



"""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


A    =			logging.get_logger(__name__)






class      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	Optional[int] 							=     ["""pixel_values"""]



		def __init__( self  :    Union[str, Any]      ,							snake_case  :    bool = True      ,							snake_case  :    Dict[str, int] = None      ,							snake_case  :    PILImageResampling = PIL.Image.BICUBIC      ,							snake_case  :    bool = True      ,							snake_case  :    Dict[str, int] = None      ,							snake_case  :    Union[int, float] = 1 / 255      ,							snake_case  :    bool = True      ,							snake_case  :    bool = True      ,							snake_case  :    Optional[Union[float, List[float]]] = None      ,							snake_case  :    Optional[Union[float, List[float]]] = None      ,							**snake_case  :    Union[str, Any]      ,							)			->						None:






									'''simple docstring'''

									super().__init__(**snake_case				)
									A         =					size if size is not None else {'height': 256, 'width': 256}
									A         =					get_size_dict(snake_case				)
									A         =					crop_size if crop_size is not None else {'height': 224, 'width': 224}
									A         =					get_size_dict(snake_case      ,							param_name='crop_size'				)

									A         =					do_resize
									A         =					size
									A         =					resample
									A         =					do_center_crop
									A         =					crop_size
									A         =					do_rescale
									A         =					rescale_factor
									A         =					do_normalize
									A         =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									A         =					image_std if image_std is not None else IMAGENET_STANDARD_STD



		def 					A_		( self  :    List[Any]      ,							snake_case  :    np.ndarray      ,							snake_case  :    Dict[str, int]      ,							snake_case  :    PILImageResampling = PIL.Image.BICUBIC      ,							snake_case  :    Optional[Union[str, ChannelDimension]] = None      ,							**snake_case  :    Tuple      ,							)			->						np.ndarray:






									'''simple docstring'''

									A         =					get_size_dict(snake_case				)
									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(
									    snake_case      ,							size=(size['height'], size['width'])      ,							resample=snake_case      ,							data_format=snake_case      ,							**snake_case				)



		def 					A_		( self  :    Optional[int]      ,							snake_case  :    np.ndarray      ,							snake_case  :    Dict[str, int]      ,							snake_case  :    Optional[Union[str, ChannelDimension]] = None      ,							**snake_case  :    List[Any]      ,							)			->						np.ndarray:






									'''simple docstring'''

									A         =					get_size_dict(snake_case				)
									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(snake_case      ,							size=(size['height'], size['width'])      ,							data_format=snake_case      ,							**snake_case				)



		def 					A_		( self  :    Union[str, Any]      ,							snake_case  :    np.ndarray      ,							snake_case  :    Union[int, float]      ,							snake_case  :    Optional[Union[str, ChannelDimension]] = None      ,							**snake_case  :    Dict      ,							)			->						List[Any]:






									'''simple docstring'''

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



		def 					A_		( self  :    Union[str, Any]      ,							snake_case  :    np.ndarray      ,							snake_case  :    Union[float, List[float]]      ,							snake_case  :    Union[float, List[float]]      ,							snake_case  :    Optional[Union[str, ChannelDimension]] = None      ,							**snake_case  :    List[str]      ,							)			->						np.ndarray:






									'''simple docstring'''

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



		def 					A_		( self  :    Optional[Any]      ,							snake_case  :    ImageInput      ,							snake_case  :    bool = None      ,							snake_case  :    Dict[str, int] = None      ,							snake_case  :    Tuple=None      ,							snake_case  :    bool = None      ,							snake_case  :    Dict[str, int] = None      ,							snake_case  :    bool = None      ,							snake_case  :    float = None      ,							snake_case  :    bool = None      ,							snake_case  :    Optional[Union[float, List[float]]] = None      ,							snake_case  :    Optional[Union[float, List[float]]] = None      ,							snake_case  :    Optional[Union[str, TensorType]] = None      ,							snake_case  :    ChannelDimension = ChannelDimension.FIRST      ,							**snake_case  :    Optional[int]      ,							)			->						PIL.Image.Image:






									'''simple docstring'''

									A         =					do_resize if do_resize is not None else self.do_resize
									A         =					resample if resample is not None else self.resample
									A         =					do_center_crop if do_center_crop is not None else self.do_center_crop
									A         =					do_rescale if do_rescale is not None else self.do_rescale
									A         =					rescale_factor if rescale_factor is not None else self.rescale_factor
									A         =					do_normalize if do_normalize is not None else self.do_normalize
									A         =					image_mean if image_mean is not None else self.image_mean
									A         =					image_std if image_std is not None else self.image_std

									A         =					size if size is not None else self.size
									A         =					get_size_dict(snake_case				)
									A         =					crop_size if crop_size is not None else self.crop_size
									A         =					get_size_dict(snake_case      ,							param_name='crop_size'				)

									A         =					make_list_of_images(snake_case				)

									if not valid_images(snake_case				):
																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.
									A         =					[to_numpy_array(snake_case				) for image in images]

									if do_resize:
																A         =					[self.resize(image=snake_case      ,							size=snake_case      ,							resample=snake_case				) for image in images]

									if do_center_crop:
																A         =					[self.center_crop(image=snake_case      ,							size=snake_case				) for image in images]

									if do_rescale:
																A         =					[self.rescale(image=snake_case      ,							scale=snake_case				) for image in images]

									if do_normalize:
																A         =					[self.normalize(image=snake_case      ,							mean=snake_case      ,							std=snake_case				) for image in images]

									A         =					[to_channel_dimension_format(snake_case      ,							snake_case				) for image in images]

									A         =					{'pixel_values': images}
									return BatchFeature(data=snake_case      ,							tensor_type=snake_case				)








"""simple docstring"""







from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

A    =			TypeVar('T')






class      UpperCAmelCase__		(      Generic[T]     ):
		lowerCAmelCase_       :	deque[T]  # Cache store of keys
		lowerCAmelCase_       :	set[T]  # References of the keys in cache
		lowerCAmelCase_       :	int 							=     10  # Maximum capacity of cache



		def __init__( self  :    int      ,							snake_case  :    int				)			->						None:






									'''simple docstring'''

									A         =					deque()
									A         =					set()
									if not n:
																A         =					sys.maxsize
									elif n < 0:
																raise ValueError('n should be an integer greater than 0.'				)
									else:
																A         =					n



		def 					A_		( self  :    Optional[Any]      ,							snake_case  :    T				)			->						None:






									'''simple docstring'''

									if x not in self.key_reference:
																if len(self.dq_store				) == LRUCache._MAX_CAPACITY:
																							A         =					self.dq_store.pop()
																							self.key_reference.remove(snake_case				)
									else:
																self.dq_store.remove(snake_case				)

									self.dq_store.appendleft(snake_case				)
									self.key_reference.add(snake_case				)



		def 					A_		( self  :    Dict				)			->						None:






									'''simple docstring'''

									for k in self.dq_store:
																print(snake_case				)



		def __repr__( self  :    int				)			->						str:






									'''simple docstring'''

									return f"""LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store				)}"""


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							A    =			LRUCache(4)
							lru_cache.refer('A')
							lru_cache.refer(2)
							lru_cache.refer(3)
							lru_cache.refer('A')
							lru_cache.refer(4)
							lru_cache.refer(5)
							lru_cache.display()

							print(lru_cache)
							assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"



