code: 
  
  
  '''simple docstring'''
  
  import math
  
  
  
  
  
  
  
  class  UpperCAmelCase :
  
   def __init__( self :	Any,					a_ :	Dict,					a_ :	int,					a_ :	Tuple					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						x
      UpperCamelCase__ 						=						y
      UpperCamelCase__ 						=						z
  
   def __repr__( self :	Tuple					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return f'Point({self.x}, {self.y}, {self.z})'
  
  
  
  
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Point    ,		_UpperCamelCase      :				Point   )		->					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()
style_context: 
  
  
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_sentencepiece_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  __lowercase:		int							=     {
      "configuration_open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
  }
  
  try:
         if not is_sentencepiece_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         __lowercase:		str							=     ["LlamaTokenizer"]
  
  try:
         if not is_tokenizers_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         __lowercase:		List[Any]							=     ["LlamaTokenizerFast"]
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         __lowercase:		Union[str, Any]							=     [
             "OpenLlamaForCausalLM",
             "OpenLlamaModel",
             "OpenLlamaPreTrainedModel",
             "OpenLlamaForSequenceClassification",
         ]
  
  
  if TYPE_CHECKING:
         from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig
  
         try:
                if not is_sentencepiece_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from transformers import LlamaTokenizer
  
         try:
                if not is_tokenizers_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from transformers import LlamaTokenizerFast
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_open_llama import (
                    OpenLlamaForCausalLM,
                    OpenLlamaForSequenceClassification,
                    OpenLlamaModel,
                    OpenLlamaPreTrainedModel,
                )
  
  
  else:
         import sys
  
         __lowercase:		Tuple							=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)