"""simple docstring"""



from typing import TYPE_CHECKING

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


__magic_name__     					=       {
    """configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
    """tokenization_xlm""": ["""XLMTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__     					=       [
         """XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """XLMForMultipleChoice""",
         """XLMForQuestionAnswering""",
         """XLMForQuestionAnsweringSimple""",
         """XLMForSequenceClassification""",
         """XLMForTokenClassification""",
         """XLMModel""",
         """XLMPreTrainedModel""",
         """XLMWithLMHeadModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__     					=       [
         """TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFXLMForMultipleChoice""",
         """TFXLMForQuestionAnsweringSimple""",
         """TFXLMForSequenceClassification""",
         """TFXLMForTokenClassification""",
         """TFXLMMainLayer""",
         """TFXLMModel""",
         """TFXLMPreTrainedModel""",
         """TFXLMWithLMHeadModel""",
     ]


if TYPE_CHECKING:
     from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
     from .tokenization_xlm import XLMTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_xlm import (
              XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              XLMForMultipleChoice,
              XLMForQuestionAnswering,
              XLMForQuestionAnsweringSimple,
              XLMForSequenceClassification,
              XLMForTokenClassification,
              XLMModel,
              XLMPreTrainedModel,
              XLMWithLMHeadModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_xlm import (
              TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFXLMForMultipleChoice,
              TFXLMForQuestionAnsweringSimple,
              TFXLMForSequenceClassification,
              TFXLMForTokenClassification,
              TFXLMMainLayer,
              TFXLMModel,
              TFXLMPreTrainedModel,
              TFXLMWithLMHeadModel,
          )

else:
     import sys

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




def 		UpperCamelCase						(   lowercase_ )		->						float:


  '''simple docstring'''






  if not nums:  # Makes sure that the list is not empty
    raise ValueError("""List is empty""" )

  lowercase__	: int     		=   sum(lowercase_ ) / len(lowercase_ )  # Calculate the average
  return sum(abs(x - average ) for x in nums ) / len(lowercase_ )


if __name__ == "__main__":
     import doctest

     doctest.testmod()






