code: 
  
  
  
  
  
  from typing import Dict, List, Optional, Union
  
  import numpy as np
  
  from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
  from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
  from ...image_utils import (
      IMAGENET_STANDARD_MEAN,
      IMAGENET_STANDARD_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, is_vision_available, logging
  
  
  if is_vision_available():
        import PIL
  
  
  _UpperCamelCase       = logging.get_logger(__name__)
  
  
  
  
  class 				_lowerCamelCase (    UpperCamelCase_    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        UpperCAmelCase_						:      List[str]       =["pixel_values"]
  
  
  
  
  
  
  
        def __init__( self				,						UpperCAmelCase = True				,						UpperCAmelCase = None				,						UpperCAmelCase = PIL.Image.NEAREST				,						UpperCAmelCase = False				,						UpperCAmelCase = None				,						UpperCAmelCase = 1 / 255				,						UpperCAmelCase = False				,						UpperCAmelCase = True				,						UpperCAmelCase = True				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = True				,						**UpperCAmelCase				,						)       ->   None:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          super().__init__(**__UpperCamelCase   )
          __snake_case   :				str        =       size if size is not None else {'''height''': 346, '''width''': 346}
          __snake_case   :				List[Any]        =       get_size_dict(__UpperCamelCase   )
          __snake_case   :				Dict        =       crop_size if crop_size is not None else {'''height''': 289, '''width''': 289}
          __snake_case   :				Optional[int]        =       get_size_dict(__UpperCamelCase				,						param_name="crop_size"   )
  
          __snake_case   :				Union[str, Any]        =       do_resize
          __snake_case   :				Tuple        =       size
          __snake_case   :				Dict        =       resample
          __snake_case   :				Optional[int]        =       do_center_crop
          __snake_case   :				Optional[int]        =       crop_size
          __snake_case   :				Optional[Any]        =       do_rescale
          __snake_case   :				Dict        =       rescale_factor
          __snake_case   :				List[str]        =       rescale_offset
          __snake_case   :				str        =       do_normalize
          __snake_case   :				Optional[int]        =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          __snake_case   :				Dict        =       image_std if image_std is not None else IMAGENET_STANDARD_STD
          __snake_case   :				Union[str, Any]        =       include_top
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = PIL.Image.NEAREST				,						UpperCAmelCase = None				,						**UpperCAmelCase				,						)       ->   np.ndarray:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Optional[int]        =       get_size_dict(__UpperCamelCase   )
          if "height" not in size or "width" not in size:
            raise ValueError(F"""The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}"""   )
          return resize(
              __UpperCamelCase				,						size=(size["height"], size["width"])				,						resample=__UpperCamelCase				,						data_format=__UpperCamelCase				,						**__UpperCamelCase   )
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = None				,						**UpperCAmelCase				,						)       ->   np.ndarray:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Tuple        =       get_size_dict(__UpperCamelCase   )
          if "height" not in size or "width" not in size:
            raise ValueError(F"""The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}"""   )
          return center_crop(__UpperCamelCase				,						size=(size["height"], size["width"])				,						data_format=__UpperCamelCase				,						**__UpperCamelCase   )
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = True				,						UpperCAmelCase = None				,						**UpperCAmelCase				,						)       ->   List[str]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          if offset:
            __snake_case   :				str        =       (image - 127.5) * scale
            if data_format is not None:
              __snake_case   :				Optional[Any]        =       to_channel_dimension_format(__UpperCamelCase				,						__UpperCamelCase   )
            __snake_case   :				Any        =       rescaled_image.astype(np.floataa   )
          else:
            __snake_case   :				List[Any]        =       rescale(__UpperCamelCase				,						scale=__UpperCamelCase				,						data_format=__UpperCamelCase				,						**__UpperCamelCase   )
          return rescaled_image
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = None				,						**UpperCAmelCase				,						)       ->   np.ndarray:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return normalize(__UpperCamelCase				,						mean=__UpperCamelCase				,						std=__UpperCamelCase				,						data_format=__UpperCamelCase				,						**__UpperCamelCase   )
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase=None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = ChannelDimension.FIRST				,						**UpperCAmelCase				,						)       ->   PIL.Image.Image:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				List[str]        =       do_resize if do_resize is not None else self.do_resize
          __snake_case   :				List[Any]        =       resample if resample is not None else self.resample
          __snake_case   :				int        =       do_center_crop if do_center_crop is not None else self.do_center_crop
          __snake_case   :				int        =       do_rescale if do_rescale is not None else self.do_rescale
          __snake_case   :				Optional[Any]        =       rescale_factor if rescale_factor is not None else self.rescale_factor
          __snake_case   :				Optional[Any]        =       rescale_offset if rescale_offset is not None else self.rescale_offset
          __snake_case   :				Optional[int]        =       do_normalize if do_normalize is not None else self.do_normalize
          __snake_case   :				int        =       image_mean if image_mean is not None else self.image_mean
          __snake_case   :				Optional[Any]        =       image_std if image_std is not None else self.image_std
          __snake_case   :				Dict        =       include_top if include_top is not None else self.include_top
  
