


from typing import TYPE_CHECKING

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


snake_case				:    List[str]       				=     {
    '''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
    '''tokenization_roc_bert''': ['''RoCBertTokenizer'''],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     pass

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case				:    Optional[Any]       				=     [
         '''ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''RoCBertForCausalLM''',
         '''RoCBertForMaskedLM''',
         '''RoCBertForMultipleChoice''',
         '''RoCBertForPreTraining''',
         '''RoCBertForQuestionAnswering''',
         '''RoCBertForSequenceClassification''',
         '''RoCBertForTokenClassification''',
         '''RoCBertLayer''',
         '''RoCBertModel''',
         '''RoCBertPreTrainedModel''',
         '''load_tf_weights_in_roc_bert''',
     ]

if TYPE_CHECKING:
     from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
     from .tokenization_roc_bert import RoCBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          raise OptionalDependencyNotAvailable()

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_roc_bert import (
              ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              RoCBertForCausalLM,
              RoCBertForMaskedLM,
              RoCBertForMultipleChoice,
              RoCBertForPreTraining,
              RoCBertForQuestionAnswering,
              RoCBertForSequenceClassification,
              RoCBertForTokenClassification,
              RoCBertLayer,
              RoCBertModel,
              RoCBertPreTrainedModel,
              load_tf_weights_in_roc_bert,
          )


else:
     import sys

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









snake_case				:    str       				=     '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

snake_case				:    List[Any]       				=     [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
snake_case				:    int       				=     {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}





