


'''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


SCREAMING_SNAKE_CASE_      =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_      =			{
    '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  lowerCAmelCase_						(       snake_case__      ):





  """simple docstring"""



  a_       :str												="""deberta-v2"""

  def __init__(					self     :							int				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]=1_2_8_1_0_0				,							SCREAMING_SNAKE_CASE__     :							List[Any]=1_5_3_6				,							SCREAMING_SNAKE_CASE__     :							Any=2_4				,							SCREAMING_SNAKE_CASE__     :							str=2_4				,							SCREAMING_SNAKE_CASE__     :							Optional[int]=6_1_4_4				,							SCREAMING_SNAKE_CASE__     :							Tuple="gelu"				,							SCREAMING_SNAKE_CASE__     :							Optional[int]=0.1				,							SCREAMING_SNAKE_CASE__     :							Optional[int]=0.1				,							SCREAMING_SNAKE_CASE__     :							List[str]=5_1_2				,							SCREAMING_SNAKE_CASE__     :							List[Any]=0				,							SCREAMING_SNAKE_CASE__     :							Dict=0.0_2				,							SCREAMING_SNAKE_CASE__     :							Tuple=1E-7				,							SCREAMING_SNAKE_CASE__     :							List[Any]=False				,							SCREAMING_SNAKE_CASE__     :							int=-1				,							SCREAMING_SNAKE_CASE__     :							Dict=0				,							SCREAMING_SNAKE_CASE__     :							Optional[int]=True				,							SCREAMING_SNAKE_CASE__     :							int=None				,							SCREAMING_SNAKE_CASE__     :							int=0				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]="gelu"				,							**SCREAMING_SNAKE_CASE__     :							str				,							):





       '''simple docstring'''

       super().__init__(**SCREAMING_SNAKE_CASE__						)

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

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

       __a       =   pos_att_type
       __a       =   vocab_size
       __a       =   layer_norm_eps

       __a       =   kwargs.get("""pooler_hidden_size"""				,							SCREAMING_SNAKE_CASE__						)
       __a       =   pooler_dropout
       __a       =   pooler_hidden_act


class  lowerCAmelCase_						(       snake_case__      ):





  """simple docstring"""



  @property
  def __a						(					self     :							int						):





       '''simple docstring'''

       if self.task == "multiple-choice":
            __a       =   {0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
            __a       =   {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 __a						(					self     :							Dict						):





       '''simple docstring'''

       return 1_2


  def __a						(					self     :							Any				,							SCREAMING_SNAKE_CASE__     :							Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]				,							SCREAMING_SNAKE_CASE__     :							int = -1				,							SCREAMING_SNAKE_CASE__     :							int = -1				,							SCREAMING_SNAKE_CASE__     :							int = -1				,							SCREAMING_SNAKE_CASE__     :							bool = False				,							SCREAMING_SNAKE_CASE__     :							Optional["TensorType"] = None				,							SCREAMING_SNAKE_CASE__     :							int = 3				,							SCREAMING_SNAKE_CASE__     :							int = 4_0				,							SCREAMING_SNAKE_CASE__     :							int = 4_0				,							SCREAMING_SNAKE_CASE__     :							"PreTrainedTokenizerBase" = None				,							):





       '''simple docstring'''

       __a       =   super().generate_dummy_inputs(preprocessor=SCREAMING_SNAKE_CASE__				,							framework=SCREAMING_SNAKE_CASE__						)
       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'''







def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					str:
     """simple docstring"""




     return " ".join(input_str.split()[::-1]		)


if __name__ == "__main__":
 import doctest

 doctest.testmod()






