code: 
  
  
  
  
  
  '''simple docstring'''
  
  import argparse
  import logging
  import os
  from pathlib import Path
  from typing import Any, Dict
  
  import pytorch_lightning as pl
  from pytorch_lightning.utilities import rank_zero_info
  
  from transformers import (
      AdamW,
      AutoConfig,
      AutoModel,
      AutoModelForPreTraining,
      AutoModelForQuestionAnswering,
      AutoModelForSeqaSeqLM,
      AutoModelForSequenceClassification,
      AutoModelForTokenClassification,
      AutoModelWithLMHead,
      AutoTokenizer,
      PretrainedConfig,
      PreTrainedTokenizer,
  )
  from transformers.optimization import (
      Adafactor,
      get_cosine_schedule_with_warmup,
      get_cosine_with_hard_restarts_schedule_with_warmup,
      get_linear_schedule_with_warmup,
      get_polynomial_decay_schedule_with_warmup,
  )
  from transformers.utils.versions import require_version
  
  
  lowercase_			       =				logging.getLogger(__name__)
  
  require_version("""pytorch_lightning>=1.0.4""")
  
  lowercase_			       =				{
      """base""": AutoModel,
      """sequence-classification""": AutoModelForSequenceClassification,
      """question-answering""": AutoModelForQuestionAnswering,
      """pretraining""": AutoModelForPreTraining,
      """token-classification""": AutoModelForTokenClassification,
      """language-modeling""": AutoModelWithLMHead,
      """summarization""": AutoModelForSeqaSeqLM,
      """translation""": AutoModelForSeqaSeqLM,
  }
  
  
  # update this and the import above to support new schedulers from transformers.optimization
  lowercase_			       =				{
      """linear""": get_linear_schedule_with_warmup,
      """cosine""": get_cosine_schedule_with_warmup,
      """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
      """polynomial""": get_polynomial_decay_schedule_with_warmup,
      # '': get_constant_schedule,             # not supported for now
      # '': get_constant_schedule_with_warmup, # not supported for now
  }
  lowercase_			       =				sorted(arg_to_scheduler.keys())
  lowercase_			       =				"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""
  
  
  class       a_  (       pl.LightningModule ):
  
      '''simple docstring'''
  
  
      def __init__(	self				,	A				,	A=None				,	A="base"				,	A=None				,	A=None				,	A=None				,	**A				,	)	->     List[str]:
          super().__init__()
          # TODO: move to self.save_hyperparameters()
          # self.save_hyperparameters()
          # can also expand arguments into trainer signature for easier reading
  
          self.save_hyperparameters(A )
          _SCREAMING_SNAKE_CASE             =    0
          _SCREAMING_SNAKE_CASE             =    Path(self.hparams.output_dir )
          _SCREAMING_SNAKE_CASE             =    self.hparams.cache_dir if self.hparams.cache_dir else None
          if config is None:
              _SCREAMING_SNAKE_CASE             =    AutoConfig.from_pretrained(
                  self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path				,	**({"""num_labels""": num_labels} if num_labels is not None else {})				,	cache_dir=A				,	**A				,	)
          else:
              _SCREAMING_SNAKE_CASE             =    config
  
          _SCREAMING_SNAKE_CASE             =    ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
          for p in extra_model_params:
              if getattr(self.hparams				,	A				,	A ):
                  assert hasattr(self.config				,	A ), f'model config doesn\'t have a `{p}` attribute'
                  setattr(self.config				,	A				,	getattr(self.hparams				,	A ) )
  
          if tokenizer is None:
              _SCREAMING_SNAKE_CASE             =    AutoTokenizer.from_pretrained(
                  self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path				,	cache_dir=A				,	)
          else:
              _SCREAMING_SNAKE_CASE             =    tokenizer
          _SCREAMING_SNAKE_CASE             =    MODEL_MODES[mode]
          if model is None:
              _SCREAMING_SNAKE_CASE             =    self.model_type.from_pretrained(
                  self.hparams.model_name_or_path				,	from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path )				,	config=self.config				,	cache_dir=A				,	)
          else:
              _SCREAMING_SNAKE_CASE             =    model
  
