code: 
  
  
  
  import unittest
  
  import numpy as np
  
  from transformers.testing_utils import require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
  
  from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
  
  
  if is_torch_available():
      import torch
  
  if is_vision_available():
      from PIL import Image
  
      from transformers import ViTImageProcessor
  
  
  
  class 		_snake_case      (			unittest.TestCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   def __init__(						self	,    a	,    a=7	,    a=3	,    a=1_8	,    a=3_0	,    a=4_0_0	,    a=True	,    a=None	,    a=True	,    a=[0.5, 0.5, 0.5]	,    a=[0.5, 0.5, 0.5]	,    )	->							Optional[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    size if size is not None else {'''height''': 1_8, '''width''': 1_8}
     _A           =    parent
     _A           =    batch_size
     _A           =    num_channels
     _A           =    image_size
     _A           =    min_resolution
     _A           =    max_resolution
     _A           =    do_resize
     _A           =    size
     _A           =    do_normalize
     _A           =    image_mean
     _A           =    image_std
  
  
  
   def lowercase_		(						self					)	->							str:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return {
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_normalize": self.do_normalize,
         "do_resize": self.do_resize,
         "size": self.size,
     }
  
  
  
  
  
  
  
  @require_torch
  @require_vision
  class 		_snake_case      (			lowerCamelCase					,unittest.TestCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   lowerCamelCase_          =			ViTImageProcessor if is_vision_available() else None
   def lowercase_		(						self					)	->							Tuple:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    ViTImageProcessingTester(self					)
   @property
   def lowercase_		(						self					)	->							List[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return self.image_processor_tester.prepare_image_processor_dict()
   def lowercase_		(						self					)	->							Optional[int]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    self.image_processing_class(**self.image_processor_dict					)
     self.assertTrue(hasattr(a	,    '''image_mean'''					)					)
     self.assertTrue(hasattr(a	,    '''image_std'''					)					)
     self.assertTrue(hasattr(a	,    '''do_normalize'''					)					)
     self.assertTrue(hasattr(a	,    '''do_resize'''					)					)
     self.assertTrue(hasattr(a	,    '''size'''					)					)
   def lowercase_		(						self					)	->							List[str]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    self.image_processing_class.from_dict(self.image_processor_dict					)
     self.assertEqual(image_processor.size	,    {'''height''': 1_8, '''width''': 1_8}					)
  
     _A           =    self.image_processing_class.from_dict(self.image_processor_dict	,    size=4_2					)
     self.assertEqual(image_processor.size	,    {'''height''': 4_2, '''width''': 4_2}					)
   def lowercase_		(						self					)	->							Optional[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     pass
   def lowercase_		(						self					)	->							Optional[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    self.image_processing_class(**self.image_processor_dict					)
     # create random PIL images
     _A           =    prepare_image_inputs(self.image_processor_tester	,    equal_resolution=a					)
     for image in image_inputs:
       self.assertIsInstance(a	,    Image.Image					)
  
     # Test not batched input
     _A           =    image_processing(image_inputs[0]	,    return_tensors='''pt'''					).pixel_values
     self.assertEqual(
         encoded_images.shape	,    (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['''height'''],
             self.image_processor_tester.size['''width'''],
         )	,    )
  
     # Test batched
     _A           =    image_processing(a	,    return_tensors='''pt'''					).pixel_values
     self.assertEqual(
         encoded_images.shape	,    (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['''height'''],
             self.image_processor_tester.size['''width'''],
         )	,    )
   def lowercase_		(						self					)	->							Tuple:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    self.image_processing_class(**self.image_processor_dict					)
     # create random numpy tensors
     _A           =    prepare_image_inputs(self.image_processor_tester	,    equal_resolution=a	,    numpify=a					)
     for image in image_inputs:
       self.assertIsInstance(a	,    np.ndarray					)
  
