


'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_						:       Optional[int]      			=				{
    """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:
     a_						:       Dict      			=				[
         """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

     a_						:       Dict      			=				_LazyModule(__name__, globals()["""__file__"""], _import_structure)







'''simple docstring'''

def __snake_case      (   UpperCAmelCase_		:		int = 100	):
 lowerCamelCase_  				=						n * (n + 1) * (2 * n + 1) / 6
 lowerCamelCase_  				=						(n * (n + 1) / 2) ** 2
 return int(square_of_sum - sum_of_squares	)


if __name__ == "__main__":
     print(f'''{solution() = }''')



