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 					_UpperCamelCase :
  
  
  
  
  
  
  
  									'''simple docstring'''
  
  
  
  
  
  
  
  									@staticmethod
  									def 		_UpperCAmelCase	(		*SCREAMING_SNAKE_CASE_  :				Dict		,				**SCREAMING_SNAKE_CASE_  :				Union[str, Any]		):
  															pass
  
  
  
  
  @is_pipeline_test
  @require_torch_or_tf
  @require_vision
  class 					_UpperCamelCase (      unittest.TestCase	):
  
  
  
  
  
  
  
  							'''simple docstring'''
  							_A  				=  MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
  							_A  				=  TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				List[str]		,				SCREAMING_SNAKE_CASE_  :				List[str]		,				SCREAMING_SNAKE_CASE_  :				List[Any]		,				SCREAMING_SNAKE_CASE_  :				Optional[Any]		):
  													_a     =			ImageClassificationPipeline(model=SCREAMING_SNAKE_CASE_		,				image_processor=SCREAMING_SNAKE_CASE_		,				top_k=2		)
  													_a     =			[
  													    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		),
  													    'http://images.cocodataset.org/val2017/000000039769.jpg',
  													]
  													return image_classifier, examples
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				List[str]		,				SCREAMING_SNAKE_CASE_  :				int		,				SCREAMING_SNAKE_CASE_  :				Any		):
  													_a     =			image_classifier('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
  
  													self.assertEqual(
  													    SCREAMING_SNAKE_CASE_		,				[
  													        {'score': ANY(SCREAMING_SNAKE_CASE_		), 'label': ANY(SCREAMING_SNAKE_CASE_		)},
  													        {'score': ANY(SCREAMING_SNAKE_CASE_		), 'label': ANY(SCREAMING_SNAKE_CASE_		)},
  													    ]		,				)
  
  													import datasets
  
  													_a     =			datasets.load_dataset('hf-internal-testing/fixtures_image_utils'		,				'image'		,				split='test'		)
  
  													# Accepts URL + PIL.Image + lists
  													_a     =			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(
  													    SCREAMING_SNAKE_CASE_		,				[
  													        [
  													            {'score': ANY(SCREAMING_SNAKE_CASE_		), 'label': ANY(SCREAMING_SNAKE_CASE_		)},
  													            {'score': ANY(SCREAMING_SNAKE_CASE_		), 'label': ANY(SCREAMING_SNAKE_CASE_		)},
  													        ],
  													        [
  													            {'score': ANY(SCREAMING_SNAKE_CASE_		), 'label': ANY(SCREAMING_SNAKE_CASE_		)},
  													            {'score': ANY(SCREAMING_SNAKE_CASE_		), 'label': ANY(SCREAMING_SNAKE_CASE_		)},
  													        ],
  													        [
  													            {'score': ANY(SCREAMING_SNAKE_CASE_		), 'label': ANY(SCREAMING_SNAKE_CASE_		)},
  													            {'score': ANY(SCREAMING_SNAKE_CASE_		), 'label': ANY(SCREAMING_SNAKE_CASE_		)},
  													        ],
  													        [
  													            {'score': ANY(SCREAMING_SNAKE_CASE_		), 'label': ANY(SCREAMING_SNAKE_CASE_		)},
  													            {'score': ANY(SCREAMING_SNAKE_CASE_		), 'label': ANY(SCREAMING_SNAKE_CASE_		)},
  													        ],
  													        [
  													            {'score': ANY(SCREAMING_SNAKE_CASE_		), 'label': ANY(SCREAMING_SNAKE_CASE_		)},
  													            {'score': ANY(SCREAMING_SNAKE_CASE_		), 'label': ANY(SCREAMING_SNAKE_CASE_		)},
  													        ],
  													    ]		,				)
  
  
  
  
  
  
  
  							@require_torch
  							def 		_UpperCAmelCase	(		self  :				List[Any]		):
  													_a     =			'hf-internal-testing/tiny-random-vit'
  													_a     =			pipeline('image-classification'		,				model=SCREAMING_SNAKE_CASE_		)
  
  													_a     =			image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'		)
  													self.assertEqual(
  													    nested_simplify(SCREAMING_SNAKE_CASE_		,				decimals=4		)		,				[{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}]		,				)
  
  													_a     =			image_classifier(
  													    [
  													        'http://images.cocodataset.org/val2017/000000039769.jpg',
  													        'http://images.cocodataset.org/val2017/000000039769.jpg',
  													    ]		,				top_k=2		,				)
  													self.assertEqual(
  													    nested_simplify(SCREAMING_SNAKE_CASE_		,				decimals=4		)		,				[
  													        [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}],
  													        [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}],
  													    ]		,				)
  
