code: 
  
  
  """simple docstring"""
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  UpperCAmelCase   =	{
      """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:
   UpperCAmelCase   =	["""MarkupLMTokenizerFast"""]
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   UpperCAmelCase   =	[
       """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
  
   UpperCAmelCase   =	_LazyModule(__name__, globals()["""__file__"""], _import_structure)
  
  
style_context: 
  
  
  """simple docstring"""
  import os
  
  from apache_beam.io.filesystems import FileSystems
  from apache_beam.pipeline import Pipeline
  
  from .logging import get_logger
  
  
  UpperCAmelCase   =	2 << 20  # 2mb
  UpperCAmelCase   =	get_logger(__name__)
  
  
  
  
  class   lowercase__							(				A_							):
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Dict:
        _lowerCamelCase  :				Tuple      =		self._options.get_all_options().get("""runner""")
        return runner in [None, "DirectRunner", "PortableRunner"]
  
  
  
  
  
  
  
  def 							_snake_case			(      __snake_case       :			Union[str, Any]	,   __snake_case       :			List[Any]	,   __snake_case       :			Any=False      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				Any      =		FileSystems
     if fs.exists(__snake_case      ):
        if force_upload:
           logger.info(F'Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True.'      )
        else:
           logger.info(F'Remote path already exist: {remote_file_path}. Skipping it as force_upload=False.'      )
           return
     with fs.create(__snake_case      ) as remote_file:
        with open(__snake_case	,   """rb"""      ) as local_file:
           _lowerCamelCase  :				Optional[Any]      =		local_file.read(__snake_case      )
           while chunk:
              remote_file.write(__snake_case      )
              _lowerCamelCase  :				Tuple      =		local_file.read(__snake_case      )
  
  
  
  
  
  def 							_snake_case			(      __snake_case       :			Union[str, Any]	,   __snake_case       :			List[Any]	,   __snake_case       :			Union[str, Any]=False      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				int      =		FileSystems
     if os.path.exists(__snake_case      ):
        if force_download:
           logger.info(F'Local path already exist: {remote_file_path}. Overwriting it as force_upload=True.'      )
        else:
           logger.info(F'Local path already exist: {remote_file_path}. Skipping it as force_upload=False.'      )
           return
     with fs.open(__snake_case      ) as remote_file:
        with open(__snake_case	,   """wb"""      ) as local_file:
           _lowerCamelCase  :				str      =		remote_file.read(__snake_case      )
           while chunk:
              local_file.write(__snake_case      )
              _lowerCamelCase  :				Any      =		remote_file.read(__snake_case      )
  
  