






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,
    get_resize_output_image_size,
    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, logging


lowercase    				=   logging.get_logger(__name__)




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					['''pixel_values''']




	def __init__(			self			,     a = True			,     a = None			,     a = PILImageResampling.BILINEAR			,     a = True			,     a = None			,     a = True			,     a = 1 / 2_55			,     a = True			,     a = None			,     a = None			,     **a			,     )			->       None:
								super().__init__(**a   )
								snake_case_           =				size if size is not None else {'shortest_edge': 2_56}
								snake_case_           =				get_size_dict(a			,     default_to_square=a   )
								snake_case_           =				crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
								snake_case_           =				get_size_dict(a   )
								snake_case_           =				do_resize
								snake_case_           =				size
								snake_case_           =				resample
								snake_case_           =				do_center_crop
								snake_case_           =				crop_size
								snake_case_           =				do_rescale
								snake_case_           =				rescale_factor
								snake_case_           =				do_normalize
								snake_case_           =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								snake_case_           =				image_std if image_std is not None else IMAGENET_STANDARD_STD




	def 				_UpperCamelCase     (			self			,     a			,     a			,     a = PILImageResampling.BICUBIC			,     a = None			,     **a			,     )			->       np.ndarray:
								snake_case_           =				get_size_dict(a			,     default_to_square=a   )
								if "shortest_edge" not in size:
															raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''   )
								snake_case_           =				get_resize_output_image_size(a			,     size=size['shortest_edge']			,     default_to_square=a   )
								return resize(a			,     size=a			,     resample=a			,     data_format=a			,     **a   )




	def 				_UpperCamelCase     (			self			,     a			,     a			,     a = None			,     **a			,     )			->       np.ndarray:
								snake_case_           =				get_size_dict(a   )
								return center_crop(a			,     size=(size['height'], size['width'])			,     data_format=a			,     **a   )




	def 				_UpperCamelCase     (			self			,     a			,     a			,     a = None			,     **a   )			->       np.ndarray:
								return rescale(a			,     scale=a			,     data_format=a			,     **a   )




	def 				_UpperCamelCase     (			self			,     a			,     a			,     a			,     a = None			,     **a			,     )			->       np.ndarray:
								return normalize(a			,     mean=a			,     std=a			,     data_format=a			,     **a   )






	def 				_UpperCamelCase     (			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			,     )			->       List[str]:
								snake_case_           =				do_resize if do_resize is not None else self.do_resize
								snake_case_           =				size if size is not None else self.size
								snake_case_           =				get_size_dict(a			,     default_to_square=a   )
								snake_case_           =				resample if resample is not None else self.resample
								snake_case_           =				do_center_crop if do_center_crop is not None else self.do_center_crop
								snake_case_           =				crop_size if crop_size is not None else self.crop_size
								snake_case_           =				get_size_dict(a   )
								snake_case_           =				do_rescale if do_rescale is not None else self.do_rescale
								snake_case_           =				rescale_factor if rescale_factor is not None else self.rescale_factor
								snake_case_           =				do_normalize if do_normalize is not None else self.do_normalize
								snake_case_           =				image_mean if image_mean is not None else self.image_mean
								snake_case_           =				image_std if image_std is not None else self.image_std

								snake_case_           =				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:
															raise ValueError('Size 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_           =				[to_numpy_array(a   ) for image in images]

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

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

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

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

								snake_case_           =				[to_channel_dimension_format(a			,     a   ) for image in images]

								snake_case_           =				{'pixel_values': images}
								return BatchFeature(data=a			,     tensor_type=a   )













from __future__ import annotations

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

lowercase    				=   TypeVar("T")




class 					UpperCamelCase_ (     Generic[T]							):



	'''simple docstring'''




	lowerCAmelCase						=					42  # Cache store of keys
	lowerCAmelCase						=					42  # References of the keys in cache
	lowerCAmelCase						=					1_0  # Maximum capacity of cache




	def __init__(			self			,     a   )			->       None:
								snake_case_           =				deque()
								snake_case_           =				set()
								if not n:
															snake_case_           =				sys.maxsize
								elif n < 0:
															raise ValueError('n should be an integer greater than 0.'   )
								else:
															snake_case_           =				n




	def 				_UpperCamelCase     (			self			,     a   )			->       None:
								if x not in self.key_reference:
															if len(self.dq_store   ) == LRUCache._MAX_CAPACITY:
																						snake_case_           =				self.dq_store.pop()
																						self.key_reference.remove(a   )
								else:
															self.dq_store.remove(a   )

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




	def 				_UpperCamelCase     (			self   )			->       None:
								for k in self.dq_store:
															print(a   )






	def __repr__(			self   )			->       str:
								return F'''LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store   )}'''


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	lowercase    				=   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]"





