





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       :   Tuple				       =							logging.get_logger(__name__)

__UpperCAmelCase       :   Union[str, 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     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     """deberta-v2"""




       def __init__(  self :    Optional[int]							,				A :    List[Any]=128_100							,				A :    int=1_536							,				A :    Dict=24							,				A :    List[str]=24							,				A :    Optional[int]=6_144							,				A :    List[str]="gelu"							,				A :    Optional[int]=0.1							,				A :    Any=0.1							,				A :    Optional[Any]=512							,				A :    Tuple=0							,				A :    List[Any]=0.02							,				A :    Dict=1E-7							,				A :    Optional[int]=False							,				A :    Dict=-1							,				A :    int=0							,				A :    Union[str, Any]=True							,				A :    Tuple=None							,				A :    Optional[Any]=0							,				A :    Union[str, Any]="gelu"							,				**A :    Tuple							,				):
         super().__init__(**A		)

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

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

         __snake_case:						Dict   =						pos_att_type
         __snake_case:						List[Any]   =						vocab_size
         __snake_case:						Optional[Any]   =						layer_norm_eps

         __snake_case:						str   =						kwargs.get("""pooler_hidden_size"""							,				A		)
         __snake_case:						Any   =						pooler_dropout
         __snake_case:						Optional[Any]   =						pooler_hidden_act







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







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






       def 							UpperCAmelCase__ (  self :    Dict							,				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 = 40							,				A :    int = 40							,				A :    "PreTrainedTokenizerBase" = None							,				):
         __snake_case:						Union[str, Any]   =						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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCAmelCase       :   Optional[int]				       =							logging.get_logger(__name__)

__UpperCAmelCase       :   Optional[int]				       =							{
    "kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json",
    "kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json",
    "kssteven/ibert-roberta-large-mnli": (
        "https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"
    ),
}







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     """ibert"""




       def __init__(  self :    Dict							,				A :    Union[str, Any]=30_522							,				A :    List[Any]=768							,				A :    List[Any]=12							,				A :    Optional[int]=12							,				A :    Optional[Any]=3_072							,				A :    int="gelu"							,				A :    str=0.1							,				A :    List[Any]=0.1							,				A :    Optional[Any]=512							,				A :    int=2							,				A :    Union[str, Any]=0.02							,				A :    List[str]=1E-12							,				A :    Optional[int]=1							,				A :    Optional[int]=0							,				A :    List[str]=2							,				A :    str="absolute"							,				A :    Any=False							,				A :    Optional[Any]="none"							,				**A :    Any							,				):
         super().__init__(pad_token_id=A							,				bos_token_id=A							,				eos_token_id=A							,				**A		)

         __snake_case:						List[Any]   =						vocab_size
         __snake_case:						Optional[Any]   =						hidden_size
         __snake_case:						List[str]   =						num_hidden_layers
         __snake_case:						Tuple   =						num_attention_heads
         __snake_case:						List[str]   =						hidden_act
         __snake_case:						Optional[Any]   =						intermediate_size
         __snake_case:						Tuple   =						hidden_dropout_prob
         __snake_case:						List[str]   =						attention_probs_dropout_prob
         __snake_case:						Any   =						max_position_embeddings
         __snake_case:						int   =						type_vocab_size
         __snake_case:						List[str]   =						initializer_range
         __snake_case:						List[Any]   =						layer_norm_eps
         __snake_case:						Optional[int]   =						position_embedding_type
         __snake_case:						str   =						quant_mode
         __snake_case:						Optional[int]   =						force_dequant







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       @property
       def 							UpperCAmelCase__ (  self :    Tuple		):
         if self.task == "multiple-choice":
           __snake_case:						List[Any]   =						{0: """batch""", 1: """choice""", 2: """sequence"""}
         else:
           __snake_case:						List[str]   =						{0: """batch""", 1: """sequence"""}
         return OrderedDict(
             [
                 ("""input_ids""", dynamic_axis),
                 ("""attention_mask""", dynamic_axis),
             ]		)






