

"""simple docstring"""





from ... import PretrainedConfig


_a       =							{
    'sijunhe/nezha-cn-base': 'https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json',
}




class 					_lowerCAmelCase  (	lowercase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Optional[Any]   		=	NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
 __UpperCAmelCase	:					Optional[Any]   		=	"nezha"


 def __init__(			self							:			Tuple,					UpperCAmelCase__							:			List[Any]=2_1_1_2_8,					UpperCAmelCase__							:			Tuple=7_6_8,					UpperCAmelCase__							:			List[str]=1_2,					UpperCAmelCase__							:			List[Any]=1_2,					UpperCAmelCase__							:			str=3_0_7_2,					UpperCAmelCase__							:			str="gelu",					UpperCAmelCase__							:			Dict=0.1,					UpperCAmelCase__							:			int=0.1,					UpperCAmelCase__							:			List[str]=5_1_2,					UpperCAmelCase__							:			List[Any]=6_4,					UpperCAmelCase__							:			Optional[int]=2,					UpperCAmelCase__							:			str=0.02,					UpperCAmelCase__							:			Optional[int]=1E-12,					UpperCAmelCase__							:			int=0.1,					UpperCAmelCase__							:			str=0,					UpperCAmelCase__							:			Optional[Any]=2,					UpperCAmelCase__							:			List[Any]=3,					UpperCAmelCase__							:			str=True,					**UpperCAmelCase__							:			int,					):
    super().__init__(pad_token_id=UpperCAmelCase__,					bos_token_id=UpperCAmelCase__,					eos_token_id=UpperCAmelCase__,					**UpperCAmelCase__	)

    __lowercase								=						vocab_size
    __lowercase								=						hidden_size
    __lowercase								=						num_hidden_layers
    __lowercase								=						num_attention_heads
    __lowercase								=						hidden_act
    __lowercase								=						intermediate_size
    __lowercase								=						hidden_dropout_prob
    __lowercase								=						attention_probs_dropout_prob
    __lowercase								=						max_position_embeddings
    __lowercase								=						max_relative_position
    __lowercase								=						type_vocab_size
    __lowercase								=						initializer_range
    __lowercase								=						layer_norm_eps
    __lowercase								=						classifier_dropout
    __lowercase								=						use_cache











from typing import TYPE_CHECKING

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


_a 						=      {
    "configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
    "tokenization_lxmert": ["LxmertTokenizer"],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a 						=      ["LxmertTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a 						=      [
			    "LxmertEncoder",
			    "LxmertForPreTraining",
			    "LxmertForQuestionAnswering",
			    "LxmertModel",
			    "LxmertPreTrainedModel",
			    "LxmertVisualFeatureEncoder",
			    "LxmertXLayer",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a 						=      [
			    "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFLxmertForPreTraining",
			    "TFLxmertMainLayer",
			    "TFLxmertModel",
			    "TFLxmertPreTrainedModel",
			    "TFLxmertVisualFeatureEncoder",
			]


if TYPE_CHECKING:
			from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
			from .tokenization_lxmert import LxmertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_lxmert_fast import LxmertTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_lxmert import (
						    LxmertEncoder,
						    LxmertForPreTraining,
						    LxmertForQuestionAnswering,
						    LxmertModel,
						    LxmertPreTrainedModel,
						    LxmertVisualFeatureEncoder,
						    LxmertXLayer,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_lxmert import (
						    TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFLxmertForPreTraining,
						    TFLxmertMainLayer,
						    TFLxmertModel,
						    TFLxmertPreTrainedModel,
						    TFLxmertVisualFeatureEncoder,
						)

else:
			import sys

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



