"""simple docstring"""







from __future__ import annotations

import math
import random
from typing import Any







class        UpperCamelCase		:
   def __init__(			self			)	->      None:


        '''simple docstring'''


        lowercase_	:				list[Any]													=	[]
        lowercase_	:				int													=	0
        lowercase_	:				int													=	0
   def 			_UpperCAmelCase   (			self			)	->      bool:


        '''simple docstring'''


        return self.head == self.tail
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      None:


        '''simple docstring'''


        self.data.append(__UpperCamelCase			)
        lowercase_	:				Tuple													=	self.tail + 1
   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	self.data[self.head]
        lowercase_	:				List[Any]													=	self.head + 1
        return ret
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        return self.tail - self.head







   def 			_UpperCAmelCase   (			self			)	->      None:


        '''simple docstring'''


        print(self.data			)
        print('**************'			)
        print(self.data[self.head : self.tail]			)







class        UpperCamelCase		:
   def __init__(			self     ,__UpperCamelCase			)	->      None:


        '''simple docstring'''


        lowercase_	:				Optional[Any]													=	data
        lowercase_	:				MyNode | None													=	None
        lowercase_	:				MyNode | None													=	None
        lowercase_	:				int													=	1
   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        return self.data
   def 			_UpperCAmelCase   (			self			)	->      MyNode | None:


        '''simple docstring'''


        return self.left
   def 			_UpperCAmelCase   (			self			)	->      MyNode | None:


        '''simple docstring'''


        return self.right
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        return self.height
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      None:


        '''simple docstring'''


        lowercase_	:				str													=	data
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      None:


        '''simple docstring'''


        lowercase_	:				Tuple													=	node
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      None:


        '''simple docstring'''


        lowercase_	:				Optional[Any]													=	node







   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      None:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	height






def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     MyNode | None      ):
     if node is None:
          return 0
     return node.get_height()



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     int      ):
     if a > b:
          return a
     return b



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     MyNode      ):
     print('left rotation node:'     ,			node.get_data()      )
     lowercase_	:				Dict													=	node.get_left()
     assert ret is not None
     node.set_left(ret.get_right()      )
     ret.set_right(__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Tuple													=	my_max(get_height(node.get_right()      )     ,			get_height(node.get_left()      )      ) + 1
     node.set_height(__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Union[str, Any]													=	my_max(get_height(ret.get_right()      )     ,			get_height(ret.get_left()      )      ) + 1
     ret.set_height(__SCREAMING_SNAKE_CASE      )
     return ret



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     MyNode      ):
     print('right rotation node:'     ,			node.get_data()      )
     lowercase_	:				Any													=	node.get_right()
     assert ret is not None
     node.set_right(ret.get_left()      )
     ret.set_left(__SCREAMING_SNAKE_CASE      )
     lowercase_	:				List[Any]													=	my_max(get_height(node.get_right()      )     ,			get_height(node.get_left()      )      ) + 1
     node.set_height(__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Dict													=	my_max(get_height(ret.get_right()      )     ,			get_height(ret.get_left()      )      ) + 1
     ret.set_height(__SCREAMING_SNAKE_CASE      )
     return ret



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     MyNode      ):
     lowercase_	:				Optional[Any]													=	node.get_left()
     assert left_child is not None
     node.set_left(left_rotation(__SCREAMING_SNAKE_CASE      )      )
     return right_rotation(__SCREAMING_SNAKE_CASE      )



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     MyNode      ):
     lowercase_	:				List[str]													=	node.get_right()
     assert right_child is not None
     node.set_right(right_rotation(__SCREAMING_SNAKE_CASE      )      )
     return left_rotation(__SCREAMING_SNAKE_CASE      )



