

'''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 					lowercase						(  unittest.TestCase ):
							_lowerCamelCase		:      Dict=   MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				Dict:
								UpperCAmelCase_		:      Any       	=					hf_hub_download(
								    repo_id='nateraw/video-demo'    ,			filename='archery.mp4'    ,			repo_type='dataset')
								UpperCAmelCase_		:      str       	=					VideoClassificationPipeline(model=_UpperCAmelCase    ,			image_processor=_UpperCAmelCase    ,			top_k=2)
								UpperCAmelCase_		:      Optional[int]       	=					[
								    example_video_filepath,
								    '''https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4''',
								]
								return video_classifier, examples


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case)							->				Dict:
								for example in examples:
									UpperCAmelCase_		:      List[Any]       	=					video_classifier(_UpperCAmelCase)

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


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

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

								UpperCAmelCase_		:      str       	=					video_classifier(
								    [
								        video_file_path,
								        video_file_path,
								    ]    ,			top_k=2    ,			)
								self.assertEqual(
								    nested_simplify(_UpperCAmelCase    ,			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 						_snake_case							(			self)							->				Optional[int]:
								pass









'''simple docstring'''


from __future__ import annotations

from typing import Any



class 					lowercase						(  a_ ):
							pass



class 					lowercase						:


							def __init__(			self    ,			_snake_case)							->				None:
								UpperCAmelCase_		:      Any       	=					data
								UpperCAmelCase_		:      Node | None       	=					None


							def __iter__(			self)							->				Optional[int]:
								UpperCAmelCase_		:      int       	=					self
								UpperCAmelCase_		:      List[str]       	=					[]
								while node:
									if node in visited:
										raise ContainsLoopError
									visited.append(_snake_case)
									yield node.data
									UpperCAmelCase_		:      Tuple       	=					node.next_node





							@property
							def 						_snake_case							(			self)							->				bool:
								try:
									list(self)
									return False
								except ContainsLoopError:
									return True


if __name__ == "__main__":
						lowerCAmelCase__										= Node(1)
						lowerCAmelCase__										= Node(2)
						lowerCAmelCase__										= Node(3)
						lowerCAmelCase__										= Node(4)
						print(root_node.has_loop)  # False
						lowerCAmelCase__										= root_node.next_node
						print(root_node.has_loop)  # True

						lowerCAmelCase__										= Node(5)
						lowerCAmelCase__										= Node(6)
						lowerCAmelCase__										= Node(5)
						lowerCAmelCase__										= Node(6)
						print(root_node.has_loop)  # False

						lowerCAmelCase__										= Node(1)
						print(root_node.has_loop)  # False