  
      def     snake_case_(	self				,	*A				,	**A )	->     List[Any]:
          _SCREAMING_SNAKE_CASE             =    self.model_type.from_pretrained(*A				,	**A )
  
  
      def     snake_case_(	self )	->     Dict:
          _SCREAMING_SNAKE_CASE             =    arg_to_scheduler[self.hparams.lr_scheduler]
          _SCREAMING_SNAKE_CASE             =    get_schedule_func(
              self.opt				,	num_warmup_steps=self.hparams.warmup_steps				,	num_training_steps=self.total_steps() )
          _SCREAMING_SNAKE_CASE             =    {"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
          return scheduler
  
  
      def     snake_case_(	self )	->     Optional[Any]:
          _SCREAMING_SNAKE_CASE             =    self.model
          _SCREAMING_SNAKE_CASE             =    ["""bias""", """LayerNorm.weight"""]
          _SCREAMING_SNAKE_CASE             =    [
              {
                  """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay )],
                  """weight_decay""": self.hparams.weight_decay,
              },
              {
                  """params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay )],
                  """weight_decay""": 0.0,
              },
          ]
          if self.hparams.adafactor:
              _SCREAMING_SNAKE_CASE             =    Adafactor(
                  A				,	lr=self.hparams.learning_rate				,	scale_parameter=A				,	relative_step=A )
  
          else:
              _SCREAMING_SNAKE_CASE             =    AdamW(
                  A				,	lr=self.hparams.learning_rate				,	eps=self.hparams.adam_epsilon )
          _SCREAMING_SNAKE_CASE             =    optimizer
  
          _SCREAMING_SNAKE_CASE             =    self.get_lr_scheduler()
  
          return [optimizer], [scheduler]
  
  
      def     snake_case_(	self				,	A				,	A )	->     str:
          return self.validation_step(A				,	A )
  
  
      def     snake_case_(	self				,	A )	->     Optional[Any]:
          return self.validation_end(A )
  
  
      def     snake_case_(	self )	->     int:
          _SCREAMING_SNAKE_CASE             =    max(1				,	self.hparams.gpus )  # TODO: consider num_tpu_cores
          _SCREAMING_SNAKE_CASE             =    self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
          return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
  
  
      def     snake_case_(	self				,	A )	->     List[Any]:
          if mode == "test":
              _SCREAMING_SNAKE_CASE             =    len(self.test_dataloader().dataset )
          else:
              _SCREAMING_SNAKE_CASE             =    self.get_dataloader("""train"""				,	self.hparams.train_batch_size				,	shuffle=A )
              _SCREAMING_SNAKE_CASE             =    len(self.train_dataloader().dataset )
  
  
      def     snake_case_(	self				,	A				,	A				,	A = False )	->     Any:
          raise NotImplementedError("""You must implement this for your task""" )
  
  
      def     snake_case_(	self )	->     Union[str, Any]:
          return self.train_loader
  
  
      def     snake_case_(	self )	->     int:
          return self.get_dataloader("""dev"""				,	self.hparams.eval_batch_size				,	shuffle=A )
  
  
      def     snake_case_(	self )	->     Optional[Any]:
          return self.get_dataloader("""test"""				,	self.hparams.eval_batch_size				,	shuffle=A )
  
  
      def     snake_case_(	self				,	A )	->     Union[str, Any]:
          return os.path.join(
              self.hparams.data_dir				,	"""cached_{}_{}_{}""".format(
                  A				,	list(filter(A				,	self.hparams.model_name_or_path.split("""/""" ) ) ).pop()				,	str(self.hparams.max_seq_length )				,	)				,	)
  
