code: 
  
  
  '''simple docstring'''
  
  import os
  from typing import List
  
  def _SCREAMING_SNAKE_CASE   (A						,				A ) -> Optional[int]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if name != "training_function" and name != "main":
                raise ValueError(f"Incorrect function name passed: {name}, choose either \'main\' or \'training_function\'" )
         lowercase__    ,lowercase__       =				[], False
         for line in lines:
                if not found_start and f"def {name}" in line:
                       lowercase__       =				True
                       good_lines.append(A )
                       continue
                if found_start:
                       if name == "training_function" and "def main" in line:
                              return good_lines
                       if name == "main" and "if __name__" in line:
                              return good_lines
                       good_lines.append(A )
  
  def _SCREAMING_SNAKE_CASE   (A ) -> Optional[int]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         return [line for line in lines if not line.lstrip().startswith('''#''' ) and line != "\n"]
  
  def _SCREAMING_SNAKE_CASE   (A						,				A						,				A						,				A = None ) -> List[str]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         with open(A						,				'''r''' ) as f:
                lowercase__       =				f.readlines()
         with open(os.path.abspath(os.path.join('''examples'''						,				'''nlp_example.py''' ) )						,				'''r''' ) as f:
                lowercase__       =				f.readlines()
         with open(A						,				'''r''' ) as f:
                lowercase__       =				f.readlines()
         if secondary_filename is not None:
                with open(A						,				'''r''' ) as f:
                       lowercase__       =				f.readlines()
  
      # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
         if parser_only:
                lowercase__       =				clean_lines(get_function_contents_by_name(A						,				'''main''' ) )
                lowercase__       =				clean_lines(get_function_contents_by_name(A						,				'''main''' ) )
                lowercase__       =				clean_lines(get_function_contents_by_name(A						,				'''main''' ) )
                if secondary_filename is not None:
                       lowercase__       =				clean_lines(get_function_contents_by_name(A						,				'''main''' ) )
         else:
                lowercase__       =				clean_lines(get_function_contents_by_name(A						,				'''training_function''' ) )
                lowercase__       =				clean_lines(get_function_contents_by_name(A						,				'''training_function''' ) )
                lowercase__       =				clean_lines(get_function_contents_by_name(A						,				'''training_function''' ) )
                if secondary_filename is not None:
                       lowercase__       =				clean_lines(
                           get_function_contents_by_name(A						,				'''training_function''' ) )
  
         lowercase__       =				'''train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n'''
  
         # Specific code in our script that differs from the full version, aka what is new
         lowercase__       =				[]
         lowercase__       =				[]  # We keep track of the idxs just in case it's a repeated statement
         lowercase__       =				iter(A )
         for i in range(len(A ) - 1 ):
                if i not in passed_idxs:
                       lowercase__       =				next(A )
                       if (line not in full_file_func) and (line.lstrip() != _dl_line):
                              if "TESTING_MOCKED_DATALOADERS" not in line:
                                     new_feature_code.append(A )
                                     passed_idxs.append(A )
                              else:
                                     # Skip over the `config['num_epochs'] = 2` statement
                                     lowercase__       =				next(A )
  
      # Extract out just the new parts from the full_file_training_func
         lowercase__       =				[]
         lowercase__       =				[]  # We keep track of the idxs just in case it's a repeated statement
         for i, line in enumerate(A ):
                if i not in passed_idxs:
                       if (line not in full_file_func) and (line.lstrip() != _dl_line):
                              if "TESTING_MOCKED_DATALOADERS" not in line:
                                     new_full_example_parts.append(A )
                                     passed_idxs.append(A )
  
      # Finally, get the overall diff
         lowercase__       =				[line for line in new_feature_code if line not in new_full_example_parts]
         if secondary_filename is not None:
                lowercase__       =				[line for line in full_file_contents if line not in secondary_file_func]
                lowercase__       =				[line for line in diff_from_example if line not in diff_from_two]
  
         return diff_from_example
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
      is_vision_available,
  )
  
  
  lowerCamelCase							:     Optional[Any]								=			{
      'configuration_mobilevitv2': [
          'MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
          'MobileViTV2Config',
          'MobileViTV2OnnxConfig',
      ],
  }
  
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   lowerCamelCase							:     Optional[int]								=			[
       'MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST',
       'MobileViTV2ForImageClassification',
       'MobileViTV2ForSemanticSegmentation',
       'MobileViTV2Model',
       'MobileViTV2PreTrainedModel',
   ]
  
  if TYPE_CHECKING:
   from .configuration_mobilevitva import (
       MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
       MobileViTVaConfig,
       MobileViTVaOnnxConfig,
   )
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_mobilevitva import (
        MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        MobileViTVaForImageClassification,
        MobileViTVaForSemanticSegmentation,
        MobileViTVaModel,
        MobileViTVaPreTrainedModel,
    )
  
  else:
   import sys
  
   lowerCamelCase							:     Dict								=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  