          __snake_case   :				List[Any]        =       size if size is not None else self.size
          __snake_case   :				Union[str, Any]        =       get_size_dict(__UpperCamelCase   )
          __snake_case   :				Optional[Any]        =       crop_size if crop_size is not None else self.crop_size
          __snake_case   :				Union[str, Any]        =       get_size_dict(__UpperCamelCase				,						param_name="crop_size"   )
  
          __snake_case   :				Tuple        =       make_list_of_images(__UpperCamelCase   )
  
          if not valid_images(__UpperCamelCase   ):
            raise ValueError(
                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                "torch.Tensor, tf.Tensor or jax.ndarray."   )
  
          if do_resize and size is None or resample is None:
            raise ValueError("Size and resample must be specified if do_resize is True."   )
  
          if do_center_crop and crop_size is None:
            raise ValueError("Crop size must be specified if do_center_crop is True."   )
  
          if do_rescale and rescale_factor is None:
            raise ValueError("Rescale factor must be specified if do_rescale is True."   )
  
          if do_normalize and (image_mean is None or image_std is None):
            raise ValueError("Image mean and std must be specified if do_normalize is True."   )
  
          # All transformations expect numpy arrays.
          __snake_case   :				int        =       [to_numpy_array(__UpperCamelCase   ) for image in images]
  
          if do_resize:
            __snake_case   :				Dict        =       [self.resize(image=__UpperCamelCase				,						size=__UpperCamelCase				,						resample=__UpperCamelCase   ) for image in images]
  
          if do_center_crop:
            __snake_case   :				int        =       [self.center_crop(image=__UpperCamelCase				,						size=__UpperCamelCase   ) for image in images]
  
          if do_rescale:
            __snake_case   :				Union[str, Any]        =       [self.rescale(image=__UpperCamelCase				,						scale=__UpperCamelCase				,						offset=__UpperCamelCase   ) for image in images]
  
          if do_normalize:
            __snake_case   :				Union[str, Any]        =       [self.normalize(image=__UpperCamelCase				,						mean=__UpperCamelCase				,						std=__UpperCamelCase   ) for image in images]
  
          if include_top:
            __snake_case   :				Tuple        =       [self.normalize(image=__UpperCamelCase				,						mean=[0, 0, 0]				,						std=__UpperCamelCase   ) for image in images]
  
          __snake_case   :				Optional[int]        =       [to_channel_dimension_format(__UpperCamelCase				,						__UpperCamelCase   ) for image in images]
  
          __snake_case   :				Optional[int]        =       {'''pixel_values''': images}
          return BatchFeature(data=__UpperCamelCase				,						tensor_type=__UpperCamelCase   )
  
  
style_context: 
  
  
  
  
  
  from __future__ import annotations
  
  from collections.abc import Callable
  from typing import Generic, TypeVar
  
  _UpperCamelCase       = TypeVar('''T''')
  _UpperCamelCase       = TypeVar('''U''')
  
  
  
  
  class 				_lowerCamelCase (    Generic[T, U]    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        def __init__( self				,						UpperCAmelCase				,						UpperCAmelCase   )       ->   Dict:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Any        =       key
          __snake_case   :				Tuple        =       val
          __snake_case   :				int        =       0
          __snake_case   :				DoubleLinkedListNode[T, U] | None        =       None
          __snake_case   :				DoubleLinkedListNode[T, U] | None        =       None
  
  
  
  
  
  
  
        def __repr__( self   )       ->   str:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
              self.key				,						self.val				,						self.freq				,						self.next is not None				,						self.prev is not None   )
  
  
  
  
  class 				_lowerCamelCase (    Generic[T, U]    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        def __init__( self   )       ->   None:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				DoubleLinkedListNode[T, U]        =       DoubleLinkedListNode(UpperCAmelCase				,						UpperCAmelCase   )
          __snake_case   :				DoubleLinkedListNode[T, U]        =       DoubleLinkedListNode(UpperCAmelCase				,						UpperCAmelCase   )
          __snake_case    ,   __snake_case   :				Any        =       self.rear, self.head
  
  
  
  
  
  
  
        def __repr__( self   )       ->   str:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Union[str, Any]        =       ["DoubleLinkedList"]
          __snake_case   :				List[Any]        =       self.head
          while node.next is not None:
            rep.append(str(UpperCAmelCase   )   )
            __snake_case   :				List[Any]        =       node.next
          rep.append(str(self.rear   )   )
          return ",\n    ".join(UpperCAmelCase   )
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   None:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Dict        =       self.rear.prev
  