  
  
  
  
  
  
  							@require_tf
  							def 		_UpperCAmelCase	(		self  :				List[str]		):
  													_a     =			'hf-internal-testing/tiny-random-vit'
  													_a     =			pipeline('image-classification'		,				model=SCREAMING_SNAKE_CASE_		,				framework='tf'		)
  
  													_a     =			image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'		)
  													self.assertEqual(
  													    nested_simplify(SCREAMING_SNAKE_CASE_		,				decimals=4		)		,				[{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}]		,				)
  
  													_a     =			image_classifier(
  													    [
  													        'http://images.cocodataset.org/val2017/000000039769.jpg',
  													        'http://images.cocodataset.org/val2017/000000039769.jpg',
  													    ]		,				top_k=2		,				)
  													self.assertEqual(
  													    nested_simplify(SCREAMING_SNAKE_CASE_		,				decimals=4		)		,				[
  													        [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}],
  													        [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}],
  													    ]		,				)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				List[str]		):
  													_a     =			PreTrainedTokenizer()
  
  													# Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
  													_a     =			pipeline(
  													    'image-classification'		,				model='hf-internal-testing/tiny-random-vit'		,				tokenizer=SCREAMING_SNAKE_CASE_		)
  
  													self.assertIs(image_classifier.tokenizer		,				SCREAMING_SNAKE_CASE_		)
  
  
  
  
  
  
  
  							@slow
  							@require_torch
  							def 		_UpperCAmelCase	(		self  :				List[str]		):
  													# Perceiver is not tested by `run_pipeline_test` properly.
  													# That is because the type of feature_extractor and model preprocessor need to be kept
  													# in sync, which is not the case in the current design
  													_a     =			pipeline('image-classification'		,				model='deepmind/vision-perceiver-conv'		)
  													_a     =			image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'		)
  													self.assertEqual(
  													    nested_simplify(SCREAMING_SNAKE_CASE_		,				decimals=4		)		,				[
  													        {'score': 0.4385, 'label': 'tabby, tabby cat'},
  													        {'score': 0.321, 'label': 'tiger cat'},
  													        {'score': 0.0502, 'label': 'Egyptian cat'},
  													        {'score': 0.0137, 'label': 'crib, cot'},
  													        {'score': 0.007, 'label': 'radiator'},
  													    ]		,				)
  
  													_a     =			pipeline('image-classification'		,				model='deepmind/vision-perceiver-fourier'		)
  													_a     =			image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'		)
  													self.assertEqual(
  													    nested_simplify(SCREAMING_SNAKE_CASE_		,				decimals=4		)		,				[
  													        {'score': 0.5658, 'label': 'tabby, tabby cat'},
  													        {'score': 0.1309, 'label': 'tiger cat'},
  													        {'score': 0.0722, 'label': 'Egyptian cat'},
  													        {'score': 0.0707, 'label': 'remote control, remote'},
  													        {'score': 0.0082, 'label': 'computer keyboard, keypad'},
  													    ]		,				)
  
  													_a     =			pipeline('image-classification'		,				model='deepmind/vision-perceiver-learned'		)
  													_a     =			image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'		)
  													self.assertEqual(
  													    nested_simplify(SCREAMING_SNAKE_CASE_		,				decimals=4		)		,				[
  													        {'score': 0.3022, 'label': 'tabby, tabby cat'},
  													        {'score': 0.2362, 'label': 'Egyptian cat'},
  													        {'score': 0.1856, 'label': 'tiger cat'},
  													        {'score': 0.0324, 'label': 'remote control, remote'},
  													        {'score': 0.0096, 'label': 'quilt, comforter, comfort, puff'},
  													    ]		,				)
  
  
  
style_context: 
  
  
  import math
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  float:
  						_a     =			math.log(2							,     10				) * number
  						_a     =			round((log_number - int(_UpperCAmelCase				))							,     15				)
  
  						return difference
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase = 678910				)    ->  int:
  						_a     =			90
  						_a     =			0
  
  						_a     =			math.log(1.23							,     10				)
  						_a     =			math.log(1.24							,     10				)
  						_a     =			0
  
  						while position < number:
  												_a     =			log_difference(_UpperCAmelCase				)
  
  												if difference >= upper_limit:
  																		power_iterator += 93
  
  												elif difference < lower_limit:
  																		power_iterator += 196
  
  												else:
  																		_a     =			power_iterator
  																		power_iterator += 196
  																		position += 1
  
  						return previous_power
  
  
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  
  		print(f"""{solution() = }""")
  
  
  