

"""simple docstring"""






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


_lowerCAmelCase       						=		logging.get_logger(__name__)







class 						__UpperCamelCase     (     __lowercase    ):
  _UpperCAmelCase        =		["pixel_values"]

  def __init__(		self     ,_A = True     ,_A = None     ,_A = PIL.Image.BICUBIC     ,_A = True     ,_A = None     ,_A = 1 / 255     ,_A = True     ,_A = True     ,_A = None     ,_A = None     ,**_A     ,):
      '''simple docstring'''

      super().__init__(**_A   )
      _lowerCAmelCase							: Dict	=     size if size is not None else {'height': 256, 'width': 256}
      _lowerCAmelCase							: Optional[Any]	=     get_size_dict(_A   )
      _lowerCAmelCase							: Optional[int]	=     crop_size if crop_size is not None else {'height': 224, 'width': 224}
      _lowerCAmelCase							: List[Any]	=     get_size_dict(_A     ,param_name='crop_size'   )

      _lowerCAmelCase							: Dict	=     do_resize
      _lowerCAmelCase							: Union[str, Any]	=     size
      _lowerCAmelCase							: Optional[Any]	=     resample
      _lowerCAmelCase							: Any	=     do_center_crop
      _lowerCAmelCase							: List[Any]	=     crop_size
      _lowerCAmelCase							: str	=     do_rescale
      _lowerCAmelCase							: Tuple	=     rescale_factor
      _lowerCAmelCase							: str	=     do_normalize
      _lowerCAmelCase							: List[str]	=     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      _lowerCAmelCase							: Union[str, Any]	=     image_std if image_std is not None else IMAGENET_STANDARD_STD

  def __lowerCamelCase		(		self     ,_A     ,_A     ,_A = PIL.Image.BICUBIC     ,_A = None     ,**_A     ,):
      '''simple docstring'''

      _lowerCAmelCase							: Optional[int]	=     get_size_dict(_A   )
      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(
          _A     ,size=(size['height'], size['width'])     ,resample=_A     ,data_format=_A     ,**_A   )

  def __lowerCamelCase		(		self     ,_A     ,_A     ,_A = None     ,**_A     ,):
      '''simple docstring'''

      _lowerCAmelCase							: Dict	=     get_size_dict(_A   )
      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(_A     ,size=(size['height'], size['width'])     ,data_format=_A     ,**_A   )

  def __lowerCamelCase		(		self     ,_A     ,_A     ,_A = None     ,**_A     ,):
      '''simple docstring'''

      return rescale(_A     ,scale=_A     ,data_format=_A     ,**_A   )

  def __lowerCamelCase		(		self     ,_A     ,_A     ,_A     ,_A = None     ,**_A     ,):
      '''simple docstring'''

      return normalize(_A     ,mean=_A     ,std=_A     ,data_format=_A     ,**_A   )







  def __lowerCamelCase		(		self     ,_A     ,_A = None     ,_A = None     ,_A=None     ,_A = None     ,_A = None     ,_A = None     ,_A = None     ,_A = None     ,_A = None     ,_A = None     ,_A = None     ,_A = ChannelDimension.FIRST     ,**_A     ,):
      '''simple docstring'''

      _lowerCAmelCase							: Tuple	=     do_resize if do_resize is not None else self.do_resize
      _lowerCAmelCase							: Union[str, Any]	=     resample if resample is not None else self.resample
      _lowerCAmelCase							: List[Any]	=     do_center_crop if do_center_crop is not None else self.do_center_crop
      _lowerCAmelCase							: Tuple	=     do_rescale if do_rescale is not None else self.do_rescale
      _lowerCAmelCase							: int	=     rescale_factor if rescale_factor is not None else self.rescale_factor
      _lowerCAmelCase							: Any	=     do_normalize if do_normalize is not None else self.do_normalize
      _lowerCAmelCase							: Any	=     image_mean if image_mean is not None else self.image_mean
      _lowerCAmelCase							: Any	=     image_std if image_std is not None else self.image_std

      _lowerCAmelCase							: str	=     size if size is not None else self.size
      _lowerCAmelCase							: Any	=     get_size_dict(_A   )
      _lowerCAmelCase							: Tuple	=     crop_size if crop_size is not None else self.crop_size
      _lowerCAmelCase							: Tuple	=     get_size_dict(_A     ,param_name='crop_size'   )

      _lowerCAmelCase							: Any	=     make_list_of_images(_A   )

      if not valid_images(_A   ):
          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.
      _lowerCAmelCase							: List[str]	=     [to_numpy_array(_A   ) for image in images]

      if do_resize:
          _lowerCAmelCase							: Dict	=     [self.resize(image=_A     ,size=_A     ,resample=_A   ) for image in images]

      if do_center_crop:
          _lowerCAmelCase							: Any	=     [self.center_crop(image=_A     ,size=_A   ) for image in images]

      if do_rescale:
          _lowerCAmelCase							: int	=     [self.rescale(image=_A     ,scale=_A   ) for image in images]

      if do_normalize:
          _lowerCAmelCase							: str	=     [self.normalize(image=_A     ,mean=_A     ,std=_A   ) for image in images]

