





"""simple docstring"""





from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass





@dataclass
class        _lowerCamelCase      :
						UpperCAmelCase_ =			42
						UpperCAmelCase_ =			42





class        _lowerCamelCase      :


						def __init__(self   ,							__a		) ->   List[Any]:
								UpperCamelCase   =	[[] for _ in range(__a		)]
								UpperCamelCase   =	size


						def __getitem__(self   ,							__a		) ->   Iterator[Edge]:
								return iter(self._graph[vertex]		)


						@property
						def     snake_case_   (self		) ->   str:
								return self._size


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a		) ->   List[Any]:
								if weight not in (0, 1):
										raise ValueError("Edge weight must be either 0 or 1."		)

								if to_vertex < 0 or to_vertex >= self.size:
										raise ValueError("Vertex indexes must be in [0; size)."		)

								self._graph[from_vertex].append(Edge(__a   ,							__a		)		)




						def     snake_case_   (self   ,							__a   ,							__a		) ->   int | None:
								UpperCamelCase   =	deque([start_vertex]		)
								UpperCamelCase   =	[None] * self.size
								UpperCamelCase   =	0

								while queue:
										UpperCamelCase   =	queue.popleft()
										UpperCamelCase   =	distances[current_vertex]
										if current_distance is None:
												continue

										for edge in self[current_vertex]:
												UpperCamelCase   =	current_distance + edge.weight
												UpperCamelCase   =	distances[edge.destination_vertex]
												if (
												    isinstance(__a   ,							__a		)
												    and new_distance >= dest_vertex_distance
												):
														continue
												UpperCamelCase   =	new_distance
												if edge.weight == 0:
														queue.appendleft(edge.destination_vertex		)
												else:
														queue.append(edge.destination_vertex		)

								if distances[finish_vertex] is None:
										raise ValueError("No path from start_vertex to finish_vertex."		)

								return distances[finish_vertex]


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