  
      @pl.utilities.rank_zero_only
      def     snake_case_(	self				,	A )	->     None:
          _SCREAMING_SNAKE_CASE             =    self.output_dir.joinpath("""best_tfmr""" )
          _SCREAMING_SNAKE_CASE             =    self.step_count
          self.model.save_pretrained(A )
          self.tokenizer.save_pretrained(A )
  
  
  
  
  
      @staticmethod
      def     snake_case_(	A				,	A )	->     Any:
          parser.add_argument(
              """--model_name_or_path"""				,	default=A				,	type=A				,	required=A				,	help="""Path to pretrained model or model identifier from huggingface.co/models"""				,	)
          parser.add_argument(
              """--config_name"""				,	default=""""""				,	type=A				,	help="""Pretrained config name or path if not the same as model_name""" )
          parser.add_argument(
              """--tokenizer_name"""				,	default=A				,	type=A				,	help="""Pretrained tokenizer name or path if not the same as model_name"""				,	)
          parser.add_argument(
              """--cache_dir"""				,	default=""""""				,	type=A				,	help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""				,	)
          parser.add_argument(
              """--encoder_layerdrop"""				,	type=A				,	help="""Encoder layer dropout probability (Optional). Goes into model.config"""				,	)
          parser.add_argument(
              """--decoder_layerdrop"""				,	type=A				,	help="""Decoder layer dropout probability (Optional). Goes into model.config"""				,	)
          parser.add_argument(
              """--dropout"""				,	type=A				,	help="""Dropout probability (Optional). Goes into model.config"""				,	)
          parser.add_argument(
              """--attention_dropout"""				,	type=A				,	help="""Attention dropout probability (Optional). Goes into model.config"""				,	)
          parser.add_argument("""--learning_rate"""				,	default=5e-5				,	type=A				,	help="""The initial learning rate for Adam.""" )
          parser.add_argument(
              """--lr_scheduler"""				,	default="""linear"""				,	choices=A				,	metavar=A				,	type=A				,	help="""Learning rate scheduler"""				,	)
          parser.add_argument("""--weight_decay"""				,	default=0.0				,	type=A				,	help="""Weight decay if we apply some.""" )
          parser.add_argument("""--adam_epsilon"""				,	default=1e-8				,	type=A				,	help="""Epsilon for Adam optimizer.""" )
          parser.add_argument("""--warmup_steps"""				,	default=0				,	type=A				,	help="""Linear warmup over warmup_steps.""" )
          parser.add_argument("""--num_workers"""				,	default=4				,	type=A				,	help="""kwarg passed to DataLoader""" )
          parser.add_argument("""--num_train_epochs"""				,	dest="""max_epochs"""				,	default=3				,	type=A )
          parser.add_argument("""--train_batch_size"""				,	default=32				,	type=A )
          parser.add_argument("""--eval_batch_size"""				,	default=32				,	type=A )
          parser.add_argument("""--adafactor"""				,	action="""store_true""" )
  
  
  
  
  class       a_  (       pl.Callback ):
  
      '''simple docstring'''
  
  
      def     snake_case_(	self				,	A				,	A )	->     List[Any]:
          _SCREAMING_SNAKE_CASE             =    trainer.lr_schedulers[0]["""scheduler"""]
          _SCREAMING_SNAKE_CASE             =    {f'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr() )}
          pl_module.logger.log_metrics(A )
  
  
      def     snake_case_(	self				,	A				,	A )	->     Dict:
          rank_zero_info("""***** Validation results *****""" )
          _SCREAMING_SNAKE_CASE             =    trainer.callback_metrics
          # Log results
          for key in sorted(A ):
              if key not in ["log", "progress_bar"]:
                  rank_zero_info("""{} = {}\n""".format(A				,	str(metrics[key] ) ) )
  
  
  
