


"""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          = logging.get_logger(__name__)

__lowercase          = {
    """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 		_A    (    _a    ):







    """simple docstring"""




    UpperCAmelCase			:				int      =			"""deberta"""






    def __init__(   self							:       List[str]							,					__UpperCAmelCase							:       int=50265							,					__UpperCAmelCase							:       str=768							,					__UpperCAmelCase							:       Union[str, Any]=12							,					__UpperCAmelCase							:       int=12							,					__UpperCAmelCase							:       Union[str, Any]=3072							,					__UpperCAmelCase							:       List[Any]="gelu"							,					__UpperCAmelCase							:       Optional[int]=0.1							,					__UpperCAmelCase							:       Any=0.1							,					__UpperCAmelCase							:       List[Any]=512							,					__UpperCAmelCase							:       Dict=0							,					__UpperCAmelCase							:       Tuple=0.02							,					__UpperCAmelCase							:       Union[str, Any]=1e-7							,					__UpperCAmelCase							:       Any=False							,					__UpperCAmelCase							:       Tuple=-1							,					__UpperCAmelCase							:       List[Any]=0							,					__UpperCAmelCase							:       Optional[int]=True							,					__UpperCAmelCase							:       List[str]=None							,					__UpperCAmelCase							:       int=0							,					__UpperCAmelCase							:       Tuple="gelu"							,					**__UpperCAmelCase							:       Optional[Any]							,					):
      super().__init__(**__UpperCAmelCase)

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

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

      a		:						Tuple 				=     pos_att_type
      a		:						List[str] 				=     vocab_size
      a		:						Dict 				=     layer_norm_eps

      a		:						int 				=     kwargs.get("pooler_hidden_size"							,					__UpperCAmelCase)
      a		:						List[Any] 				=     pooler_dropout
      a		:						Tuple 				=     pooler_hidden_act






class 		_A    (    _a    ):







    """simple docstring"""




    @property
    def       __snake_case				(   self							:       int):
      if self.task == "multiple-choice":
        a		:						List[Any] 				=     {0: "batch", 1: "choice", 2: "sequence"}
      else:
        a		:						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       __snake_case				(   self							:       Tuple):
      return 12






    def       __snake_case				(   self							:       Optional[int]							,					__UpperCAmelCase							:       Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]							,					__UpperCAmelCase							:       int = -1							,					__UpperCAmelCase							:       int = -1							,					__UpperCAmelCase							:       int = -1							,					__UpperCAmelCase							:       bool = False							,					__UpperCAmelCase							:       Optional["TensorType"] = None							,					__UpperCAmelCase							:       int = 3							,					__UpperCAmelCase							:       int = 40							,					__UpperCAmelCase							:       int = 40							,					__UpperCAmelCase							:       "PreTrainedTokenizerBase" = None							,					):
      a		:						Any 				=     super().generate_dummy_inputs(preprocessor=__UpperCAmelCase							,					framework=__UpperCAmelCase)
      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 ..utils import DummyObject, requires_backends






class 		_A    (    metaclass=_a    ):







    """simple docstring"""




    UpperCAmelCase			:				int      =			["""torch""", """transformers""", """invisible_watermark"""]






    def __init__(   self							:       int							,					*__UpperCAmelCase							:       List[Any]							,					**__UpperCAmelCase							:       Optional[int]):
      requires_backends(self							,					["torch", "transformers", "invisible_watermark"])






    @classmethod
    def       __snake_case				(   cls							:       Optional[Any]							,					*__UpperCAmelCase							:       Any							,					**__UpperCAmelCase							:       int):
      requires_backends(cls							,					["torch", "transformers", "invisible_watermark"])






    @classmethod
    def       __snake_case				(   cls							:       List[Any]							,					*__UpperCAmelCase							:       Optional[Any]							,					**__UpperCAmelCase							:       Tuple):
      requires_backends(cls							,					["torch", "transformers", "invisible_watermark"])






class 		_A    (    metaclass=_a    ):







    """simple docstring"""




    UpperCAmelCase			:				Optional[int]      =			["""torch""", """transformers""", """invisible_watermark"""]






    def __init__(   self							:       int							,					*__UpperCAmelCase							:       Tuple							,					**__UpperCAmelCase							:       Tuple):
      requires_backends(self							,					["torch", "transformers", "invisible_watermark"])






    @classmethod
    def       __snake_case				(   cls							:       int							,					*__UpperCAmelCase							:       str							,					**__UpperCAmelCase							:       Optional[int]):
      requires_backends(cls							,					["torch", "transformers", "invisible_watermark"])






    @classmethod
    def       __snake_case				(   cls							:       Any							,					*__UpperCAmelCase							:       Optional[int]							,					**__UpperCAmelCase							:       Union[str, Any]):
      requires_backends(cls							,					["torch", "transformers", "invisible_watermark"])






