
from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE							:str							 =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:Tuple							 =				{
    'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/config.json',
    'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/config.json',
    'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/config.json',
    'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/config.json',
    'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json',
    'roberta-large-openai-detector': 'https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json',
}





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     "roberta"






	def __init__(       self					: Optional[int]							,A					: Optional[int]=5_02_65							,A					: Union[str, Any]=7_68							,A					: Tuple=12							,A					: Any=12							,A					: List[Any]=30_72							,A					: str="gelu"							,A					: Dict=0.1							,A					: Union[str, Any]=0.1							,A					: Dict=5_12							,A					: Union[str, Any]=2							,A					: Tuple=0.02							,A					: Dict=1E-12							,A					: Optional[Any]=1							,A					: int=0							,A					: Union[str, Any]=2							,A					: str="absolute"							,A					: Optional[int]=True							,A					: Dict=None							,**A					: Optional[Any]							,):
					super().__init__(pad_token_id=A							,bos_token_id=A							,eos_token_id=A							,**A )

					__A   			= vocab_size
					__A   			= hidden_size
					__A   			= num_hidden_layers
					__A   			= num_attention_heads
					__A   			= hidden_act
					__A   			= intermediate_size
					__A   			= hidden_dropout_prob
					__A   			= attention_probs_dropout_prob
					__A   			= max_position_embeddings
					__A   			= type_vocab_size
					__A   			= initializer_range
					__A   			= layer_norm_eps
					__A   			= position_embedding_type
					__A   			= use_cache
					__A   			= classifier_dropout





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_ : int    		=       {
    "configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"],
    "tokenization_deberta": ["DebertaTokenizer"],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : int    		=       ["DebertaTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : List[str]    		=       [
        "DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
        "DebertaForMaskedLM",
        "DebertaForQuestionAnswering",
        "DebertaForSequenceClassification",
        "DebertaForTokenClassification",
        "DebertaModel",
        "DebertaPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Any    		=       [
        "TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFDebertaForMaskedLM",
        "TFDebertaForQuestionAnswering",
        "TFDebertaForSequenceClassification",
        "TFDebertaForTokenClassification",
        "TFDebertaModel",
        "TFDebertaPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
    from .tokenization_deberta import DebertaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_deberta_fast import DebertaTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_deberta import (
            DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
            DebertaForMaskedLM,
            DebertaForQuestionAnswering,
            DebertaForSequenceClassification,
            DebertaForTokenClassification,
            DebertaModel,
            DebertaPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_deberta import (
            TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFDebertaForMaskedLM,
            TFDebertaForQuestionAnswering,
            TFDebertaForSequenceClassification,
            TFDebertaForTokenClassification,
            TFDebertaModel,
            TFDebertaPreTrainedModel,
        )


else:
    import sys

    snake_case_ : Dict    		=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






