



from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

__lowerCamelCase     :							int  							=				{
    "SCUT-DLVCLab/lilt-roberta-en-base": (
        "https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
    ),
}





class  __magic_name__   (							A__						):
		lowercase      :       Union[str, Any]          ='''lilt'''






		def __init__(						self      :     Optional[int]				,		UpperCamelCase__      :     Optional[Any]=3_05_22				,		UpperCamelCase__      :     Optional[int]=7_68				,		UpperCamelCase__      :     Union[str, Any]=12				,		UpperCamelCase__      :     Optional[Any]=12				,		UpperCamelCase__      :     Optional[int]=30_72				,		UpperCamelCase__      :     Optional[int]="gelu"				,		UpperCamelCase__      :     Union[str, Any]=0.1				,		UpperCamelCase__      :     Dict=0.1				,		UpperCamelCase__      :     Dict=5_12				,		UpperCamelCase__      :     Tuple=2				,		UpperCamelCase__      :     str=0.02				,		UpperCamelCase__      :     Optional[int]=1e-1_2				,		UpperCamelCase__      :     Tuple=0				,		UpperCamelCase__      :     Optional[int]="absolute"				,		UpperCamelCase__      :     int=None				,		UpperCamelCase__      :     Union[str, Any]=4				,		UpperCamelCase__      :     List[str]=10_24				,		**UpperCamelCase__      :     List[str]				,		)				->					Optional[Any]:
								'''simple docstring'''







								super().__init__(pad_token_id=UpperCamelCase__				,		**UpperCamelCase__      )

								UpperCAmelCase         =     vocab_size
								UpperCAmelCase         =     hidden_size
								UpperCAmelCase         =     num_hidden_layers
								UpperCAmelCase         =     num_attention_heads
								UpperCAmelCase         =     hidden_act
								UpperCAmelCase         =     intermediate_size
								UpperCAmelCase         =     hidden_dropout_prob
								UpperCAmelCase         =     attention_probs_dropout_prob
								UpperCAmelCase         =     max_position_embeddings
								UpperCAmelCase         =     type_vocab_size
								UpperCAmelCase         =     initializer_range
								UpperCAmelCase         =     layer_norm_eps
								UpperCAmelCase         =     position_embedding_type
								UpperCAmelCase         =     classifier_dropout
								UpperCAmelCase         =     channel_shrink_ratio
								UpperCAmelCase         =     max_ad_position_embeddings








from typing import TYPE_CHECKING

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


__lowerCamelCase     :							Any  							=				{
    "configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase     :							Optional[int]  							=				[
		    "MEGA_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "MegaForCausalLM",
		    "MegaForMaskedLM",
		    "MegaForMultipleChoice",
		    "MegaForQuestionAnswering",
		    "MegaForSequenceClassification",
		    "MegaForTokenClassification",
		    "MegaModel",
		    "MegaPreTrainedModel",
		]

if TYPE_CHECKING:
		from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mega import (
				    MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MegaForCausalLM,
				    MegaForMaskedLM,
				    MegaForMultipleChoice,
				    MegaForQuestionAnswering,
				    MegaForSequenceClassification,
				    MegaForTokenClassification,
				    MegaModel,
				    MegaPreTrainedModel,
				)

else:
		import sys

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



