
"""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 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


__a			 =   logging.get_logger(__name__)
class 							lowerCamelCase       (			__SCREAMING_SNAKE_CASE ):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      ["""pixel_values"""]






							def __init__(						self :     Dict		, lowerCamelCase_ :     bool = True		, lowerCamelCase_ :     Optional[Dict[str, int]] = None		, lowerCamelCase_ :     PILImageResampling = PILImageResampling.BILINEAR		, lowerCamelCase_ :     bool = True		, lowerCamelCase_ :     Union[int, float] = 1 / 255		, lowerCamelCase_ :     bool = True		, lowerCamelCase_ :     Optional[Union[float, List[float]]] = None		, lowerCamelCase_ :     Optional[Union[float, List[float]]] = None		, **lowerCamelCase_ :     Any		, ):
													super().__init__(**lowerCamelCase_				)
													UpperCAmelCase__		=    size if size is not None else {'''height''': 224, '''width''': 224}
													UpperCAmelCase__		=    get_size_dict(lowerCamelCase_				)
													UpperCAmelCase__		=    do_resize
													UpperCAmelCase__		=    do_rescale
													UpperCAmelCase__		=    do_normalize
													UpperCAmelCase__		=    size
													UpperCAmelCase__		=    resample
													UpperCAmelCase__		=    rescale_factor
													UpperCAmelCase__		=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													UpperCAmelCase__		=    image_std if image_std is not None else IMAGENET_STANDARD_STD






							def 					lowerCAmelCase     (						self :     Union[str, Any]		, lowerCamelCase_ :     np.ndarray		, lowerCamelCase_ :     Dict[str, int]		, lowerCamelCase_ :     PILImageResampling = PILImageResampling.BILINEAR		, lowerCamelCase_ :     Optional[Union[str, ChannelDimension]] = None		, **lowerCamelCase_ :     str		, ):
													UpperCAmelCase__		=    get_size_dict(lowerCamelCase_				)
													if "height" not in size or "width" not in size:
																			raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""				)
													return resize(
													    lowerCamelCase_		, size=(size['''height'''], size['''width'''])		, resample=lowerCamelCase_		, data_format=lowerCamelCase_		, **lowerCamelCase_				)






							def 					lowerCAmelCase     (						self :     str		, lowerCamelCase_ :     np.ndarray		, lowerCamelCase_ :     float		, lowerCamelCase_ :     Optional[Union[str, ChannelDimension]] = None		, **lowerCamelCase_ :     int				):
													return rescale(lowerCamelCase_		, scale=lowerCamelCase_		, data_format=lowerCamelCase_		, **lowerCamelCase_				)






							def 					lowerCAmelCase     (						self :     List[Any]		, lowerCamelCase_ :     np.ndarray		, lowerCamelCase_ :     Union[float, List[float]]		, lowerCamelCase_ :     Union[float, List[float]]		, lowerCamelCase_ :     Optional[Union[str, ChannelDimension]] = None		, **lowerCamelCase_ :     Dict		, ):
													return normalize(lowerCamelCase_		, mean=lowerCamelCase_		, std=lowerCamelCase_		, data_format=lowerCamelCase_		, **lowerCamelCase_				)






							def 					lowerCAmelCase     (						self :     Dict		, lowerCamelCase_ :     ImageInput		, lowerCamelCase_ :     Optional[bool] = None		, lowerCamelCase_ :     Dict[str, int] = None		, lowerCamelCase_ :     PILImageResampling = None		, lowerCamelCase_ :     Optional[bool] = None		, lowerCamelCase_ :     Optional[float] = None		, lowerCamelCase_ :     Optional[bool] = None		, lowerCamelCase_ :     Optional[Union[float, List[float]]] = None		, lowerCamelCase_ :     Optional[Union[float, List[float]]] = None		, lowerCamelCase_ :     Optional[Union[str, TensorType]] = None		, lowerCamelCase_ :     Union[str, ChannelDimension] = ChannelDimension.FIRST		, **lowerCamelCase_ :     Dict		, ):
													UpperCAmelCase__		=    do_resize if do_resize is not None else self.do_resize
													UpperCAmelCase__		=    do_rescale if do_rescale is not None else self.do_rescale
													UpperCAmelCase__		=    do_normalize if do_normalize is not None else self.do_normalize
													UpperCAmelCase__		=    resample if resample is not None else self.resample
													UpperCAmelCase__		=    rescale_factor if rescale_factor is not None else self.rescale_factor
													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(lowerCamelCase_				)

													UpperCAmelCase__		=    make_list_of_images(lowerCamelCase_				)

													if not valid_images(lowerCamelCase_				):
																			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_rescale and rescale_factor is None:
																			raise ValueError('''Rescale factor must be specified if do_rescale is True.'''				)

