


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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


_snake_case   = logging.get_logger(__name__)


if is_vision_available():
      import PIL




class    A_   ( a_     ):







 """simple docstring"""





 _UpperCamelCase				: str      =					['''pixel_values''']
 def __init__(			self					: Optional[int]       ,    _a					: bool = True       ,    _a					: Dict[str, int] = None       ,    _a					: PILImageResampling = PILImageResampling.BILINEAR       ,    _a					: bool = True       ,    _a					: bool = False       ,    _a					: bool = True       ,    _a					: bool = True       ,    _a					: Union[int, float] = 1 / 2_5_5       ,    _a					: bool = True       ,    _a					: Optional[Union[float, List[float]]] = None       ,    _a					: Optional[Union[float, List[float]]] = None       ,    **_a					: Optional[Any]       ,    )    ->			None:
      super().__init__(**_a				)

      UpperCAmelCase						:				Optional[int]		  =			size if size is not None else {"height": 2_5_6_0, "width": 1_9_2_0}
      if isinstance(_a       ,    (tuple, list)				):
           # The previous feature extractor size parameter was in (width, height) format
           UpperCAmelCase						:				Dict		  =			size[::-1]
      UpperCAmelCase						:				Tuple		  =			get_size_dict(_a				)

      UpperCAmelCase						:				Optional[int]		  =			do_resize
      UpperCAmelCase						:				Tuple		  =			size
      UpperCAmelCase						:				str		  =			resample
      UpperCAmelCase						:				Optional[Any]		  =			do_thumbnail
      UpperCAmelCase						:				Optional[int]		  =			do_align_long_axis
      UpperCAmelCase						:				List[str]		  =			do_pad
      UpperCAmelCase						:				Dict		  =			do_rescale
      UpperCAmelCase						:				Dict		  =			rescale_factor
      UpperCAmelCase						:				Union[str, Any]		  =			do_normalize
      UpperCAmelCase						:				Any		  =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      UpperCAmelCase						:				List[str]		  =			image_std if image_std is not None else IMAGENET_STANDARD_STD
 def   _UpperCamelCase    (			self					: Dict       ,    _a					: np.ndarray       ,    _a					: Dict[str, int]       ,    _a					: Optional[Union[str, ChannelDimension]] = None				)    ->			np.ndarray:
      UpperCAmelCase  , UpperCAmelCase						:				Any		  =			get_image_size(_a				)
      UpperCAmelCase  , UpperCAmelCase						:				Optional[Any]		  =			size["height"], size["width"]

      if (output_width < output_height and input_width > input_height) or (
          output_width > output_height and input_width < input_height
      ):
           UpperCAmelCase						:				Union[str, Any]		  =			np.rotaa(_a       ,    3				)

      if data_format is not None:
           UpperCAmelCase						:				Any		  =			to_channel_dimension_format(_a       ,    _a				)

      return image
 def   _UpperCamelCase    (			self					: int       ,    _a					: np.ndarray       ,    _a					: Dict[str, int]       ,    _a					: bool = False       ,    _a					: Optional[Union[str, ChannelDimension]] = None       ,    )    ->			np.ndarray:
      UpperCAmelCase  , UpperCAmelCase						:				str		  =			size["height"], size["width"]
      UpperCAmelCase  , UpperCAmelCase						:				List[str]		  =			get_image_size(_a				)

      UpperCAmelCase						:				List[str]		  =			output_width - input_width
      UpperCAmelCase						:				Any		  =			output_height - input_height

      if random_padding:
           UpperCAmelCase						:				Tuple		  =			np.random.randint(low=0       ,    high=delta_height + 1				)
           UpperCAmelCase						:				Optional[int]		  =			np.random.randint(low=0       ,    high=delta_width + 1				)
      else:
           UpperCAmelCase						:				str		  =			delta_height // 2
           UpperCAmelCase						:				Union[str, Any]		  =			delta_width // 2

      UpperCAmelCase						:				Dict		  =			delta_height - pad_top
      UpperCAmelCase						:				Optional[Any]		  =			delta_width - pad_left

