





'''simple docstring'''

def 		UpperCamelCase       (							_lowerCamelCase      :      int = 10_00 ):
    A__       =    0
    A__       =    (n - 1) // 3
    total += ((terms) * (6 + (terms - 1) * 3)) // 2  # total of an A.P.
    A__       =    (n - 1) // 5
    total += ((terms) * (10 + (terms - 1) * 5)) // 2
    A__       =    (n - 1) // 15
    total -= ((terms) * (30 + (terms - 1) * 15)) // 2
    return total


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










'''simple docstring'''

from typing import TYPE_CHECKING

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


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

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

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

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase							:			str				=[
      "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

  __lowerCAmelCase							:			int				=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



