code: 
  
  
  
  
  
  import unittest
  
  from transformers import (
      MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
      TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
      PreTrainedTokenizer,
      is_vision_available,
  )
  from transformers.pipelines import ImageClassificationPipeline, pipeline
  from transformers.testing_utils import (
      is_pipeline_test,
      nested_simplify,
      require_tf,
      require_torch,
      require_torch_or_tf,
      require_vision,
      slow,
  )
  
  from .test_pipelines_common import ANY
  
  
  if is_vision_available():
  			from PIL import Image
  else:
  
  
  
  
  
  
  			class 							__snake_case    :
  
  
  
  
  
  				@staticmethod
  				def      _snake_case      (		*UpperCamelCase_			,					**UpperCamelCase_		)			->		Optional[Any]:
  					pass
  
  
  
  
  
  
  @is_pipeline_test
  @require_torch_or_tf
  @require_vision
  class 							__snake_case    (				unittest.TestCase       ):
  	__lowerCAmelCase						=     MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
  	__lowerCAmelCase						=     TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_		)			->		Union[str, Any]:
  		snake_case__  					=      ImageClassificationPipeline(model=UpperCamelCase_			,					image_processor=UpperCamelCase_			,					top_k=2		)
  		snake_case__  					=      [
  		    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		),
  		    "http://images.cocodataset.org/val2017/000000039769.jpg",
  		]
  		return image_classifier, examples
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_			,					UpperCamelCase_		)			->		Optional[Any]:
  		snake_case__  					=      image_classifier('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
  
  		self.assertEqual(
  		    UpperCamelCase_			,					[
  		        {'score': ANY(UpperCamelCase_		), 'label': ANY(UpperCamelCase_		)},
  		        {'score': ANY(UpperCamelCase_		), 'label': ANY(UpperCamelCase_		)},
  		    ]			,					)
  
  		import datasets
  
  		snake_case__  					=      datasets.load_dataset('hf-internal-testing/fixtures_image_utils'			,					'image'			,					split='test'		)
  
  		# Accepts URL + PIL.Image + lists
  		snake_case__  					=      image_classifier(
  		    [
  		        Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		),
  		        'http://images.cocodataset.org/val2017/000000039769.jpg',
  		        # RGBA
  		        dataset[0]['file'],
  		        # LA
  		        dataset[1]['file'],
  		        # L
  		        dataset[2]['file'],
  		    ]		)
  		self.assertEqual(
  		    UpperCamelCase_			,					[
  		        [
  		            {'score': ANY(UpperCamelCase_		), 'label': ANY(UpperCamelCase_		)},
  		            {'score': ANY(UpperCamelCase_		), 'label': ANY(UpperCamelCase_		)},
  		        ],
  		        [
  		            {'score': ANY(UpperCamelCase_		), 'label': ANY(UpperCamelCase_		)},
  		            {'score': ANY(UpperCamelCase_		), 'label': ANY(UpperCamelCase_		)},
  		        ],
  		        [
  		            {'score': ANY(UpperCamelCase_		), 'label': ANY(UpperCamelCase_		)},
  		            {'score': ANY(UpperCamelCase_		), 'label': ANY(UpperCamelCase_		)},
  		        ],
  		        [
  		            {'score': ANY(UpperCamelCase_		), 'label': ANY(UpperCamelCase_		)},
  		            {'score': ANY(UpperCamelCase_		), 'label': ANY(UpperCamelCase_		)},
  		        ],
  		        [
  		            {'score': ANY(UpperCamelCase_		), 'label': ANY(UpperCamelCase_		)},
  		            {'score': ANY(UpperCamelCase_		), 'label': ANY(UpperCamelCase_		)},
  		        ],
  		    ]			,					)
  
  
  
  
  
  	@require_torch
  	def      _snake_case      (		self		)			->		Optional[Any]:
  		snake_case__  					=      "hf-internal-testing/tiny-random-vit"
  		snake_case__  					=      pipeline('image-classification'			,					model=UpperCamelCase_		)
  
  		snake_case__  					=      image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'		)
  		self.assertEqual(
  		    nested_simplify(UpperCamelCase_			,					decimals=4		)			,					[{'label': 'LABEL_1', 'score': 0.5_7_4}, {'label': 'LABEL_0', 'score': 0.4_2_6}]			,					)
  
  		snake_case__  					=      image_classifier(
  		    [
  		        'http://images.cocodataset.org/val2017/000000039769.jpg',
  		        'http://images.cocodataset.org/val2017/000000039769.jpg',
  		    ]			,					top_k=2			,					)
  		self.assertEqual(
  		    nested_simplify(UpperCamelCase_			,					decimals=4		)			,					[
  		        [{'label': 'LABEL_1', 'score': 0.5_7_4}, {'label': 'LABEL_0', 'score': 0.4_2_6}],
  		        [{'label': 'LABEL_1', 'score': 0.5_7_4}, {'label': 'LABEL_0', 'score': 0.4_2_6}],
  		    ]			,					)
  
  
  
  
  
