code: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
  
  
  UpperCAmelCase      :						Optional[int]    =		{
      "configuration_esm": ["ESM_PRETRAINED_CONFIG_ARCHIVE_MAP", "EsmConfig"],
      "tokenization_esm": ["EsmTokenizer"],
  }
  
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    UpperCAmelCase      :						Tuple    =		[
        "ESM_PRETRAINED_MODEL_ARCHIVE_LIST",
        "EsmForMaskedLM",
        "EsmForSequenceClassification",
        "EsmForTokenClassification",
        "EsmModel",
        "EsmPreTrainedModel",
    ]
    UpperCAmelCase      :						Any    =		["EsmForProteinFolding", "EsmFoldPreTrainedModel"]
  
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    UpperCAmelCase      :						List[str]    =		[
        "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
  
    UpperCAmelCase      :						Dict    =		_LazyModule(__name__, globals()["__file__"], _import_structure)
  
  
style_context: 
  
  
  def   __lowerCamelCase	(       lowerCamelCase__		:	int = 100     ,       lowerCamelCase__		:	int = 100			):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			return max(
  			    sum(int(lowerCamelCase__			) for x in str(base**power			)			)
  			    for base in range(lowerCamelCase__			)
  			    for power in range(lowerCamelCase__			)			)
  
  
  # Tests
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  
  