code: 
  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
  
  
  UpperCamelCase_	:    List[Any]  					=       logging.getLogger(__name__)
  
  require_version("""pytorch_lightning>=1.0.4""")
  
  UpperCamelCase_	:    Union[str, Any]  					=       {
      """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
  UpperCamelCase_	:    Optional[int]  					=       {
      """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
  }
  UpperCamelCase_	:    Tuple  					=       sorted(arg_to_scheduler.keys())
  UpperCamelCase_	:    Optional[int]  					=       """{""" + """, """.join(arg_to_scheduler_choices) + """}"""
  
  
  
  
  class      __lowercase	(	pl.LightningModule					):
  
  
  
  
  
  
   def __init__(self							:   Any			,  snake_case							:   Optional[Any]			,  snake_case							:   List[Any]=None			,  snake_case							:   Tuple="base"			,  snake_case							:   Union[str, Any]=None			,  snake_case							:   str=None			,  snake_case							:   Any=None			,  **snake_case							:   Optional[int]			,  )   -> 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(lowerCamelCase_						)
         _lowercase					:  Dict    			=      0
         _lowercase					:  Tuple    			=      Path(self.hparams.output_dir						)
         _lowercase					:  Any    			=      self.hparams.cache_dir if self.hparams.cache_dir else None
         if config is None:
               _lowercase					:  Tuple    			=      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=lowerCamelCase_			,  **lowerCamelCase_			,  )
         else:
               _lowercase					:  Optional[int]    			=      config
  
         _lowercase					:  Optional[int]    			=      ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
         for p in extra_model_params:
               if getattr(self.hparams			,  lowerCamelCase_			,  lowerCamelCase_						):
                     assert hasattr(self.config			,  lowerCamelCase_						), F"""model config doesn\'t have a `{p}` attribute"""
                     setattr(self.config			,  lowerCamelCase_			,  getattr(self.hparams			,  lowerCamelCase_						)						)
  
         if tokenizer is None:
               _lowercase					:  List[str]    			=      AutoTokenizer.from_pretrained(
                   self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path			,  cache_dir=lowerCamelCase_			,  )
         else:
               _lowercase					:  Any    			=      tokenizer
         _lowercase					:  Any    			=      MODEL_MODES[mode]
         if model is None:
               _lowercase					:  Dict    			=      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=lowerCamelCase_			,  )
         else:
               _lowercase					:  List[Any]    			=      model
  
  
  
  
  
  
   def 			_a(self							:   Any			,  *snake_case							:   List[Any]			,  **snake_case							:   Tuple						)   -> Tuple:
         _lowercase					:  List[Any]    			=      self.model_type.from_pretrained(*lowerCamelCase_			,  **lowerCamelCase_						)
  
  
  
  
  
  
   def 			_a(self							:   List[str]						)   -> List[Any]:
         _lowercase					:  Any    			=      arg_to_scheduler[self.hparams.lr_scheduler]
         _lowercase					:  Optional[Any]    			=      get_schedule_func(
             self.opt			,  num_warmup_steps=self.hparams.warmup_steps			,  num_training_steps=self.total_steps()						)
         _lowercase					:  Optional[int]    			=      {"scheduler": scheduler, "interval": "step", "frequency": 1}
         return scheduler
  
  
  
  
  
  
   def 			_a(self							:   Tuple						)   -> Union[str, Any]:
         _lowercase					:  Optional[Any]    			=      self.model
         _lowercase					:  Optional[int]    			=      ["bias", "LayerNorm.weight"]
         _lowercase					:  List[Any]    			=      [
             {
                 "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:
               _lowercase					:  Optional[int]    			=      Adafactor(
                   lowerCamelCase_			,  lr=self.hparams.learning_rate			,  scale_parameter=lowerCamelCase_			,  relative_step=lowerCamelCase_						)
  
         else:
               _lowercase					:  Dict    			=      AdamW(
                   lowerCamelCase_			,  lr=self.hparams.learning_rate			,  eps=self.hparams.adam_epsilon						)
         _lowercase					:  Union[str, Any]    			=      optimizer
  
         _lowercase					:  Optional[int]    			=      self.get_lr_scheduler()
  
         return [optimizer], [scheduler]
  
  
  
