



'''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 		__snake_case(							)						->					Optional[Any]:
      for n in range(1	,  1_000_000						):
            yield n * (n + 1) // 2





def 		__snake_case(							_lowerCAmelCase						)						->					str:
      snake_case__    :       Optional[int]       		= 1
      snake_case__    :       List[Any]       		= 2
      while i * i <= n:
            snake_case__    :       Dict       		= 0
            while n % i == 0:
                  n //= i
                  multiplicity += 1
            divisors_count *= multiplicity + 1
            i += 1
      if n > 1:
            divisors_count *= 2
      return divisors_count





def 		__snake_case(							)						->					List[str]:
      return next(i for i in triangle_number_generator() if count_divisors(_lowerCAmelCase						) > 500						)


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