code: 
  
  
  '''simple docstring'''
  
  
  
  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 DeiTImageProcessor
  
  
  
  
  
  class      UpperCAmelCase_						(unittest.TestCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      def __init__(							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_=7    ,  SCREAMING_SNAKE_CASE_=3    ,  SCREAMING_SNAKE_CASE_=18    ,  SCREAMING_SNAKE_CASE_=30    ,  SCREAMING_SNAKE_CASE_=4_00    ,  SCREAMING_SNAKE_CASE_=True    ,  SCREAMING_SNAKE_CASE_=None    ,  SCREAMING_SNAKE_CASE_=True    ,  SCREAMING_SNAKE_CASE_=None    ,  SCREAMING_SNAKE_CASE_=True    ,  SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]    ,  SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]    ,  )       ->  Optional[int]:
           __lowerCamelCase     :			Optional[int]              =	size if size is not None else {'height': 20, 'width': 20}
           __lowerCamelCase     :			int              =	crop_size if crop_size is not None else {'height': 18, 'width': 18}
  
           __lowerCamelCase     :			Optional[int]              =	parent
           __lowerCamelCase     :			List[str]              =	batch_size
           __lowerCamelCase     :			int              =	num_channels
           __lowerCamelCase     :			Dict              =	image_size
           __lowerCamelCase     :			Optional[int]              =	min_resolution
           __lowerCamelCase     :			List[Any]              =	max_resolution
           __lowerCamelCase     :			int              =	do_resize
           __lowerCamelCase     :			Dict              =	size
           __lowerCamelCase     :			Union[str, Any]              =	do_center_crop
           __lowerCamelCase     :			Any              =	crop_size
           __lowerCamelCase     :			Dict              =	do_normalize
           __lowerCamelCase     :			str              =	image_mean
           __lowerCamelCase     :			List[Any]              =	image_std
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  str:
           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_center_crop": self.do_center_crop,
               "crop_size": self.crop_size,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
           }
  
  
  
  
  
  
  
  @require_torch
  @require_vision
  class      UpperCAmelCase_						(_UpperCAmelCase      ,	unittest.TestCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      Any							  =    DeiTImageProcessor if is_vision_available() else None
      lowerCamelCase						:      int							  =    True
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Union[str, Any]:
           __lowerCamelCase     :			Any              =	DeiTImageProcessingTester(self			)
  
  
  
  
  
  
  
      @property
      def    lowercase_       (							self			)       ->  Any:
           return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Optional[int]:
           __lowerCamelCase     :			Optional[Any]              =	self.image_processing_class(**self.image_processor_dict			)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,  'do_resize'			)			)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,  'size'			)			)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,  'do_center_crop'			)			)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,  'center_crop'			)			)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,  'do_normalize'			)			)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,  'image_mean'			)			)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,  'image_std'			)			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Optional[Any]:
           __lowerCamelCase     :			List[str]              =	self.image_processing_class.from_dict(self.image_processor_dict			)
           self.assertEqual(image_processor.size    ,  {'height': 20, 'width': 20}			)
           self.assertEqual(image_processor.crop_size    ,  {'height': 18, 'width': 18}			)
  
           __lowerCamelCase     :			Optional[Any]              =	self.image_processing_class.from_dict(self.image_processor_dict    ,  size=42    ,  crop_size=84			)
           self.assertEqual(image_processor.size    ,  {'height': 42, 'width': 42}			)
           self.assertEqual(image_processor.crop_size    ,  {'height': 84, 'width': 84}			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Optional[int]:
           pass
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Optional[int]:
           # Initialize image_processing
           __lowerCamelCase     :			Any              =	self.image_processing_class(**self.image_processor_dict			)
           # create random PIL images
           __lowerCamelCase     :			Any              =	prepare_image_inputs(self.image_processor_tester    ,  equal_resolution=SCREAMING_SNAKE_CASE_			)
           for image in image_inputs:
                self.assertIsInstance(SCREAMING_SNAKE_CASE_    ,  Image.Image			)
  
           # Test not batched input
           __lowerCamelCase     :			str              =	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.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )    ,  )
  
           # Test batched
           __lowerCamelCase     :			Dict              =	image_processing(SCREAMING_SNAKE_CASE_    ,  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.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )    ,  )
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[Any]:
           # Initialize image_processing
           __lowerCamelCase     :			Optional[int]              =	self.image_processing_class(**self.image_processor_dict			)
           # create random numpy tensors
           __lowerCamelCase     :			List[str]              =	prepare_image_inputs(self.image_processor_tester    ,  equal_resolution=SCREAMING_SNAKE_CASE_    ,  numpify=SCREAMING_SNAKE_CASE_			)
           for image in image_inputs:
                self.assertIsInstance(SCREAMING_SNAKE_CASE_    ,  np.ndarray			)
  
           # Test not batched input
           __lowerCamelCase     :			Optional[Any]              =	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.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )    ,  )
  
           # Test batched
           __lowerCamelCase     :			Dict              =	image_processing(SCREAMING_SNAKE_CASE_    ,  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.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )    ,  )
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Optional[int]:
           # Initialize image_processing
           __lowerCamelCase     :			int              =	self.image_processing_class(**self.image_processor_dict			)
           # create random PyTorch tensors
           __lowerCamelCase     :			Tuple              =	prepare_image_inputs(self.image_processor_tester    ,  equal_resolution=SCREAMING_SNAKE_CASE_    ,  torchify=SCREAMING_SNAKE_CASE_			)
           for image in image_inputs:
                self.assertIsInstance(SCREAMING_SNAKE_CASE_    ,  torch.Tensor			)
  
           # Test not batched input
           __lowerCamelCase     :			Tuple              =	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.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )    ,  )
  
           # Test batched
           __lowerCamelCase     :			str              =	image_processing(SCREAMING_SNAKE_CASE_    ,  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.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )    ,  )
  
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       str							)  ->	int:
       __lowerCamelCase     :			Tuple              =	str(UpperCAmelCase_							).strip()
       if not bin_string:
            raise ValueError('Empty string was passed to the function'							)
       __lowerCamelCase     :			Optional[int]              =	bin_string[0] == '-'
       if is_negative:
            __lowerCamelCase     :			List[Any]              =	bin_string[1:]
       if not all(char in '01' for char in bin_string							):
            raise ValueError('Non-binary value was passed to the function'							)
       __lowerCamelCase     :			Optional[int]              =	0
       for char in bin_string:
            __lowerCamelCase     :			str              =	2 * decimal_number + int(UpperCAmelCase_							)
       return -decimal_number if is_negative else decimal_number
  
  
  if __name__ == "__main__":
        from doctest import testmod
  
        testmod()
  
  
  
  
  
  
  