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

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
    from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__lowerCAmelCase      :					List[str]   	=       logging.get_logger(__name__)

__lowerCAmelCase      :					Optional[int]   	=       {
    'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/config.json',
    'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/config.json',
    'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json',
    'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json',
    'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json',
    'microsoft/deberta-xlarge-mnli': 'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json',
}




class 	UpperCAmelCase_      (			_A					):


  '''simple docstring'''







  a__   	=		"""deberta"""
  def __init__(			self      :			Tuple      ,							UpperCamelCase__      :			Union[str, Any]=5_0265      ,							UpperCamelCase__      :			List[str]=768      ,							UpperCamelCase__      :			Optional[int]=12      ,							UpperCamelCase__      :			int=12      ,							UpperCamelCase__      :			List[Any]=3072      ,							UpperCamelCase__      :			Tuple="gelu"      ,							UpperCamelCase__      :			Optional[int]=0.1      ,							UpperCamelCase__      :			Optional[int]=0.1      ,							UpperCamelCase__      :			str=512      ,							UpperCamelCase__      :			Tuple=0      ,							UpperCamelCase__      :			int=0.02      ,							UpperCamelCase__      :			int=1E-7      ,							UpperCamelCase__      :			Tuple=False      ,							UpperCamelCase__      :			Union[str, Any]=-1      ,							UpperCamelCase__      :			Dict=0      ,							UpperCamelCase__      :			List[str]=True      ,							UpperCamelCase__      :			int=None      ,							UpperCamelCase__      :			Dict=0      ,							UpperCamelCase__      :			int="gelu"      ,							**UpperCamelCase__      :			List[Any]      ,							)      ->     Any:







    """simple docstring"""






    super().__init__(**UpperCamelCase__   )

    __magic_name__    					=	hidden_size
    __magic_name__    					=	num_hidden_layers
    __magic_name__    					=	num_attention_heads
    __magic_name__    					=	intermediate_size
    __magic_name__    					=	hidden_act
    __magic_name__    					=	hidden_dropout_prob
    __magic_name__    					=	attention_probs_dropout_prob
    __magic_name__    					=	max_position_embeddings
    __magic_name__    					=	type_vocab_size
    __magic_name__    					=	initializer_range
    __magic_name__    					=	relative_attention
    __magic_name__    					=	max_relative_positions
    __magic_name__    					=	pad_token_id
    __magic_name__    					=	position_biased_input

    # Backwards compatibility
    if type(UpperCamelCase__   ) == str:
      __magic_name__    					=	[x.strip() for x in pos_att_type.lower().split("""|"""   )]

    __magic_name__    					=	pos_att_type
    __magic_name__    					=	vocab_size
    __magic_name__    					=	layer_norm_eps

    __magic_name__    					=	kwargs.get("""pooler_hidden_size"""      ,							UpperCamelCase__   )
    __magic_name__    					=	pooler_dropout
    __magic_name__    					=	pooler_hidden_act







class 	UpperCAmelCase_      (			_A					):


  '''simple docstring'''







  @property
  def 							_lowercase							(			self      :			str   )      ->     Mapping[str, Mapping[int, str]]:







    """simple docstring"""






    if self.task == "multiple-choice":
      __magic_name__    					=	{0: """batch""", 1: """choice""", 2: """sequence"""}
    else:
      __magic_name__    					=	{0: """batch""", 1: """sequence"""}
    if self._config.type_vocab_size > 0:
      return OrderedDict(
          [("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)]   )
    else:
      return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)]   )
  @property
  def 							_lowercase							(			self      :			Tuple   )      ->     int:







    """simple docstring"""






    return 12





  def 							_lowercase							(			self      :			List[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      ,							UpperCamelCase__      :			"PreTrainedTokenizerBase" = None      ,							)      ->     Mapping[str, Any]:







    """simple docstring"""






    __magic_name__    					=	super().generate_dummy_inputs(preprocessor=UpperCamelCase__      ,							framework=UpperCamelCase__   )
    if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
      del dummy_inputs["token_type_ids"]
    return dummy_inputs



from itertools import compress, repeat
from math import ceil, sqrt

def a__   (	A_ ):






  '''simple docstring'''





  if num <= 2:
    return []
  if num == 3:
    return [2]

  # Odd sieve for numbers in range [3, num - 1]
  __magic_name__    					=	bytearray(b"""\x01""" ) * ((num >> 1) - 1)

  for i in range(3,					int(sqrt(A_ ) ) + 1,					2 ):
    if sieve[(i >> 1) - 1]:
      __magic_name__    					=	i**2
      __magic_name__    					=	repeat(
          0,					ceil((num - i_squared) / (i << 1) ) )

  return [2] + list(compress(range(3,					A_,					2 ),					A_ ) )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