      UpperCAmelCase						:				List[str]		  =			((pad_top, pad_bottom), (pad_left, pad_right))
      return pad(_a       ,    _a       ,    data_format=_a				)
 def   _UpperCamelCase    (			self					: int       ,    *_a					: Tuple       ,    **_a					: str				)    ->			List[str]:
      logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."				)
      return self.pad_image(*_a       ,    **_a				)
 def   _UpperCamelCase    (			self					: Dict       ,    _a					: np.ndarray       ,    _a					: Dict[str, int]       ,    _a					: PILImageResampling = PILImageResampling.BICUBIC       ,    _a					: Optional[Union[str, ChannelDimension]] = None       ,    **_a					: Union[str, Any]       ,    )    ->			np.ndarray:
      UpperCAmelCase  , UpperCAmelCase						:				int		  =			get_image_size(_a				)
      UpperCAmelCase  , UpperCAmelCase						:				Dict		  =			size["height"], size["width"]

      # We always resize to the smallest of either the input or output size.
      UpperCAmelCase						:				Any		  =			min(_a       ,    _a				)
      UpperCAmelCase						:				List[Any]		  =			min(_a       ,    _a				)

      if height == input_height and width == input_width:
           return image

      if input_height > input_width:
           UpperCAmelCase						:				Dict		  =			int(input_width * height / input_height				)
      elif input_width > input_height:
           UpperCAmelCase						:				Tuple		  =			int(input_height * width / input_width				)

      return resize(
          _a       ,    size=(height, width)       ,    resample=_a       ,    reducing_gap=2.0       ,    data_format=_a       ,    **_a				)
 def   _UpperCamelCase    (			self					: List[Any]       ,    _a					: np.ndarray       ,    _a					: Dict[str, int]       ,    _a					: PILImageResampling = PILImageResampling.BICUBIC       ,    _a					: Optional[Union[str, ChannelDimension]] = None       ,    **_a					: Dict       ,    )    ->			np.ndarray:
      UpperCAmelCase						:				Any		  =			get_size_dict(_a				)
      UpperCAmelCase						:				int		  =			min(size["height"]       ,    size["width"]				)
      UpperCAmelCase						:				List[Any]		  =			get_resize_output_image_size(_a       ,    size=_a       ,    default_to_square=_a				)
      UpperCAmelCase						:				List[str]		  =			resize(_a       ,    size=_a       ,    resample=_a       ,    data_format=_a       ,    **_a				)
      return resized_image
 def   _UpperCamelCase    (			self					: int       ,    _a					: np.ndarray       ,    _a					: Union[int, float]       ,    _a					: Optional[Union[str, ChannelDimension]] = None       ,    **_a					: Union[str, Any]       ,    )    ->			Any:
      return rescale(_a       ,    scale=_a       ,    data_format=_a       ,    **_a				)
 def   _UpperCamelCase    (			self					: Optional[Any]       ,    _a					: np.ndarray       ,    _a					: Union[float, List[float]]       ,    _a					: Union[float, List[float]]       ,    _a					: Optional[Union[str, ChannelDimension]] = None       ,    **_a					: Optional[Any]       ,    )    ->			np.ndarray:
      return normalize(_a       ,    mean=_a       ,    std=_a       ,    data_format=_a       ,    **_a				)