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     MyNode | None     ,			__SCREAMING_SNAKE_CASE							:     Any      ):
     if node is None:
          return MyNode(__SCREAMING_SNAKE_CASE      )
     if data < node.get_data():
          node.set_left(insert_node(node.get_left()     ,			__SCREAMING_SNAKE_CASE      )      )
          if (
              get_height(node.get_left()      ) - get_height(node.get_right()      ) == 2
          ):  # an unbalance detected
               lowercase_	:				Optional[Any]													=	node.get_left()
               assert left_child is not None
               if (
                   data < left_child.get_data()
               ):  # new node is the left child of the left child
                    lowercase_	:				Union[str, Any]													=	right_rotation(__SCREAMING_SNAKE_CASE      )
               else:
                    lowercase_	:				Optional[Any]													=	lr_rotation(__SCREAMING_SNAKE_CASE      )
     else:
          node.set_right(insert_node(node.get_right()     ,			__SCREAMING_SNAKE_CASE      )      )
          if get_height(node.get_right()      ) - get_height(node.get_left()      ) == 2:
               lowercase_	:				Tuple													=	node.get_right()
               assert right_child is not None
               if data < right_child.get_data():
                    lowercase_	:				Union[str, Any]													=	rl_rotation(__SCREAMING_SNAKE_CASE      )
               else:
                    lowercase_	:				int													=	left_rotation(__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Optional[Any]													=	my_max(get_height(node.get_right()      )     ,			get_height(node.get_left()      )      ) + 1
     node.set_height(__SCREAMING_SNAKE_CASE      )
     return node



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     MyNode      ):
     while True:
          lowercase_	:				int													=	root.get_right()
          if right_child is None:
               break
          lowercase_	:				Optional[int]													=	right_child
     return root.get_data()



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     MyNode      ):
     while True:
          lowercase_	:				Optional[int]													=	root.get_left()
          if left_child is None:
               break
          lowercase_	:				Dict													=	left_child
     return root.get_data()



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     MyNode     ,			__SCREAMING_SNAKE_CASE							:     Any      ):
     lowercase_	:				int													=	root.get_left()
     lowercase_	:				int													=	root.get_right()
     if root.get_data() == data:
          if left_child is not None and right_child is not None:
               lowercase_	:				List[str]													=	get_left_most(__SCREAMING_SNAKE_CASE      )
               root.set_data(__SCREAMING_SNAKE_CASE      )
               root.set_right(del_node(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )      )
          elif left_child is not None:
               lowercase_	:				List[Any]													=	left_child
          elif right_child is not None:
               lowercase_	:				str													=	right_child
          else:
               return None
     elif root.get_data() > data:
          if left_child is None:
               print('No such data'      )
               return root
          else:
               root.set_left(del_node(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )      )
     else:  # root.get_data() < data
          if right_child is None:
               return root
          else:
               root.set_right(del_node(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )      )

     if get_height(__SCREAMING_SNAKE_CASE      ) - get_height(__SCREAMING_SNAKE_CASE      ) == 2:
          assert right_child is not None
          if get_height(right_child.get_right()      ) > get_height(right_child.get_left()      ):
               lowercase_	:				Tuple													=	left_rotation(__SCREAMING_SNAKE_CASE      )
          else:
               lowercase_	:				List[Any]													=	rl_rotation(__SCREAMING_SNAKE_CASE      )
     elif get_height(__SCREAMING_SNAKE_CASE      ) - get_height(__SCREAMING_SNAKE_CASE      ) == -2:
          assert left_child is not None
          if get_height(left_child.get_left()      ) > get_height(left_child.get_right()      ):
               lowercase_	:				Optional[int]													=	right_rotation(__SCREAMING_SNAKE_CASE      )
          else:
               lowercase_	:				Optional[Any]													=	lr_rotation(__SCREAMING_SNAKE_CASE      )
     lowercase_	:				List[str]													=	my_max(get_height(root.get_right()      )     ,			get_height(root.get_left()      )      ) + 1
     root.set_height(__SCREAMING_SNAKE_CASE      )
     return root







class        UpperCamelCase		:
   def __init__(			self			)	->      None:


        '''simple docstring'''


        lowercase_	:				MyNode | None													=	None
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        return get_height(self.root			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      None:


        '''simple docstring'''


        print('insert:' + str(__UpperCamelCase			)			)
        lowercase_	:				Any													=	insert_node(self.root     ,__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      None:


        '''simple docstring'''


        print('delete:' + str(__UpperCamelCase			)			)
        if self.root is None:
             print('Tree is empty!'			)
             return
        lowercase_	:				List[str]													=	del_node(self.root     ,__UpperCamelCase			)







   def __str__(			self     ,)	->      str:  # a level traversale, gives a more intuitive look on the tree


