

'''simple docstring'''

from __future__ import annotations

import math
import random
from typing import Any




class 	A_			:






     '''simple docstring'''



     def __init__(		self			:					Tuple	)						->     None:
      UpperCAmelCase	:     list[Any]						 =			[]
      UpperCAmelCase	:     int						 =			0
      UpperCAmelCase	:     int						 =			0


     def        UpperCAmelCase_	(		self			:					List[Any]	)						->     bool:
      return self.head == self.tail


     def        UpperCAmelCase_	(		self			:					Optional[Any]					,      lowercase_			:					Any	)						->     None:
      self.data.append(lowercase_	)
      UpperCAmelCase	:     Dict						 =			self.tail + 1


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


     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     int:
      return self.tail - self.head




     def        UpperCAmelCase_	(		self			:					Tuple	)						->     None:
      print(self.data	)
      print('**************'	)
      print(self.data[self.head : self.tail]	)




class 	A_			:






     '''simple docstring'''



     def __init__(		self			:					Union[str, Any]					,      lowercase_			:					Any	)						->     None:
      UpperCAmelCase	:     Optional[int]						 =			data
      UpperCAmelCase	:     MyNode | None						 =			None
      UpperCAmelCase	:     MyNode | None						 =			None
      UpperCAmelCase	:     int						 =			1


     def        UpperCAmelCase_	(		self			:					List[Any]	)						->     Any:
      return self.data


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


     def        UpperCAmelCase_	(		self			:					Any	)						->     MyNode | None:
      return self.right


     def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     int:
      return self.height


     def        UpperCAmelCase_	(		self			:					List[str]					,      lowercase_			:					Any	)						->     None:
      UpperCAmelCase	:     List[str]						 =			data


     def        UpperCAmelCase_	(		self			:					int					,      lowercase_			:					MyNode | None	)						->     None:
      UpperCAmelCase	:     Dict						 =			node


     def        UpperCAmelCase_	(		self			:					Optional[int]					,      lowercase_			:					MyNode | None	)						->     None:
      UpperCAmelCase	:     Any						 =			node




     def        UpperCAmelCase_	(		self			:					Any					,      lowercase_			:					int	)						->     None:
      UpperCAmelCase	:     List[Any]						 =			height




def 				UpperCamelCase(					UpperCAmelCase_     ):
 if node is None:
  return 0
 return node.get_height()




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 if a > b:
  return a
 return b




def 				UpperCamelCase(					UpperCAmelCase_     ):
 print('left rotation node:'  ,	node.get_data()     )
 UpperCAmelCase	:     List[str]						 =			node.get_left()
 assert ret is not None
 node.set_left(ret.get_right()     )
 ret.set_right(UpperCAmelCase_     )
 UpperCAmelCase	:     str						 =			my_max(get_height(node.get_right()     )  ,	get_height(node.get_left()     )     ) + 1
 node.set_height(UpperCAmelCase_     )
 UpperCAmelCase	:     str						 =			my_max(get_height(ret.get_right()     )  ,	get_height(ret.get_left()     )     ) + 1
 ret.set_height(UpperCAmelCase_     )
 return ret




def 				UpperCamelCase(					UpperCAmelCase_     ):
 print('right rotation node:'  ,	node.get_data()     )
 UpperCAmelCase	:     Dict						 =			node.get_right()
 assert ret is not None
 node.set_right(ret.get_left()     )
 ret.set_left(UpperCAmelCase_     )
 UpperCAmelCase	:     Tuple						 =			my_max(get_height(node.get_right()     )  ,	get_height(node.get_left()     )     ) + 1
 node.set_height(UpperCAmelCase_     )
 UpperCAmelCase	:     Tuple						 =			my_max(get_height(ret.get_right()     )  ,	get_height(ret.get_left()     )     ) + 1
 ret.set_height(UpperCAmelCase_     )
 return ret




def 				UpperCamelCase(					UpperCAmelCase_     ):
 UpperCAmelCase	:     List[str]						 =			node.get_left()
 assert left_child is not None
 node.set_left(left_rotation(UpperCAmelCase_     )     )
 return right_rotation(UpperCAmelCase_     )




def 				UpperCamelCase(					UpperCAmelCase_     ):
 UpperCAmelCase	:     List[Any]						 =			node.get_right()
 assert right_child is not None
 node.set_right(right_rotation(UpperCAmelCase_     )     )
 return left_rotation(UpperCAmelCase_     )




