




from __future__ import annotations

from random import random


class        UpperCamelCase_					:

    '''simple docstring'''






    def __init__(    self   :			Tuple      ,				UpperCAmelCase__   :			int | None = None)		->Union[str, Any]:





           '''simple docstring'''

           A__   				=					value
           A__   				=					random()
           A__   				=					None
           A__   				=					None


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





           '''simple docstring'''

           from pprint import pformat

           if self.left is None and self.right is None:
                  return f"""'{self.value}: {self.prior:.5}'"""
           else:
                  return pformat(
                      {f"""{self.value}: {self.prior:.5}""": (self.left, self.right)}      ,				indent=1)





    def __str__(    self   :			Optional[int])		->str:





           '''simple docstring'''

           A__   				=					str(self.value) + ''' '''
           A__   				=					str(self.left or '''''')
           A__   				=					str(self.right or '''''')
           return value + left + right







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> tuple[Node | None, Node | None]:



       """simple docstring"""

       if root is None:  # None tree is split into 2 Nones
              return None, None
       elif root.value is None:
              return None, None
       else:
              if value < root.value:
                     A__				,							A__   				=					split(root.left				,       lowercase_		)
                     return left, root
              else:
                     A__				,							A__   				=					split(root.right				,       lowercase_		)
                     return root, right







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Node | None:



       """simple docstring"""

       if (not left) or (not right):  # If one node is None, return the other
              return left or right
       elif left.prior < right.prior:
              A__   				=					merge(left.right				,       lowercase_		)
              return left
       else:
              A__   				=					merge(lowercase_				,       right.left		)
              return right







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Node | None:



       """simple docstring"""

       A__   				=					Node(lowercase_		)
       A__				,							A__   				=					split(lowercase_				,       lowercase_		)
       return merge(merge(lowercase_				,       lowercase_		)				,       lowercase_		)







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Node | None:



       """simple docstring"""

       A__				,							A__   				=					split(lowercase_				,       value - 1		)
       A__				,							A__   				=					split(lowercase_				,       lowercase_		)
       return merge(lowercase_				,       lowercase_		)







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> None:



       """simple docstring"""

       if not root:  # None
              return
       else:
              inorder(root.left		)
              print(root.value				,       end=''','''		)
              inorder(root.right		)







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Node | None:



       """simple docstring"""

       for arg in args.split():
              if arg[0] == "+":
                     A__   				=					insert(lowercase_				,       int(arg[1:]		)		)

              elif arg[0] == "-":
                     A__   				=					erase(lowercase_				,       int(arg[1:]		)		)

              else:
                     print('''Unknown command'''		)

       return root







def SCREAMING_SNAKE_CASE       ( )     -> None:



       """simple docstring"""

       A__   				=					None
       print(
           '''enter numbers to create a tree, + value to add value into treap, '''
           '''- value to erase all nodes with value. \'q\' to quit. '''		)

       A__   				=					input()
       while args != "q":
              A__   				=					interact_treap(lowercase_				,       lowercase_		)
              print(lowercase_		)
              A__   				=					input()

       print('''good by!'''		)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()








from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_lowerCamelCase			:	str							 =     logging.get_logger(__name__)


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    UpperCAmelCase__													=   ['''pixel_values''']


    def __init__(    self   :			List[Any]      ,				UpperCAmelCase__   :			bool = True      ,				UpperCAmelCase__   :			int = 32      ,				UpperCAmelCase__   :			Dict=PILImageResampling.BILINEAR      ,				UpperCAmelCase__   :			bool = True      ,				**UpperCAmelCase__   :			Optional[int]      ,				)		->None:





           '''simple docstring'''

           A__   				=					do_resize
           A__   				=					do_rescale
           A__   				=					size_divisor
           A__   				=					resample
           super().__init__(**UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			np.ndarray      ,				UpperCAmelCase__   :			int      ,				UpperCAmelCase__   :			List[str]      ,				UpperCAmelCase__   :			Optional[ChannelDimension] = None      ,				**UpperCAmelCase__   :			Dict)		->np.ndarray:





           '''simple docstring'''

           A__				,							A__   				=					get_image_size(UpperCAmelCase__)
           # Rounds the height and width down to the closest multiple of size_divisor
           A__   				=					height // size_divisor * size_divisor
           A__   				=					width // size_divisor * size_divisor
           A__   				=					resize(UpperCAmelCase__      ,				(new_h, new_w)      ,				resample=UpperCAmelCase__      ,				data_format=UpperCAmelCase__      ,				**UpperCAmelCase__)
           return image


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any]      ,				UpperCAmelCase__   :			np.ndarray      ,				UpperCAmelCase__   :			float      ,				UpperCAmelCase__   :			Optional[ChannelDimension] = None      ,				**UpperCAmelCase__   :			List[str])		->np.ndarray:





           '''simple docstring'''

           return rescale(image=UpperCAmelCase__      ,				scale=UpperCAmelCase__      ,				data_format=UpperCAmelCase__      ,				**UpperCAmelCase__)





    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int]      ,				UpperCAmelCase__   :			Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]      ,				UpperCAmelCase__   :			Optional[bool] = None      ,				UpperCAmelCase__   :			Optional[int] = None      ,				UpperCAmelCase__   :			Dict=None      ,				UpperCAmelCase__   :			Optional[bool] = None      ,				UpperCAmelCase__   :			Optional[Union[TensorType, str]] = None      ,				UpperCAmelCase__   :			ChannelDimension = ChannelDimension.FIRST      ,				**UpperCAmelCase__   :			List[str]      ,				)		->BatchFeature:





           '''simple docstring'''

           A__   				=					do_resize if do_resize is not None else self.do_resize
           A__   				=					do_rescale if do_rescale is not None else self.do_rescale
           A__   				=					size_divisor if size_divisor is not None else self.size_divisor
           A__   				=					resample if resample is not None else self.resample

           if do_resize and size_divisor is None:
                  raise ValueError('''size_divisor is required for resizing''')

           A__   				=					make_list_of_images(UpperCAmelCase__)

           if not valid_images(UpperCAmelCase__):
                  raise ValueError('''Invalid image(s)''')

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

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

           if do_rescale:
                  A__   				=					[self.rescale(UpperCAmelCase__      ,				scale=1 / 255) for image in images]

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

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