  
  
  
   def 			_a(self							:   Optional[Any]			,  snake_case							:   Optional[Any]			,  snake_case							:   Union[str, Any]						)   -> Any:
         return self.validation_step(lowerCamelCase_			,  lowerCamelCase_						)
  
  
  
  
  
  
   def 			_a(self							:   Optional[Any]			,  snake_case							:   Dict						)   -> Tuple:
         return self.validation_end(lowerCamelCase_						)
  
  
  
  
  
  
   def 			_a(self							:   Tuple						)   -> Optional[int]:
         _lowercase					:  Any    			=      max(1			,  self.hparams.gpus						)  # TODO: consider num_tpu_cores
         _lowercase					:  int    			=      self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
         return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
  
  
  
  
  
  
   def 			_a(self							:   str			,  snake_case							:   str						)   -> List[Any]:
  
         if stage == "test":
               _lowercase					:  Union[str, Any]    			=      len(self.test_dataloader().dataset						)
         else:
               _lowercase					:  Tuple    			=      self.get_dataloader("train"			,  self.hparams.train_batch_size			,  shuffle=lowerCamelCase_						)
               _lowercase					:  List[Any]    			=      len(self.train_dataloader().dataset						)
  
  
  
  
  
  
   def 			_a(self							:   str			,  snake_case							:   int			,  snake_case							:   int			,  snake_case							:   List[str] = False						)   -> str:
         raise NotImplementedError("You must implement this for your task"						)
  
  
  
  
  
  
   def 			_a(self							:   Dict						)   -> List[str]:
         return self.train_loader
  
  
  
  
  
  
   def 			_a(self							:   Optional[int]						)   -> Tuple:
         return self.get_dataloader("dev"			,  self.hparams.eval_batch_size			,  shuffle=lowerCamelCase_						)
  
  
  
  
  
  
   def 			_a(self							:   List[Any]						)   -> List[Any]:
         return self.get_dataloader("test"			,  self.hparams.eval_batch_size			,  shuffle=lowerCamelCase_						)
  
  
  
  
  
  
   def 			_a(self							:   Union[str, Any]			,  snake_case							:   int						)   -> Union[str, Any]:
         return os.path.join(
             self.hparams.data_dir			,  "cached_{}_{}_{}".format(
                 lowerCamelCase_			,  list(filter(lowerCamelCase_			,  self.hparams.model_name_or_path.split("/"						)						)						).pop()			,  str(self.hparams.max_seq_length						)			,  )			,  )
  
  
  
  
  
  
   @pl.utilities.rank_zero_only
   def 			_a(self							:   Dict			,  snake_case							:   Dict						)   -> Optional[Any]:
         _lowercase					:  str    			=      self.output_dir.joinpath("best_tfmr"						)
         _lowercase					:  Optional[Any]    			=      self.step_count
         self.model.save_pretrained(lowerCamelCase_						)
         self.tokenizer.save_pretrained(lowerCamelCase_						)
  
  
  
  
  