													# All transformations expect numpy arrays.
													UpperCAmelCase__		=    [to_numpy_array(lowerCamelCase_				) for image in images]

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

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

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

													UpperCAmelCase__		=    [to_channel_dimension_format(lowerCamelCase_		, lowerCamelCase_				) for image in images]

													UpperCAmelCase__		=    {'''pixel_values''': images}
													return BatchFeature(data=lowerCamelCase_		, tensor_type=lowerCamelCase_				)



"""simple docstring"""







from __future__ import annotations

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

__a			 =   TypeVar("T")
__a			 =   TypeVar("U")
class 							lowerCamelCase       (			Generic[T, U] ):


							"""simple docstring"""






							def __init__(						self :     str		, lowerCamelCase_ :     T | None		, lowerCamelCase_ :     U | None				):
													UpperCAmelCase__		=    key
													UpperCAmelCase__		=    val
													UpperCAmelCase__		=    0
													UpperCAmelCase__		=    None
													UpperCAmelCase__		=    None






							def __repr__(						self :     Tuple				):
													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 :     List[str]				):
													UpperCAmelCase__		=    DoubleLinkedListNode(lowerCamelCase_		, lowerCamelCase_				)
													UpperCAmelCase__		=    DoubleLinkedListNode(lowerCamelCase_		, lowerCamelCase_				)
													UpperCAmelCase__	,				UpperCAmelCase__		=    self.rear, self.head






							def __repr__(						self :     int				):
													UpperCAmelCase__		=    ['''DoubleLinkedList''']
													UpperCAmelCase__		=    self.head
													while node.next is not None:
																			rep.append(str(lowerCamelCase_				)				)
																			UpperCAmelCase__		=    node.next
													rep.append(str(self.rear				)				)
													return ",\n    ".join(lowerCamelCase_				)






							def 					lowerCAmelCase     (						self :     List[Any]		, lowerCamelCase_ :     DoubleLinkedListNode[T, U]				):
													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
													node.freq += 1
													self._position_node(lowerCamelCase_				)






							def 					lowerCAmelCase     (						self :     Optional[Any]		, lowerCamelCase_ :     DoubleLinkedListNode[T, U]				):

													while node.prev is not None and node.prev.freq > node.freq:
																			# swap node with previous node
																			UpperCAmelCase__		=    node.prev

																			UpperCAmelCase__		=    previous_node.prev
																			UpperCAmelCase__		=    node.prev
																			UpperCAmelCase__		=    previous_node
																			UpperCAmelCase__		=    node






							def 					lowerCAmelCase     (						self :     Union[str, Any]		, lowerCamelCase_ :     DoubleLinkedListNode[T, U]				):

													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] ):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      {}






							def __init__(						self :     str		, lowerCamelCase_ :     int				):
													UpperCAmelCase__		=    DoubleLinkedList()
													UpperCAmelCase__		=    capacity
													UpperCAmelCase__		=    0
													UpperCAmelCase__		=    0
													UpperCAmelCase__		=    0
													UpperCAmelCase__		=    {}






							def __repr__(						self :     Tuple				):
													return (
													    f"""CacheInfo(hits={self.hits}, misses={self.miss}, """
													    f"""capacity={self.capacity}, current_size={self.num_keys})"""
													)






							def __contains__(						self :     Optional[Any]		, lowerCamelCase_ :     T				):
													return key in self.cache






							def 					lowerCAmelCase     (						self :     Any		, lowerCamelCase_ :     T				):

													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(lowerCamelCase_				)
																			return node.val
													self.miss += 1
													return None






							def 					lowerCAmelCase     (						self :     Tuple		, lowerCamelCase_ :     T		, lowerCamelCase_ :     U				):

													if key not in self.cache:
																			if self.num_keys >= self.capacity:
																									# delete first node 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(lowerCamelCase_				) is not None
																									# first_node guaranteed to be in list

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

													else:
																			UpperCAmelCase__		=    self.list.remove(self.cache[key]				)
																			assert node is not None  # node guaranteed to be in list
																			UpperCAmelCase__		=    value
																			self.list.add(lowerCamelCase_				)






							@classmethod
							def 					lowerCAmelCase     (						cls :     type[LFUCache[T, U]]		, lowerCamelCase_ :     int = 128				):

													def cache_decorator_inner(lowerCamelCase_ :     Callable[[T], U]				) -> Callable[..., U]:
																			def cache_decorator_wrapper(*lowerCamelCase_ :     T				) -> U:
																									if func not in cls.decorator_function_to_instance_map:
																															UpperCAmelCase__		=    LFUCache(lowerCamelCase_				)

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

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

																			setattr(lowerCamelCase_		, '''cache_info'''		, lowerCamelCase_				)  # noqa: B010

																			return cache_decorator_wrapper

													return cache_decorator_inner


if __name__ == "__main__":
						import doctest

						doctest.testmod()