




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__    :							Optional[Any]       		=     {'configuration_qdqbert': ['QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'QDQBertConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__    :							List[Any]       		=     [
       'QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'QDQBertForMaskedLM',
       'QDQBertForMultipleChoice',
       'QDQBertForNextSentencePrediction',
       'QDQBertForQuestionAnswering',
       'QDQBertForSequenceClassification',
       'QDQBertForTokenClassification',
       'QDQBertLayer',
       'QDQBertLMHeadModel',
       'QDQBertModel',
       'QDQBertPreTrainedModel',
       'load_tf_weights_in_qdqbert',
   ]


if TYPE_CHECKING:
   from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_qdqbert import (
          QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          QDQBertForMaskedLM,
          QDQBertForMultipleChoice,
          QDQBertForNextSentencePrediction,
          QDQBertForQuestionAnswering,
          QDQBertForSequenceClassification,
          QDQBertForTokenClassification,
          QDQBertLayer,
          QDQBertLMHeadModel,
          QDQBertModel,
          QDQBertPreTrainedModel,
          load_tf_weights_in_qdqbert,
      )


else:
   import sys

   UpperCAmelCase__    :							Optional[Any]       		=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


UpperCAmelCase__    :							List[str]       		=     {
    'configuration_ernie_m': ['ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieMConfig'],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__    :							Any       		=     ['ErnieMTokenizer']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__    :							Dict       		=     [
       'ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ErnieMForMultipleChoice',
       'ErnieMForQuestionAnswering',
       'ErnieMForSequenceClassification',
       'ErnieMForTokenClassification',
       'ErnieMModel',
       'ErnieMPreTrainedModel',
       'ErnieMForInformationExtraction',
   ]


if TYPE_CHECKING:
   from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_ernie_m import ErnieMTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_ernie_m import (
          ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
          ErnieMForInformationExtraction,
          ErnieMForMultipleChoice,
          ErnieMForQuestionAnswering,
          ErnieMForSequenceClassification,
          ErnieMForTokenClassification,
          ErnieMModel,
          ErnieMPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase__    :							List[Any]       		=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
