code: 
  
  
  """simple docstring"""
  
  
  
  
  from typing import List, Union
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  __SCREAMING_SNAKE_CASE					=						logging.get_logger(__name__)
  
  __SCREAMING_SNAKE_CASE					=						{
      """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/config.json""",
      # See all LED models at https://huggingface.co/models?filter=led
  }
  
  
  
  
  
  class __snake_case		(					_SCREAMING_SNAKE_CASE      ):
  
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			lowerCAmelCase_				:   str     	=			'led'
  			lowerCAmelCase_				:   str     	=			{
  			    'num_attention_heads': 'encoder_attention_heads',
  			    'hidden_size': 'd_model',
  			    'attention_probs_dropout_prob': 'attention_dropout',
  			    'initializer_range': 'init_std',
  			}
  
  
  
  
  			def __init__(		self							:Tuple				,  UpperCamelCase__							:Any=50_265				,  UpperCamelCase__							:Optional[Any]=16_384				,  UpperCamelCase__							:Optional[int]=1_024				,  UpperCamelCase__							:Union[str, Any]=12				,  UpperCamelCase__							:Dict=4_096				,  UpperCamelCase__							:Dict=16				,  UpperCamelCase__							:List[Any]=12				,  UpperCamelCase__							:Optional[int]=4_096				,  UpperCamelCase__							:Dict=16				,  UpperCamelCase__							:Union[str, Any]=0.0				,  UpperCamelCase__							:Optional[Any]=0.0				,  UpperCamelCase__							:Tuple=True				,  UpperCamelCase__							:List[str]=True				,  UpperCamelCase__							:Optional[int]="gelu"				,  UpperCamelCase__							:Union[str, Any]=1_024				,  UpperCamelCase__							:str=0.1				,  UpperCamelCase__							:Optional[int]=0.0				,  UpperCamelCase__							:Union[str, Any]=0.0				,  UpperCamelCase__							:Union[str, Any]=0.02				,  UpperCamelCase__							:List[str]=2				,  UpperCamelCase__							:Tuple=0.0				,  UpperCamelCase__							:Optional[Any]=1				,  UpperCamelCase__							:str=0				,  UpperCamelCase__							:Optional[Any]=2				,  UpperCamelCase__							:Union[List[int], int] = 512				,  **UpperCamelCase__							:Any				,  ):
  										_a          =				vocab_size
  										_a          =				max_encoder_position_embeddings
  										_a          =				max_decoder_position_embeddings
  										_a          =				d_model
  										_a          =				encoder_ffn_dim
  										_a          =				encoder_layers
  										_a          =				encoder_attention_heads
  										_a          =				decoder_ffn_dim
  										_a          =				decoder_layers
  										_a          =				decoder_attention_heads
  										_a          =				dropout
  										_a          =				attention_dropout
  										_a          =				activation_dropout
  										_a          =				activation_function
  										_a          =				init_std
  										_a          =				encoder_layerdrop
  										_a          =				decoder_layerdrop
  										_a          =				classifier_dropout
  										_a          =				use_cache
  										_a          =				encoder_layers
  										_a          =				attention_window
  
  										super().__init__(
  										    pad_token_id=UpperCamelCase__				,  bos_token_id=UpperCamelCase__				,  eos_token_id=UpperCamelCase__				,  is_encoder_decoder=UpperCamelCase__				,  decoder_start_token_id=UpperCamelCase__				,  **UpperCamelCase__				,  )
  
style_context: 
  
  
  """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 						__a      (  a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if name != "training_function" and name != "main":
                raise ValueError(F'Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''					)
         _a   , _a          =				[], False
         for line in lines:
                if not found_start and F'def {name}' in line:
                       _a          =				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 						__a      (  a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         return [line for line in lines if not line.lstrip().startswith("#"					) and line != "\n"]
  
  
  
  def 						__a      (  a,		a,		a,		a = None					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         with open(a,		"r"					) as f:
                _a          =				f.readlines()
         with open(os.path.abspath(os.path.join("examples",		"nlp_example.py"					)					),		"r"					) as f:
                _a          =				f.readlines()
         with open(a,		"r"					) as f:
                _a          =				f.readlines()
         if secondary_filename is not None:
                with open(a,		"r"					) as f:
                       _a          =				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          =				clean_lines(get_function_contents_by_name(a,		"main"					)					)
                _a          =				clean_lines(get_function_contents_by_name(a,		"main"					)					)
                _a          =				clean_lines(get_function_contents_by_name(a,		"main"					)					)
                if secondary_filename is not None:
                       _a          =				clean_lines(get_function_contents_by_name(a,		"main"					)					)
         else:
                _a          =				clean_lines(get_function_contents_by_name(a,		"training_function"					)					)
                _a          =				clean_lines(get_function_contents_by_name(a,		"training_function"					)					)
                _a          =				clean_lines(get_function_contents_by_name(a,		"training_function"					)					)
                if secondary_filename is not None:
                       _a          =				clean_lines(
                           get_function_contents_by_name(a,		"training_function"					)					)
  
         _a          =				"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          =				[]
         _a          =				[]  # We keep track of the idxs just in case it's a repeated statement
         _a          =				iter(a					)
         for i in range(len(a					) - 1					):
                if i not in passed_idxs:
                       _a          =				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
                                     _a          =				next(a					)
  
      # Extract out just the new parts from the full_file_training_func
         _a          =				[]
         _a          =				[]  # 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
         _a          =				[line for line in new_feature_code if line not in new_full_example_parts]
         if secondary_filename is not None:
                _a          =				[line for line in full_file_contents if line not in secondary_file_func]
                _a          =				[line for line in diff_from_example if line not in diff_from_two]
  
         return diff_from_example
  