          # All nodes other than self.head are guaranteed to have non-None previous
          assert previous is not None
  
          __snake_case   :				int        =       node
          __snake_case   :				List[str]        =       previous
          __snake_case   :				Optional[int]        =       node
          __snake_case   :				int        =       self.rear
          node.freq += 1
          self._position_node(UpperCAmelCase   )
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   None:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          while node.prev is not None and node.prev.freq > node.freq:
            # swap node with previous node
            __snake_case   :				List[Any]        =       node.prev
  
            __snake_case   :				Dict        =       previous_node.prev
            __snake_case   :				Union[str, Any]        =       node.prev
            __snake_case   :				Union[str, Any]        =       previous_node
            __snake_case   :				int        =       node
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   DoubleLinkedListNode[T, U] | None:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          if node.prev is None or node.next is None:
            return None
  
          __snake_case   :				Optional[int]        =       node.next
          __snake_case   :				Optional[Any]        =       node.prev
          __snake_case   :				Optional[int]        =       None
          __snake_case   :				Any        =       None
          return node
  
  
  
  
  class 				_lowerCamelCase (    Generic[T, U]    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        UpperCAmelCase_						:      dict[Callable[[T], U], LFUCache[T, U]]       ={}
  
  
  
  
  
  
  
        def __init__( self				,						UpperCAmelCase   )       ->   Tuple:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				DoubleLinkedList[T, U]        =       DoubleLinkedList()
          __snake_case   :				str        =       capacity
          __snake_case   :				str        =       0
          __snake_case   :				Optional[int]        =       0
          __snake_case   :				Any        =       0
          __snake_case   :				dict[T, DoubleLinkedListNode[T, U]]        =       {}
  
  
  
  
  
  
  
        def __repr__( self   )       ->   str:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return (
              F"""CacheInfo(hits={self.hits}, misses={self.miss}, """
              F"""capacity={self.capacity}, current_size={self.num_keys})"""
          )
  
  
  
  
  
  
  
        def __contains__( self				,						UpperCAmelCase   )       ->   bool:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return key in self.cache
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   U | None:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          if key in self.cache:
            self.hits += 1
            __snake_case   :				DoubleLinkedListNode[T, U]        =       self.cache[key]
            __snake_case   :				Union[str, Any]        =       self.list.remove(self.cache[key]   )
            assert node == value_node
  
            # node is guaranteed not None because it is in self.cache
            assert node is not None
            self.list.add(UpperCAmelCase   )
            return node.val
          self.miss += 1
          return None
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase   )       ->   None:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          if key not in self.cache:
            if self.num_keys >= self.capacity:
              # delete first node when over capacity
              __snake_case   :				Optional[Any]        =       self.list.head.next
  
              # guaranteed to have a non-None first node when num_keys > 0
              # explain to type checker via assertions
              assert first_node is not None
              assert first_node.key is not None
              assert self.list.remove(UpperCAmelCase   ) is not None
              # first_node guaranteed to be in list
  
              del self.cache[first_node.key]
              self.num_keys -= 1
            __snake_case   :				Union[str, Any]        =       DoubleLinkedListNode(UpperCAmelCase				,						UpperCAmelCase   )
            self.list.add(self.cache[key]   )
            self.num_keys += 1
  
          else:
            __snake_case   :				Union[str, Any]        =       self.list.remove(self.cache[key]   )
            assert node is not None  # node guaranteed to be in list
            __snake_case   :				Optional[Any]        =       value
            self.list.add(UpperCAmelCase   )
  
  
  
  
  
  
  
        @classmethod
        def 						UpperCAmelCase		( cls				,						UpperCAmelCase = 128   )       ->   Callable[[Callable[[T], U]], Callable[..., U]]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          def cache_decorator_inner(UpperCAmelCase   ) -> Callable[..., U]:
            def cache_decorator_wrapper(*UpperCAmelCase   ) -> U:
              if func not in cls.decorator_function_to_instance_map:
                __snake_case   :				str        =       LFUCache(UpperCAmelCase   )
  
              __snake_case   :				Dict        =       cls.decorator_function_to_instance_map[func].get(args[0]   )
              if result is None:
                __snake_case   :				Optional[int]        =       func(*UpperCAmelCase   )
                cls.decorator_function_to_instance_map[func].put(args[0]				,						UpperCAmelCase   )
              return result
  
            def cache_info() -> LFUCache[T, U]:
              return cls.decorator_function_to_instance_map[func]
  
            setattr(UpperCAmelCase				,						"cache_info"				,						UpperCAmelCase   )  # noqa: B010
  
            return cache_decorator_wrapper
  
          return cache_decorator_inner
  
  
  if __name__ == "__main__":
       import doctest
  
       doctest.testmod()
  
  