
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_A   =     {
    "configuration_nezha": ["NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP", "NezhaConfig"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "NezhaForNextSentencePrediction",
			    "NezhaForMaskedLM",
			    "NezhaForPreTraining",
			    "NezhaForMultipleChoice",
			    "NezhaForQuestionAnswering",
			    "NezhaForSequenceClassification",
			    "NezhaForTokenClassification",
			    "NezhaModel",
			    "NezhaPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_nezha import (
						    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    NezhaForMaskedLM,
						    NezhaForMultipleChoice,
						    NezhaForNextSentencePrediction,
						    NezhaForPreTraining,
						    NezhaForQuestionAnswering,
						    NezhaForSequenceClassification,
						    NezhaForTokenClassification,
						    NezhaModel,
						    NezhaPreTrainedModel,
						)


else:
			import sys

			_A   =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





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


_A   =     logging.get_logger(__name__)

_A   =     {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}





class 	_lowerCAmelCase					(	__a       ):
				_lowercase					='''megatron-bert'''




				def __init__(					self				,				_UpperCamelCase=29_056				,				_UpperCamelCase=1_024				,				_UpperCamelCase=24				,				_UpperCamelCase=16				,				_UpperCamelCase=4_096				,				_UpperCamelCase="gelu"				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.1				,				_UpperCamelCase=512				,				_UpperCamelCase=2				,				_UpperCamelCase=0.02				,				_UpperCamelCase=1e-1_2				,				_UpperCamelCase=0				,				_UpperCamelCase="absolute"				,				_UpperCamelCase=True				,				**_UpperCamelCase				,				)				->      int:
							super().__init__(pad_token_id=_UpperCamelCase				,				**_UpperCamelCase     )

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



