






from typing import TYPE_CHECKING

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


__lowerCamelCase :      Optional[Any]										=				{
    """configuration_deberta""": ["""DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaConfig""", """DebertaOnnxConfig"""],
    """tokenization_deberta""": ["""DebertaTokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase :      Union[str, Any]										=				["""DebertaTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase :      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:
	__lowerCamelCase :      Union[str, 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

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











from collections import OrderedDict
from typing import Mapping

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


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

__lowerCamelCase :      str										=				{
    """andreasmadsen/efficient_mlm_m0.40""": (
        """https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"""
    ),
}



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		"roberta-prelayernorm"



					def __init__( self					:  Tuple							,		__A					:  Any=5_0_2_6_5							,		__A					:  Optional[int]=7_6_8							,		__A					:  Dict=1_2							,		__A					:  Union[str, Any]=1_2							,		__A					:  List[Any]=3_0_7_2							,		__A					:  Optional[Any]="gelu"							,		__A					:  Optional[int]=0.1							,		__A					:  Tuple=0.1							,		__A					:  Optional[Any]=5_1_2							,		__A					:  List[str]=2							,		__A					:  Optional[int]=0.0_2							,		__A					:  Tuple=1e-1_2							,		__A					:  Any=1							,		__A					:  str=0							,		__A					:  int=2							,		__A					:  List[str]="absolute"							,		__A					:  Optional[Any]=True							,		__A					:  List[Any]=None							,		**__A					:  Optional[Any]							,		):
						super().__init__(pad_token_id=__A							,		bos_token_id=__A							,		eos_token_id=__A							,		**__A	)

						snake_case__			:				Tuple       =  vocab_size
						snake_case__			:				Optional[Any]       =  hidden_size
						snake_case__			:				List[Any]       =  num_hidden_layers
						snake_case__			:				List[Any]       =  num_attention_heads
						snake_case__			:				Dict       =  hidden_act
						snake_case__			:				Union[str, Any]       =  intermediate_size
						snake_case__			:				List[Any]       =  hidden_dropout_prob
						snake_case__			:				Any       =  attention_probs_dropout_prob
						snake_case__			:				int       =  max_position_embeddings
						snake_case__			:				Tuple       =  type_vocab_size
						snake_case__			:				Optional[int]       =  initializer_range
						snake_case__			:				int       =  layer_norm_eps
						snake_case__			:				Dict       =  position_embedding_type
						snake_case__			:				int       =  use_cache
						snake_case__			:				Dict       =  classifier_dropout



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






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



