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 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Union[str, Any]		,   _SCREAMING_SNAKE_CASE   :     int  ):
  
  
  
  
  
  
  					if name != "training_function" and name != "main":
  										raise ValueError(F"""Incorrect function name passed: {name}, choose either \'main\' or \'training_function\'"""  )
  					__a					,       __a    : List[Any]      					=						[], False
  					for line in lines:
  										if not found_start and F"""def {name}""" in line:
  															__a    : Any      					=						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 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Optional[int]  ):
  					return [line for line in lines if not line.lstrip().startswith('#'  ) and line != "\n"]
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     List[str]		,   _SCREAMING_SNAKE_CASE   :     Dict		,   _SCREAMING_SNAKE_CASE   :     int		,   _SCREAMING_SNAKE_CASE   :     Any = None  ):
  
  
  
  
  
  
  					with open(UpperCamelCase_		,   'r'  ) as f:
  										__a    : Any      					=						f.readlines()
  					with open(os.path.abspath(os.path.join('examples'		,   'nlp_example.py'  )  )		,   'r'  ) as f:
  										__a    : List[Any]      					=						f.readlines()
  					with open(UpperCamelCase_		,   'r'  ) as f:
  										__a    : List[Any]      					=						f.readlines()
  					if secondary_filename is not None:
  										with open(UpperCamelCase_		,   'r'  ) as f:
  															__a    : List[Any]      					=						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:
  										__a    : Any      					=						clean_lines(get_function_contents_by_name(UpperCamelCase_		,   'main'  )  )
  										__a    : Union[str, Any]      					=						clean_lines(get_function_contents_by_name(UpperCamelCase_		,   'main'  )  )
  										__a    : Dict      					=						clean_lines(get_function_contents_by_name(UpperCamelCase_		,   'main'  )  )
  										if secondary_filename is not None:
  															__a    : Dict      					=						clean_lines(get_function_contents_by_name(UpperCamelCase_		,   'main'  )  )
  					else:
  										__a    : int      					=						clean_lines(get_function_contents_by_name(UpperCamelCase_		,   'training_function'  )  )
  										__a    : Union[str, Any]      					=						clean_lines(get_function_contents_by_name(UpperCamelCase_		,   'training_function'  )  )
  										__a    : Optional[Any]      					=						clean_lines(get_function_contents_by_name(UpperCamelCase_		,   'training_function'  )  )
  										if secondary_filename is not None:
  															__a    : Union[str, Any]      					=						clean_lines(
  															    get_function_contents_by_name(UpperCamelCase_		,   'training_function'  )  )
  
  					__a    : Tuple      					=						'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
  					__a    : int      					=						[]
  					__a    : Union[str, Any]      					=						[]  # We keep track of the idxs just in case it's a repeated statement
  					__a    : Tuple      					=						iter(UpperCamelCase_  )
  					for i in range(len(UpperCamelCase_  ) - 1  ):
  										if i not in passed_idxs:
  															__a    : Any      					=						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
  																									__a    : Dict      					=						next(UpperCamelCase_  )
  
      # Extract out just the new parts from the full_file_training_func
  					__a    : Union[str, Any]      					=						[]
  					__a    : int      					=						[]  # 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
  					__a    : Union[str, Any]      					=						[line for line in new_feature_code if line not in new_full_example_parts]
  					if secondary_filename is not None:
  										__a    : str      					=						[line for line in full_file_contents if line not in secondary_file_func]
  										__a    : int      					=						[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
  
  
  __lowercase			:		Any =			{'configuration_visual_bert': ['VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VisualBertConfig']}
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	__lowercase			:		Optional[int] =			[
  	    'VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
  	    'VisualBertForMultipleChoice',
  	    'VisualBertForPreTraining',
  	    'VisualBertForQuestionAnswering',
  	    'VisualBertForRegionToPhraseAlignment',
  	    'VisualBertForVisualReasoning',
  	    'VisualBertLayer',
  	    'VisualBertModel',
  	    'VisualBertPreTrainedModel',
  	]
  
  
  if TYPE_CHECKING:
  	from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_visual_bert import (
  		    VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    VisualBertForMultipleChoice,
  		    VisualBertForPreTraining,
  		    VisualBertForQuestionAnswering,
  		    VisualBertForRegionToPhraseAlignment,
  		    VisualBertForVisualReasoning,
  		    VisualBertLayer,
  		    VisualBertModel,
  		    VisualBertPreTrainedModel,
  		)
  
  
  else:
  	import sys
  
  	__lowercase			:		Tuple =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  