


"""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


UpperCAmelCase				=						logging.get_logger(__name__)

UpperCAmelCase				=						{
    """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 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					'''deberta-v2'''





   def __init__(							self      ,   SCREAMING_SNAKE_CASE=12_8100      ,   SCREAMING_SNAKE_CASE=1536      ,   SCREAMING_SNAKE_CASE=24      ,   SCREAMING_SNAKE_CASE=24      ,   SCREAMING_SNAKE_CASE=6144      ,   SCREAMING_SNAKE_CASE="gelu"      ,   SCREAMING_SNAKE_CASE=0.1      ,   SCREAMING_SNAKE_CASE=0.1      ,   SCREAMING_SNAKE_CASE=512      ,   SCREAMING_SNAKE_CASE=0      ,   SCREAMING_SNAKE_CASE=0.02      ,   SCREAMING_SNAKE_CASE=1e-7      ,   SCREAMING_SNAKE_CASE=False      ,   SCREAMING_SNAKE_CASE=-1      ,   SCREAMING_SNAKE_CASE=0      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=None      ,   SCREAMING_SNAKE_CASE=0      ,   SCREAMING_SNAKE_CASE="gelu"      ,   **SCREAMING_SNAKE_CASE      ,   )  ->  Tuple:
      super().__init__(**SCREAMING_SNAKE_CASE)

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

      # Backwards compatibility
      if type(SCREAMING_SNAKE_CASE) == str:
         _lowerCamelCase	:					Optional[int]   =		[x.strip() for x in pos_att_type.lower().split("""|""")]

      _lowerCamelCase	:					int   =		pos_att_type
      _lowerCamelCase	:					Optional[Any]   =		vocab_size
      _lowerCamelCase	:					Optional[Any]   =		layer_norm_eps

      _lowerCamelCase	:					Optional[Any]   =		kwargs.get("""pooler_hidden_size"""      ,   SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Any   =		pooler_dropout
      _lowerCamelCase	:					Optional[int]   =		pooler_hidden_act







class 	lowercase__				(					A_				):





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





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = -1      ,   SCREAMING_SNAKE_CASE = -1      ,   SCREAMING_SNAKE_CASE = -1      ,   SCREAMING_SNAKE_CASE = False      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = 3      ,   SCREAMING_SNAKE_CASE = 40      ,   SCREAMING_SNAKE_CASE = 40      ,   SCREAMING_SNAKE_CASE = None      ,   )  ->  Mapping[str, Any]:
      _lowerCamelCase	:					int   =		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"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments

def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					Any   =		HfArgumentParser(__snake_case   )
   _lowerCamelCase	:					int   =		parser.parse_args_into_dataclasses()[0]
   _lowerCamelCase	:					Dict   =		TensorFlowBenchmark(args=__snake_case   )
   try:
      _lowerCamelCase	:					Optional[int]   =		parser.parse_args_into_dataclasses()[0]
   except ValueError as e:
      _lowerCamelCase	:					Union[str, Any]   =		"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
      _lowerCamelCase	:					List[str]   =		""" """.join(str(__snake_case   ).split(""" """   )[:-1]   )
      _lowerCamelCase	:					Dict   =		""""""
      _lowerCamelCase	:					List[Any]   =		eval(str(__snake_case   ).split(""" """   )[-1]   )
      _lowerCamelCase	:					Tuple   =		[]
      for arg in depreciated_args:
         # arg[2:] removes '--'
         if arg[2:] in TensorFlowBenchmark.deprecated_args:
            # arg[5:] removes '--no_'
            full_error_msg += arg_error_msg.format(arg[5:]   )
         else:
            wrong_args.append(__snake_case   )
      if len(__snake_case   ) > 0:
         _lowerCamelCase	:					Tuple   =		full_error_msg + begin_error_msg + str(__snake_case   )
      raise ValueError(__snake_case   )
   benchmark.run()


if __name__ == "__main__":
 main()

