
from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE							:Union[str, Any]											=       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:Optional[int]											=       {
    'google/electra-small-generator': 'https://huggingface.co/google/electra-small-generator/resolve/main/config.json',
    'google/electra-base-generator': 'https://huggingface.co/google/electra-base-generator/resolve/main/config.json',
    'google/electra-large-generator': 'https://huggingface.co/google/electra-large-generator/resolve/main/config.json',
    'google/electra-small-discriminator': (
        'https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json'
    ),
    'google/electra-base-discriminator': (
        'https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json'
    ),
    'google/electra-large-discriminator': (
        'https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json'
    ),
}





class 							UpperCAmelCase   (						__SCREAMING_SNAKE_CASE						):




	'''simple docstring'''





	snake_case_      					=  "electra"






	def __init__( self      :				List[str]							,A      :				int=3_05_22							,A      :				List[str]=1_28							,A      :				Optional[int]=2_56							,A      :				Dict=12							,A      :				int=4							,A      :				Union[str, Any]=10_24							,A      :				Optional[Any]="gelu"							,A      :				List[str]=0.1							,A      :				List[str]=0.1							,A      :				Any=5_12							,A      :				Optional[int]=2							,A      :				Dict=0.02							,A      :				int=1E-12							,A      :				Any="first"							,A      :				List[str]=True							,A      :				Optional[int]="gelu"							,A      :				str=0.1							,A      :				int=0							,A      :				Optional[int]="absolute"							,A      :				Dict=True							,A      :				Optional[Any]=None							,**A      :				int							,):
					super().__init__(pad_token_id=A							,**A				)

					__A         =				vocab_size
					__A         =				embedding_size
					__A         =				hidden_size
					__A         =				num_hidden_layers
					__A         =				num_attention_heads
					__A         =				intermediate_size
					__A         =				hidden_act
					__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         =				summary_type
					__A         =				summary_use_proj
					__A         =				summary_activation
					__A         =				summary_last_dropout
					__A         =				position_embedding_type
					__A         =				use_cache
					__A         =				classifier_dropout





class 							UpperCAmelCase   (						__SCREAMING_SNAKE_CASE						):




	'''simple docstring'''





	@property
	def        UpperCamelCase_						( self      :				str				):
					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),
					        ("token_type_ids", dynamic_axis),
					    ]				)


from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE							:str											=       {
    'configuration_deberta_v2': ['DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaV2Config', 'DebertaV2OnnxConfig'],
    'tokenization_deberta_v2': ['DebertaV2Tokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:int											=       ['DebertaV2TokenizerFast']

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Any											=       [
					    'TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFDebertaV2ForMaskedLM',
					    'TFDebertaV2ForQuestionAnswering',
					    'TFDebertaV2ForSequenceClassification',
					    'TFDebertaV2ForTokenClassification',
					    'TFDebertaV2Model',
					    'TFDebertaV2PreTrainedModel',
					]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Any											=       [
					    'DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'DebertaV2ForMaskedLM',
					    'DebertaV2ForMultipleChoice',
					    'DebertaV2ForQuestionAnswering',
					    'DebertaV2ForSequenceClassification',
					    'DebertaV2ForTokenClassification',
					    'DebertaV2Model',
					    'DebertaV2PreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_deberta_va import (
					    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    DebertaVaConfig,
					    DebertaVaOnnxConfig,
					)
					from .tokenization_deberta_va import DebertaVaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_deberta_va import (
										    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFDebertaVaForMaskedLM,
										    TFDebertaVaForQuestionAnswering,
										    TFDebertaVaForSequenceClassification,
										    TFDebertaVaForTokenClassification,
										    TFDebertaVaModel,
										    TFDebertaVaPreTrainedModel,
										)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_deberta_va import (
										    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DebertaVaForMaskedLM,
										    DebertaVaForMultipleChoice,
										    DebertaVaForQuestionAnswering,
										    DebertaVaForSequenceClassification,
										    DebertaVaForTokenClassification,
										    DebertaVaModel,
										    DebertaVaPreTrainedModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE							:List[Any]											=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
