code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  # 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	(  snake_case	,     snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     if name != "training_function" and name != "main":
        raise ValueError(f"""Incorrect function name passed: {name}, choose either 'main' or 'training_function'"""   )
     SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= [], False
     for line in lines:
        if not found_start and f"""def {name}""" in line:
           SCREAMING_SNAKE_CASE__			:				Dict       						= True
           good_lines.append(snake_case   )
           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(snake_case   )
  
  
  def 				UpperCamelCase	(  snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     return [line for line in lines if not line.lstrip().startswith('''#'''   ) and line != "\n"]
  
  
  
  
  
  
  
  def 				UpperCamelCase	(  snake_case	,     snake_case	,     snake_case	,     snake_case = None   ):
  
  
  
  
     '''simple docstring'''
  
     with open(snake_case	,     '''r'''   ) as f:
        SCREAMING_SNAKE_CASE__			:				List[Any]       						= f.readlines()
     with open(os.path.abspath(os.path.join('''examples'''	,     '''nlp_example.py'''   )   )	,     '''r'''   ) as f:
        SCREAMING_SNAKE_CASE__			:				str       						= f.readlines()
     with open(snake_case	,     '''r'''   ) as f:
        SCREAMING_SNAKE_CASE__			:				List[str]       						= f.readlines()
     if secondary_filename is not None:
        with open(snake_case	,     '''r'''   ) as f:
           SCREAMING_SNAKE_CASE__			:				Tuple       						= 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:
        SCREAMING_SNAKE_CASE__			:				int       						= clean_lines(get_function_contents_by_name(snake_case	,     '''main'''   )   )
        SCREAMING_SNAKE_CASE__			:				Any       						= clean_lines(get_function_contents_by_name(snake_case	,     '''main'''   )   )
        SCREAMING_SNAKE_CASE__			:				Optional[int]       						= clean_lines(get_function_contents_by_name(snake_case	,     '''main'''   )   )
        if secondary_filename is not None:
           SCREAMING_SNAKE_CASE__			:				Any       						= clean_lines(get_function_contents_by_name(snake_case	,     '''main'''   )   )
     else:
        SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= clean_lines(get_function_contents_by_name(snake_case	,     '''training_function'''   )   )
        SCREAMING_SNAKE_CASE__			:				str       						= clean_lines(get_function_contents_by_name(snake_case	,     '''training_function'''   )   )
        SCREAMING_SNAKE_CASE__			:				Optional[int]       						= clean_lines(get_function_contents_by_name(snake_case	,     '''training_function'''   )   )
        if secondary_filename is not None:
           SCREAMING_SNAKE_CASE__			:				Tuple       						= clean_lines(
               get_function_contents_by_name(snake_case	,     '''training_function'''   )   )
  
     SCREAMING_SNAKE_CASE__			:				Any       						= '''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
     SCREAMING_SNAKE_CASE__			:				int       						= []
     SCREAMING_SNAKE_CASE__			:				int       						= []  # We keep track of the idxs just in case it's a repeated statement
     SCREAMING_SNAKE_CASE__			:				Dict       						= iter(snake_case   )
     for i in range(len(snake_case   ) - 1   ):
        if i not in passed_idxs:
           SCREAMING_SNAKE_CASE__			:				Dict       						= next(snake_case   )
           if (line not in full_file_func) and (line.lstrip() != _dl_line):
              if "TESTING_MOCKED_DATALOADERS" not in line:
                 new_feature_code.append(snake_case   )
                 passed_idxs.append(snake_case   )
              else:
                 # Skip over the `config['num_epochs'] = 2` statement
                 SCREAMING_SNAKE_CASE__			:				List[str]       						= next(snake_case   )
  
      # Extract out just the new parts from the full_file_training_func
     SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= []
     SCREAMING_SNAKE_CASE__			:				List[Any]       						= []  # We keep track of the idxs just in case it's a repeated statement
     for i, line in enumerate(snake_case   ):
        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(snake_case   )
                 passed_idxs.append(snake_case   )
  
      # Finally, get the overall diff
     SCREAMING_SNAKE_CASE__			:				Dict       						= [line for line in new_feature_code if line not in new_full_example_parts]
     if secondary_filename is not None:
        SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= [line for line in full_file_contents if line not in secondary_file_func]
        SCREAMING_SNAKE_CASE__			:				str       						= [line for line in diff_from_example if line not in diff_from_two]
  
     return diff_from_example
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  # Copyright 2023 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.
  from typing import TYPE_CHECKING
  
  # rely on isort to merge the imports
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
  )
  
  
  lowercase__			=	{
      '''configuration_convnextv2''': [
          '''CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
          '''ConvNextV2Config''',
      ]
  }
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         lowercase__			=	[
             '''CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''ConvNextV2ForImageClassification''',
             '''ConvNextV2Model''',
             '''ConvNextV2PreTrainedModel''',
             '''ConvNextV2Backbone''',
         ]
  
  
  if TYPE_CHECKING:
         from .configuration_convnextva import (
             CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
             ConvNextVaConfig,
         )
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_convnextva import (
                    CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                    ConvNextVaBackbone,
                    ConvNextVaForImageClassification,
                    ConvNextVaModel,
                    ConvNextVaPreTrainedModel,
                )
  
  else:
         import sys
  
         lowercase__			=	_LazyModule(__name__, globals()['''__file__'''], _import_structure)
  
  