
'''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,
    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


a_											=						logging.get_logger(__name__)






class    UpperCAmelCase_				(	lowercase__					):
 UpperCAmelCase_		    =						["""pixel_values"""]





 def __init__(    self ,       lowercase_ = True ,       lowercase_ = None ,       lowercase_ = PILImageResampling.BILINEAR ,       lowercase_ = True ,       lowercase_ = None ,       lowercase_ = True ,       lowercase_ = 1 / 2_55 ,       lowercase_ = True ,       lowercase_ = None ,       lowercase_ = None ,       **lowercase_ ,       ):
     super().__init__(**lowercase_)
     snake_case_ :    List[str]							=						size if size is not None else {"shortest_edge": 2_56}
     snake_case_ :    Any							=						get_size_dict(lowercase_ ,       default_to_square=lowercase_)
     snake_case_ :    List[Any]							=						crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
     snake_case_ :    Dict							=						get_size_dict(lowercase_)
     snake_case_ :    List[str]							=						do_resize
     snake_case_ :    List[str]							=						size
     snake_case_ :    List[Any]							=						resample
     snake_case_ :    int							=						do_center_crop
     snake_case_ :    Optional[Any]							=						crop_size
     snake_case_ :    Any							=						do_rescale
     snake_case_ :    Any							=						rescale_factor
     snake_case_ :    int							=						do_normalize
     snake_case_ :    str							=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     snake_case_ :    int							=						image_std if image_std is not None else IMAGENET_STANDARD_STD





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_ = PILImageResampling.BICUBIC ,       lowercase_ = None ,       **lowercase_ ,       ):
     snake_case_ :    List[Any]							=						get_size_dict(lowercase_ ,       default_to_square=lowercase_)
     if "shortest_edge" not in size:
         raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}')
     snake_case_ :    Optional[int]							=						get_resize_output_image_size(lowercase_ ,       size=size["shortest_edge"] ,       default_to_square=lowercase_)
     return resize(lowercase_ ,       size=lowercase_ ,       resample=lowercase_ ,       data_format=lowercase_ ,       **lowercase_)





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_ = None ,       **lowercase_ ,       ):
     snake_case_ :    Tuple							=						get_size_dict(lowercase_)
     return center_crop(lowercase_ ,       size=(size["height"], size["width"]) ,       data_format=lowercase_ ,       **lowercase_)





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_ = None ,       **lowercase_):
     return rescale(lowercase_ ,       scale=lowercase_ ,       data_format=lowercase_ ,       **lowercase_)





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ = None ,       **lowercase_ ,       ):
     return normalize(lowercase_ ,       mean=lowercase_ ,       std=lowercase_ ,       data_format=lowercase_ ,       **lowercase_)





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ = None ,       lowercase_ = None ,       lowercase_ = None ,       lowercase_ = None ,       lowercase_ = None ,       lowercase_ = None ,       lowercase_ = None ,       lowercase_ = None ,       lowercase_ = None ,       lowercase_ = None ,       lowercase_ = None ,       lowercase_ = ChannelDimension.FIRST ,       **lowercase_ ,       ):
     snake_case_ :    Optional[Any]							=						do_resize if do_resize is not None else self.do_resize
     snake_case_ :    Union[str, Any]							=						size if size is not None else self.size
     snake_case_ :    Dict							=						get_size_dict(lowercase_ ,       default_to_square=lowercase_)
     snake_case_ :    str							=						resample if resample is not None else self.resample
     snake_case_ :    List[str]							=						do_center_crop if do_center_crop is not None else self.do_center_crop
     snake_case_ :    Optional[int]							=						crop_size if crop_size is not None else self.crop_size
     snake_case_ :    Dict							=						get_size_dict(lowercase_)
     snake_case_ :    List[str]							=						do_rescale if do_rescale is not None else self.do_rescale
     snake_case_ :    int							=						rescale_factor if rescale_factor is not None else self.rescale_factor
     snake_case_ :    Dict							=						do_normalize if do_normalize is not None else self.do_normalize
     snake_case_ :    Optional[int]							=						image_mean if image_mean is not None else self.image_mean
     snake_case_ :    Tuple							=						image_std if image_std is not None else self.image_std

     snake_case_ :    Union[str, Any]							=						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.
     snake_case_ :    List[str]							=						[to_numpy_array(lowercase_) for image in images]

