






"""simple docstring"""
snake_case   :   Dict    		=		"""
# 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
"""

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







"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case   :   str    		=		{"""configuration_sew_d""": ["""SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWDConfig"""]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case   :   Union[str, Any]    		=		[
     """SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """SEWDForCTC""",
     """SEWDForSequenceClassification""",
     """SEWDModel""",
     """SEWDPreTrainedModel""",
 ]

if TYPE_CHECKING:
 from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_sew_d import (
      SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
      SEWDForCTC,
      SEWDForSequenceClassification,
      SEWDModel,
      SEWDPreTrainedModel,
  )


else:
 import sys

 snake_case   :   Union[str, Any]    		=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)