

'''simple docstring'''







import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available


class        A__   (   _snake_case      ):
    lowercase						       =		["image_processor", "tokenizer"]
    lowercase						       =		"OwlViTImageProcessor"
    lowercase						       =		("CLIPTokenizer", "CLIPTokenizerFast")



    def __init__(       self		,					UpperCamelCase__=None		,					UpperCamelCase__=None		,					**UpperCamelCase__							)    ->   str:





           '''simple docstring'''
           A_						       =   None
           if "feature_extractor" in kwargs:
                  warnings.warn(
                      """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                      """ instead."""		,					UpperCamelCase__		,					)
                  A_						       =   kwargs.pop("""feature_extractor"""							)

           A_						       =   image_processor if image_processor is not None else feature_extractor
           if image_processor is None:
                  raise ValueError("""You need to specify an `image_processor`."""							)
           if tokenizer is None:
                  raise ValueError("""You need to specify a `tokenizer`."""							)

           super().__init__(UpperCamelCase__		,					UpperCamelCase__							)



    def __call__(       self		,					UpperCamelCase__=None		,					UpperCamelCase__=None		,					UpperCamelCase__=None		,					UpperCamelCase__="max_length"		,					UpperCamelCase__="np"		,					**UpperCamelCase__							)    ->   int:





           '''simple docstring'''

           if text is None and query_images is None and images is None:
                  raise ValueError(
                      """You have to specify at least one text or query image or image. All three cannot be none."""							)

           if text is not None:
                  if isinstance(UpperCamelCase__		,					UpperCamelCase__							) or (isinstance(UpperCamelCase__		,					UpperCamelCase__							) and not isinstance(text[0]		,					UpperCamelCase__							)):
                         A_						       =   [self.tokenizer(UpperCamelCase__		,					padding=UpperCamelCase__		,					return_tensors=UpperCamelCase__		,					**UpperCamelCase__							)]

                  elif isinstance(UpperCamelCase__		,					UpperCamelCase__							) and isinstance(text[0]		,					UpperCamelCase__							):
                         A_						       =   []

                         # Maximum number of queries across batch
                         A_						       =   max([len(UpperCamelCase__							) for t in text]							)

                         # Pad all batch samples to max number of text queries
                         for t in text:
                                if len(UpperCamelCase__							) != max_num_queries:
                                       A_						       =   t + [""" """] * (max_num_queries - len(UpperCamelCase__							))

                                A_						       =   self.tokenizer(UpperCamelCase__		,					padding=UpperCamelCase__		,					return_tensors=UpperCamelCase__		,					**UpperCamelCase__							)
                                encodings.append(UpperCamelCase__							)
                  else:
                         raise TypeError("""Input text should be a string, a list of strings or a nested list of strings"""							)

                  if return_tensors == "np":
                         A_						       =   np.concatenate([encoding["""input_ids"""] for encoding in encodings]		,					axis=0							)
                         A_						       =   np.concatenate([encoding["""attention_mask"""] for encoding in encodings]		,					axis=0							)

                  elif return_tensors == "jax" and is_flax_available():
                         import jax.numpy as jnp

                         A_						       =   jnp.concatenate([encoding["""input_ids"""] for encoding in encodings]		,					axis=0							)
                         A_						       =   jnp.concatenate([encoding["""attention_mask"""] for encoding in encodings]		,					axis=0							)

                  elif return_tensors == "pt" and is_torch_available():
                         import torch

                         A_						       =   torch.cat([encoding["""input_ids"""] for encoding in encodings]		,					dim=0							)
                         A_						       =   torch.cat([encoding["""attention_mask"""] for encoding in encodings]		,					dim=0							)

                  elif return_tensors == "tf" and is_tf_available():
                         import tensorflow as tf

                         A_						       =   tf.stack([encoding["""input_ids"""] for encoding in encodings]		,					axis=0							)
                         A_						       =   tf.stack([encoding["""attention_mask"""] for encoding in encodings]		,					axis=0							)

                  else:
                         raise ValueError("""Target return tensor type could not be returned"""							)