     # Test not batched input
     _A           =    image_processing(image_inputs[0]	,    return_tensors='''pt'''					).pixel_values
     self.assertEqual(
         encoded_images.shape	,    (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['''height'''],
             self.image_processor_tester.size['''width'''],
         )	,    )
  
     # Test batched
     _A           =    image_processing(a	,    return_tensors='''pt'''					).pixel_values
     self.assertEqual(
         encoded_images.shape	,    (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['''height'''],
             self.image_processor_tester.size['''width'''],
         )	,    )
  
  
  
   def lowercase_		(						self					)	->							Optional[int]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    self.image_processing_class(**self.image_processor_dict					)
     # create random PyTorch tensors
     _A           =    prepare_image_inputs(self.image_processor_tester	,    equal_resolution=a	,    torchify=a					)
     for image in image_inputs:
       self.assertIsInstance(a	,    torch.Tensor					)
  
     # Test not batched input
     _A           =    image_processing(image_inputs[0]	,    return_tensors='''pt'''					).pixel_values
     self.assertEqual(
         encoded_images.shape	,    (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['''height'''],
             self.image_processor_tester.size['''width'''],
         )	,    )
  
     # Test batched
     _A           =    image_processing(a	,    return_tensors='''pt'''					).pixel_values
     self.assertEqual(
         encoded_images.shape	,    (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['''height'''],
             self.image_processor_tester.size['''width'''],
         )	,    )
style_context: 
  
  
  
  from sklearn.metrics import accuracy_score
  
  import datasets
  
  
  __lowerCamelCase  				=					"""
  Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
  Accuracy = (TP + TN) / (TP + TN + FP + FN)
   Where:
  TP: True positive
  TN: True negative
  FP: False positive
  FN: False negative
  """
  
  
  __lowerCamelCase  				=					"""
  Args:
      predictions (`list` of `int`): Predicted labels.
      references (`list` of `int`): Ground truth labels.
      normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
      sample_weight (`list` of `float`): Sample weights Defaults to None.
  
  Returns:
      accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.
  
  Examples:
  
      Example 1-A simple example
          >>> accuracy_metric = datasets.load_metric(\"accuracy\")
          >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
          >>> print(results)
          {'accuracy': 0.5}
  
      Example 2-The same as Example 1, except with `normalize` set to `False`.
          >>> accuracy_metric = datasets.load_metric(\"accuracy\")
          >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
          >>> print(results)
          {'accuracy': 3.0}
  
      Example 3-The same as Example 1, except with `sample_weight` set.
          >>> accuracy_metric = datasets.load_metric(\"accuracy\")
          >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
          >>> print(results)
          {'accuracy': 0.8778625954198473}
  """
  
  
  __lowerCamelCase  				=					"""
  @article{scikit-learn,
    title={Scikit-learn: Machine Learning in {P}ython},
    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
    journal={Journal of Machine Learning Research},
    volume={12},
    pages={2825--2830},
    year={2011}
  }
  """
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,_KWARGS_DESCRIPTION				)
  class 		_snake_case      (			datasets.Metric				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   def lowercase_		(						self					)	->							Optional[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return datasets.MetricInfo(
         description=_DESCRIPTION	,    citation=_CITATION	,    inputs_description=_KWARGS_DESCRIPTION	,    features=datasets.Features(
             {
                 '''predictions''': datasets.Sequence(datasets.Value('''int32'''					)					),
                 '''references''': datasets.Sequence(datasets.Value('''int32'''					)					),
             }
             if self.config_name == '''multilabel'''
             else {
                 '''predictions''': datasets.Value('''int32'''					),
                 '''references''': datasets.Value('''int32'''					),
             }					)	,    reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html''']	,    )
  
  
  
   def lowercase_		(						self	,    a	,    a	,    a=True	,    a=None					)	->							Dict:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return {
         "accuracy": float(
             accuracy_score(a	,    a	,    normalize=a	,    sample_weight=a					)					)
     }