  
  
      def     snake_case_(	self				,	A				,	A )	->     List[Any]:
          rank_zero_info("""***** Test results *****""" )
          _SCREAMING_SNAKE_CASE             =    trainer.callback_metrics
          # Log and save results to file
          _SCREAMING_SNAKE_CASE             =    os.path.join(pl_module.hparams.output_dir				,	"""test_results.txt""" )
          with open(A				,	"""w""" ) as writer:
              for key in sorted(A ):
                  if key not in ["log", "progress_bar"]:
                      rank_zero_info("""{} = {}\n""".format(A				,	str(metrics[key] ) ) )
                      writer.write("""{} = {}\n""".format(A				,	str(metrics[key] ) ) )
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					int      , __lowerCamelCase				:					List[str]					)		->None:
      #  To allow all pl args uncomment the following line
      #  parser = pl.Trainer.add_argparse_args(parser)
      parser.add_argument(
          """--output_dir"""      , default=__lowerCamelCase      , type=__lowerCamelCase      , required=__lowerCamelCase      , help="""The output directory where the model predictions and checkpoints will be written."""      , )
      parser.add_argument(
          """--fp16"""      , action="""store_true"""      , help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""      , )
  
      parser.add_argument(
          """--fp16_opt_level"""      , type=__lowerCamelCase      , default="""O2"""      , help=(
              """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
              """See details at https://nvidia.github.io/apex/amp.html"""
          )      , )
      parser.add_argument("""--n_tpu_cores"""      , dest="""tpu_cores"""      , type=__lowerCamelCase					)
      parser.add_argument("""--max_grad_norm"""      , dest="""gradient_clip_val"""      , default=1.0      , type=__lowerCamelCase      , help="""Max gradient norm"""					)
      parser.add_argument("""--do_train"""      , action="""store_true"""      , help="""Whether to run training."""					)
      parser.add_argument("""--do_predict"""      , action="""store_true"""      , help="""Whether to run predictions on the test set."""					)
      parser.add_argument(
          """--gradient_accumulation_steps"""      , dest="""accumulate_grad_batches"""      , type=__lowerCamelCase      , default=1      , help="""Number of updates steps to accumulate before performing a backward/update pass."""      , )
      parser.add_argument("""--seed"""      , type=__lowerCamelCase      , default=42      , help="""random seed for initialization"""					)
      parser.add_argument(
          """--data_dir"""      , default=__lowerCamelCase      , type=__lowerCamelCase      , required=__lowerCamelCase      , help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""      , )
  
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					BaseTransformer      , __lowerCamelCase				:					argparse.Namespace      , __lowerCamelCase				:					List[Any]=None      , __lowerCamelCase				:					int=True      , __lowerCamelCase				:					List[str]=[]      , __lowerCamelCase				:					Union[str, Any]=None      , __lowerCamelCase				:					int=None      , **__lowerCamelCase				:					str      , )		->List[Any]:
      pl.seed_everything(args.seed					)
  
      # init model
      _SCREAMING_SNAKE_CASE             =    Path(model.hparams.output_dir					)
      odir.mkdir(exist_ok=__lowerCamelCase					)
  
      # add custom checkpoints
      if checkpoint_callback is None:
          _SCREAMING_SNAKE_CASE             =    pl.callbacks.ModelCheckpoint(
              filepath=args.output_dir      , prefix="""checkpoint"""      , monitor="""val_loss"""      , mode="""min"""      , save_top_k=1					)
      if early_stopping_callback:
          extra_callbacks.append(__lowerCamelCase					)
      if logging_callback is None:
          _SCREAMING_SNAKE_CASE             =    LoggingCallback()
  
      _SCREAMING_SNAKE_CASE             =    {}
  
      # TODO: remove with PyTorch 1.6 since pl uses native amp
      if args.fpaa:
          _SCREAMING_SNAKE_CASE             =    16
          _SCREAMING_SNAKE_CASE             =    args.fpaa_opt_level
  
      if args.gpus > 1:
          _SCREAMING_SNAKE_CASE             =    """ddp"""
  