                  A_						       =   BatchEncoding()
                  A_						       =   input_ids
                  A_						       =   attention_mask

           if query_images is not None:
                  A_						       =   BatchEncoding()
                  A_						       =   self.image_processor(
                      UpperCamelCase__		,					return_tensors=UpperCamelCase__		,					**UpperCamelCase__							).pixel_values
                  A_						       =   query_pixel_values

           if images is not None:
                  A_						       =   self.image_processor(UpperCamelCase__		,					return_tensors=UpperCamelCase__		,					**UpperCamelCase__							)

           if text is not None and images is not None:
                  A_						       =   image_features.pixel_values
                  return encoding
           elif query_images is not None and images is not None:
                  A_						       =   image_features.pixel_values
                  return encoding
           elif text is not None or query_images is not None:
                  return encoding
           else:
                  return BatchEncoding(data=dict(**UpperCamelCase__							)		,					tensor_type=UpperCamelCase__							)



    def        snake_case_      (       self		,					*UpperCamelCase__		,					**UpperCamelCase__							)    ->   int:





           '''simple docstring'''
           return self.image_processor.post_process(*UpperCamelCase__		,					**UpperCamelCase__							)



    def        snake_case_      (       self		,					*UpperCamelCase__		,					**UpperCamelCase__							)    ->   List[Any]:





           '''simple docstring'''
           return self.image_processor.post_process_object_detection(*UpperCamelCase__		,					**UpperCamelCase__							)



    def        snake_case_      (       self		,					*UpperCamelCase__		,					**UpperCamelCase__							)    ->   Tuple:





           '''simple docstring'''
           return self.image_processor.post_process_image_guided_detection(*UpperCamelCase__		,					**UpperCamelCase__							)



    def        snake_case_      (       self		,					*UpperCamelCase__		,					**UpperCamelCase__							)    ->   Any:





           '''simple docstring'''
           return self.tokenizer.batch_decode(*UpperCamelCase__		,					**UpperCamelCase__							)



    def        snake_case_      (       self		,					*UpperCamelCase__		,					**UpperCamelCase__							)    ->   List[str]:





           '''simple docstring'''
           return self.tokenizer.decode(*UpperCamelCase__		,					**UpperCamelCase__							)



    @property
    def        snake_case_      (       self							)    ->   Optional[int]:





           '''simple docstring'''
           warnings.warn(
               """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""		,					UpperCamelCase__		,					)
           return self.image_processor_class







    @property
    def        snake_case_      (       self							)    ->   List[str]:





           '''simple docstring'''
           warnings.warn(
               """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""		,					UpperCamelCase__		,					)
           return self.image_processor



'''simple docstring'''







from __future__ import annotations

import math
import random
from typing import Any


class        A__   :



    def __init__(       self							)    ->   None:





           '''simple docstring'''
           A_						       =   []
           A_						       =   0
           A_						       =   0



    def        snake_case_      (       self							)    ->   bool:





           '''simple docstring'''
           return self.head == self.tail



    def        snake_case_      (       self		,					UpperCamelCase__							)    ->   None:





           '''simple docstring'''
           self.data.append(UpperCamelCase__							)
           A_						       =   self.tail + 1



    def        snake_case_      (       self							)    ->   Any:





           '''simple docstring'''
           A_						       =   self.data[self.head]
           A_						       =   self.head + 1
           return ret



    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           return self.tail - self.head







    def        snake_case_      (       self							)    ->   None:





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


class        A__   :



    def __init__(       self		,					UpperCamelCase__							)    ->   None:





           '''simple docstring'''
           A_						       =   data
           A_						       =   None
           A_						       =   None
           A_						       =   1



    def        snake_case_      (       self							)    ->   Any:





           '''simple docstring'''
           return self.data



    def        snake_case_      (       self							)    ->   MyNode | None:





           '''simple docstring'''
           return self.left



    def        snake_case_      (       self							)    ->   MyNode | None:





           '''simple docstring'''
           return self.right



    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           return self.height



    def        snake_case_      (       self		,					UpperCamelCase__							)    ->   None:





           '''simple docstring'''
           A_						       =   data



    def        snake_case_      (       self		,					UpperCamelCase__							)    ->   None:





           '''simple docstring'''
           A_						       =   node



    def        snake_case_      (       self		,					UpperCamelCase__							)    ->   None:





           '''simple docstring'''
           A_						       =   node







    def        snake_case_      (       self		,					UpperCamelCase__							)    ->   None:





