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 							_A (    unittest.TestCase							):
      """simple docstring"""
  
      def __init__(							self			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    Dict   ,    __SCREAMING_SNAKE_CASE			:    int=7   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]=3   ,    __SCREAMING_SNAKE_CASE			:    int=18   ,    __SCREAMING_SNAKE_CASE			:    Dict=30   ,    __SCREAMING_SNAKE_CASE			:    List[Any]=400   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]=True   ,    __SCREAMING_SNAKE_CASE			:    int=None   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]=True   ,    __SCREAMING_SNAKE_CASE			:    List[str]=None   ,    __SCREAMING_SNAKE_CASE			:    List[Any]=True   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]=[0.48_145_466, 0.4_578_275, 0.40_821_073]   ,    __SCREAMING_SNAKE_CASE			:    Any=[0.26_862_954, 0.26_130_258, 0.27_577_711]   ,    __SCREAMING_SNAKE_CASE			:    Dict=True   ,    )			->       int:
             __UpperCAmelCase    							=size if size is not None else {"""shortest_edge""": 20}
             __UpperCAmelCase    							=crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
             __UpperCAmelCase    							=parent
             __UpperCAmelCase    							=batch_size
             __UpperCAmelCase    							=num_channels
             __UpperCAmelCase    							=image_size
             __UpperCAmelCase    							=min_resolution
             __UpperCAmelCase    							=max_resolution
             __UpperCAmelCase    							=do_resize
             __UpperCAmelCase    							=size
             __UpperCAmelCase    							=do_center_crop
             __UpperCAmelCase    							=crop_size
             __UpperCAmelCase    							=do_normalize
             __UpperCAmelCase    							=image_mean
             __UpperCAmelCase    							=image_std
             __UpperCAmelCase    							=do_convert_rgb
  
  
  
      def _a       (							self			:    int			)			->       Optional[Any]:
             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 _a       (							self			:    str   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]=False   ,    __SCREAMING_SNAKE_CASE			:    Dict=False   ,    __SCREAMING_SNAKE_CASE			:    str=False			)			->       Any:
             assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
  
             if equal_resolution:
                    __UpperCAmelCase    							=[]
                    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:
                    __UpperCAmelCase    							=[]
                    for i in range(self.batch_size			):
                           __UpperCAmelCase							,   __UpperCAmelCase    							=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
                    __UpperCAmelCase    							=[Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE   ,    0   ,    -1			)			) for x in image_inputs]
  
             if torchify:
                    __UpperCAmelCase    							=[torch.from_numpy(__SCREAMING_SNAKE_CASE			) for x in image_inputs]
  
             return image_inputs
  
  
  
  
  @require_torch
  @require_vision
  class 							_A (    UpperCamelCase				,				unittest.TestCase							):
      """simple docstring"""
  
      lowerCamelCase  :       Union[str, Any]   =   CLIPImageProcessor if is_vision_available() else None
  
  
  
      def _a       (							self			:    Union[str, Any]			)			->       int:
             __UpperCAmelCase    							=CLIPImageProcessingTester(self			)
  
  
  
      @property
      def _a       (							self			:    List[Any]			)			->       Tuple:
             return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
      def _a       (							self			:    Tuple			)			->       Tuple:
             __UpperCAmelCase    							=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"""			)			)
             self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE   ,    """do_convert_rgb"""			)			)
  
  
  
      def _a       (							self			:    str			)			->       List[Any]:
             __UpperCAmelCase    							=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}			)
  
             __UpperCAmelCase    							=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 _a       (							self			:    str			)			->       str:
             pass
  
  
  
      def _a       (							self			:    List[Any]			)			->       List[str]:
             # Initialize image_processing
             __UpperCAmelCase    							=self.image_processing_class(**self.image_processor_dict			)
             # create random PIL images
             __UpperCAmelCase    							=self.image_processor_tester.prepare_inputs(equal_resolution=__SCREAMING_SNAKE_CASE			)
             for image in image_inputs:
                    self.assertIsInstance(__SCREAMING_SNAKE_CASE   ,    Image.Image			)
  
             # Test not batched input
             __UpperCAmelCase    							=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
             __UpperCAmelCase    							=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 _a       (							self			:    List[Any]			)			->       Optional[int]:
             # Initialize image_processing
             __UpperCAmelCase    							=self.image_processing_class(**self.image_processor_dict			)
             # create random numpy tensors
             __UpperCAmelCase    							=self.image_processor_tester.prepare_inputs(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
             __UpperCAmelCase    							=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
             __UpperCAmelCase    							=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 _a       (							self			:    List[Any]			)			->       List[Any]:
             # Initialize image_processing
             __UpperCAmelCase    							=self.image_processing_class(**self.image_processor_dict			)
             # create random PyTorch tensors
             __UpperCAmelCase    							=self.image_processor_tester.prepare_inputs(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
             __UpperCAmelCase    							=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
             __UpperCAmelCase    							=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"""],
                 )   ,    )
  
  
  
  
  @require_torch
  @require_vision
  class 							_A (    UpperCamelCase				,				unittest.TestCase							):
      """simple docstring"""
  
      lowerCamelCase  :       Any   =   CLIPImageProcessor if is_vision_available() else None
  
