

'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCamelCase		       =    {
    '''configuration_xlm_roberta_xl''': [
        '''XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XLMRobertaXLConfig''',
        '''XLMRobertaXLOnnxConfig''',
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase		       =    [
          '''XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''XLMRobertaXLForCausalLM''',
          '''XLMRobertaXLForMaskedLM''',
          '''XLMRobertaXLForMultipleChoice''',
          '''XLMRobertaXLForQuestionAnswering''',
          '''XLMRobertaXLForSequenceClassification''',
          '''XLMRobertaXLForTokenClassification''',
          '''XLMRobertaXLModel''',
          '''XLMRobertaXLPreTrainedModel''',
      ]

if TYPE_CHECKING:
      from .configuration_xlm_roberta_xl import (
          XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
          XLMRobertaXLConfig,
          XLMRobertaXLOnnxConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_xlm_roberta_xl import (
                XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
                XLMRobertaXLForCausalLM,
                XLMRobertaXLForMaskedLM,
                XLMRobertaXLForMultipleChoice,
                XLMRobertaXLForQuestionAnswering,
                XLMRobertaXLForSequenceClassification,
                XLMRobertaXLForTokenClassification,
                XLMRobertaXLModel,
                XLMRobertaXLPreTrainedModel,
            )

else:
      import sys

      __lowerCamelCase		       =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)



'''simple docstring'''







def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__				)			->			float:
       _validate_point(UpperCAmelCase__				)
       _validate_point(UpperCAmelCase__				)
       if len(UpperCAmelCase__				) != len(UpperCAmelCase__				):
              raise ValueError("""Both points must be in the same n-dimensional space"""				)

       return float(sum(abs(a - b				) for a, b in zip(UpperCAmelCase__,       UpperCAmelCase__				)				)				)
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			None:
       if point:
              if isinstance(UpperCAmelCase__,       UpperCAmelCase__				):
                     for item in point:
                            if not isinstance(UpperCAmelCase__,       (int, float)				):
                                   A_						=      (
                                       """Expected a list of numbers as input, found """
                                       F'''{type(UpperCAmelCase__				).__name__}'''
                                   )
                                   raise TypeError(UpperCAmelCase__				)
              else:
                     A_						=      F'''Expected a list of numbers as input, found {type(UpperCAmelCase__				).__name__}'''
                     raise TypeError(UpperCAmelCase__				)
       else:
              raise ValueError("""Missing an input"""				)
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__				)			->			float:
       _validate_point(UpperCAmelCase__				)
       _validate_point(UpperCAmelCase__				)
       if len(UpperCAmelCase__				) != len(UpperCAmelCase__				):
              raise ValueError("""Both points must be in the same n-dimensional space"""				)

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


if __name__ == "__main__":
      import doctest

      doctest.testmod()
