code: 
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  A           =   {
      "configuration_markuplm": ["MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarkupLMConfig"],
      "feature_extraction_markuplm": ["MarkupLMFeatureExtractor"],
      "processing_markuplm": ["MarkupLMProcessor"],
      "tokenization_markuplm": ["MarkupLMTokenizer"],
  }
  
  try:
       if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       A           =   ["MarkupLMTokenizerFast"]
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       A           =   [
           "MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "MarkupLMForQuestionAnswering",
           "MarkupLMForSequenceClassification",
           "MarkupLMForTokenClassification",
           "MarkupLMModel",
           "MarkupLMPreTrainedModel",
       ]
  
  
  if TYPE_CHECKING:
       from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
       from .feature_extraction_markuplm import MarkupLMFeatureExtractor
       from .processing_markuplm import MarkupLMProcessor
       from .tokenization_markuplm import MarkupLMTokenizer
  
       try:
            if not is_tokenizers_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .tokenization_markuplm_fast import MarkupLMTokenizerFast
  
       try:
            if not is_torch_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_markuplm import (
                MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                MarkupLMForQuestionAnswering,
                MarkupLMForSequenceClassification,
                MarkupLMForTokenClassification,
                MarkupLMModel,
                MarkupLMPreTrainedModel,
            )
  
  
  else:
       import sys
  
       A           =   _LazyModule(__name__, globals()["__file__"], _import_structure)
  
  
style_context: 
  '''simple docstring'''
  
  
  
  
  
  def   snake_case_       (							_a     :							int = 1_000			):
        return sum(i for i in range(_a			) if i % 3 == 0 or i % 5 == 0			)
  
  
  if __name__ == "__main__":
       print(F"""{solution() = }""")
  
  