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
  
  
  if is_torch_available():
     import torch
  
  if is_vision_available():
     from PIL import Image
  
     from transformers import CLIPImageProcessor
  
  class 					snake_case_							(       unittest.TestCase      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def __init__(    self:	str   ,       snake_case__:	Any   ,       snake_case__:	Optional[Any]=7   ,       snake_case__:	Optional[Any]=3   ,       snake_case__:	List[str]=18   ,       snake_case__:	Any=30   ,       snake_case__:	List[Any]=400   ,       snake_case__:	Any=True   ,       snake_case__:	Tuple=None   ,       snake_case__:	Any=True   ,       snake_case__:	List[Any]=None   ,       snake_case__:	Optional[int]=True   ,       snake_case__:	Optional[int]=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]   ,       snake_case__:	str=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]   ,       snake_case__:	int=True   ,       )  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	size if size is not None else {'shortest_edge': 20}
         _lowercase     =	crop_size if crop_size is not None else {'height': 18, 'width': 18}
         _lowercase     =	parent
         _lowercase     =	batch_size
         _lowercase     =	num_channels
         _lowercase     =	image_size
         _lowercase     =	min_resolution
         _lowercase     =	max_resolution
         _lowercase     =	do_resize
         _lowercase     =	size
         _lowercase     =	do_center_crop
         _lowercase     =	crop_size
         _lowercase     =	do_normalize
         _lowercase     =	image_mean
         _lowercase     =	image_std
         _lowercase     =	do_convert_rgb
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	int	)  ->		Any:
  
  
         """simple docstring"""
  
  
  
         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,
             "do_convert_rgb": self.do_convert_rgb,
         }
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[Any]   ,       snake_case__:	Optional[int]=False   ,       snake_case__:	int=False   ,       snake_case__:	Dict=False	)  ->		int:
  
  
         """simple docstring"""
  
  
  
         assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
  
         if equal_resolution:
             _lowercase     =	[]
             for i in range(self.batch_size	):
                 image_inputs.append(
                     np.random.randint(
                         255   ,       size=(self.num_channels, self.max_resolution, self.max_resolution)   ,       dtype=np.uinta	)	)
         else:
             _lowercase     =	[]
             for i in range(self.batch_size	):
                 _lowercase ,    _lowercase     =	np.random.choice(np.arange(self.min_resolution   ,       self.max_resolution	)   ,       2	)
                 image_inputs.append(np.random.randint(255   ,       size=(self.num_channels, width, height)   ,       dtype=np.uinta	)	)
  
         if not numpify and not torchify:
             # PIL expects the channel dimension as last dimension
             _lowercase     =	[Image.fromarray(np.moveaxis(snake_case__   ,       0   ,       -1	)	) for x in image_inputs]
  
         if torchify:
             _lowercase     =	[torch.from_numpy(snake_case__	) for x in image_inputs]
  
         return image_inputs
  
  
  
  
  
  
  @require_torch
  @require_vision
  class 					snake_case_							(       lowercase_      ,			unittest.TestCase      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     __lowerCAmelCase      =						CLIPImageProcessor if is_vision_available() else None
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Tuple	)  ->		Union[str, Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	CLIPImageProcessingTester(self	)
  
     @property
     def 			SCREAMING_SNAKE_CASE__				(    self:	Any	)  ->		List[str]:
  
  
         """simple docstring"""
  
  
  
         return self.image_processor_tester.prepare_image_processor_dict()
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	str	)  ->		Any:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.image_processing_class(**self.image_processor_dict	)
         self.assertTrue(hasattr(snake_case__   ,       'do_resize'	)	)
         self.assertTrue(hasattr(snake_case__   ,       'size'	)	)
         self.assertTrue(hasattr(snake_case__   ,       'do_center_crop'	)	)
         self.assertTrue(hasattr(snake_case__   ,       'center_crop'	)	)
         self.assertTrue(hasattr(snake_case__   ,       'do_normalize'	)	)
         self.assertTrue(hasattr(snake_case__   ,       'image_mean'	)	)
         self.assertTrue(hasattr(snake_case__   ,       'image_std'	)	)
         self.assertTrue(hasattr(snake_case__   ,       'do_convert_rgb'	)	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	List[Any]	)  ->		int:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.image_processing_class.from_dict(self.image_processor_dict	)
         self.assertEqual(image_processor.size   ,       {'shortest_edge': 20}	)
         self.assertEqual(image_processor.crop_size   ,       {'height': 18, 'width': 18}	)
  
         _lowercase     =	self.image_processing_class.from_dict(self.image_processor_dict   ,       size=42   ,       crop_size=84	)
         self.assertEqual(image_processor.size   ,       {'shortest_edge': 42}	)
         self.assertEqual(image_processor.crop_size   ,       {'height': 84, 'width': 84}	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	List[str]	)  ->		str:
  
  
         """simple docstring"""
  
  
  
         pass
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Dict	)  ->		List[str]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.image_processing_class(**self.image_processor_dict	)
         # create random PIL images
         _lowercase     =	self.image_processor_tester.prepare_inputs(equal_resolution=snake_case__	)
         for image in image_inputs:
             self.assertIsInstance(snake_case__   ,       Image.Image	)
  
