



'''simple docstring'''






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


A_				:					List[Any]							=      logging.get_logger(__name__)

A_				:					Optional[Any]							=      {
    """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 				lowercase			(    _lowerCamelCase							):


 """simple docstring"""


 UpperCAmelCase		      =    """deberta"""







 def __init__(				self    ,a_=50_265    ,a_=768    ,a_=12    ,a_=12    ,a_=3_072    ,a_="gelu"    ,a_=0.1    ,a_=0.1    ,a_=512    ,a_=0    ,a_=0.02    ,a_=1E-7    ,a_=False    ,a_=-1    ,a_=0    ,a_=True    ,a_=None    ,a_=0    ,a_="gelu"    ,**a_    ,)						->  Optional[Any]:
        super().__init__(**a_				)

        _UpperCAmelCase    :		Tuple     	=					hidden_size
        _UpperCAmelCase    :		List[str]     	=					num_hidden_layers
        _UpperCAmelCase    :		Dict     	=					num_attention_heads
        _UpperCAmelCase    :		Optional[Any]     	=					intermediate_size
        _UpperCAmelCase    :		Any     	=					hidden_act
        _UpperCAmelCase    :		List[str]     	=					hidden_dropout_prob
        _UpperCAmelCase    :		str     	=					attention_probs_dropout_prob
        _UpperCAmelCase    :		List[str]     	=					max_position_embeddings
        _UpperCAmelCase    :		List[str]     	=					type_vocab_size
        _UpperCAmelCase    :		Optional[Any]     	=					initializer_range
        _UpperCAmelCase    :		Dict     	=					relative_attention
        _UpperCAmelCase    :		int     	=					max_relative_positions
        _UpperCAmelCase    :		int     	=					pad_token_id
        _UpperCAmelCase    :		Any     	=					position_biased_input

        # Backwards compatibility
        if type(a_				) == str:
               _UpperCAmelCase    :		Optional[Any]     	=					[x.strip() for x in pos_att_type.lower().split("""|"""				)]

        _UpperCAmelCase    :		Optional[Any]     	=					pos_att_type
        _UpperCAmelCase    :		str     	=					vocab_size
        _UpperCAmelCase    :		List[Any]     	=					layer_norm_eps

        _UpperCAmelCase    :		Dict     	=					kwargs.get("""pooler_hidden_size"""    ,a_				)
        _UpperCAmelCase    :		List[str]     	=					pooler_dropout
        _UpperCAmelCase    :		Any     	=					pooler_hidden_act







class 				lowercase			(    _lowerCamelCase							):


 """simple docstring"""


 @property
 def _snake_case					(				self				)						->  Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
               _UpperCAmelCase    :		str     	=					{0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
               _UpperCAmelCase    :		Optional[int]     	=					{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 _snake_case					(				self				)						->  int:
        return 12







 def _snake_case					(				self    ,a_    ,a_ = -1    ,a_ = -1    ,a_ = -1    ,a_ = False    ,a_ = None    ,a_ = 3    ,a_ = 40    ,a_ = 40    ,a_ = None    ,)						->  Mapping[str, Any]:
        _UpperCAmelCase    :		Dict     	=					super().generate_dummy_inputs(preprocessor=a_    ,framework=a_				)
        if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
               del dummy_inputs["token_type_ids"]
        return dummy_inputs






def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   Optional[int]:

     """simple docstring"""







     if power < 0:
          return -1
     base %= mod
     _lowerCAmelCase :       Tuple	   =     1

     while power > 0:
          if power & 1:
               _lowerCAmelCase :       Optional[Any]	   =     (result * base) % mod
          _lowerCAmelCase :       int	   =     power >> 1
          _lowerCAmelCase :       Optional[int]	   =     (base * base) % mod

     return result







def 						_snake_case		(     )							->   Optional[int]:

     """simple docstring"""







     print(modular_exponential(3		,				200		,				13					)					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  main()






