


from typing import TYPE_CHECKING

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


a__  : Union[str, Any]				=	{
    '''configuration_esm''': ['''ESM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''EsmConfig'''],
    '''tokenization_esm''': ['''EsmTokenizer'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__  : Union[str, Any]				=	[
      '''ESM_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''EsmForMaskedLM''',
      '''EsmForSequenceClassification''',
      '''EsmForTokenClassification''',
      '''EsmModel''',
      '''EsmPreTrainedModel''',
  ]
  a__  : Union[str, Any]				=	['''EsmForProteinFolding''', '''EsmFoldPreTrainedModel''']

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__  : int				=	[
      '''TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFEsmForMaskedLM''',
      '''TFEsmForSequenceClassification''',
      '''TFEsmForTokenClassification''',
      '''TFEsmModel''',
      '''TFEsmPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
  from .tokenization_esm import EsmTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_esm import (
        ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
        EsmForMaskedLM,
        EsmForSequenceClassification,
        EsmForTokenClassification,
        EsmModel,
        EsmPreTrainedModel,
    )
    from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_esm import (
        TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFEsmForMaskedLM,
        TFEsmForSequenceClassification,
        TFEsmForTokenClassification,
        TFEsmModel,
        TFEsmPreTrainedModel,
    )


else:
  import sys

  a__  : Any				=	_LazyModule(__name__, globals()['__file__'], _import_structure)









'''simple docstring'''







from itertools import permutations







def _UpperCamelCase	(					)							->		Optional[int]:


      '''simple docstring'''



      UpperCamelCase__  =    list(map("".join				,      permutations("0123456789"   )   )   )
      return result[999999]


if __name__ == "__main__":
  print(solution())





