code: 
  
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase				)					-> list:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			Any    			=      []
      lowerCAmelCase				:			Union[str, Any]    			=      1
  
      for i in range(len(_UpperCAmelCase				)				):
          if i + 1 < len(_UpperCAmelCase				) and text[i] == text[i + 1]:
              count += 1
          else:
              encoded.append((text[i], count)				)
              lowerCAmelCase				:			Optional[int]    			=      1
  
      return encoded
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase				)					-> str:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      return "".join(char * length for char, length in encoded				)
  
  
  if __name__ == "__main__":
   from doctest import testmod
  
   testmod(name='''run_length_encode''', verbose=True)
   testmod(name='''run_length_decode''', verbose=True)
  
  
  
  
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  __A   :     List[Any]      =			{'''configuration_umt5''': ['''UMT5Config''', '''UMT5OnnxConfig''']}
  
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __A   :     Tuple      =			[
       '''UMT5EncoderModel''',
       '''UMT5ForConditionalGeneration''',
       '''UMT5ForQuestionAnswering''',
       '''UMT5Model''',
       '''UMT5PreTrainedModel''',
   ]
  
  if TYPE_CHECKING:
   from .configuration_umta import UMTaConfig, UMTaOnnxConfig
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_umta import (
        UMTaEncoderModel,
        UMTaForConditionalGeneration,
        UMTaForQuestionAnswering,
        UMTaModel,
        UMTaPreTrainedModel,
    )
  else:
   import sys
  
   __A   :     Any      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  
  