"""simple docstring"""



from __future__ import annotations

from random import random
from typing import Generic, TypeVar

__lowercase		:		Union[str, Any]														=							TypeVar("KT")
__lowercase		:		int														=							TypeVar("VT")



class      _A	(						Generic[KT, VT]					):




    """simple docstring"""




    def __init__(		self							:     Union[str, Any]	,      A_							:     KT | str = "root"	,      A_							:     VT | None = None    )    ->       List[Any]:
         __snake_case				=							key
         __snake_case				=							value
         __snake_case				=							[]
    def __repr__(		self							:     str    )    ->       str:
         return f"Node({self.key}: {self.value})"







    @property
    def  lowercase					(		self							:     Union[str, Any]    )    ->       int:
         return len(self.forward    )






class      _A	(						Generic[KT, VT]					):




    """simple docstring"""




    def __init__(		self							:     int	,      A_							:     float = 0.5	,      A_							:     int = 16    )    ->       Optional[Any]:
         __snake_case				=							Node[KT, VT]()
         __snake_case				=							0
         __snake_case				=							p
         __snake_case				=							max_level
    def __str__(		self							:     List[str]    )    ->       str:
         __snake_case				=							list(self    )

         if len(A_    ) == 0:
              return f"SkipList(level={self.level})"

         __snake_case				=							max((len(str(A_    )    ) for item in items)	,      default=4    )
         __snake_case				=							max(A_	,      4    ) + 4

         __snake_case				=							self.head
         __snake_case				=							[]

         __snake_case				=							node.forward.copy()
         lines.append(f"[{node.key}]".ljust(A_	,      '''-'''    ) + '''* ''' * len(A_    )    )
         lines.append(''' ''' * label_size + '''| ''' * len(A_    )    )

         while len(node.forward    ) != 0:
              __snake_case				=							node.forward[0]

              lines.append(
                  f"[{node.key}]".ljust(A_	,      '''-'''    )
                  + ''' '''.join(str(n.key    ) if n.key == node.key else '''|''' for n in forwards    )    )
              lines.append(''' ''' * label_size + '''| ''' * len(A_    )    )
              __snake_case				=							node.forward

         lines.append('''None'''.ljust(A_    ) + '''* ''' * len(A_    )    )
         return f"SkipList(level={self.level})\n" + "\n".join(A_    )
    def __iter__(		self							:     Optional[int]    )    ->       Union[str, Any]:
         __snake_case				=							self.head

         while len(node.forward    ) != 0:
              yield node.forward[0].key
              __snake_case				=							node.forward[0]
    def  lowercase					(		self							:     List[str]    )    ->       int:
         __snake_case				=							1
         while random() < self.p and level < self.max_level:
              level += 1

         return level
    def  lowercase					(		self							:     Dict	,      A_							:     int    )    ->       tuple[Node[KT, VT] | None, list[Node[KT, VT]]]:
         __snake_case				=							[]

         __snake_case				=							self.head

         for i in reversed(range(self.level    )    ):
              # i < node.level - When node level is lesser than `i` decrement `i`.
              # node.forward[i].key < key - Jumping to node with key value higher
              #                             or equal to searched key would result
              #                             in skipping searched key.
              while i < node.level and node.forward[i].key < key:
                   __snake_case				=							node.forward[i]
              # Each leftmost node (relative to searched node) will potentially have to
              # be updated.
              update_vector.append(A_    )

         update_vector.reverse()  # Note that we were inserting values in reverse order.

