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
  
  
  __SCREAMING_SNAKE_CASE					=						logging.getLogger(__name__)
  
  require_version("""pytorch_lightning>=1.0.4""")
  
  __SCREAMING_SNAKE_CASE					=						{
      """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
  __SCREAMING_SNAKE_CASE					=						{
      """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
  }
  __SCREAMING_SNAKE_CASE					=						sorted(arg_to_scheduler.keys())
  __SCREAMING_SNAKE_CASE					=						"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""
  
  
  
  
  
  class __snake_case		(					pl.LightningModule      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def __init__(		self							:int				,  UpperCamelCase__							:argparse.Namespace				,  UpperCamelCase__							:str=None				,  UpperCamelCase__							:str="base"				,  UpperCamelCase__							:Optional[Any]=None				,  UpperCamelCase__							:Dict=None				,  UpperCamelCase__							:Any=None				,  **UpperCamelCase__							:Dict				,  ):
            super().__init__()
            # TODO: move to self.save_hyperparameters()
            # self.save_hyperparameters()
            # can also expand arguments into trainer signature for easier reading
  
            self.save_hyperparameters(UpperCamelCase__       )
            _a          =				0
            _a          =				Path(self.hparams.output_dir       )
            _a          =				self.hparams.cache_dir if self.hparams.cache_dir else None
            if config is None:
                   _a          =				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=UpperCamelCase__				,  **UpperCamelCase__				,  )
            else:
                   _a          =				config
  
            _a          =				("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
            for p in extra_model_params:
                   if getattr(self.hparams				,  UpperCamelCase__				,  UpperCamelCase__       ):
                          assert hasattr(self.config				,  UpperCamelCase__       ), f'model config doesn\'t have a `{p}` attribute'
                          setattr(self.config				,  UpperCamelCase__				,  getattr(self.hparams				,  UpperCamelCase__       )       )
  
            if tokenizer is None:
                   _a          =				AutoTokenizer.from_pretrained(
                       self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path				,  cache_dir=UpperCamelCase__				,  )
            else:
                   _a          =				tokenizer
            _a          =				MODEL_MODES[mode]
            if model is None:
                   _a          =				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=UpperCamelCase__				,  )
            else:
                   _a          =				model
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Optional[Any]				,  *UpperCamelCase__							:Any				,  **UpperCamelCase__							:List[str]       ):
            _a          =				self.model_type.from_pretrained(*UpperCamelCase__				,  **UpperCamelCase__       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Any       ):
            _a          =				arg_to_scheduler[self.hparams.lr_scheduler]
            _a          =				get_schedule_func(
                self.opt				,  num_warmup_steps=self.hparams.warmup_steps				,  num_training_steps=self.total_steps()       )
            _a          =				{"scheduler": scheduler, "interval": "step", "frequency": 1}
            return scheduler
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:List[str]       ):
            _a          =				self.model
            _a          =				["bias", "LayerNorm.weight"]
            _a          =				[
                {
                    "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:
                   _a          =				Adafactor(
                       UpperCamelCase__				,  lr=self.hparams.learning_rate				,  scale_parameter=UpperCamelCase__				,  relative_step=UpperCamelCase__       )
  
            else:
                   _a          =				AdamW(
                       UpperCamelCase__				,  lr=self.hparams.learning_rate				,  eps=self.hparams.adam_epsilon       )
            _a          =				optimizer
  
            _a          =				self.get_lr_scheduler()
  
