"""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 			UpperCamelCase__(	unittest.TestCase       ):
						lowerCAmelCase__     :  List[str]  							=						MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Tuple:
													A__		   =							hf_hub_download(
													    repo_id='nateraw/video-demo'						,filename='archery.mp4'						,repo_type='dataset'   )
													A__		   =							VideoClassificationPipeline(model=__UpperCAmelCase						,image_processor=__UpperCAmelCase						,top_k=2   )
													A__		   =							[
													    example_video_filepath,
													    'https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4',
													]
													return video_classifier, examples





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Any:
													for example in examples:
																				A__		   =							video_classifier(__UpperCAmelCase   )

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





						@require_torch
						def  snake_case__		(				self   )    -> Union[str, Any]:
													A__		   =							'hf-internal-testing/tiny-random-VideoMAEForVideoClassification'
													A__		   =							VideoMAEFeatureExtractor(
													    size={'shortest_edge': 10}						,crop_size={'height': 10, 'width': 10}   )
													A__		   =							pipeline(
													    'video-classification'						,model=__UpperCAmelCase						,feature_extractor=__UpperCAmelCase						,frame_sampling_rate=4   )

													A__		   =							hf_hub_download(repo_id='nateraw/video-demo'						,filename='archery.mp4'						,repo_type='dataset'   )
													A__		   =							video_classifier(__UpperCAmelCase						,top_k=2   )
													self.assertEqual(
													    nested_simplify(__UpperCAmelCase						,decimals=4   )						,[{'score': 0.5_1_9_9, 'label': 'LABEL_0'}, {'score': 0.4_8_0_1, 'label': 'LABEL_1'}]						,)

													A__		   =							video_classifier(
													    [
													        video_file_path,
													        video_file_path,
													    ]						,top_k=2						,)
													self.assertEqual(
													    nested_simplify(__UpperCAmelCase						,decimals=4   )						,[
													        [{'score': 0.5_1_9_9, 'label': 'LABEL_0'}, {'score': 0.4_8_0_1, 'label': 'LABEL_1'}],
													        [{'score': 0.5_1_9_9, 'label': 'LABEL_0'}, {'score': 0.4_8_0_1, 'label': 'LABEL_1'}],
													    ]						,)





						@require_tf
						def  snake_case__		(				self   )    -> Optional[Any]:
													pass






"""simple docstring"""




from __future__ import annotations

from typing import Generic, TypeVar

__lowerCamelCase							 =     TypeVar("T")
class 			UpperCamelCase__(	Generic[T]       ):





						def __init__(				self						,__UpperCAmelCase   )    -> None:
													A__		   =							data
													A__		   =							self
													A__		   =							0






class 			UpperCamelCase__(	Generic[T]       ):





						def __init__(				self   )    -> None:
													# map from node name to the node object
													A__		   =							{}





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> None:
													# create a new set with x as its member
													A__		   =							DisjointSetTreeNode(__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> DisjointSetTreeNode[T]:
													# find the set x belongs to (with path-compression)
													A__		   =							self.map[data]
													if elem_ref != elem_ref.parent:
																				A__		   =							self.find_set(elem_ref.parent.data   )
													return elem_ref.parent





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> None:
													# helper function for union operation
													if nodea.rank > nodea.rank:
																				A__		   =							nodea
													else:
																				A__		   =							nodea
																				if nodea.rank == nodea.rank:
																											nodea.rank += 1





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> None:
													# merge 2 disjoint sets
													self.link(self.find_set(__UpperCAmelCase   )						,self.find_set(__UpperCAmelCase   )   )






class 			UpperCamelCase__(	Generic[T]       ):





						def __init__(				self   )    -> None:
													# connections: map from the node to the neighbouring nodes (with weights)
													A__		   =							{}





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> None:
													# add a node ONLY if its not present in the graph
													if node not in self.connections:
																				A__		   =							{}





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> None:
													# add an edge with the given weight
													self.add_node(__UpperCAmelCase   )
													self.add_node(__UpperCAmelCase   )
													A__		   =							weight
													A__		   =							weight





						def  snake_case__		(				self   )    -> GraphUndirectedWeighted[T]:
													A__		   =							[]
													A__		   =							set()
													for start in self.connections:
																				for end in self.connections[start]:
																											if (start, end) not in seen:
																																		seen.add((end, start)   )
																																		edges.append((start, end, self.connections[start][end])   )
													edges.sort(key=lambda __UpperCAmelCase   : x[2]   )

													# creating the disjoint set
													A__		   =							DisjointSetTree[T]()
													for node in self.connections:
																				disjoint_set.make_set(__UpperCAmelCase   )

													# MST generation
													A__		   =							0
													A__		   =							0
													A__		   =							GraphUndirectedWeighted[T]()
													while num_edges < len(self.connections   ) - 1:
																				A__   ,     A__   ,     A__		   =							edges[index]
																				index += 1
																				A__		   =							disjoint_set.find_set(__UpperCAmelCase   )
																				A__		   =							disjoint_set.find_set(__UpperCAmelCase   )
																				if parent_u != parent_v:
																											num_edges += 1
																											graph.add_edge(__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )
																											disjoint_set.union(__UpperCAmelCase						,__UpperCAmelCase   )
													return graph