        '''simple docstring'''


        lowercase_	:				Any													=	''
        lowercase_	:				Optional[int]													=	MyQueue()
        q.push(self.root			)
        lowercase_	:				Any													=	self.get_height()
        if layer == 0:
             return output
        lowercase_	:				str													=	0
        while not q.is_empty():
             lowercase_	:				List[Any]													=	q.pop()
             lowercase_	:				List[Any]													=	' ' * int(math.pow(2     ,layer - 1			)			)
             output += space
             if node is None:
                  output += "*"
                  q.push(__UpperCamelCase			)
                  q.push(__UpperCamelCase			)
             else:
                  output += str(node.get_data()			)
                  q.push(node.get_left()			)
                  q.push(node.get_right()			)
             output += space
             lowercase_	:				str													=	cnt + 1
             for i in range(100			):
                  if cnt == math.pow(2     ,__UpperCamelCase			) - 1:
                       lowercase_	:				List[str]													=	layer - 1
                       if layer == 0:
                            output += "\n*************************************"
                            return output
                       output += "\n"
                       break
        output += "\n*************************************"
        return output






def 			lowercase__(       ):
     import doctest

     doctest.testmod()


if __name__ == "__main__":
       _test()
       __SCREAMING_SNAKE_CASE    	=AVLtree()
       __SCREAMING_SNAKE_CASE    	=list(range(10))
       random.shuffle(lst)
       for i in lst:
              t.insert(i)
              print(str(t))
       random.shuffle(lst)
       for i in lst:
              t.del_node(i)
              print(str(t))


"""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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
       import PIL


__SCREAMING_SNAKE_CASE    	=logging.get_logger(__name__)







class        UpperCamelCase		(			lowercase_  ):
   lowercase							=  ['pixel_values']
   def __init__(			self     ,__UpperCamelCase = True     ,__UpperCamelCase = None     ,__UpperCamelCase = 0.9     ,__UpperCamelCase = PILImageResampling.BICUBIC     ,__UpperCamelCase = True     ,__UpperCamelCase = None     ,__UpperCamelCase = 1 / 255     ,__UpperCamelCase = True     ,__UpperCamelCase = True     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      None:


        '''simple docstring'''


        super().__init__(**__UpperCamelCase			)
        lowercase_	:				Optional[int]													=	size if size is not None else {'shortest_edge': 224}
        lowercase_	:				Union[str, Any]													=	get_size_dict(__UpperCamelCase     ,default_to_square=__UpperCamelCase			)
        lowercase_	:				Union[str, Any]													=	crop_size if crop_size is not None else {'height': 224, 'width': 224}
        lowercase_	:				Optional[int]													=	get_size_dict(__UpperCamelCase     ,param_name='crop_size'			)

        lowercase_	:				List[str]													=	do_resize
        lowercase_	:				List[Any]													=	size
        lowercase_	:				int													=	crop_pct
        lowercase_	:				Dict													=	resample
        lowercase_	:				List[str]													=	do_center_crop
        lowercase_	:				Union[str, Any]													=	crop_size
        lowercase_	:				List[Any]													=	do_rescale
        lowercase_	:				Tuple													=	rescale_factor
        lowercase_	:				Tuple													=	do_normalize
        lowercase_	:				Union[str, Any]													=	image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        lowercase_	:				int													=	image_std if image_std is not None else IMAGENET_DEFAULT_STD
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = None     ,__UpperCamelCase = PILImageResampling.BICUBIC     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      np.ndarray:


        '''simple docstring'''


        lowercase_	:				Any													=	get_size_dict(__UpperCamelCase     ,default_to_square=__UpperCamelCase			)
        if "shortest_edge" not in size and ("height" not in size or "width" not in size):
             raise ValueError(f'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''			)
        if crop_pct is not None:
             if "shortest_edge" in size:
                  lowercase_	:				Union[str, Any]													=	int(size['shortest_edge'] / crop_pct			)
             elif "height" in size and "width" in size:
                  if size["height"] == size["width"]:
                       lowercase_	:				Tuple													=	int(size['height'] / crop_pct			)
                  else:
                       lowercase_	:				Dict													=	(int(size['height'] / crop_pct			), int(size['width'] / crop_pct			))
             else:
                  raise ValueError('Invalid size for resize: {}'.format(__UpperCamelCase			)			)

