code: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  __A   :     List[Any]      =			{'''configuration_umt5''': ['''UMT5Config''', '''UMT5OnnxConfig''']}
  
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __A   :     Tuple      =			[
       '''UMT5EncoderModel''',
       '''UMT5ForConditionalGeneration''',
       '''UMT5ForQuestionAnswering''',
       '''UMT5Model''',
       '''UMT5PreTrainedModel''',
   ]
  
  if TYPE_CHECKING:
   from .configuration_umta import UMTaConfig, UMTaOnnxConfig
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_umta import (
        UMTaEncoderModel,
        UMTaForConditionalGeneration,
        UMTaForQuestionAnswering,
        UMTaModel,
        UMTaPreTrainedModel,
    )
  else:
   import sys
  
   __A   :     Any      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
style_context: 
  
  __A   :     List[Any]      =			'''
  # Installation de Transformers
  ! pip install transformers datasets
  # Pour installer à partir du code source au lieu de la dernière version, commentez la commande ci-dessus et décommentez la suivante.
  # ! pip install git+https://github.com/huggingface/transformers.git
  '''
  
  __A   :     Dict      =			[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
  __A   :     Tuple      =			{
      '''{processor_class}''': '''FakeProcessorClass''',
      '''{model_class}''': '''FakeModelClass''',
      '''{object_class}''': '''FakeObjectClass''',
  }
  
  
  
  
  
  