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 if node is None:
  return MyNode(UpperCAmelCase_     )
 if data < node.get_data():
  node.set_left(insert_node(node.get_left()  ,	UpperCAmelCase_     )     )
  if (
      get_height(node.get_left()     ) - get_height(node.get_right()     ) == 2
  ):  # an unbalance detected
   UpperCAmelCase	:     Union[str, 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
    UpperCAmelCase	:     List[str]						 =			right_rotation(UpperCAmelCase_     )
   else:
    UpperCAmelCase	:     int						 =			lr_rotation(UpperCAmelCase_     )
 else:
  node.set_right(insert_node(node.get_right()  ,	UpperCAmelCase_     )     )
  if get_height(node.get_right()     ) - get_height(node.get_left()     ) == 2:
   UpperCAmelCase	:     str						 =			node.get_right()
   assert right_child is not None
   if data < right_child.get_data():
    UpperCAmelCase	:     str						 =			rl_rotation(UpperCAmelCase_     )
   else:
    UpperCAmelCase	:     Optional[Any]						 =			left_rotation(UpperCAmelCase_     )
 UpperCAmelCase	:     Optional[Any]						 =			my_max(get_height(node.get_right()     )  ,	get_height(node.get_left()     )     ) + 1
 node.set_height(UpperCAmelCase_     )
 return node




def 				UpperCamelCase(					UpperCAmelCase_     ):
 while True:
  UpperCAmelCase	:     int						 =			root.get_right()
  if right_child is None:
   break
  UpperCAmelCase	:     Union[str, Any]						 =			right_child
 return root.get_data()




def 				UpperCamelCase(					UpperCAmelCase_     ):
 while True:
  UpperCAmelCase	:     List[str]						 =			root.get_left()
  if left_child is None:
   break
  UpperCAmelCase	:     Optional[int]						 =			left_child
 return root.get_data()




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Optional[Any]						 =			root.get_left()
 UpperCAmelCase	:     List[Any]						 =			root.get_right()
 if root.get_data() == data:
  if left_child is not None and right_child is not None:
   UpperCAmelCase	:     List[Any]						 =			get_left_most(UpperCAmelCase_     )
   root.set_data(UpperCAmelCase_     )
   root.set_right(del_node(UpperCAmelCase_  ,	UpperCAmelCase_     )     )
  elif left_child is not None:
   UpperCAmelCase	:     List[str]						 =			left_child
  elif right_child is not None:
   UpperCAmelCase	:     Optional[int]						 =			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(UpperCAmelCase_  ,	UpperCAmelCase_     )     )
 else:  # root.get_data() < data
  if right_child is None:
   return root
  else:
   root.set_right(del_node(UpperCAmelCase_  ,	UpperCAmelCase_     )     )

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




class 	A_			:






     '''simple docstring'''



     def __init__(		self			:					List[str]	)						->     None:
      UpperCAmelCase	:     MyNode | None						 =			None


     def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     int:
      return get_height(self.root	)


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


     def        UpperCAmelCase_	(		self			:					Optional[int]					,      lowercase_			:					Any	)						->     None:
      print('delete:' + str(lowercase_	)	)
      if self.root is None:
       print('Tree is empty!'	)
       return
      UpperCAmelCase	:     Tuple						 =			del_node(self.root					,      lowercase_	)




     def __str__(		self			:					Optional[int]					,      )						->     str:  # a level traversale, gives a more intuitive look on the tree
      UpperCAmelCase	:     Dict						 =			''
      UpperCAmelCase	:     Tuple						 =			MyQueue()
      q.push(self.root	)
      UpperCAmelCase	:     Dict						 =			self.get_height()
      if layer == 0:
       return output
      UpperCAmelCase	:     List[Any]						 =			0
      while not q.is_empty():
       UpperCAmelCase	:     Dict						 =			q.pop()
       UpperCAmelCase	:     Dict						 =			' ' * int(math.pow(2					,      layer - 1	)	)
       output += space
       if node is None:
        output += "*"
        q.push(lowercase_	)
        q.push(lowercase_	)
       else:
        output += str(node.get_data()	)
        q.push(node.get_left()	)
        q.push(node.get_right()	)
       output += space
       UpperCAmelCase	:     Dict						 =			cnt + 1
       for i in range(100	):
        if cnt == math.pow(2					,      lowercase_	) - 1:
         UpperCAmelCase	:     Optional[int]						 =			layer - 1
         if layer == 0:
          output += "\n*************************************"
          return output
         output += "\n"
         break
      output += "\n*************************************"
      return output




def 				UpperCamelCase(					):
 import doctest

 doctest.testmod()


if __name__ == "__main__":
 _test()
 lowercase__         =      AVLtree()
 lowercase__         =      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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    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


lowercase__         =      logging.get_logger(__name__)




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Optional[Any]						 =			b.T
 UpperCAmelCase	:     Optional[int]						 =			np.sum(np.square(UpperCAmelCase_     )  ,	axis=1     )
 UpperCAmelCase	:     List[Any]						 =			np.sum(np.square(UpperCAmelCase_     )  ,	axis=0     )
 UpperCAmelCase	:     List[str]						 =			np.matmul(UpperCAmelCase_  ,	UpperCAmelCase_     )
 UpperCAmelCase	:     Union[str, Any]						 =			aa[:, None] - 2 * ab + ba[None, :]
 return d




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     int						 =			x.reshape(-1  ,	3     )
 UpperCAmelCase	:     Optional[int]						 =			squared_euclidean_distance(UpperCAmelCase_  ,	UpperCAmelCase_     )
 return np.argmin(UpperCAmelCase_  ,	axis=1     )




