



'''simple docstring'''


_lowerCamelCase	:       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"

_lowerCamelCase	:       Dict      	=	[{"type": "code", "content": INSTALL_CONTENT}]
_lowerCamelCase	:       List[Any]      	=	{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}






'''simple docstring'''


from typing import TYPE_CHECKING

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


_lowerCamelCase	:       List[Any]      	=	{
    "configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfig"],
    "tokenization_m2m_100": ["M2M100Tokenizer"],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase	:       int      	=	[
       "M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST",
       "M2M100ForConditionalGeneration",
       "M2M100Model",
       "M2M100PreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
   from .tokenization_mam_aaa import MaMaaaTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mam_aaa import (
          M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
          MaMaaaForConditionalGeneration,
          MaMaaaModel,
          MaMaaaPreTrainedModel,
      )


else:
   import sys

   _lowerCamelCase	:       Optional[int]      	=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