 def   _UpperCamelCase    (			self					: Tuple       ,    _a					: ImageInput       ,    _a					: bool = None       ,    _a					: Dict[str, int] = None       ,    _a					: PILImageResampling = None       ,    _a					: bool = None       ,    _a					: bool = None       ,    _a					: bool = None       ,    _a					: bool = False       ,    _a					: bool = None       ,    _a					: float = None       ,    _a					: bool = None       ,    _a					: Optional[Union[float, List[float]]] = None       ,    _a					: Optional[Union[float, List[float]]] = None       ,    _a					: Optional[Union[str, TensorType]] = None       ,    _a					: Optional[ChannelDimension] = ChannelDimension.FIRST       ,    **_a					: Union[str, Any]       ,    )    ->			PIL.Image.Image:
      UpperCAmelCase						:				int		  =			do_resize if do_resize is not None else self.do_resize
      UpperCAmelCase						:				Tuple		  =			size if size is not None else self.size
      if isinstance(_a       ,    (tuple, list)				):
           # Previous feature extractor had size in (width, height) format
           UpperCAmelCase						:				Optional[Any]		  =			size[::-1]
      UpperCAmelCase						:				int		  =			get_size_dict(_a				)
      UpperCAmelCase						:				Optional[int]		  =			resample if resample is not None else self.resample
      UpperCAmelCase						:				Dict		  =			do_thumbnail if do_thumbnail is not None else self.do_thumbnail
      UpperCAmelCase						:				Tuple		  =			do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
      UpperCAmelCase						:				Optional[int]		  =			do_pad if do_pad is not None else self.do_pad
      UpperCAmelCase						:				Optional[Any]		  =			do_rescale if do_rescale is not None else self.do_rescale
      UpperCAmelCase						:				List[str]		  =			rescale_factor if rescale_factor is not None else self.rescale_factor
      UpperCAmelCase						:				List[Any]		  =			do_normalize if do_normalize is not None else self.do_normalize
      UpperCAmelCase						:				str		  =			image_mean if image_mean is not None else self.image_mean
      UpperCAmelCase						:				Dict		  =			image_std if image_std is not None else self.image_std

      UpperCAmelCase						:				Any		  =			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_rescale and rescale_factor is None:
           raise ValueError("Rescale factor must be specified if do_rescale is True."				)

      if do_pad and size is None:
           raise ValueError("Size must be specified if do_pad 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						:				Optional[Any]		  =			[to_numpy_array(_a				) for image in images]

      if do_align_long_axis:
           UpperCAmelCase						:				Any		  =			[self.align_long_axis(_a       ,    size=_a				) for image in images]

      if do_resize:
           UpperCAmelCase						:				List[str]		  =			[self.resize(image=_a       ,    size=_a       ,    resample=_a				) for image in images]

      if do_thumbnail:
           UpperCAmelCase						:				List[Any]		  =			[self.thumbnail(image=_a       ,    size=_a				) for image in images]

      if do_pad:
           UpperCAmelCase						:				Tuple		  =			[self.pad_image(image=_a       ,    size=_a       ,    random_padding=_a				) for image in images]

      if do_rescale:
           UpperCAmelCase						:				Optional[Any]		  =			[self.rescale(image=_a       ,    scale=_a				) for image in images]

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

      UpperCAmelCase						:				Dict		  =			[to_channel_dimension_format(_a       ,    _a				) for image in images]

      UpperCAmelCase						:				int		  =			{"pixel_values": images}
      return BatchFeature(data=_a       ,    tensor_type=_a				)







from __future__ import annotations

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

_snake_case   = TypeVar("T")
_snake_case   = TypeVar("U")




class    A_   ( Generic[T, U]     ):







 """simple docstring"""





 def __init__(			self					: List[Any]       ,    _a					: T | None       ,    _a					: U | None				)    ->			Dict:
      UpperCAmelCase						:				str		  =			key
      UpperCAmelCase						:				Dict		  =			val
      UpperCAmelCase						:				int		  =			0
      UpperCAmelCase						:				DoubleLinkedListNode[T, U] | None		  =			None
      UpperCAmelCase						:				DoubleLinkedListNode[T, U] | None		  =			None

 def __repr__(			self					: List[str]				)    ->			str:
      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    A_   ( Generic[T, U]     ):







 """simple docstring"""





 def __init__(			self					: Any				)    ->			None:
      UpperCAmelCase						:				DoubleLinkedListNode[T, U]		  =			DoubleLinkedListNode(_a       ,    _a				)
      UpperCAmelCase						:				DoubleLinkedListNode[T, U]		  =			DoubleLinkedListNode(_a       ,    _a				)
      UpperCAmelCase  , UpperCAmelCase						:				Optional[int]		  =			self.rear, self.head
 def __repr__(			self					: int				)    ->			str:
      UpperCAmelCase						:				Union[str, Any]		  =			["DoubleLinkedList"]
      UpperCAmelCase						:				int		  =			self.head
      while node.next is not None:
           rep.append(str(_a				)				)
           UpperCAmelCase						:				Union[str, Any]		  =			node.next
      rep.append(str(self.rear				)				)
      return ",\n    ".join(_a				)
 def   _UpperCamelCase    (			self					: List[Any]       ,    _a					: DoubleLinkedListNode[T, U]				)    ->			None:
      UpperCAmelCase						:				str		  =			self.rear.prev

