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()
  
  