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     List[Any]  		=  ["""pixel_values"""]


     def __init__(		self			:					List[Any]					,      lowercase_			:					Optional[Union[List[List[int]], np.ndarray]] = None					,      lowercase_			:					bool = True					,      lowercase_			:					Dict[str, int] = None					,      lowercase_			:					PILImageResampling = PILImageResampling.BILINEAR					,      lowercase_			:					bool = True					,      lowercase_			:					bool = True					,      **lowercase_			:					Optional[Any]					,      )						->     None:
      super().__init__(**lowercase_	)
      UpperCAmelCase	:     Any						 =			size if size is not None else {'height': 256, 'width': 256}
      UpperCAmelCase	:     List[Any]						 =			get_size_dict(lowercase_	)
      UpperCAmelCase	:     str						 =			np.array(lowercase_	) if clusters is not None else None
      UpperCAmelCase	:     Any						 =			do_resize
      UpperCAmelCase	:     List[Any]						 =			size
      UpperCAmelCase	:     Any						 =			resample
      UpperCAmelCase	:     Dict						 =			do_normalize
      UpperCAmelCase	:     List[Any]						 =			do_color_quantize


     def        UpperCAmelCase_	(		self			:					int					,      lowercase_			:					np.ndarray					,      lowercase_			:					Dict[str, int]					,      lowercase_			:					PILImageResampling = PILImageResampling.BILINEAR					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = None					,      **lowercase_			:					Any					,      )						->     np.ndarray:
      UpperCAmelCase	:     Dict						 =			get_size_dict(lowercase_	)
      if "height" not in size or "width" not in size:
       raise ValueError(f"""Size dictionary must contain both height and width keys. Got {size.keys()}"""	)
      return resize(
          lowercase_					,      size=(size['height'], size['width'])					,      resample=lowercase_					,      data_format=lowercase_					,      **lowercase_	)


     def        UpperCAmelCase_	(		self			:					Optional[int]					,      lowercase_			:					np.ndarray					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = None					,      )						->     np.ndarray:
      UpperCAmelCase	:     int						 =			rescale(image=lowercase_					,      scale=1 / 127.5					,      data_format=lowercase_	)
      UpperCAmelCase	:     Dict						 =			image - 1
      return image




     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					ImageInput					,      lowercase_			:					bool = None					,      lowercase_			:					Dict[str, int] = None					,      lowercase_			:					PILImageResampling = None					,      lowercase_			:					bool = None					,      lowercase_			:					Optional[bool] = None					,      lowercase_			:					Optional[Union[List[List[int]], np.ndarray]] = None					,      lowercase_			:					Optional[Union[str, TensorType]] = None					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST					,      **lowercase_			:					List[str]					,      )						->     PIL.Image.Image:
      UpperCAmelCase	:     Optional[int]						 =			do_resize if do_resize is not None else self.do_resize
      UpperCAmelCase	:     Optional[Any]						 =			size if size is not None else self.size
      UpperCAmelCase	:     Optional[int]						 =			get_size_dict(lowercase_	)
      UpperCAmelCase	:     Any						 =			resample if resample is not None else self.resample
      UpperCAmelCase	:     Optional[Any]						 =			do_normalize if do_normalize is not None else self.do_normalize
      UpperCAmelCase	:     str						 =			do_color_quantize if do_color_quantize is not None else self.do_color_quantize
      UpperCAmelCase	:     Optional[int]						 =			clusters if clusters is not None else self.clusters
      UpperCAmelCase	:     List[str]						 =			np.array(lowercase_	)

      UpperCAmelCase	:     int						 =			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 or resample is None:
       raise ValueError('Size and resample must be specified if do_resize is True.'	)

      if do_color_quantize and clusters is None:
       raise ValueError('Clusters must be specified if do_color_quantize is True.'	)

      # All transformations expect numpy arrays.
      UpperCAmelCase	:     Dict						 =			[to_numpy_array(lowercase_	) for image in images]

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

      if do_normalize:
       UpperCAmelCase	:     Tuple						 =			[self.normalize(image=lowercase_	) for image in images]

      if do_color_quantize:
       UpperCAmelCase	:     List[str]						 =			[to_channel_dimension_format(lowercase_					,      ChannelDimension.LAST	) for image in images]
       # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
       UpperCAmelCase	:     int						 =			np.array(lowercase_	)
       UpperCAmelCase	:     str						 =			color_quantize(lowercase_					,      lowercase_	).reshape(images.shape[:-1]	)

       # flatten to (batch_size, height*width)
       UpperCAmelCase	:     Optional[int]						 =			images.shape[0]
       UpperCAmelCase	:     Union[str, Any]						 =			images.reshape(lowercase_					,      -1	)

       # We need to convert back to a list of images to keep consistent behaviour across processors.
       UpperCAmelCase	:     int						 =			list(lowercase_	)
      else:
       UpperCAmelCase	:     Dict						 =			[to_channel_dimension_format(lowercase_					,      lowercase_	) for image in images]

      UpperCAmelCase	:     Any						 =			{'input_ids': images}
      return BatchFeature(data=lowercase_					,      tensor_type=lowercase_	)