      _lowerCAmelCase							: int	=     [to_channel_dimension_format(_A     ,_A   ) for image in images]

      _lowerCAmelCase							: Union[str, Any]	=     {'pixel_values': images}
      return BatchFeature(data=_A     ,tensor_type=_A   )







from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

lowerCamelCase__						=				TypeVar("T")
lowerCamelCase__						=				TypeVar("U")







class 							lowerCAmelCase__   ( Generic[T, U]						):

						def __init__(      self							,				a							,				a					)       ->   Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase         =						key
								_UpperCamelCase         =						val
								_UpperCamelCase         =						None
								_UpperCamelCase         =						None







						def __repr__(      self					)       ->   str:


								'''simple docstring'''

								return (
								    F'Node: key: {self.key}, val: {self.val}, '
								    F'has next: {bool(self.next					)}, has prev: {bool(self.prev					)}'
								)







class 							lowerCAmelCase__   ( Generic[T, U]						):

						def __init__(      self					)       ->   None:


								'''simple docstring'''

								_UpperCamelCase         =						DoubleLinkedListNode(a							,				a					)
								_UpperCamelCase         =						DoubleLinkedListNode(a							,				a					)
								_UpperCamelCase     ,						_UpperCamelCase         =						self.rear, self.head

						def __repr__(      self					)       ->   str:


								'''simple docstring'''

								_UpperCamelCase         =						["""DoubleLinkedList"""]
								_UpperCamelCase         =						self.head
								while node.next is not None:
										rep.append(str(a					)					)
										_UpperCamelCase         =						node.next
								rep.append(str(self.rear					)					)
								return ",\n    ".join(a					)

						def 			A_					(      self							,				a					)       ->   None:


								'''simple docstring'''

								_UpperCamelCase         =						self.rear.prev

								# All nodes other than self.head are guaranteed to have non-None previous
								assert previous is not None

								_UpperCamelCase         =						node
								_UpperCamelCase         =						previous
								_UpperCamelCase         =						node
								_UpperCamelCase         =						self.rear







						def 			A_					(      self							,				a					)       ->   DoubleLinkedListNode[T, U] | None:


								'''simple docstring'''

								if node.prev is None or node.next is None:
										return None

								_UpperCamelCase         =						node.next
								_UpperCamelCase         =						node.prev
								_UpperCamelCase         =						None
								_UpperCamelCase         =						None
								return node







class 							lowerCAmelCase__   ( Generic[T, U]						):
						UpperCamelCase_				:			dict[Callable[[T], U], LRUCache[T, U]]						=		{}

						def __init__(      self							,				a					)       ->   int:


								'''simple docstring'''

								_UpperCamelCase         =						DoubleLinkedList()
								_UpperCamelCase         =						capacity
								_UpperCamelCase         =						0
								_UpperCamelCase         =						0
								_UpperCamelCase         =						0
								_UpperCamelCase         =						{}

						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							,				a					)       ->   bool:


								'''simple docstring'''

								return key in self.cache

						def 			A_					(      self							,				a					)       ->   U | None:


								'''simple docstring'''

								if key in self.cache:
										self.hits += 1
										_UpperCamelCase         =						self.cache[key]
										_UpperCamelCase         =						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(a					)
										return node.val
								self.miss += 1
								return None

						def 			A_					(      self							,				a							,				a					)       ->   None:


								'''simple docstring'''

								if key not in self.cache:
										if self.num_keys >= self.capacity:
												# delete first node (oldest) when over capacity
												_UpperCamelCase         =						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(a					) is not None
												)  # node guaranteed to be in list assert node.key is not None

												del self.cache[first_node.key]
												self.num_keys -= 1
										_UpperCamelCase         =						DoubleLinkedListNode(a							,				a					)
										self.list.add(self.cache[key]					)
										self.num_keys += 1

								else:
										# bump node to the end of the list, update value
										_UpperCamelCase         =						self.list.remove(self.cache[key]					)
										assert node is not None  # node guaranteed to be in list
										_UpperCamelCase         =						value
										self.list.add(a					)







						@classmethod
						def 			A_					(      cls							,				a = 1_28					)       ->   Callable[[Callable[[T], U]], Callable[..., U]]:


								'''simple docstring'''

								def cache_decorator_inner(a					) -> Callable[..., U]:
										def cache_decorator_wrapper(*a					) -> U:
												if func not in cls.decorator_function_to_instance_map:
														_UpperCamelCase         =						LRUCache(a					)

												_UpperCamelCase         =						cls.decorator_function_to_instance_map[func].get(args[0]					)
												if result is None:
														_UpperCamelCase         =						func(*a					)
														cls.decorator_function_to_instance_map[func].put(args[0]							,				a					)
												return result

										def cache_info() -> LRUCache[T, U]:
												return cls.decorator_function_to_instance_map[func]

										setattr(a							,				"""cache_info"""							,				a					)  # noqa: B010

										return cache_decorator_wrapper

								return cache_decorator_inner


if __name__ == "__main__":
		import doctest

		doctest.testmod()



