

'''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 collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


lowercase__         =      logging.get_logger(__name__)

lowercase__         =      {
    "deepmind/language-perceiver": "https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json",
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     Optional[int]  		=  """perceiver"""


     def __init__(		self			:					str					,      lowercase_			:					List[str]=256					,      lowercase_			:					List[str]=1_280					,      lowercase_			:					str=768					,      lowercase_			:					Tuple=1					,      lowercase_			:					str=26					,      lowercase_			:					List[Any]=8					,      lowercase_			:					int=8					,      lowercase_			:					List[str]=None					,      lowercase_			:					Dict=None					,      lowercase_			:					int="kv"					,      lowercase_			:					Union[str, Any]=1					,      lowercase_			:					List[str]=1					,      lowercase_			:					Any="gelu"					,      lowercase_			:					Optional[Any]=0.1					,      lowercase_			:					str=0.02					,      lowercase_			:					Optional[Any]=1E-12					,      lowercase_			:					Any=True					,      lowercase_			:					Union[str, Any]=262					,      lowercase_			:					Union[str, Any]=2_048					,      lowercase_			:					Optional[int]=56					,      lowercase_			:					int=[368, 496]					,      lowercase_			:					str=16					,      lowercase_			:					Optional[int]=1_920					,      lowercase_			:					Tuple=16					,      lowercase_			:					int=[1, 16, 224, 224]					,      **lowercase_			:					Union[str, Any]					,      )						->     List[str]:
      super().__init__(**lowercase_	)

      UpperCAmelCase	:     Union[str, Any]						 =			num_latents
      UpperCAmelCase	:     List[Any]						 =			d_latents
      UpperCAmelCase	:     Dict						 =			d_model
      UpperCAmelCase	:     Dict						 =			num_blocks
      UpperCAmelCase	:     Optional[int]						 =			num_self_attends_per_block
      UpperCAmelCase	:     Optional[Any]						 =			num_self_attention_heads
      UpperCAmelCase	:     Optional[Any]						 =			num_cross_attention_heads
      UpperCAmelCase	:     Tuple						 =			qk_channels
      UpperCAmelCase	:     Optional[int]						 =			v_channels
      UpperCAmelCase	:     str						 =			cross_attention_shape_for_attention
      UpperCAmelCase	:     Union[str, Any]						 =			self_attention_widening_factor
      UpperCAmelCase	:     List[Any]						 =			cross_attention_widening_factor
      UpperCAmelCase	:     Optional[Any]						 =			hidden_act
      UpperCAmelCase	:     int						 =			attention_probs_dropout_prob
      UpperCAmelCase	:     Optional[int]						 =			initializer_range
      UpperCAmelCase	:     Dict						 =			layer_norm_eps
      UpperCAmelCase	:     Any						 =			use_query_residual
      # masked language modeling attributes
      UpperCAmelCase	:     Any						 =			vocab_size
      UpperCAmelCase	:     List[Any]						 =			max_position_embeddings
      # image classification attributes
      UpperCAmelCase	:     str						 =			image_size
      # flow attributes
      UpperCAmelCase	:     Any						 =			train_size
      # multimodal autoencoding attributes
      UpperCAmelCase	:     Any						 =			num_frames
      UpperCAmelCase	:     List[Any]						 =			audio_samples_per_frame
      UpperCAmelCase	:     Tuple						 =			samples_per_patch
      UpperCAmelCase	:     Union[str, Any]						 =			output_shape




class 	A_			(    _snake_case							):






     '''simple docstring'''



     @property
     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
       UpperCAmelCase	:     Tuple						 =			{0: 'batch', 1: 'choice', 2: 'sequence'}
      else:
       UpperCAmelCase	:     Tuple						 =			{0: 'batch', 1: 'sequence'}
      return OrderedDict(
          [
              ('inputs', dynamic_axis),
              ('attention_mask', dynamic_axis),
          ]	)


     @property
     def        UpperCAmelCase_	(		self			:					str	)						->     float:
      return 1E-4




     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]					,      lowercase_			:					int = -1					,      lowercase_			:					int = -1					,      lowercase_			:					int = -1					,      lowercase_			:					bool = False					,      lowercase_			:					Optional[TensorType] = None					,      lowercase_			:					int = 3					,      lowercase_			:					int = 40					,      lowercase_			:					int = 40					,      )						->     Mapping[str, Any]:
      # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

      if isinstance(lowercase_					,      lowercase_	):
       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       UpperCAmelCase	:     Tuple						 =			compute_effective_axis_dimension(
           lowercase_					,      fixed_dimension=OnnxConfig.default_fixed_batch					,      num_token_to_add=0	)
       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       UpperCAmelCase	:     int						 =			preprocessor.num_special_tokens_to_add(lowercase_	)
       UpperCAmelCase	:     Union[str, Any]						 =			compute_effective_axis_dimension(
           lowercase_					,      fixed_dimension=OnnxConfig.default_fixed_sequence					,      num_token_to_add=lowercase_	)
       # Generate dummy inputs according to compute batch and sequence
       UpperCAmelCase	:     List[Any]						 =			[' '.join(['a']	) * seq_length] * batch_size
       UpperCAmelCase	:     Union[str, Any]						 =			dict(preprocessor(lowercase_					,      return_tensors=lowercase_	)	)
       UpperCAmelCase	:     Union[str, Any]						 =			inputs.pop('input_ids'	)
       return inputs
      elif isinstance(lowercase_					,      lowercase_	) and preprocessor.model_input_names[0] == "pixel_values":
       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       UpperCAmelCase	:     Tuple						 =			compute_effective_axis_dimension(lowercase_					,      fixed_dimension=OnnxConfig.default_fixed_batch	)
       UpperCAmelCase	:     Any						 =			self._generate_dummy_images(lowercase_					,      lowercase_					,      lowercase_					,      lowercase_	)
       UpperCAmelCase	:     Tuple						 =			dict(preprocessor(images=lowercase_					,      return_tensors=lowercase_	)	)
       UpperCAmelCase	:     Dict						 =			inputs.pop('pixel_values'	)
       return inputs
      else:
       raise ValueError(
           'Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.'	)