            return [optimizer], [scheduler]
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Any				,  UpperCamelCase__							:Any				,  UpperCamelCase__							:Optional[int]       ):
            return self.validation_step(UpperCamelCase__				,  UpperCamelCase__       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Tuple				,  UpperCamelCase__							:Optional[int]       ):
            return self.validation_end(UpperCamelCase__       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:int       ):
            _a          =				max(1				,  self.hparams.gpus       )  # TODO: consider num_tpu_cores
            _a          =				self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
            return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:List[str]				,  UpperCamelCase__							:Dict       ):
            if stage == "test":
                   _a          =				len(self.test_dataloader().dataset       )
            else:
                   _a          =				self.get_dataloader("train"				,  self.hparams.train_batch_size				,  shuffle=UpperCamelCase__       )
                   _a          =				len(self.train_dataloader().dataset       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:List[str]				,  UpperCamelCase__							:str				,  UpperCamelCase__							:int				,  UpperCamelCase__							:bool = False       ):
            raise NotImplementedError("You must implement this for your task"       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:List[Any]       ):
            return self.train_loader
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Union[str, Any]       ):
            return self.get_dataloader("dev"				,  self.hparams.eval_batch_size				,  shuffle=UpperCamelCase__       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Union[str, Any]       ):
            return self.get_dataloader("test"				,  self.hparams.eval_batch_size				,  shuffle=UpperCamelCase__       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Optional[Any]				,  UpperCamelCase__							:Any       ):
            return os.path.join(
                self.hparams.data_dir				,  "cached_{}_{}_{}".format(
                    UpperCamelCase__				,  list(filter(UpperCamelCase__				,  self.hparams.model_name_or_path.split("/"       )       )       ).pop()				,  str(self.hparams.max_seq_length       )				,  )				,  )
  
  
  
  
     @pl.utilities.rank_zero_only
     def SCREAMING_SNAKE_CASE_      (		self							:Any				,  UpperCamelCase__							:Dict[str, Any]       ):
            _a          =				self.output_dir.joinpath("best_tfmr"       )
            _a          =				self.step_count
            self.model.save_pretrained(UpperCamelCase__       )
            self.tokenizer.save_pretrained(UpperCamelCase__       )
  
  
  
  
     @staticmethod
     def SCREAMING_SNAKE_CASE_      (		UpperCamelCase__							:int				,  UpperCamelCase__							:Union[str, Any]       ):
            parser.add_argument(
                "--model_name_or_path"				,  default=UpperCamelCase__				,  type=UpperCamelCase__				,  required=UpperCamelCase__				,  help="Path to pretrained model or model identifier from huggingface.co/models"				,  )
            parser.add_argument(
                "--config_name"				,  default=""				,  type=UpperCamelCase__				,  help="Pretrained config name or path if not the same as model_name"       )
            parser.add_argument(
                "--tokenizer_name"				,  default=UpperCamelCase__				,  type=UpperCamelCase__				,  help="Pretrained tokenizer name or path if not the same as model_name"				,  )
            parser.add_argument(
                "--cache_dir"				,  default=""				,  type=UpperCamelCase__				,  help="Where do you want to store the pre-trained models downloaded from huggingface.co"				,  )
            parser.add_argument(
                "--encoder_layerdrop"				,  type=UpperCamelCase__				,  help="Encoder layer dropout probability (Optional). Goes into model.config"				,  )
            parser.add_argument(
                "--decoder_layerdrop"				,  type=UpperCamelCase__				,  help="Decoder layer dropout probability (Optional). Goes into model.config"				,  )
            parser.add_argument(
                "--dropout"				,  type=UpperCamelCase__				,  help="Dropout probability (Optional). Goes into model.config"				,  )
            parser.add_argument(
                "--attention_dropout"				,  type=UpperCamelCase__				,  help="Attention dropout probability (Optional). Goes into model.config"				,  )
            parser.add_argument("--learning_rate"				,  default=5E-5				,  type=UpperCamelCase__				,  help="The initial learning rate for Adam."       )
            parser.add_argument(
                "--lr_scheduler"				,  default="linear"				,  choices=UpperCamelCase__				,  metavar=UpperCamelCase__				,  type=UpperCamelCase__				,  help="Learning rate scheduler"				,  )
            parser.add_argument("--weight_decay"				,  default=0.0				,  type=UpperCamelCase__				,  help="Weight decay if we apply some."       )
            parser.add_argument("--adam_epsilon"				,  default=1E-8				,  type=UpperCamelCase__				,  help="Epsilon for Adam optimizer."       )
            parser.add_argument("--warmup_steps"				,  default=0				,  type=UpperCamelCase__				,  help="Linear warmup over warmup_steps."       )
            parser.add_argument("--num_workers"				,  default=4				,  type=UpperCamelCase__				,  help="kwarg passed to DataLoader"       )
            parser.add_argument("--num_train_epochs"				,  dest="max_epochs"				,  default=3				,  type=UpperCamelCase__       )
            parser.add_argument("--train_batch_size"				,  default=32				,  type=UpperCamelCase__       )
            parser.add_argument("--eval_batch_size"				,  default=32				,  type=UpperCamelCase__       )
            parser.add_argument("--adafactor"				,  action="store_true"       )
  