           '''simple docstring'''
           A_						       =   height

def    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      int:
       if node is None:
              return 0
       return node.get_height()
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__					)      ->      int:
       if a > b:
              return a
       return b
def    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      MyNode:
       print("""left rotation node:""",							node.get_data()					)
       A_						       =   node.get_left()
       assert ret is not None
       node.set_left(ret.get_right()					)
       ret.set_right(UpperCAmelCase__					)
       A_						       =   my_max(get_height(node.get_right()					),							get_height(node.get_left()					)					) + 1
       node.set_height(UpperCAmelCase__					)
       A_						       =   my_max(get_height(ret.get_right()					),							get_height(ret.get_left()					)					) + 1
       ret.set_height(UpperCAmelCase__					)
       return ret
def    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      MyNode:
       print("""right rotation node:""",							node.get_data()					)
       A_						       =   node.get_right()
       assert ret is not None
       node.set_right(ret.get_left()					)
       ret.set_left(UpperCAmelCase__					)
       A_						       =   my_max(get_height(node.get_right()					),							get_height(node.get_left()					)					) + 1
       node.set_height(UpperCAmelCase__					)
       A_						       =   my_max(get_height(ret.get_right()					),							get_height(ret.get_left()					)					) + 1
       ret.set_height(UpperCAmelCase__					)
       return ret
def    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      MyNode:
       A_						       =   node.get_left()
       assert left_child is not None
       node.set_left(left_rotation(UpperCAmelCase__					)					)
       return right_rotation(UpperCAmelCase__					)
def    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      MyNode:
       A_						       =   node.get_right()
       assert right_child is not None
       node.set_right(right_rotation(UpperCAmelCase__					)					)
       return left_rotation(UpperCAmelCase__					)
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__					)      ->      MyNode | None:
       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
                     A_						       =   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
                            A_						       =   right_rotation(UpperCAmelCase__					)
                     else:
                            A_						       =   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:
                     A_						       =   node.get_right()
                     assert right_child is not None
                     if data < right_child.get_data():
                            A_						       =   rl_rotation(UpperCAmelCase__					)
                     else:
                            A_						       =   left_rotation(UpperCAmelCase__					)
       A_						       =   my_max(get_height(node.get_right()					),							get_height(node.get_left()					)					) + 1
       node.set_height(UpperCAmelCase__					)
       return node
def    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      Any:
       while True:
              A_						       =   root.get_right()
              if right_child is None:
                     break
              A_						       =   right_child
       return root.get_data()
def    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      Any:
       while True:
              A_						       =   root.get_left()
              if left_child is None:
                     break
              A_						       =   left_child
       return root.get_data()
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__					)      ->      MyNode | None:
       A_						       =   root.get_left()
       A_						       =   root.get_right()
       if root.get_data() == data:
              if left_child is not None and right_child is not None:
                     A_						       =   get_left_most(UpperCAmelCase__					)
                     root.set_data(UpperCAmelCase__					)
                     root.set_right(del_node(UpperCAmelCase__,							UpperCAmelCase__					)					)
              elif left_child is not None:
                     A_						       =   left_child
              elif right_child is not None:
                     A_						       =   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()					):
                     A_						       =   left_rotation(UpperCAmelCase__					)
              else:
                     A_						       =   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()					):
                     A_						       =   right_rotation(UpperCAmelCase__					)
              else:
                     A_						       =   lr_rotation(UpperCAmelCase__					)
       A_						       =   my_max(get_height(root.get_right()					),							get_height(root.get_left()					)					) + 1
       root.set_height(UpperCAmelCase__					)
       return root


class        A__   :



    def __init__(       self							)    ->   None:





           '''simple docstring'''
           A_						       =   None



    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           return get_height(self.root							)



    def        snake_case_      (       self		,					UpperCamelCase__							)    ->   None:





           '''simple docstring'''
           print("""insert:""" + str(UpperCamelCase__							)							)
           A_						       =   insert_node(self.root		,					UpperCamelCase__							)



    def        snake_case_      (       self		,					UpperCamelCase__							)    ->   None:





           '''simple docstring'''
           print("""delete:""" + str(UpperCamelCase__							)							)
           if self.root is None:
                  print("""Tree is empty!"""							)
                  return
           A_						       =   del_node(self.root		,					UpperCamelCase__							)







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





           '''simple docstring'''
           A_						       =   """"""
           A_						       =   MyQueue()
           q.push(self.root							)
           A_						       =   self.get_height()
           if layer == 0:
                  return output
           A_						       =   0
           while not q.is_empty():
                  A_						       =   q.pop()
                  A_						       =   """ """ * 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
                  A_						       =   cnt + 1
                  for i in range(100							):
                         if cnt == math.pow(2		,					UpperCamelCase__							) - 1:
                                A_						       =   layer - 1
                                if layer == 0:
                                       output += "\n*************************************"
                                       return output
                                output += "\n"
                                break
           output += "\n*************************************"
           return output

def    UpperCAmelCase__     (   )      ->      None:
       import doctest

       doctest.testmod()


if __name__ == "__main__":
      _test()
      __lowerCamelCase									=     AVLtree()
      __lowerCamelCase									=     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))
