





"""simple docstring"""




from typing import TYPE_CHECKING

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


UpperCamelCase_									={
    """configuration_bert""": ["""BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BertConfig""", """BertOnnxConfig"""],
    """tokenization_bert""": ["""BasicTokenizer""", """BertTokenizer""", """WordpieceTokenizer"""],
}

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_									=[
          """BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """BertForMaskedLM""",
          """BertForMultipleChoice""",
          """BertForNextSentencePrediction""",
          """BertForPreTraining""",
          """BertForQuestionAnswering""",
          """BertForSequenceClassification""",
          """BertForTokenClassification""",
          """BertLayer""",
          """BertLMHeadModel""",
          """BertModel""",
          """BertPreTrainedModel""",
          """load_tf_weights_in_bert""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_									=[
          """TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFBertEmbeddings""",
          """TFBertForMaskedLM""",
          """TFBertForMultipleChoice""",
          """TFBertForNextSentencePrediction""",
          """TFBertForPreTraining""",
          """TFBertForQuestionAnswering""",
          """TFBertForSequenceClassification""",
          """TFBertForTokenClassification""",
          """TFBertLMHeadModel""",
          """TFBertMainLayer""",
          """TFBertModel""",
          """TFBertPreTrainedModel""",
      ]
try:
      if not is_tensorflow_text_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_									=["""TFBertTokenizer"""]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_									=[
          """FlaxBertForCausalLM""",
          """FlaxBertForMaskedLM""",
          """FlaxBertForMultipleChoice""",
          """FlaxBertForNextSentencePrediction""",
          """FlaxBertForPreTraining""",
          """FlaxBertForQuestionAnswering""",
          """FlaxBertForSequenceClassification""",
          """FlaxBertForTokenClassification""",
          """FlaxBertModel""",
          """FlaxBertPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
      from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_bert_fast import BertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_bert import (
                BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BertForMaskedLM,
                BertForMultipleChoice,
                BertForNextSentencePrediction,
                BertForPreTraining,
                BertForQuestionAnswering,
                BertForSequenceClassification,
                BertForTokenClassification,
                BertLayer,
                BertLMHeadModel,
                BertModel,
                BertPreTrainedModel,
                load_tf_weights_in_bert,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_bert import (
                TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFBertEmbeddings,
                TFBertForMaskedLM,
                TFBertForMultipleChoice,
                TFBertForNextSentencePrediction,
                TFBertForPreTraining,
                TFBertForQuestionAnswering,
                TFBertForSequenceClassification,
                TFBertForTokenClassification,
                TFBertLMHeadModel,
                TFBertMainLayer,
                TFBertModel,
                TFBertPreTrainedModel,
            )

      try:
            if not is_tensorflow_text_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_bert_tf import TFBertTokenizer

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_bert import (
                FlaxBertForCausalLM,
                FlaxBertForMaskedLM,
                FlaxBertForMultipleChoice,
                FlaxBertForNextSentencePrediction,
                FlaxBertForPreTraining,
                FlaxBertForQuestionAnswering,
                FlaxBertForSequenceClassification,
                FlaxBertForTokenClassification,
                FlaxBertModel,
                FlaxBertPreTrainedModel,
            )

else:
      import sys

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










"""simple docstring"""




import logging

from transformers.configuration_utils import PretrainedConfig


UpperCamelCase_									=logging.getLogger(__name__)




class 			_a						(				_lowerCAmelCase    ):
				UpperCamelCase								=   '''masked_bert'''






				def __init__(			self		:   Optional[Any],			lowerCAmelCase__		:   Dict=3_0_5_2_2,			lowerCAmelCase__		:   int=7_6_8,			lowerCAmelCase__		:   Tuple=1_2,			lowerCAmelCase__		:   Optional[Any]=1_2,			lowerCAmelCase__		:   Tuple=3_0_7_2,			lowerCAmelCase__		:   Optional[int]="gelu",			lowerCAmelCase__		:   Tuple=0.1,			lowerCAmelCase__		:   Tuple=0.1,			lowerCAmelCase__		:   Any=5_1_2,			lowerCAmelCase__		:   Optional[int]=2,			lowerCAmelCase__		:   Optional[int]=0.02,			lowerCAmelCase__		:   Union[str, Any]=1e-1_2,			lowerCAmelCase__		:   Union[str, Any]=0,			lowerCAmelCase__		:   Dict="topK",			lowerCAmelCase__		:   Union[str, Any]="constant",			lowerCAmelCase__		:   Union[str, Any]=0.0,			**lowerCAmelCase__		:   Any,			)     ->				List[Any]:




										'''simple docstring'''



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

										_UpperCamelCase      : Optional[Any]         =   vocab_size
										_UpperCamelCase      : int         =   hidden_size
										_UpperCamelCase      : List[Any]         =   num_hidden_layers
										_UpperCamelCase      : Any         =   num_attention_heads
										_UpperCamelCase      : List[str]         =   hidden_act
										_UpperCamelCase      : Tuple         =   intermediate_size
										_UpperCamelCase      : int         =   hidden_dropout_prob
										_UpperCamelCase      : str         =   attention_probs_dropout_prob
										_UpperCamelCase      : Optional[int]         =   max_position_embeddings
										_UpperCamelCase      : str         =   type_vocab_size
										_UpperCamelCase      : Optional[Any]         =   initializer_range
										_UpperCamelCase      : List[str]         =   layer_norm_eps
										_UpperCamelCase      : int         =   pruning_method
										_UpperCamelCase      : Union[str, Any]         =   mask_init
										_UpperCamelCase      : Any         =   mask_scale