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

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

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

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

     snake_case_ :    Dict							=						[to_channel_dimension_format(lowercase_ ,       lowercase_) for image in images]

     snake_case_ :    Optional[int]							=						{"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

a_											=						TypeVar("T")
a_											=						TypeVar("U")






class    UpperCAmelCase_				(	Generic[T, U]					):





 def __init__(    self ,       lowercase_ ,       lowercase_):
     snake_case_ :    Any     			=						key
     snake_case_ :    List[Any]     			=						val
     snake_case_ :    DoubleLinkedListNode[T, U] | None     			=						None
     snake_case_ :    DoubleLinkedListNode[T, U] | None     			=						None





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






class    UpperCAmelCase_				(	Generic[T, U]					):





 def __init__(    self):
     snake_case_ :    DoubleLinkedListNode[T, U]     			=						DoubleLinkedListNode(lowercase_ ,       lowercase_)
     snake_case_ :    DoubleLinkedListNode[T, U]     			=						DoubleLinkedListNode(lowercase_ ,       lowercase_)
     snake_case_			, snake_case_ :    Union[str, Any]     			=						self.rear, self.head





 def __repr__(    self):
     snake_case_ :    Dict     			=						["DoubleLinkedList"]
     snake_case_ :    Dict     			=						self.head
     while node.next is not None:
         rep.append(str(lowercase_))
         snake_case_ :    List[str]     			=						node.next
     rep.append(str(self.rear))
     return ",\n    ".join(lowercase_)





 def        snake_case__		(    self ,       lowercase_):
     snake_case_ :    List[str]     			=						self.rear.prev

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

     snake_case_ :    Tuple     			=						node
     snake_case_ :    str     			=						previous
     snake_case_ :    Optional[Any]     			=						node
     snake_case_ :    Any     			=						self.rear





 def        snake_case__		(    self ,       lowercase_):

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

     snake_case_ :    Union[str, Any]     			=						node.next
     snake_case_ :    Optional[int]     			=						node.prev
     snake_case_ :    str     			=						None
     snake_case_ :    int     			=						None
     return node






class    UpperCAmelCase_				(	Generic[T, U]					):
 UpperCAmelCase_		    =						{}





 def __init__(    self ,       lowercase_):
     snake_case_ :    DoubleLinkedList[T, U]     			=						DoubleLinkedList()
     snake_case_ :    List[str]     			=						capacity
     snake_case_ :    Any     			=						0
     snake_case_ :    Dict     			=						0
     snake_case_ :    Union[str, Any]     			=						0
     snake_case_ :    dict[T, DoubleLinkedListNode[T, U]]     			=						{}





 def __repr__(    self):
     return (
         F'CacheInfo(hits={self.hits}, misses={self.miss}, '
         F'capacity={self.capacity}, current size={self.num_keys})'
     )





 def __contains__(    self ,       lowercase_):
     return key in self.cache





 def        snake_case__		(    self ,       lowercase_):
     # Note: pythonic interface would throw KeyError rather than return None

     if key in self.cache:
         self.hits += 1
         snake_case_ :    DoubleLinkedListNode[T, U]     			=						self.cache[key]
         snake_case_ :    Optional[Any]     			=						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        snake_case__		(    self ,       lowercase_ ,       lowercase_):

     if key not in self.cache:
         if self.num_keys >= self.capacity:
             # delete first node (oldest) when over capacity
             snake_case_ :    Optional[Any]     			=						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
         snake_case_ :    Dict     			=						DoubleLinkedListNode(lowercase_ ,       lowercase_)
         self.list.add(self.cache[key])
         self.num_keys += 1

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





 @classmethod
 def        snake_case__		(    cls ,       lowercase_ = 1_28):

     def cache_decorator_inner(lowercase_) -> Callable[..., U]:
         def cache_decorator_wrapper(*lowercase_) -> U:
             if func not in cls.decorator_function_to_instance_map:
                 snake_case_ :    List[str]     			=						LRUCache(lowercase_)

             snake_case_ :    Optional[Any]     			=						cls.decorator_function_to_instance_map[func].get(args[0])
             if result is None:
                 snake_case_ :    Any     			=						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()