         # Test not batched input
         _lowercase     =	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
         _lowercase     =	image_processing(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 			SCREAMING_SNAKE_CASE__				(    self:	Any	)  ->		Optional[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.image_processing_class(**self.image_processor_dict	)
         # create random numpy tensors
         _lowercase     =	self.image_processor_tester.prepare_inputs(equal_resolution=snake_case__   ,       numpify=snake_case__	)
         for image in image_inputs:
             self.assertIsInstance(snake_case__   ,       np.ndarray	)
  
         # Test not batched input
         _lowercase     =	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
         _lowercase     =	image_processing(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 			SCREAMING_SNAKE_CASE__				(    self:	Any	)  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.image_processing_class(**self.image_processor_dict	)
         # create random PyTorch tensors
         _lowercase     =	self.image_processor_tester.prepare_inputs(equal_resolution=snake_case__   ,       torchify=snake_case__	)
         for image in image_inputs:
             self.assertIsInstance(snake_case__   ,       torch.Tensor	)
  
         # Test not batched input
         _lowercase     =	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
         _lowercase     =	image_processing(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'],
             )   ,       )
  
  
  
  
  
  
  @require_torch
  @require_vision
  class 					snake_case_							(       lowercase_      ,			unittest.TestCase      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     __lowerCAmelCase      =						CLIPImageProcessor if is_vision_available() else None
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[Any]	)  ->		Tuple:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	CLIPImageProcessingTester(self   ,       num_channels=4	)
         _lowercase     =	3
  
     @property
     def 			SCREAMING_SNAKE_CASE__				(    self:	Dict	)  ->		Optional[int]:
  
  
         """simple docstring"""
  
  
  
         return self.image_processor_tester.prepare_image_processor_dict()
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Any	)  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.image_processing_class(**self.image_processor_dict	)
         self.assertTrue(hasattr(snake_case__   ,       'do_resize'	)	)
         self.assertTrue(hasattr(snake_case__   ,       'size'	)	)
         self.assertTrue(hasattr(snake_case__   ,       'do_center_crop'	)	)
         self.assertTrue(hasattr(snake_case__   ,       'center_crop'	)	)
         self.assertTrue(hasattr(snake_case__   ,       'do_normalize'	)	)
         self.assertTrue(hasattr(snake_case__   ,       'image_mean'	)	)
         self.assertTrue(hasattr(snake_case__   ,       'image_std'	)	)
         self.assertTrue(hasattr(snake_case__   ,       'do_convert_rgb'	)	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[Any]	)  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         pass
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[Any]	)  ->		Optional[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.image_processing_class(**self.image_processor_dict	)
         # create random PIL images
         _lowercase     =	self.image_processor_tester.prepare_inputs(equal_resolution=snake_case__	)
         for image in image_inputs:
             self.assertIsInstance(snake_case__   ,       Image.Image	)
  
         # Test not batched input
         _lowercase     =	image_processing(image_inputs[0]   ,       return_tensors='pt'	).pixel_values
         self.assertEqual(
             encoded_images.shape   ,       (
                 1,
                 self.expected_encoded_image_num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )   ,       )
  
         # Test batched
         _lowercase     =	image_processing(snake_case__   ,       return_tensors='pt'	).pixel_values
         self.assertEqual(
             encoded_images.shape   ,       (
                 self.image_processor_tester.batch_size,
                 self.expected_encoded_image_num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )   ,       )
  
style_context: 
  
  
  
  
  
  
  from __future__ import annotations
  def        UpperCAmelCase      (   _lowercase,      _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	[None] * (len(_lowercase      ) + len(_lowercase      ))
  
      _lowercase     =	0  # pointer to current index for left Half
      _lowercase     =	0  # pointer to current index for the right Half
      _lowercase     =	0  # pointer to current index for the sorted array Half
  
      while pointera < len(_lowercase      ) and pointera < len(_lowercase      ):
          if left_half[pointera] < right_half[pointera]:
              _lowercase     =	left_half[pointera]
              pointera += 1
              index += 1
          else:
              _lowercase     =	right_half[pointera]
              pointera += 1
              index += 1
      while pointera < len(_lowercase      ):
          _lowercase     =	left_half[pointera]
          pointera += 1
          index += 1
  
      while pointera < len(_lowercase      ):
          _lowercase     =	right_half[pointera]
          pointera += 1
          index += 1
  
      return sorted_array
  def        UpperCAmelCase      (   _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      if len(_lowercase      ) <= 1:
          return array
      # the actual formula to calculate the middle element = left + (right - left) // 2
      # this avoids integer overflow in case of large N
      _lowercase     =	0 + (len(_lowercase      ) - 0) // 2
  
      # Split the array into halves till the array length becomes equal to One
      # merge the arrays of single length returned by mergeSort function and
      # pass them into the merge arrays function which merges the array
      _lowercase     =	array[:middle]
      _lowercase     =	array[middle:]
  
      return merge(merge_sort(_lowercase      ),      merge_sort(_lowercase      )      )
  
  
  if __name__ == "__main__":
     import doctest
  
     doctest.testmod()
  