





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


lowerCAmelCase_						   =    logging.get_logger(__name__)

lowerCAmelCase_						   =    {
    """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        _lowerCAmelCase    (			UpperCAmelCase_							):


		'''simple docstring'''
		a_							:       Optional[Any]      					="""perceiver"""







		def __init__(      self      :					Dict      ,				UpperCamelCase      :					int=2_56      ,				UpperCamelCase      :					str=12_80      ,				UpperCamelCase      :					List[str]=7_68      ,				UpperCamelCase      :					Tuple=1      ,				UpperCamelCase      :					Optional[int]=26      ,				UpperCamelCase      :					Tuple=8      ,				UpperCamelCase      :					Dict=8      ,				UpperCamelCase      :					Dict=None      ,				UpperCamelCase      :					Dict=None      ,				UpperCamelCase      :					List[Any]="kv"      ,				UpperCamelCase      :					List[Any]=1      ,				UpperCamelCase      :					Optional[Any]=1      ,				UpperCamelCase      :					Optional[Any]="gelu"      ,				UpperCamelCase      :					Tuple=0.1      ,				UpperCamelCase      :					int=0.02      ,				UpperCamelCase      :					Tuple=1e-1_2      ,				UpperCamelCase      :					List[str]=True      ,				UpperCamelCase      :					Any=2_62      ,				UpperCamelCase      :					Any=20_48      ,				UpperCamelCase      :					Optional[Any]=56      ,				UpperCamelCase      :					Any=[3_68, 4_96]      ,				UpperCamelCase      :					List[Any]=16      ,				UpperCamelCase      :					List[str]=19_20      ,				UpperCamelCase      :					int=16      ,				UpperCamelCase      :					Any=[1, 16, 2_24, 2_24]      ,				**UpperCamelCase      :					Tuple      ,				):


				'''simple docstring'''





				super().__init__(**UpperCamelCase							)

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




class        _lowerCAmelCase    (			UpperCAmelCase_							):


		'''simple docstring'''







		@property
		def UpperCamelCase_ (      self      :					Dict							):


				'''simple docstring'''





				if self.task == "multiple-choice":
						_snake_case   :				int														=	{0: 'batch', 1: 'choice', 2: 'sequence'}
				else:
						_snake_case   :				Any														=	{0: 'batch', 1: 'sequence'}
				return OrderedDict(
				    [
				        ('inputs', dynamic_axis),
				        ('attention_mask', dynamic_axis),
				    ]							)







		@property
		def UpperCamelCase_ (      self      :					Tuple							):


				'''simple docstring'''





				return 1e-4







		def UpperCamelCase_ (      self      :					Optional[Any]      ,				UpperCamelCase      :					Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]      ,				UpperCamelCase      :					int = -1      ,				UpperCamelCase      :					int = -1      ,				UpperCamelCase      :					int = -1      ,				UpperCamelCase      :					bool = False      ,				UpperCamelCase      :					Optional[TensorType] = None      ,				UpperCamelCase      :					int = 3      ,				UpperCamelCase      :					int = 40      ,				UpperCamelCase      :					int = 40      ,				):


				'''simple docstring'''





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











from __future__ import annotations

from random import random



class        _lowerCAmelCase    :


  '''simple docstring'''







  def __init__(      self      :					Dict      ,				UpperCamelCase      :					int | None = None							):


    '''simple docstring'''





    _snake_case   :				str														=	value
    _snake_case   :				List[Any]														=	random()
    _snake_case   :				Node | None														=	None
    _snake_case   :				Node | None														=	None







  def __repr__(      self      :					Optional[Any]							):


    '''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      :					Dict							):


    '''simple docstring'''





    _snake_case   :				List[str]														=	str(self.value							) + ' '
    _snake_case   :				List[Any]														=	str(self.left or ''							)
    _snake_case   :				int														=	str(self.right or ''							)
    return value + left + right




def     lowerCamelCase_	(      lowerCAmelCase:	Node | None    ,							lowerCAmelCase:	int				)->		tuple[Node | None, Node | None]:
  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:
      _snake_case							,					_snake_case   :				Optional[Any]														=	split(root.left    ,							lowerCAmelCase				)
      return left, root
    else:
      _snake_case							,					_snake_case   :				List[str]														=	split(root.right    ,							lowerCAmelCase				)
      return root, right


def     lowerCamelCase_	(      lowerCAmelCase:	Node | None    ,							lowerCAmelCase:	Node | None				)->		Node | None:
  if (not left) or (not right):  # If one node is None, return the other
    return left or right
  elif left.prior < right.prior:
    _snake_case   :				str														=	merge(left.right    ,							lowerCAmelCase				)
    return left
  else:
    _snake_case   :				Union[str, Any]														=	merge(lowerCAmelCase    ,							right.left				)
    return right


def     lowerCamelCase_	(      lowerCAmelCase:	Node | None    ,							lowerCAmelCase:	int				)->		Node | None:
  _snake_case   :				Tuple														=	Node(lowerCAmelCase				)
  _snake_case							,					_snake_case   :				Optional[int]														=	split(lowerCAmelCase    ,							lowerCAmelCase				)
  return merge(merge(lowerCAmelCase    ,							lowerCAmelCase				)    ,							lowerCAmelCase				)


def     lowerCamelCase_	(      lowerCAmelCase:	Node | None    ,							lowerCAmelCase:	int				)->		Node | None:
  _snake_case							,					_snake_case   :				Optional[int]														=	split(lowerCAmelCase    ,							value - 1				)
  _snake_case							,					_snake_case   :				List[str]														=	split(lowerCAmelCase    ,							lowerCAmelCase				)
  return merge(lowerCAmelCase    ,							lowerCAmelCase				)


def     lowerCamelCase_	(      lowerCAmelCase:	Node | None				)->		None:
  if not root:  # None
    return
  else:
    inorder(root.left				)
    print(root.value    ,							end=','				)
    inorder(root.right				)


def     lowerCamelCase_	(      lowerCAmelCase:	Node | None    ,							lowerCAmelCase:	str				)->		Node | None:
  for arg in args.split():
    if arg[0] == "+":
      _snake_case   :				List[str]														=	insert(lowerCAmelCase    ,							int(arg[1:]				)				)

    elif arg[0] == "-":
      _snake_case   :				Any														=	erase(lowerCAmelCase    ,							int(arg[1:]				)				)

    else:
      print('Unknown command'				)

  return root


def     lowerCamelCase_	(      )->		None:
  _snake_case   :				Tuple														=	None
  print(
      'enter numbers to create a tree, + value to add value into treap, '
      '- value to erase all nodes with value. \'q\' to quit. '				)

  _snake_case   :				List[Any]														=	input()
  while args != "q":
    _snake_case   :				int														=	interact_treap(lowerCAmelCase    ,							lowerCAmelCase				)
    print(lowerCAmelCase				)
    _snake_case   :				Tuple														=	input()

  print('good by!'				)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()