  
  
  
  
  
  class __snake_case		(					pl.Callback      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Dict				,  UpperCamelCase__							:int				,  UpperCamelCase__							:str       ):
            if (
                trainer.is_global_zero and trainer.global_rank == 0
            ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
                   pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.
  
  
  
  
  
  
  class __snake_case		(					pl.Callback      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Optional[Any]				,  UpperCamelCase__							:Any				,  UpperCamelCase__							:Union[str, Any]       ):
            _a          =				trainer.lr_schedulers[0]["scheduler"]
            _a          =				{f'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()       )}
            pl_module.logger.log_metrics(UpperCamelCase__       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:List[str]				,  UpperCamelCase__							:pl.Trainer				,  UpperCamelCase__							:pl.LightningModule       ):
            rank_zero_info("***** Validation results *****"       )
            _a          =				trainer.callback_metrics
            # Log results
            for key in sorted(UpperCamelCase__       ):
                   if key not in ["log", "progress_bar"]:
                          rank_zero_info("{} = {}\n".format(UpperCamelCase__				,  str(metrics[key]       )       )       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:int				,  UpperCamelCase__							:pl.Trainer				,  UpperCamelCase__							:pl.LightningModule       ):
            rank_zero_info("***** Test results *****"       )
            _a          =				trainer.callback_metrics
            # Log and save results to file
            _a          =				os.path.join(pl_module.hparams.output_dir				,  "test_results.txt"       )
            with open(UpperCamelCase__				,  "w"       ) as writer:
                   for key in sorted(UpperCamelCase__       ):
                          if key not in ["log", "progress_bar"]:
                                 rank_zero_info("{} = {}\n".format(UpperCamelCase__				,  str(metrics[key]       )       )       )
                                 writer.write("{} = {}\n".format(UpperCamelCase__				,  str(metrics[key]       )       )       )
  
  
  
  
  
  
  def 						__a      (  a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         parser.add_argument(
             "--output_dir",		default=a,		type=a,		required=a,		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=a,		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=a					)
         parser.add_argument("--max_grad_norm",		dest="gradient_clip_val",		default=1.0,		type=a,		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=a,		default=1,		help="Number of updates steps to accumulate before performing a backward/update pass.",		)
         parser.add_argument("--seed",		type=a,		default=4_2,		help="random seed for initialization"					)
         parser.add_argument(
             "--data_dir",		default=a,		type=a,		required=a,		help="The input data dir. Should contain the training files for the CoNLL-2003 NER task.",		)
  
  
  
  def 						__a      (  a,		a,		a=None,		a=True,		a=None,		a=[],		a=None,		a=None,		**a,		):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         pl.seed_everything(args.seed					)
  
         # init model
         _a          =				Path(model.hparams.output_dir					)
         odir.mkdir(exist_ok=a					)
  
         # add custom checkpoints
         if checkpoint_callback is None:
                _a          =				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(a					)
         if logging_callback is None:
                _a          =				LoggingCallback()
  
         _a          =				{}
  
         # TODO: remove with PyTorch 1.6 since pl uses native amp
         if args.fpaa:
                _a          =				1_6
                # train_params["amp_level"] = args.fp16_opt_level
  
         if args.gpus > 1:
                _a          =				"auto"  # "ddp"
                _a          =				"ddp"
  
         _a          =				args.accumulate_grad_batches
         _a          =				None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
         _a          =				"auto"
  
         _a          =				pl.Trainer.from_argparse_args(
             a,		weights_summary=a,		callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()],		logger=a,		**a,		)
  
         if args.do_train:
                trainer.fit(a					)
  
         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 						__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
  