

"""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,
)


_a							=							{
    '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:
   _a							=							['BertTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a							=							[
       '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:
   _a							=							[
       '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:
   _a							=							['TFBertTokenizer']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a							=							[
       '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

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







"""simple docstring"""





_a							=							[
    'DownloadConfig',
    'DownloadManager',
    'DownloadMode',
    'StreamingDownloadManager',
]

from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager