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

      UpperCAmelCase						:				Any		  =			node
      UpperCAmelCase						:				Optional[int]		  =			previous
      UpperCAmelCase						:				Optional[int]		  =			node
      UpperCAmelCase						:				Union[str, Any]		  =			self.rear
      node.freq += 1
      self._position_node(_a				)
 def   _UpperCamelCase    (			self					: Any       ,    _a					: DoubleLinkedListNode[T, U]				)    ->			None:

      while node.prev is not None and node.prev.freq > node.freq:
           # swap node with previous node
           UpperCAmelCase						:				List[str]		  =			node.prev

           UpperCAmelCase						:				Any		  =			previous_node.prev
           UpperCAmelCase						:				int		  =			node.prev
           UpperCAmelCase						:				Tuple		  =			previous_node
           UpperCAmelCase						:				Optional[int]		  =			node

 def   _UpperCamelCase    (			self					: str       ,    _a					: DoubleLinkedListNode[T, U]				)    ->			DoubleLinkedListNode[T, U] | None:

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

      UpperCAmelCase						:				Dict		  =			node.next
      UpperCAmelCase						:				Optional[Any]		  =			node.prev
      UpperCAmelCase						:				List[Any]		  =			None
      UpperCAmelCase						:				int		  =			None
      return node




class    A_   ( Generic[T, U]     ):







 """simple docstring"""





 _UpperCamelCase				: dict[Callable[[T], U], LFUCache[T, U]]      =					{}
 def __init__(			self					: List[Any]       ,    _a					: int				)    ->			int:
      UpperCAmelCase						:				DoubleLinkedList[T, U]		  =			DoubleLinkedList()
      UpperCAmelCase						:				Any		  =			capacity
      UpperCAmelCase						:				Union[str, Any]		  =			0
      UpperCAmelCase						:				Any		  =			0
      UpperCAmelCase						:				Dict		  =			0
      UpperCAmelCase						:				dict[T, DoubleLinkedListNode[T, U]]		  =			{}
 def __repr__(			self					: Union[str, Any]				)    ->			str:
      return (
          f'CacheInfo(hits={self.hits}, misses={self.miss}, '
          f'capacity={self.capacity}, current_size={self.num_keys})'
      )
 def __contains__(			self					: Optional[int]       ,    _a					: T				)    ->			bool:
      return key in self.cache
 def   _UpperCamelCase    (			self					: Optional[int]       ,    _a					: T				)    ->			U | None:

      if key in self.cache:
           self.hits += 1
           UpperCAmelCase						:				DoubleLinkedListNode[T, U]		  =			self.cache[key]
           UpperCAmelCase						:				Optional[int]		  =			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(_a				)
           return node.val
      self.miss += 1
      return None
 def   _UpperCamelCase    (			self					: str       ,    _a					: T       ,    _a					: U				)    ->			None:

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

                del self.cache[first_node.key]
                self.num_keys -= 1
           UpperCAmelCase						:				Optional[Any]		  =			DoubleLinkedListNode(_a       ,    _a				)
           self.list.add(self.cache[key]				)
           self.num_keys += 1

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

 @classmethod
 def   _UpperCamelCase    (			cls					: type[LFUCache[T, U]]       ,    _a					: int = 1_2_8				)    ->			Callable[[Callable[[T], U]], Callable[..., U]]:

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

                UpperCAmelCase						:				List[Any]		  =			cls.decorator_function_to_instance_map[func].get(args[0]				)
                if result is None:
                     UpperCAmelCase						:				List[Any]		  =			func(*_a				)
                     cls.decorator_function_to_instance_map[func].put(args[0]       ,    _a				)
                return result

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

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

           return cache_decorator_wrapper

      return cache_decorator_inner


if __name__ == "__main__":
      import doctest

      doctest.testmod()