             lowercase_	:				int													=	get_resize_output_image_size(__UpperCamelCase     ,size=__UpperCamelCase     ,default_to_square=__UpperCamelCase			)
        else:
             if "shortest_edge" in size:
                  lowercase_	:				Optional[int]													=	get_resize_output_image_size(__UpperCamelCase     ,size=size['shortest_edge']     ,default_to_square=__UpperCamelCase			)
             elif "height" in size and "width" in size:
                  lowercase_	:				Dict													=	(size['height'], size['width'])
             else:
                  raise ValueError('Invalid size for resize: {}'.format(__UpperCamelCase			)			)

        return resize(__UpperCamelCase     ,size=__UpperCamelCase     ,resample=__UpperCamelCase     ,data_format=__UpperCamelCase     ,**__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      np.ndarray:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	get_size_dict(__UpperCamelCase			)
        if "height" not in size or "width" not in size:
             raise ValueError(f'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}'''			)
        return center_crop(__UpperCamelCase     ,size=(size['height'], size['width'])     ,data_format=__UpperCamelCase     ,**__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      str:


        '''simple docstring'''


        return rescale(__UpperCamelCase     ,scale=__UpperCamelCase     ,data_format=__UpperCamelCase     ,**__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      np.ndarray:


        '''simple docstring'''


        return normalize(__UpperCamelCase     ,mean=__UpperCamelCase     ,std=__UpperCamelCase     ,data_format=__UpperCamelCase     ,**__UpperCamelCase			)







   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = ChannelDimension.FIRST     ,**__UpperCamelCase     ,)	->      PIL.Image.Image:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	do_resize if do_resize is not None else self.do_resize
        lowercase_	:				Optional[int]													=	crop_pct if crop_pct is not None else self.crop_pct
        lowercase_	:				List[str]													=	resample if resample is not None else self.resample
        lowercase_	:				str													=	do_center_crop if do_center_crop is not None else self.do_center_crop
        lowercase_	:				Tuple													=	do_rescale if do_rescale is not None else self.do_rescale
        lowercase_	:				Union[str, Any]													=	rescale_factor if rescale_factor is not None else self.rescale_factor
        lowercase_	:				str													=	do_normalize if do_normalize is not None else self.do_normalize
        lowercase_	:				str													=	image_mean if image_mean is not None else self.image_mean
        lowercase_	:				Tuple													=	image_std if image_std is not None else self.image_std

        lowercase_	:				Optional[Any]													=	size if size is not None else self.size
        lowercase_	:				Tuple													=	get_size_dict(__UpperCamelCase     ,default_to_square=__UpperCamelCase			)
        lowercase_	:				Union[str, Any]													=	crop_size if crop_size is not None else self.crop_size
        lowercase_	:				List[str]													=	get_size_dict(__UpperCamelCase     ,param_name='crop_size'			)

        lowercase_	:				str													=	make_list_of_images(__UpperCamelCase			)

        if not valid_images(__UpperCamelCase			):
             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 or resample is None:
             raise ValueError('Size and resample must be specified if do_resize is True.'			)

        if do_center_crop and crop_pct is None:
             raise ValueError('Crop_pct 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.
        lowercase_	:				Optional[Any]													=	[to_numpy_array(__UpperCamelCase			) for image in images]

        if do_resize:
             lowercase_	:				str													=	[self.resize(image=__UpperCamelCase     ,size=__UpperCamelCase     ,crop_pct=__UpperCamelCase     ,resample=__UpperCamelCase			) for image in images]

        if do_center_crop:
             lowercase_	:				str													=	[self.center_crop(image=__UpperCamelCase     ,size=__UpperCamelCase			) for image in images]

        if do_rescale:
             lowercase_	:				Any													=	[self.rescale(image=__UpperCamelCase     ,scale=__UpperCamelCase			) for image in images]

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

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

        lowercase_	:				Any													=	{'pixel_values': images}
        return BatchFeature(data=__UpperCamelCase     ,tensor_type=__UpperCamelCase			)