         # len(node.forward) != 0 - If current node doesn't contain any further
         #                          references then searched key is not present.
         # node.forward[0].key == key - Next node key should be equal to search key
         #                              if key is present.
         if len(node.forward    ) != 0 and node.forward[0].key == key:
              return node.forward[0], update_vector
         else:
              return None, update_vector
    def  lowercase					(		self							:     List[Any]	,      A_							:     KT    )    ->       Any:
         __snake_case		,	__snake_case				=							self._locate_node(A_    )

         if node is not None:
              for i, update_node in enumerate(A_    ):
                   # Remove or replace all references to removed node.
                   if update_node.level > i and update_node.forward[i].key == key:
                        if node.level > i:
                             __snake_case				=							node.forward[i]
                        else:
                             __snake_case				=							update_node.forward[:i]
    def  lowercase					(		self							:     Tuple	,      A_							:     KT	,      A_							:     VT    )    ->       Union[str, Any]:
         __snake_case		,	__snake_case				=							self._locate_node(A_    )
         if node is not None:
              __snake_case				=							value
         else:
              __snake_case				=							self.random_level()

              if level > self.level:
                   # After level increase we have to add additional nodes to head.
                   for _ in range(self.level - 1	,      A_    ):
                        update_vector.append(self.head    )
                   __snake_case				=							level

              __snake_case				=							Node(A_	,      A_    )

              for i, update_node in enumerate(update_vector[:level]    ):
                   # Change references to pass through new node.
                   if update_node.level > i:
                        new_node.forward.append(update_node.forward[i]    )

                   if update_node.level < i + 1:
                        update_node.forward.append(A_    )
                   else:
                        __snake_case				=							new_node







    def  lowercase					(		self							:     str	,      A_							:     VT    )    ->       VT | None:
         __snake_case		,	__snake_case				=							self._locate_node(A_    )

         if node is not None:
              return node.value

         return None






def    SCREAMING_SNAKE_CASE       (     ):
     __snake_case				=							SkipList()
     skip_list.insert('''Key1''',      3)
     skip_list.insert('''Key2''',      12)
     skip_list.insert('''Key3''',      41)
     skip_list.insert('''Key4''',      -19)

     __snake_case				=							skip_list.head
     __snake_case				=							{}
     while node.level != 0:
          __snake_case				=							node.forward[0]
          __snake_case				=							node.value

     assert len(snake_case) == 4
     assert all_values["Key1"] == 3
     assert all_values["Key2"] == 12
     assert all_values["Key3"] == 41
     assert all_values["Key4"] == -19





def    SCREAMING_SNAKE_CASE       (     ):
     __snake_case				=							SkipList()
     skip_list.insert('''Key1''',      10)
     skip_list.insert('''Key1''',      12)

     skip_list.insert('''Key5''',      7)
     skip_list.insert('''Key7''',      10)
     skip_list.insert('''Key10''',      5)

     skip_list.insert('''Key7''',      7)
     skip_list.insert('''Key5''',      5)
     skip_list.insert('''Key10''',      10)

     __snake_case				=							skip_list.head
     __snake_case				=							{}
     while node.level != 0:
          __snake_case				=							node.forward[0]
          __snake_case				=							node.value

     if len(snake_case) != 4:
          print()
     assert len(snake_case) == 4
     assert all_values["Key1"] == 12
     assert all_values["Key7"] == 7
     assert all_values["Key5"] == 5
     assert all_values["Key10"] == 10





def    SCREAMING_SNAKE_CASE       (     ):
     __snake_case				=							SkipList()
     assert skip_list.find('''Some key''') is None





def    SCREAMING_SNAKE_CASE       (     ):
     __snake_case				=							SkipList()

     skip_list.insert('''Key2''',      20)
     assert skip_list.find('''Key2''') == 20

     skip_list.insert('''Some Key''',      10)
     skip_list.insert('''Key2''',      8)
     skip_list.insert('''V''',      13)

     assert skip_list.find('''Y''') is None
     assert skip_list.find('''Key2''') == 8
     assert skip_list.find('''Some Key''') == 10
     assert skip_list.find('''V''') == 13





def    SCREAMING_SNAKE_CASE       (     ):
     __snake_case				=							SkipList()
     skip_list.delete('''Some key''')

