




'''simple docstring'''







from typing import TYPE_CHECKING

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


_lowerCamelCase							:						int							     =  {
    'configuration_esm': ['ESM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'EsmConfig'],
    'tokenization_esm': ['EsmTokenizer'],
}

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

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCamelCase							:						Union[str, Any]							     =  [
						    '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

						_lowerCamelCase							:						List[str]							     =  _LazyModule(__name__, globals()['__file__'], _import_structure)











'''simple docstring'''







import math





class 					lowerCamelCase_							:




					'''simple docstring'''






					def __init__(   self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase			):
							_snake_case     	=	x
							_snake_case     	=	y
							_snake_case     	=	z






					def __repr__(   self			):
							return f'''Point({self.x}, {self.y}, {self.z})'''






def 		_lowercase		(     lowercase_						,							lowercase_  ):



		'''simple docstring'''







		return math.sqrt(abs((b.x - a.x) ** 2 + (b.y - a.y) ** 2 + (b.z - a.z) ** 2  )  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()





