




'''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


lowercase	:      Optional[Any]       	=       logging.get_logger(__name__)

lowercase	:      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   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_				):
 __snake_case	:     str			   =      "deberta"



 def __init__(  self   ,		A=50265   ,		A=768   ,		A=12   ,		A=12   ,		A=3072   ,		A="gelu"   ,		A=0.1   ,		A=0.1   ,		A=512   ,		A=0   ,		A=0.0_2   ,		A=1E-7   ,		A=False   ,		A=-1   ,		A=0   ,		A=True   ,		A=None   ,		A=0   ,		A="gelu"   ,		**A   ,		):






        '''simple docstring'''




        super().__init__(**A )

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

        # Backwards compatibility
        if type(A ) == str:
               lowercase  :		Optional[Any]						=      [x.strip() for x in pos_att_type.lower().split('''|''' )]

        lowercase  :		Tuple						=      pos_att_type
        lowercase  :		List[Any]						=      vocab_size
        lowercase  :		int						=      layer_norm_eps

        lowercase  :		Optional[Any]						=      kwargs.get('''pooler_hidden_size'''   ,		A )
        lowercase  :		List[Any]						=      pooler_dropout
        lowercase  :		Tuple						=      pooler_hidden_act






class   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_				):



 @property
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        if self.task == "multiple-choice":
               lowercase  :		Any						=      {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
        else:
               lowercase  :		List[Any]						=      {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    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        return 12





 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A = -1   ,		A = -1   ,		A = -1   ,		A = False   ,		A = None   ,		A = 3   ,		A = 40   ,		A = 40   ,		A = None   ,		):






        '''simple docstring'''




        lowercase  :		Optional[Any]						=      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










'''simple docstring'''






import math
from timeit import timeit




def 		a_( lowerCamelCase      )				->int:






       '''simple docstring'''




       lowercase  :		Dict						=      0
       lowercase  :		Union[str, Any]						=      abs(lowerCamelCase      )
       while True:
              lowercase  :		List[Any]						=      n // 10
              digits += 1
              if n == 0:
                     break
       return digits




def 		a_( lowerCamelCase      )				->int:






       '''simple docstring'''




       return 1 if n == 0 else math.floor(math.log(abs(lowerCamelCase      )   ,				10      ) + 1      )




def 		a_( lowerCamelCase      )				->int:






       '''simple docstring'''




       return len(str(abs(lowerCamelCase      )      )      )




def 		a_( )				->None:






       '''simple docstring'''




       from collections.abc import Callable

       def benchmark_a_function(lowerCamelCase   ,				lowerCamelCase      ) -> None:
              lowercase  :		str						=      f"""{func.__name__}({value})"""
              lowercase  :		Optional[Any]						=      timeit(f"""__main__.{call}"""   ,				setup='''import __main__'''      )
              print(f"""{call}: {func(lowerCamelCase      )} -- {timing} seconds"""      )

       for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
              for func in (num_digits, num_digits_fast, num_digits_faster):
                     benchmark_a_function(lowerCamelCase   ,				lowerCamelCase      )
              print()


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      benchmark()




