
from typing import TYPE_CHECKING

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


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

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__    :		Any          =   [
         """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:
     lowerCamelCase__    :		int          =   [
         """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

     lowerCamelCase__    :		Tuple          =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








import sys

lowerCamelCase__    :		List[Any]          =   (
    """73167176531330624919225119674426574742355349194934"""
    """96983520312774506326239578318016984801869478851843"""
    """85861560789112949495459501737958331952853208805511"""
    """12540698747158523863050715693290963295227443043557"""
    """66896648950445244523161731856403098711121722383113"""
    """62229893423380308135336276614282806444486645238749"""
    """30358907296290491560440772390713810515859307960866"""
    """70172427121883998797908792274921901699720888093776"""
    """65727333001053367881220235421809751254540594752243"""
    """52584907711670556013604839586446706324415722155397"""
    """53697817977846174064955149290862569321978468622482"""
    """83972241375657056057490261407972968652414535100474"""
    """82166370484403199890008895243450658541227588666881"""
    """16427171479924442928230863465674813919123162824586"""
    """17866458359124566529476545682848912883142607690042"""
    """24219022671055626321111109370544217506941658960408"""
    """07198403850962455444362981230987879927244284909188"""
    """84580156166097919133875499200524063689912560717606"""
    """05886116467109405077541002256983155200055935729725"""
    """71636269561882670428252483600823257530420752963450"""
)

def 		UpperCamelCase	(      lowercase_ = N   )		->	int:


  '''simple docstring'''






  lowercase__  :   int    		=      -sys.maxsize - 1
  for i in range(len(lowercase_   ) - 12   ):
    lowercase__  :   Optional[int]    		=      1
    for j in range(13   ):
      product *= int(n[i + j]   )
    if product > largest_product:
      lowercase__  :   int    		=      product
  return largest_product


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






