






'''simple docstring'''


from typing import Dict, Iterable, 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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCamelCase_				: Optional[int]    	=							logging.get_logger(__name__)
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			__a							:							int					     =					["pixel_values"]






			def __init__(   self							:						List[Any] ,      lowercase							:						bool = True ,      lowercase							:						Dict[str, int] = None ,      lowercase							:						PILImageResampling = PILImageResampling.BICUBIC ,      lowercase							:						bool = True ,      lowercase							:						Dict[str, int] = None ,      lowercase							:						bool = True ,      lowercase							:						Union[int, float] = 1 / 2_5_5 ,      lowercase							:						bool = True ,      lowercase							:						Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN ,      lowercase							:						Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD ,      **lowercase							:						Union[str, Any] ,      )							->    None:

									'''simple docstring'''







									super().__init__(**lowercase			)
									UpperCamelCase__         = size if size is not None else {"""shortest_edge""": 2_2_4}
									UpperCamelCase__         = get_size_dict(lowercase ,      default_to_square=lowercase			)
									UpperCamelCase__         = crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
									UpperCamelCase__         = get_size_dict(lowercase ,      param_name="""crop_size"""			)

									UpperCamelCase__         = do_resize
									UpperCamelCase__         = size
									UpperCamelCase__         = resample
									UpperCamelCase__         = do_center_crop
									UpperCamelCase__         = crop_size
									UpperCamelCase__         = do_rescale
									UpperCamelCase__         = rescale_factor
									UpperCamelCase__         = do_normalize
									UpperCamelCase__         = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
									UpperCamelCase__         = image_std if image_std is not None else IMAGENET_DEFAULT_STD






			def 				A      (   self							:						Any ,      lowercase							:						np.ndarray ,      lowercase							:						Dict[str, int] ,      lowercase							:						PILImageResampling = PILImageResampling.BICUBIC ,      lowercase							:						Optional[Union[str, ChannelDimension]] = None ,      **lowercase							:						Dict ,      )							->    np.ndarray:

									'''simple docstring'''







									UpperCamelCase__         = get_size_dict(lowercase ,      default_to_square=lowercase			)
									# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
									if "shortest_edge" in size:
															UpperCamelCase__         = int((2_5_6 / 2_2_4) * size["""shortest_edge"""]			)
															UpperCamelCase__         = get_resize_output_image_size(lowercase ,      size=lowercase ,      default_to_square=lowercase			)
															UpperCamelCase__         = {"""height""": output_size[0], """width""": output_size[1]}
									if "height" not in size_dict or "width" not in size_dict:
															raise ValueError(
															    f"Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"			)
									return resize(
									    lowercase ,      size=(size_dict["""height"""], size_dict["""width"""]) ,      resample=lowercase ,      data_format=lowercase ,      **lowercase			)






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

									'''simple docstring'''







									UpperCamelCase__         = get_size_dict(lowercase			)
									if "height" not in size or "width" not in size:
															raise ValueError(f"Size dict must have keys 'height' and 'width'. Got {size.keys()}"			)
									return center_crop(lowercase ,      size=(size["""height"""], size["""width"""]) ,      data_format=lowercase ,      **lowercase			)






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

									'''simple docstring'''







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






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

									'''simple docstring'''







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







			def 				A      (   self							:						Optional[int] ,      lowercase							:						ImageInput ,      lowercase							:						Optional[bool] = None ,      lowercase							:						Optional[Dict[str, int]] = None ,      lowercase							:						PILImageResampling = None ,      lowercase							:						Optional[bool] = None ,      lowercase							:						Optional[Dict[str, int]] = None ,      lowercase							:						Optional[bool] = None ,      lowercase							:						Optional[float] = None ,      lowercase							:						Optional[bool] = None ,      lowercase							:						Optional[Union[float, Iterable[float]]] = None ,      lowercase							:						Optional[Union[float, Iterable[float]]] = None ,      lowercase							:						Optional[TensorType] = None ,      lowercase							:						ChannelDimension = ChannelDimension.FIRST ,      **lowercase							:						List[str] ,      )							->    BatchFeature:

									'''simple docstring'''







