame_sampling_rate=4  )

							lowerCAmelCase_      			=			hf_hub_download(repo_id="nateraw/video-demo"       ,       filename="archery.mp4"       ,       repo_type="dataset"  )
							lowerCAmelCase_      			=			video_classifier(_UpperCamelCase       ,       top_k=2  )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase       ,       decimals=4  )       ,       [{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}]       ,       )

							lowerCAmelCase_      			=			video_classifier(
							    [
							        video_file_path,
							        video_file_path,
							    ]       ,       top_k=2       ,       )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase       ,       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   __a		(      self  )       ->   Dict:
							pass





import math





class 			_lowerCAmelCase      :




				def __init__(      self       ,       _UpperCamelCase=0  )       ->   Tuple:  # a graph with Node 0,1,...,N-1
							lowerCAmelCase_      			=			n
							lowerCAmelCase_      			=			[
							    [math.inf for j in range(0       ,       _UpperCamelCase  )] for i in range(0       ,       _UpperCamelCase  )
							]  # adjacency matrix for weight
							lowerCAmelCase_      			=			[
							    [math.inf for j in range(0       ,       _UpperCamelCase  )] for i in range(0       ,       _UpperCamelCase  )
							]  # dp[i][j] stores minimum distance from i to j




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   List[Any]:
							lowerCAmelCase_      			=			w




				def   __a		(      self  )       ->   List[str]:
							for k in range(0       ,       self.n  ):
										for i in range(0       ,       self.n  ):
													for j in range(0       ,       self.n  ):
																lowerCAmelCase_      			=			min(self.dp[i][j]       ,       self.dp[i][k] + self.dp[k][j]  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Dict:
							return self.dp[u][v]


if __name__ == "__main__":
			_A     =  Graph(5)
			graph.add_edge(0, 2, 9)
			graph.add_edge(0, 4, 10)
			graph.add_edge(1, 3, 5)
			graph.add_edge(2, 3, 7)
			graph.add_edge(3, 0, 10)
			graph.add_edge(3, 1, 2)
			graph.add_edge(3, 2, 1)
			graph.add_edge(3, 4, 6)
			graph.add_edge(4, 1, 3)
			graph.add_edge(4, 2, 4)
			graph.add_edge(4, 3, 9)
			graph.floyd_warshall()
			graph.show_min(1, 4)
			graph.show_min(0, 3)