  
  
      def _a       (							self			:    List[str]			)			->       int:
             __UpperCAmelCase    							=CLIPImageProcessingTester(self   ,    num_channels=4			)
             __UpperCAmelCase    							=3
  
  
  
      @property
      def _a       (							self			:    List[Any]			)			->       str:
             return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
      def _a       (							self			:    List[str]			)			->       str:
             __UpperCAmelCase    							=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"""			)			)
             self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE   ,    """do_convert_rgb"""			)			)
  
  
  
      def _a       (							self			:    str			)			->       Union[str, Any]:
             pass
  
  
  
      def _a       (							self			:    Tuple			)			->       Any:
             # Initialize image_processing
             __UpperCAmelCase    							=self.image_processing_class(**self.image_processor_dict			)
             # create random PIL images
             __UpperCAmelCase    							=self.image_processor_tester.prepare_inputs(equal_resolution=__SCREAMING_SNAKE_CASE			)
             for image in image_inputs:
                    self.assertIsInstance(__SCREAMING_SNAKE_CASE   ,    Image.Image			)
  
             # Test not batched input
             __UpperCAmelCase    							=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
             __UpperCAmelCase    							=image_processing(__SCREAMING_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
  
  __A  							=  [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]
  
  
  
  class 							_A :
      """simple docstring"""
  
      def __init__(							self			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    int			)			->       None:
             __UpperCAmelCase    							=-1
             __UpperCAmelCase    							=-1
             __UpperCAmelCase    							=[-1] * length
             __UpperCAmelCase    							=None
             __UpperCAmelCase    							=None
  
  
  
      def __repr__(							self			:    Optional[int]			)			->       str:
             return f'''Node(min_value={self.minn} max_value={self.maxx})'''
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	list[int]		)  ->							Node | None:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=Node(len(A_		)		)
         __UpperCAmelCase							,   __UpperCAmelCase    							=min(A_		), max(A_		)
         # Leaf node case where the node contains only one unique value
         if root.minn == root.maxx:
                return root
         __UpperCAmelCase    							=(root.minn + root.maxx) // 2
  
         __UpperCAmelCase    							=[]
         __UpperCAmelCase    							=[]
  
         for index, num in enumerate(A_		):
                if num <= pivot:
                       left_arr.append(A_		)
                else:
                       right_arr.append(A_		)
                __UpperCAmelCase    							=len(A_		)
         __UpperCAmelCase    							=build_tree(A_		)
         __UpperCAmelCase    							=build_tree(A_		)
         return root
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	Node | None     ,			A_:	int     ,			A_:	int		)  ->							int:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         if index < 0 or node is None:
                return 0
         # Leaf node cases
         if node.minn == node.maxx:
                return index + 1 if node.minn == num else 0
         __UpperCAmelCase    							=(node.minn + node.maxx) // 2
         if num <= pivot:
                # go the left subtree and map index to the left subtree
                return rank_till_index(node.left     ,			A_     ,			node.map_left[index] - 1		)
         else:
                # go to the right subtree and map index to the right subtree
                return rank_till_index(node.right     ,			A_     ,			index - node.map_left[index]		)
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	Node | None     ,			A_:	int     ,			A_:	int     ,			A_:	int		)  ->							int:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         if start > end:
                return 0
         __UpperCAmelCase    							=rank_till_index(A_     ,			A_     ,			A_		)
         __UpperCAmelCase    							=rank_till_index(A_     ,			A_     ,			start - 1		)
         return rank_till_end - rank_before_start
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	Node | None     ,			A_:	int     ,			A_:	int     ,			A_:	int		)  ->							int:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         if index > (end - start) or start > end or node is None:
                return -1
         # Leaf node case
         if node.minn == node.maxx:
                return node.minn
         # Number of elements in the left subtree in interval [start, end]
         __UpperCAmelCase    							=node.map_left[end] - (
             node.map_left[start - 1] if start else 0
         )
         if num_elements_in_left_tree > index:
                return quantile(
                    node.left     ,			A_     ,			(node.map_left[start - 1] if start else 0)     ,			node.map_left[end] - 1     ,			)
         else:
                return quantile(
                    node.right     ,			index - num_elements_in_left_tree     ,			start - (node.map_left[start - 1] if start else 0)     ,			end - node.map_left[end]     ,			)
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	Node | None     ,			A_:	int     ,			A_:	int     ,			A_:	int     ,			A_:	int		)  ->							int:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         if (
             start > end
             or node is None
             or start_num > end_num
             or node.minn > end_num
             or node.maxx < start_num
         ):
                return 0
         if start_num <= node.minn and node.maxx <= end_num:
                return end - start + 1
         __UpperCAmelCase    							=range_counting(
             node.left     ,			(node.map_left[start - 1] if start else 0)     ,			node.map_left[end] - 1     ,			A_     ,			A_     ,			)
         __UpperCAmelCase    							=range_counting(
             node.right     ,			start - (node.map_left[start - 1] if start else 0)     ,			end - node.map_left[end]     ,			A_     ,			A_     ,			)
         return left + right
  
  
  if __name__ == "__main__":
     import doctest
  
     doctest.testmod()
  
  
  
  
  