




def 		a_							( __lowerCAmelCase   , __lowerCAmelCase				):
  _validate_point(__lowerCAmelCase				)
  _validate_point(__lowerCAmelCase				)
  if len(__lowerCAmelCase				) != len(__lowerCAmelCase				):
    raise ValueError('''Both points must be in the same n-dimensional space'''				)

  return float(sum(abs(a - b				) for a, b in zip(__lowerCAmelCase   , __lowerCAmelCase				)				)				)
def 		a_							( __lowerCAmelCase				):
  if point:
    if isinstance(__lowerCAmelCase   , __lowerCAmelCase				):
      for item in point:
        if not isinstance(__lowerCAmelCase   , (int, float)				):
          lowerCAmelCase__										=      (
              '''Expected a list of numbers as input, found '''
              F"""{type(__lowerCAmelCase				).__name__}"""
          )
          raise TypeError(__lowerCAmelCase				)
    else:
      lowerCAmelCase__										=      F"""Expected a list of numbers as input, found {type(__lowerCAmelCase				).__name__}"""
      raise TypeError(__lowerCAmelCase				)
  else:
    raise ValueError('''Missing an input'''				)
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase				):
  _validate_point(__lowerCAmelCase				)
  _validate_point(__lowerCAmelCase				)
  if len(__lowerCAmelCase				) != len(__lowerCAmelCase				):
    raise ValueError('''Both points must be in the same n-dimensional space'''				)

  return float(sum(abs(x - y				) for x, y in zip(__lowerCAmelCase   , __lowerCAmelCase				)				)				)


if __name__ == "__main__":
      import doctest

      doctest.testmod()







from typing import TYPE_CHECKING

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


__magic_name__  :  Optional[int]					=       {
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  int					=       [
          """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """BioGptForCausalLM""",
          """BioGptForTokenClassification""",
          """BioGptForSequenceClassification""",
          """BioGptModel""",
          """BioGptPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
      from .tokenization_biogpt import BioGptTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_biogpt import (
                BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BioGptForCausalLM,
                BioGptForSequenceClassification,
                BioGptForTokenClassification,
                BioGptModel,
                BioGptPreTrainedModel,
            )


else:
      import sys

      __magic_name__  :  int					=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