     assert len(skip_list.head.forward) == 0





def    SCREAMING_SNAKE_CASE       (     ):
     __snake_case				=							SkipList()

     skip_list.insert('''Key1''',      12)
     skip_list.insert('''V''',      13)
     skip_list.insert('''X''',      14)
     skip_list.insert('''Key2''',      15)

     skip_list.delete('''V''')
     skip_list.delete('''Key2''')

     assert skip_list.find('''V''') is None
     assert skip_list.find('''Key2''') is None





def    SCREAMING_SNAKE_CASE       (     ):
     __snake_case				=							SkipList()

     skip_list.insert('''Key1''',      12)
     skip_list.insert('''V''',      13)
     skip_list.insert('''X''',      14)
     skip_list.insert('''Key2''',      15)

     skip_list.delete('''V''')
     assert skip_list.find('''V''') is None
     assert skip_list.find('''X''') == 14
     assert skip_list.find('''Key1''') == 12
     assert skip_list.find('''Key2''') == 15

     skip_list.delete('''X''')
     assert skip_list.find('''V''') is None
     assert skip_list.find('''X''') is None
     assert skip_list.find('''Key1''') == 12
     assert skip_list.find('''Key2''') == 15

     skip_list.delete('''Key1''')
     assert skip_list.find('''V''') is None
     assert skip_list.find('''X''') is None
     assert skip_list.find('''Key1''') is None
     assert skip_list.find('''Key2''') == 15

     skip_list.delete('''Key2''')
     assert skip_list.find('''V''') is None
     assert skip_list.find('''X''') is None
     assert skip_list.find('''Key1''') is None
     assert skip_list.find('''Key2''') is None





def    SCREAMING_SNAKE_CASE       (     ):
     __snake_case				=							SkipList()

     skip_list.insert('''Key1''',      12)
     skip_list.insert('''V''',      13)
     skip_list.insert('''X''',      1_42)
     skip_list.insert('''Key2''',      15)

     skip_list.delete('''X''')

     def traverse_keys(snake_case):
          yield node.key
          for forward_node in node.forward:
               yield from traverse_keys(snake_case)

     assert len(set(traverse_keys(skip_list.head))) == 4





def    SCREAMING_SNAKE_CASE       (     ):
     def is_sorted(snake_case):
          return all(next_item >= item for item, next_item in zip(snake_case,      lst[1:]))

     __snake_case				=							SkipList()
     for i in range(10):
          skip_list.insert(snake_case,      snake_case)
     assert is_sorted(list(snake_case))
     skip_list.delete(5)
     skip_list.delete(8)
     skip_list.delete(2)
     assert is_sorted(list(snake_case))
     skip_list.insert(-12,      -12)
     skip_list.insert(77,      77)
     assert is_sorted(list(snake_case))





def    SCREAMING_SNAKE_CASE       (     ):
     for _ in range(1_00):
          # Repeat test 100 times due to the probabilistic nature of skip list
          # random values == random bugs
          test_insert()
          test_insert_overrides_existing_value()

          test_searching_empty_list_returns_none()
          test_search()

          test_deleting_item_from_empty_list_do_nothing()
          test_deleted_items_are_not_founded_by_find_method()
          test_delete_removes_only_given_key()
          test_delete_doesnt_leave_dead_nodes()

          test_iter_always_yields_sorted_values()





def    SCREAMING_SNAKE_CASE       (     ):
     __snake_case				=							SkipList()
     skip_list.insert(2,      '''2''')
     skip_list.insert(4,      '''4''')
     skip_list.insert(6,      '''4''')
     skip_list.insert(4,      '''5''')
     skip_list.insert(8,      '''4''')
     skip_list.insert(9,      '''4''')

     skip_list.delete(4)

     print(snake_case)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()
"""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


