





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'},
		    ]			,					)










'''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)