  	@require_tf
  	def      _snake_case      (		self		)			->		str:
  		snake_case__  					=      "hf-internal-testing/tiny-random-vit"
  		snake_case__  					=      pipeline('image-classification'			,					model=UpperCamelCase_			,					framework='tf'		)
  
  		snake_case__  					=      image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'		)
  		self.assertEqual(
  		    nested_simplify(UpperCamelCase_			,					decimals=4		)			,					[{'label': 'LABEL_1', 'score': 0.5_7_4}, {'label': 'LABEL_0', 'score': 0.4_2_6}]			,					)
  
  		snake_case__  					=      image_classifier(
  		    [
  		        'http://images.cocodataset.org/val2017/000000039769.jpg',
  		        'http://images.cocodataset.org/val2017/000000039769.jpg',
  		    ]			,					top_k=2			,					)
  		self.assertEqual(
  		    nested_simplify(UpperCamelCase_			,					decimals=4		)			,					[
  		        [{'label': 'LABEL_1', 'score': 0.5_7_4}, {'label': 'LABEL_0', 'score': 0.4_2_6}],
  		        [{'label': 'LABEL_1', 'score': 0.5_7_4}, {'label': 'LABEL_0', 'score': 0.4_2_6}],
  		    ]			,					)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		List[str]:
  		snake_case__  					=      PreTrainedTokenizer()
  
  		# Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
  		snake_case__  					=      pipeline(
  		    'image-classification'			,					model='hf-internal-testing/tiny-random-vit'			,					tokenizer=UpperCamelCase_		)
  
  		self.assertIs(image_classifier.tokenizer			,					UpperCamelCase_		)
  
  
  
  
  
  	@slow
  	@require_torch
  	def      _snake_case      (		self		)			->		List[str]:
  		snake_case__  					=      pipeline('image-classification'			,					model='deepmind/vision-perceiver-conv'		)
  		snake_case__  					=      image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'		)
  		self.assertEqual(
  		    nested_simplify(UpperCamelCase_			,					decimals=4		)			,					[
  		        {'score': 0.4_3_8_5, 'label': 'tabby, tabby cat'},
  		        {'score': 0.3_2_1, 'label': 'tiger cat'},
  		        {'score': 0.0_5_0_2, 'label': 'Egyptian cat'},
  		        {'score': 0.0_1_3_7, 'label': 'crib, cot'},
  		        {'score': 0.0_0_7, 'label': 'radiator'},
  		    ]			,					)
  
  		snake_case__  					=      pipeline('image-classification'			,					model='deepmind/vision-perceiver-fourier'		)
  		snake_case__  					=      image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'		)
  		self.assertEqual(
  		    nested_simplify(UpperCamelCase_			,					decimals=4		)			,					[
  		        {'score': 0.5_6_5_8, 'label': 'tabby, tabby cat'},
  		        {'score': 0.1_3_0_9, 'label': 'tiger cat'},
  		        {'score': 0.0_7_2_2, 'label': 'Egyptian cat'},
  		        {'score': 0.0_7_0_7, 'label': 'remote control, remote'},
  		        {'score': 0.0_0_8_2, 'label': 'computer keyboard, keypad'},
  		    ]			,					)
  
  		snake_case__  					=      pipeline('image-classification'			,					model='deepmind/vision-perceiver-learned'		)
  		snake_case__  					=      image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'		)
  		self.assertEqual(
  		    nested_simplify(UpperCamelCase_			,					decimals=4		)			,					[
  		        {'score': 0.3_0_2_2, 'label': 'tabby, tabby cat'},
  		        {'score': 0.2_3_6_2, 'label': 'Egyptian cat'},
  		        {'score': 0.1_8_5_6, 'label': 'tiger cat'},
  		        {'score': 0.0_3_2_4, 'label': 'remote control, remote'},
  		        {'score': 0.0_0_9_6, 'label': 'quilt, comforter, comfort, puff'},
  		    ]			,					)
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  a__				: Any									=    {
      0: '''underwear''',
      1: '''pants''',
      2: '''belt''',
      3: '''suit''',
      4: '''shoe''',
      5: '''socks''',
      6: '''shirt''',
      7: '''tie''',
      8: '''watch''',
  }
  
  a__				: Optional[Any]									=    [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]
  
  a__				: List[str]									=    [0 for x in range(len(graph))]
  a__				: List[Any]									=    []
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_       )      ->Dict:
  	snake_case__  					=      1
  	while stack:
  		snake_case__  					=      stack.pop()
  		print(UpperCAmelCase_				,							clothes[current_clothing]       )
  		order += 1
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       )      ->Dict:
  	snake_case__  					=      1
  	for v in graph[u]:
  		if not visited[v]:
  			depth_first_search(UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       )
  
  	stack.append(UpperCAmelCase_       )
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_       )      ->Union[str, Any]:
  	for v in range(len(UpperCAmelCase_       )       ):
  		if not visited[v]:
  			depth_first_search(UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       )
  
  
  if __name__ == "__main__":
  			topological_sort(graph, visited)
  			print(stack)
  			print_stack(stack, clothes)
  
  
  
  