code: 
  
  
  # Copyright 2022 The HuggingFace Team. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  import os
  from typing import List
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if name != "training_function" and name != "main":
            raise ValueError(F'''Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''''       )
       snake_case				,						snake_case					=       [], False
       for line in lines:
            if not found_start and F'''def {name}''' in line:
                 snake_case					=       True
                 good_lines.append(UpperCamelCase_       )
                 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(UpperCamelCase_       )
  def     UpperCAmelCase__		(UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       return [line for line in lines if not line.lstrip().startswith('''#'''       ) and line != "\n"]
  
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_ = None       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       with open(UpperCamelCase_							,'''r'''       ) as f:
            snake_case					=       f.readlines()
       with open(os.path.abspath(os.path.join('''examples'''							,'''nlp_example.py'''       )       )							,'''r'''       ) as f:
            snake_case					=       f.readlines()
       with open(UpperCamelCase_							,'''r'''       ) as f:
            snake_case					=       f.readlines()
       if secondary_filename is not None:
            with open(UpperCamelCase_							,'''r'''       ) as f:
                 snake_case					=       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:
            snake_case					=       clean_lines(get_function_contents_by_name(UpperCamelCase_							,'''main'''       )       )
            snake_case					=       clean_lines(get_function_contents_by_name(UpperCamelCase_							,'''main'''       )       )
            snake_case					=       clean_lines(get_function_contents_by_name(UpperCamelCase_							,'''main'''       )       )
            if secondary_filename is not None:
                 snake_case					=       clean_lines(get_function_contents_by_name(UpperCamelCase_							,'''main'''       )       )
       else:
            snake_case					=       clean_lines(get_function_contents_by_name(UpperCamelCase_							,'''training_function'''       )       )
            snake_case					=       clean_lines(get_function_contents_by_name(UpperCamelCase_							,'''training_function'''       )       )
            snake_case					=       clean_lines(get_function_contents_by_name(UpperCamelCase_							,'''training_function'''       )       )
            if secondary_filename is not None:
                 snake_case					=       clean_lines(
                     get_function_contents_by_name(UpperCamelCase_							,'''training_function'''       )       )
  
       snake_case					=       '''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
       snake_case					=       []
       snake_case					=       []  # We keep track of the idxs just in case it's a repeated statement
       snake_case					=       iter(UpperCamelCase_       )
       for i in range(len(UpperCamelCase_       ) - 1       ):
            if i not in passed_idxs:
                 snake_case					=       next(UpperCamelCase_       )
                 if (line not in full_file_func) and (line.lstrip() != _dl_line):
                      if "TESTING_MOCKED_DATALOADERS" not in line:
                           new_feature_code.append(UpperCamelCase_       )
                           passed_idxs.append(UpperCamelCase_       )
                      else:
                           # Skip over the `config['num_epochs'] = 2` statement
                           snake_case					=       next(UpperCamelCase_       )
  
      # Extract out just the new parts from the full_file_training_func
       snake_case					=       []
       snake_case					=       []  # We keep track of the idxs just in case it's a repeated statement
       for i, line in enumerate(UpperCamelCase_       ):
            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(UpperCamelCase_       )
                           passed_idxs.append(UpperCamelCase_       )
  
      # Finally, get the overall diff
       snake_case					=       [line for line in new_feature_code if line not in new_full_example_parts]
       if secondary_filename is not None:
            snake_case					=       [line for line in full_file_contents if line not in secondary_file_func]
            snake_case					=       [line for line in diff_from_example if line not in diff_from_two]
  
       return diff_from_example
  
  
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _SCREAMING_SNAKE_CASE    :				Optional[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:
   _SCREAMING_SNAKE_CASE    :				str       =					["LayoutLMTokenizerFast"]
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				List[str]       =					[
       "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "LayoutLMForMaskedLM",
       "LayoutLMForSequenceClassification",
       "LayoutLMForTokenClassification",
       "LayoutLMForQuestionAnswering",
       "LayoutLMModel",
       "LayoutLMPreTrainedModel",
   ]
  
  try:
   if not is_tf_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				Optional[int]       =					[
       "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
  
   _SCREAMING_SNAKE_CASE    :				Optional[int]       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  