

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__)








__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''',
}





