
'''simple docstring'''





import math


class 			UpperCamelCase__	:






 def __init__(      self	:Any						,					_A	:Dict						,					_A	:int						,					_A	:List[str]      )					->      List[Any]:







       '''simple docstring'''



       __A													=      x
       __A													=      y
       __A													=      z






 def __repr__(      self	:Dict      )					->      str:







       '''simple docstring'''



       return F'Point({self.x}, {self.y}, {self.z})'





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase						)->      float:







      """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()














from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_			:Union[str, Any]	    =		{'''configuration_nat''': ['''NAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NatConfig''']}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_			:Optional[Any]	    =		[
							    '''NAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''NatForImageClassification''',
							    '''NatModel''',
							    '''NatPreTrainedModel''',
							    '''NatBackbone''',
							]

if TYPE_CHECKING:
							from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_nat import (
														    NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    NatBackbone,
														    NatForImageClassification,
														    NatModel,
														    NatPreTrainedModel,
														)

else:
							import sys

							A_			:Tuple	    =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

