





"""simple docstring"""



from typing import TYPE_CHECKING

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


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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """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:
       a_			=      [
           """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

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











"""simple docstring"""



def 		__lowercase					(       )       ->Tuple:



    '''simple docstring'''







    __A   :						str									=					[]
    __A   :						List[Any]									=					1

    while len(snake_case_						) < 1e6:
        constant.append(str(snake_case_						)						)
        i += 1

    __A   :						Any									=					''''''.join(snake_case_						)

    return (
        int(constant[0]						)
        * int(constant[9]						)
        * int(constant[99]						)
        * int(constant[999]						)
        * int(constant[9999]						)
        * int(constant[99999]						)
        * int(constant[999999]						)
    )


if __name__ == "__main__":
       print(solution())




