




'''simple docstring'''


from typing import TYPE_CHECKING

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


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

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

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









'''simple docstring'''


def 						SCREAMING_SNAKE_CASE       (      a_	:							int			):
  __a			=					0
  while num > 0:
    digit_sum += num % 10
    num //= 10
  return digit_sum


def 						SCREAMING_SNAKE_CASE       (      a_	:							int = 100			):
  __a			=					1
  __a			=					2

  for i in range(2				,					max_n + 1			):
    __a			=					pre_numerator
    __a			=					2 * i // 3 if i % 3 == 0 else 1
    __a			=					cur_numerator
    __a			=					e_cont * pre_numerator + temp

  return sum_digits(a_			)


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