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

									UpperCamelCase__         = size if size is not None else self.size
									UpperCamelCase__         = get_size_dict(lowercase ,      default_to_square=lowercase			)
									UpperCamelCase__         = crop_size if crop_size is not None else self.crop_size
									UpperCamelCase__         = get_size_dict(lowercase ,      param_name="""crop_size"""			)

									UpperCamelCase__         = make_list_of_images(lowercase			)

									if not valid_images(lowercase			):
															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.
									UpperCamelCase__         = [to_numpy_array(lowercase			) for image in images]

									if do_resize:
															UpperCamelCase__         = [self.resize(lowercase ,      lowercase ,      lowercase			) for image in images]

									if do_center_crop:
															UpperCamelCase__         = [self.center_crop(lowercase ,      lowercase			) for image in images]

									if do_rescale:
															UpperCamelCase__         = [self.rescale(lowercase ,      lowercase			) for image in images]

									if do_normalize:
															UpperCamelCase__         = [self.normalize(lowercase ,      lowercase ,      lowercase			) for image in images]

									UpperCamelCase__         = [to_channel_dimension_format(lowercase ,      lowercase			) for image in images]

									UpperCamelCase__         = {"""pixel_values""": images}
									return BatchFeature(data=lowercase ,      tensor_type=lowercase			)








'''simple docstring'''


from __future__ import annotations

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

lowerCamelCase_				: List[str]    	=							TypeVar('''T''')
lowerCamelCase_				: Optional[int]    	=							TypeVar('''U''')
class    _SCREAMING_SNAKE_CASE							(		Generic[T, U]	):


			'''simple docstring'''





			def __init__(   self							:						Dict ,      lowercase							:						T | None ,      lowercase							:						U | None			)							->    List[Any]:

									'''simple docstring'''







									UpperCamelCase__         = key
									UpperCamelCase__         = val
									UpperCamelCase__         = None
									UpperCamelCase__         = None







			def __repr__(   self							:						List[Any]			)							->    str:

									'''simple docstring'''







									return (
									    f"Node: key: {self.key}, val: {self.val}, "
									    f"has next: {bool(self.next			)}, has prev: {bool(self.prev			)}"
									)
class    _SCREAMING_SNAKE_CASE							(		Generic[T, U]	):


			'''simple docstring'''





			def __init__(   self							:						Union[str, Any]			)							->    None:

									'''simple docstring'''







									UpperCamelCase__         = DoubleLinkedListNode(lowercase ,      lowercase			)
									UpperCamelCase__         = DoubleLinkedListNode(lowercase ,      lowercase			)
									UpperCamelCase__    ,   UpperCamelCase__         = self.rear, self.head






			def __repr__(   self							:						int			)							->    str:

									'''simple docstring'''







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






			def 				A      (   self							:						str ,      lowercase							:						DoubleLinkedListNode[T, U]			)							->    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							:						Any ,      lowercase							:						DoubleLinkedListNode[T, U]			)							->    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    _SCREAMING_SNAKE_CASE							(		Generic[T, U]	):


			'''simple docstring'''





			__a							:							dict[Callable[[T], U], LRUCache[T, U]]					     =					{}






			def __init__(   self							:						int ,      lowercase							:						int			)							->    Union[str, Any]:

									'''simple docstring'''







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






			def __repr__(   self							:						Any			)							->    str:

									'''simple docstring'''







									return (
									    f"CacheInfo(hits={self.hits}, misses={self.miss}, "
									    f"capacity={self.capacity}, current size={self.num_keys})"
									)






			def __contains__(   self							:						Any ,      lowercase							:						T			)							->    bool:

									'''simple docstring'''







									return key in self.cache






			def 				A      (   self							:						Tuple ,      lowercase							:						T			)							->    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(lowercase			)
															return node.val
									self.miss += 1
									return None






			def 				A      (   self							:						Dict ,      lowercase							:						T ,      lowercase							:						U			)							->    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(lowercase			) 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(lowercase ,      lowercase			)
															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(lowercase			)







			@classmethod
			def 				A      (   cls							:						Optional[int] ,      lowercase							:						int = 1_2_8			)							->    Callable[[Callable[[T], U]], Callable[..., U]]:

									'''simple docstring'''







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

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

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

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

															return cache_decorator_wrapper

									return cache_decorator_inner


if __name__ == "__main__":
					import doctest

					doctest.testmod()
