






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__		:							Union[str, Any] 				=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:							List[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_  (		__lowerCamelCase		):
     __lowerCamelCase      				=  'deberta-v2'




     def __init__(				self	,						_lowerCAmelCase=128100	,						_lowerCAmelCase=1536	,						_lowerCAmelCase=24	,						_lowerCAmelCase=24	,						_lowerCAmelCase=6144	,						_lowerCAmelCase="gelu"	,						_lowerCAmelCase=0.1	,						_lowerCAmelCase=0.1	,						_lowerCAmelCase=512	,						_lowerCAmelCase=0	,						_lowerCAmelCase=0.0_2	,						_lowerCAmelCase=1e-7	,						_lowerCAmelCase=False	,						_lowerCAmelCase=-1	,						_lowerCAmelCase=0	,						_lowerCAmelCase=True	,						_lowerCAmelCase=None	,						_lowerCAmelCase=0	,						_lowerCAmelCase="gelu"	,						**_lowerCAmelCase	,						):
          super().__init__(**_lowerCAmelCase		)

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

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

          UpperCAmelCase__      :  Dict				      =			pos_att_type
          UpperCAmelCase__      :  Tuple				      =			vocab_size
          UpperCAmelCase__      :  Dict				      =			layer_norm_eps

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







class 				UpperCAmelCase_  (		__lowerCamelCase		):




     @property
     def   __UpperCAmelCase  (				self		):
          if self.task == "multiple-choice":
               UpperCAmelCase__      :  Union[str, Any]				      =			{0: """batch""", 1: """choice""", 2: """sequence"""}
          else:
               UpperCAmelCase__      :  Tuple				      =			{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		):
          return 12




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase = -1	,						_lowerCAmelCase = -1	,						_lowerCAmelCase = -1	,						_lowerCAmelCase = False	,						_lowerCAmelCase = None	,						_lowerCAmelCase = 3	,						_lowerCAmelCase = 40	,						_lowerCAmelCase = 40	,						_lowerCAmelCase = None	,						):
          UpperCAmelCase__      :  int				      =			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













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


lowerCAmelCase							:       Tuple					 =	logging.get_logger(__name__)

lowerCAmelCase							:       List[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 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   str		      =     '''deberta-v2'''






				def __init__(  self			, _lowerCAmelCase=128100			, _lowerCAmelCase=1536			, _lowerCAmelCase=24			, _lowerCAmelCase=24			, _lowerCAmelCase=6144			, _lowerCAmelCase="gelu"			, _lowerCAmelCase=0.1			, _lowerCAmelCase=0.1			, _lowerCAmelCase=512			, _lowerCAmelCase=0			, _lowerCAmelCase=0.02			, _lowerCAmelCase=1e-7			, _lowerCAmelCase=False			, _lowerCAmelCase=-1			, _lowerCAmelCase=0			, _lowerCAmelCase=True			, _lowerCAmelCase=None			, _lowerCAmelCase=0			, _lowerCAmelCase="gelu"			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										super().__init__(**_lowerCAmelCase					)

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

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

										__SCREAMING_SNAKE_CASE:				int           =   pos_att_type
										__SCREAMING_SNAKE_CASE:				List[Any]           =   vocab_size
										__SCREAMING_SNAKE_CASE:				List[str]           =   layer_norm_eps

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   kwargs.get('''pooler_hidden_size'''			, _lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Dict           =   pooler_dropout
										__SCREAMING_SNAKE_CASE:				int           =   pooler_hidden_act





class 				a			(      __lowercase			):






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										if self.task == "multiple-choice":
																__SCREAMING_SNAKE_CASE:				Any           =   {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
										else:
																__SCREAMING_SNAKE_CASE:				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 			snake_case_    (  self					):

										"""simple docstring"""




										return 12






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase = -1			, _lowerCAmelCase = -1			, _lowerCAmelCase = -1			, _lowerCAmelCase = False			, _lowerCAmelCase = None			, _lowerCAmelCase = 3			, _lowerCAmelCase = 40			, _lowerCAmelCase = 40			, _lowerCAmelCase = None			, ):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   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