__lowercase		:		str														=							logging.get_logger(__name__)



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					str							=					['''pixel_values''']
    def __init__(		self							:     Optional[int]	,      A_							:     bool = True	,      A_							:     Dict[str, int] = None	,      A_							:     PILImageResampling = PILImageResampling.BICUBIC	,      A_							:     bool = True	,      A_							:     Dict[str, int] = None	,      A_							:     bool = True	,      A_							:     Union[int, float] = 1 / 255	,      A_							:     bool = True	,      A_							:     Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN	,      A_							:     Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD	,      **A_							:     Dict	,      )    ->       None:
         super().__init__(**A_    )
         __snake_case				=							size if size is not None else {'''shortest_edge''': 224}
         __snake_case				=							get_size_dict(A_	,      default_to_square=A_    )
         __snake_case				=							crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
         __snake_case				=							get_size_dict(A_	,      param_name='''crop_size'''    )

         __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_DEFAULT_MEAN
         __snake_case				=							image_std if image_std is not None else IMAGENET_DEFAULT_STD
    def  lowercase					(		self							:     Any	,      A_							:     np.ndarray	,      A_							:     Dict[str, int]	,      A_							:     PILImageResampling = PILImageResampling.BICUBIC	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     int	,      )    ->       np.ndarray:
         __snake_case				=							get_size_dict(A_	,      default_to_square=A_    )
         # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
         if "shortest_edge" in size:
              __snake_case				=							int((256 / 224) * size['''shortest_edge''']    )
              __snake_case				=							get_resize_output_image_size(A_	,      size=A_	,      default_to_square=A_    )
              __snake_case				=							{'''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(
             A_	,      size=(size_dict['''height'''], size_dict['''width'''])	,      resample=A_	,      data_format=A_	,      **A_    )
    def  lowercase					(		self							:     Any	,      A_							:     np.ndarray	,      A_							:     Dict[str, int]	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     List[Any]	,      )    ->       np.ndarray:
         __snake_case				=							get_size_dict(A_    )
         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(A_	,      size=(size['''height'''], size['''width'''])	,      data_format=A_	,      **A_    )
    def  lowercase					(		self							:     List[str]	,      A_							:     np.ndarray	,      A_							:     Union[int, float]	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     Optional[Any]	,      )    ->       np.ndarray:
         return rescale(A_	,      scale=A_	,      data_format=A_	,      **A_    )
    def  lowercase					(		self							:     Dict	,      A_							:     np.ndarray	,      A_							:     Union[float, List[float]]	,      A_							:     Union[float, List[float]]	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     Optional[int]	,      )    ->       np.ndarray:
         return normalize(A_	,      mean=A_	,      std=A_	,      data_format=A_	,      **A_    )







    def  lowercase					(		self							:     Union[str, Any]	,      A_							:     ImageInput	,      A_							:     Optional[bool] = None	,      A_							:     Optional[Dict[str, int]] = None	,      A_							:     PILImageResampling = None	,      A_							:     Optional[bool] = None	,      A_							:     Optional[Dict[str, int]] = None	,      A_							:     Optional[bool] = None	,      A_							:     Optional[float] = None	,      A_							:     Optional[bool] = None	,      A_							:     Optional[Union[float, Iterable[float]]] = None	,      A_							:     Optional[Union[float, Iterable[float]]] = None	,      A_							:     Optional[TensorType] = None	,      A_							:     ChannelDimension = ChannelDimension.FIRST	,      **A_							:     Any	,      )    ->       BatchFeature:
         __snake_case				=							do_resize if do_resize is not None else self.do_resize
         __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				=							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				=							size if size is not None else self.size
         __snake_case				=							get_size_dict(A_	,      default_to_square=A_    )
         __snake_case				=							crop_size if crop_size is not None else self.crop_size
         __snake_case				=							get_size_dict(A_	,      param_name='''crop_size'''    )

         __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(A_	,      A_	,      A_    ) for image in images]

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

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

         if do_normalize:
              __snake_case				=							[self.normalize(A_	,      A_	,      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_    )