
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


_A   =			logging.get_logger(__name__)

_A   =			{
    "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		(  A_  ):
     UpperCAmelCase__    :							Tuple									=		"deberta-v2"





     def __init__(self				:   Tuple    ,   _A				:   Tuple=1_2_8_1_0_0    ,   _A				:   List[str]=1_5_3_6    ,   _A				:   int=2_4    ,   _A				:   List[str]=2_4    ,   _A				:   str=6_1_4_4    ,   _A				:   Tuple="gelu"    ,   _A				:   Optional[int]=0.1    ,   _A				:   str=0.1    ,   _A				:   Union[str, Any]=5_1_2    ,   _A				:   Union[str, Any]=0    ,   _A				:   Dict=0.02    ,   _A				:   Dict=1E-7    ,   _A				:   Tuple=False    ,   _A				:   str=-1    ,   _A				:   Optional[Any]=0    ,   _A				:   Any=True    ,   _A				:   int=None    ,   _A				:   Dict=0    ,   _A				:   Optional[Any]="gelu"    ,   **_A				:   List[Any]    ,   )						->						List[Any]:
         super().__init__(**_A       )

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

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

         snake_case								= pos_att_type
         snake_case								= vocab_size
         snake_case								= layer_norm_eps

         snake_case								= kwargs.get("pooler_hidden_size"    ,   _A       )
         snake_case								= pooler_dropout
         snake_case								= pooler_hidden_act







class 					lowerCamelCase		(  A_  ):





     @property
     def 		UpperCAmelCase(self				:   str       )						->						Mapping[str, Mapping[int, str]]:
         if self.task == "multiple-choice":
             snake_case								= {0: "batch", 1: "choice", 2: "sequence"}
         else:
             snake_case								= {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				:   List[str]       )						->						int:
         return 1_2





     def 		UpperCAmelCase(self				:   Tuple    ,   _A				:   Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]    ,   _A				:   int = -1    ,   _A				:   int = -1    ,   _A				:   int = -1    ,   _A				:   bool = False    ,   _A				:   Optional["TensorType"] = None    ,   _A				:   int = 3    ,   _A				:   int = 4_0    ,   _A				:   int = 4_0    ,   _A				:   "PreTrainedTokenizerBase" = None    ,   )						->						Mapping[str, Any]:
         snake_case								= 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



from ....utils import logging


_A   =			logging.get_logger(__name__)







class 					lowerCamelCase		(  A_  ):





     def __init__(self				:   Tuple    ,   _A				:   Optional[int]    ,   _A				:   Tuple=None    ,   _A				:   Union[str, Any]=2_0_4_8       )						->						List[Any]:
         snake_case								= config.__dict__
         snake_case								= modal_hidden_size
         if num_labels:
             snake_case								= num_labels

