


"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase    				=      {
    "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
    "tokenization_layoutlm": ["LayoutLMTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase    				=      ["LayoutLMTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase    				=      [
          "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
          "LayoutLMForMaskedLM",
          "LayoutLMForSequenceClassification",
          "LayoutLMForTokenClassification",
          "LayoutLMForQuestionAnswering",
          "LayoutLMModel",
          "LayoutLMPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase    				=      [
          "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFLayoutLMForMaskedLM",
          "TFLayoutLMForSequenceClassification",
          "TFLayoutLMForTokenClassification",
          "TFLayoutLMForQuestionAnswering",
          "TFLayoutLMMainLayer",
          "TFLayoutLMModel",
          "TFLayoutLMPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
      from .tokenization_layoutlm import LayoutLMTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_layoutlm import (
                LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                LayoutLMForMaskedLM,
                LayoutLMForQuestionAnswering,
                LayoutLMForSequenceClassification,
                LayoutLMForTokenClassification,
                LayoutLMModel,
                LayoutLMPreTrainedModel,
            )
      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_layoutlm import (
                TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFLayoutLMForMaskedLM,
                TFLayoutLMForQuestionAnswering,
                TFLayoutLMForSequenceClassification,
                TFLayoutLMForTokenClassification,
                TFLayoutLMMainLayer,
                TFLayoutLMModel,
                TFLayoutLMPreTrainedModel,
            )

else:
      import sys

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




"""simple docstring"""





from math import factorial





def     lowercase__	(  lowerCamelCase     :  Dict				,	lowerCamelCase     :  int				)					->     Tuple:
  return factorial(lowerCamelCase				) / (factorial(lowerCamelCase				) * factorial(n - r				))





def     lowercase__	(  )					->     Optional[Any]:
  lowerCAmelCase__    :    List[Any]								=					0

  for i in range(1				,	1_0_1				):
    for j in range(1				,	i + 1				):
      if combinations(lowerCamelCase				,	lowerCamelCase				) > 1e6:
        total += 1
  return total


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