"""simple docstring"""




from typing import TYPE_CHECKING

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


_a				:		Optional[int]	   =       {
    'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a				:		Dict	   =       [
         'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MegaForCausalLM',
         'MegaForMaskedLM',
         'MegaForMultipleChoice',
         'MegaForQuestionAnswering',
         'MegaForSequenceClassification',
         'MegaForTokenClassification',
         'MegaModel',
         'MegaPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mega import (
              MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
              MegaForCausalLM,
              MegaForMaskedLM,
              MegaForMultipleChoice,
              MegaForQuestionAnswering,
              MegaForSequenceClassification,
              MegaForTokenClassification,
              MegaModel,
              MegaPreTrainedModel,
          )

else:
     import sys

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





"""simple docstring"""




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

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