      _SCREAMING_SNAKE_CASE             =    args.accumulate_grad_batches
      _SCREAMING_SNAKE_CASE             =    extra_train_kwargs.get("""accelerator"""      , __lowerCamelCase					)
      _SCREAMING_SNAKE_CASE             =    extra_train_kwargs.get("""profiler"""      , __lowerCamelCase					)
  
      _SCREAMING_SNAKE_CASE             =    pl.Trainer.from_argparse_args(
          __lowerCamelCase      , weights_summary=__lowerCamelCase      , callbacks=[logging_callback] + extra_callbacks      , logger=__lowerCamelCase      , checkpoint_callback=__lowerCamelCase      , **__lowerCamelCase      , )
  
      if args.do_train:
          trainer.fit(__lowerCamelCase					)
  
      return trainer
  
  
  
  
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 lowerCamelCase		(  __lowerCamelCase				:					List[str]      , __lowerCamelCase				:					str					)		->Tuple:
      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             =    [], False
      for line in lines:
          if not found_start and F'def {name}' in line:
              _SCREAMING_SNAKE_CASE             =    True
              good_lines.append(__lowerCamelCase					)
              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(__lowerCamelCase					)
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					List[str]					)		->List[str]:
      return [line for line in lines if not line.lstrip().startswith("""#"""					) and line != "\n"]
  
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					str      , __lowerCamelCase				:					str      , __lowerCamelCase				:					bool      , __lowerCamelCase				:					str = None					)		->List[str]:
      with open(__lowerCamelCase      , """r"""					) as f:
          _SCREAMING_SNAKE_CASE             =    f.readlines()
      with open(os.path.abspath(os.path.join("""examples"""      , """nlp_example.py"""					)					)      , """r"""					) as f:
          _SCREAMING_SNAKE_CASE             =    f.readlines()
      with open(__lowerCamelCase      , """r"""					) as f:
          _SCREAMING_SNAKE_CASE             =    f.readlines()
      if secondary_filename is not None:
          with open(__lowerCamelCase      , """r"""					) as f:
              _SCREAMING_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:
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
          if secondary_filename is not None:
              _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
      else:
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)
          if secondary_filename is not None:
              _SCREAMING_SNAKE_CASE             =    clean_lines(
                  get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)
  
      _SCREAMING_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
      _SCREAMING_SNAKE_CASE             =    []
      _SCREAMING_SNAKE_CASE             =    []  # We keep track of the idxs just in case it's a repeated statement
      _SCREAMING_SNAKE_CASE             =    iter(__lowerCamelCase					)
      for i in range(len(__lowerCamelCase					) - 1					):
          if i not in passed_idxs:
              _SCREAMING_SNAKE_CASE             =    next(__lowerCamelCase					)
              if (line not in full_file_func) and (line.lstrip() != _dl_line):
                  if "TESTING_MOCKED_DATALOADERS" not in line:
                      new_feature_code.append(__lowerCamelCase					)
                      passed_idxs.append(__lowerCamelCase					)
                  else:
                      # Skip over the `config['num_epochs'] = 2` statement
                      _SCREAMING_SNAKE_CASE             =    next(__lowerCamelCase					)
  
      # Extract out just the new parts from the full_file_training_func
      _SCREAMING_SNAKE_CASE             =    []
      _SCREAMING_SNAKE_CASE             =    []  # We keep track of the idxs just in case it's a repeated statement
      for i, line in enumerate(__lowerCamelCase					):
          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(__lowerCamelCase					)
                      passed_idxs.append(__lowerCamelCase					)
  
      # Finally, get the overall diff
      _SCREAMING_SNAKE_CASE             =    [line for line in new_feature_code if line not in new_full_example_parts]
      if secondary_filename is not None:
          _SCREAMING_SNAKE_CASE             =    [line for line in full_file_contents if line not in secondary_file_func]
          _SCREAMING_SNAKE_CASE             =    [line for line in diff_from_example if line not in diff_from_two]
  
      return diff_from_example
  
  
  
  