





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


_snake_case  :    int						    =  logging.get_logger(__name__)

_snake_case  :    Any						    =  {
    'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
    ),
    'microsoft/deberta-v2-xxlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
    ),
}



class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  a_        = """deberta-v2"""






  def __init__(				self :		Optional[Any]					,  lowerCAmelCase_ :		Tuple=1_2_8_1_0_0					,  lowerCAmelCase_ :		Any=1_5_3_6					,  lowerCAmelCase_ :		Optional[Any]=2_4					,  lowerCAmelCase_ :		str=2_4					,  lowerCAmelCase_ :		Dict=6_1_4_4					,  lowerCAmelCase_ :		List[Any]="gelu"					,  lowerCAmelCase_ :		str=0.1					,  lowerCAmelCase_ :		List[str]=0.1					,  lowerCAmelCase_ :		List[Any]=5_1_2					,  lowerCAmelCase_ :		str=0					,  lowerCAmelCase_ :		List[Any]=0.02					,  lowerCAmelCase_ :		Any=1e-7					,  lowerCAmelCase_ :		List[str]=False					,  lowerCAmelCase_ :		int=-1					,  lowerCAmelCase_ :		Any=0					,  lowerCAmelCase_ :		Optional[Any]=True					,  lowerCAmelCase_ :		Any=None					,  lowerCAmelCase_ :		Tuple=0					,  lowerCAmelCase_ :		Union[str, Any]="gelu"					,  **lowerCAmelCase_ :		int					,  )							->						str:
     super().__init__(**lowerCAmelCase_     )

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

     # Backwards compatibility
     if type(lowerCAmelCase_     ) == str:
        __lowerCAmelCase								=	[x.strip() for x in pos_att_type.lower().split('|'     )]

     __lowerCAmelCase								=	pos_att_type
     __lowerCAmelCase								=	vocab_size
     __lowerCAmelCase								=	layer_norm_eps

     __lowerCAmelCase								=	kwargs.get('pooler_hidden_size'					,  lowerCAmelCase_     )
     __lowerCAmelCase								=	pooler_dropout
     __lowerCAmelCase								=	pooler_hidden_act





class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  @property
  def 				lowercase		(				self :		Optional[Any]     )							->						Mapping[str, Mapping[int, str]]:
     if self.task == "multiple-choice":
        __lowerCAmelCase								=	{0: 'batch', 1: 'choice', 2: 'sequence'}
     else:
        __lowerCAmelCase								=	{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 :		List[str]     )							->						int:
     return 1_2






  def 				lowercase		(				self :		int					,  lowerCAmelCase_ :		Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]					,  lowerCAmelCase_ :		int = -1					,  lowerCAmelCase_ :		int = -1					,  lowerCAmelCase_ :		int = -1					,  lowerCAmelCase_ :		bool = False					,  lowerCAmelCase_ :		Optional["TensorType"] = None					,  lowerCAmelCase_ :		int = 3					,  lowerCAmelCase_ :		int = 4_0					,  lowerCAmelCase_ :		int = 4_0					,  lowerCAmelCase_ :		"PreTrainedTokenizerBase" = None					,  )							->						Mapping[str, Any]:
     __lowerCAmelCase								=	super().generate_dummy_inputs(preprocessor=lowerCAmelCase_					,  framework=lowerCAmelCase_     )
     if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
        del dummy_inputs["token_type_ids"]
     return dummy_inputs








def   a_    ( lowerCAmelCase_      :			int	):

   if p < 2:
      raise ValueError('p should not be less than 2!'	)
   elif p == 2:
      return True

   __lowerCAmelCase								=	4
   __lowerCAmelCase								=	(1 << p) - 1
   for _ in range(p - 2	):
      __lowerCAmelCase								=	((s * s) - 2) % m
   return s == 0


if __name__ == "__main__":
     print(lucas_lehmer_test(7))
     print(lucas_lehmer_test(11))