  
   @staticmethod
   def 			_a(snake_case							:   Union[str, Any]			,  snake_case							:   Tuple						)   -> List[Any]:
         parser.add_argument(
             "--model_name_or_path"			,  default=lowerCamelCase_			,  type=lowerCamelCase_			,  required=lowerCamelCase_			,  help="Path to pretrained model or model identifier from huggingface.co/models"			,  )
         parser.add_argument(
             "--config_name"			,  default=""			,  type=lowerCamelCase_			,  help="Pretrained config name or path if not the same as model_name"						)
         parser.add_argument(
             "--tokenizer_name"			,  default=lowerCamelCase_			,  type=lowerCamelCase_			,  help="Pretrained tokenizer name or path if not the same as model_name"			,  )
         parser.add_argument(
             "--cache_dir"			,  default=""			,  type=lowerCamelCase_			,  help="Where do you want to store the pre-trained models downloaded from huggingface.co"			,  )
         parser.add_argument(
             "--encoder_layerdrop"			,  type=lowerCamelCase_			,  help="Encoder layer dropout probability (Optional). Goes into model.config"			,  )
         parser.add_argument(
             "--decoder_layerdrop"			,  type=lowerCamelCase_			,  help="Decoder layer dropout probability (Optional). Goes into model.config"			,  )
         parser.add_argument(
             "--dropout"			,  type=lowerCamelCase_			,  help="Dropout probability (Optional). Goes into model.config"			,  )
         parser.add_argument(
             "--attention_dropout"			,  type=lowerCamelCase_			,  help="Attention dropout probability (Optional). Goes into model.config"			,  )
         parser.add_argument("--learning_rate"			,  default=5e-5			,  type=lowerCamelCase_			,  help="The initial learning rate for Adam."						)
         parser.add_argument(
             "--lr_scheduler"			,  default="linear"			,  choices=lowerCamelCase_			,  metavar=lowerCamelCase_			,  type=lowerCamelCase_			,  help="Learning rate scheduler"			,  )
         parser.add_argument("--weight_decay"			,  default=0.0			,  type=lowerCamelCase_			,  help="Weight decay if we apply some."						)
         parser.add_argument("--adam_epsilon"			,  default=1e-8			,  type=lowerCamelCase_			,  help="Epsilon for Adam optimizer."						)
         parser.add_argument("--warmup_steps"			,  default=0			,  type=lowerCamelCase_			,  help="Linear warmup over warmup_steps."						)
         parser.add_argument("--num_workers"			,  default=4			,  type=lowerCamelCase_			,  help="kwarg passed to DataLoader"						)
         parser.add_argument("--num_train_epochs"			,  dest="max_epochs"			,  default=3			,  type=lowerCamelCase_						)
         parser.add_argument("--train_batch_size"			,  default=32			,  type=lowerCamelCase_						)
         parser.add_argument("--eval_batch_size"			,  default=32			,  type=lowerCamelCase_						)
         parser.add_argument("--adafactor"			,  action="store_true"						)
  
  
  
  
  class      __lowercase	(	pl.Callback					):
  
  
  
  
  
  
   def 			_a(self							:   Union[str, Any]			,  snake_case							:   Optional[int]			,  snake_case							:   Optional[Any]						)   -> 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      __lowercase	(	pl.Callback					):
  
  
  
  
  
  
   def 			_a(self							:   int			,  snake_case							:   int			,  snake_case							:   Tuple						)   -> Optional[int]:
         _lowercase					:  Dict    			=      trainer.lr_schedulers[0]["scheduler"]
         _lowercase					:  Union[str, Any]    			=      {F"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()						)}
         pl_module.logger.log_metrics(lowerCamelCase_						)
  
  
  
  
  
  
   def 			_a(self							:   Union[str, Any]			,  snake_case							:   Optional[Any]			,  snake_case							:   Optional[int]						)   -> Dict:
         rank_zero_info("***** Validation results *****"						)
         _lowercase					:  Optional[int]    			=      trainer.callback_metrics
         # Log results
         for key in sorted(lowerCamelCase_						):
               if key not in ["log", "progress_bar"]:
                     rank_zero_info("{} = {}\n".format(lowerCamelCase_			,  str(metrics[key]						)						)						)
  
  
  
  
  
  
   def 			_a(self							:   str			,  snake_case							:   List[str]			,  snake_case							:   Dict						)   -> Dict:
         rank_zero_info("***** Test results *****"						)
         _lowercase					:  str    			=      trainer.callback_metrics
         # Log and save results to file
         _lowercase					:  List[str]    			=      os.path.join(pl_module.hparams.output_dir			,  "test_results.txt"						)
         with open(lowerCamelCase_			,  "w"						) as writer:
               for key in sorted(lowerCamelCase_						):
                     if key not in ["log", "progress_bar"]:
                           rank_zero_info("{} = {}\n".format(lowerCamelCase_			,  str(metrics[key]						)						)						)
                           writer.write("{} = {}\n".format(lowerCamelCase_			,  str(metrics[key]						)						)						)
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      Optional[Any]   , _UpperCAmelCase     :      Optional[Any]     )      -> None:
  
  
  
  
  
  
        '''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=42   , 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 		UpperCamelCase    (					_UpperCAmelCase     :      BaseTransformer   , _UpperCAmelCase     :      argparse.Namespace   , _UpperCAmelCase     :      int=None   , _UpperCAmelCase     :      str=True   , _UpperCAmelCase     :      List[Any]=None   , _UpperCAmelCase     :      List[str]=[]   , _UpperCAmelCase     :      Any=None   , _UpperCAmelCase     :      List[str]=None   , **_UpperCAmelCase     :      int   , )      -> Optional[Any]:
  
  
  
  
  
  
        '''simple docstring'''
  
        pl.seed_everything(args.seed     )
  
        # init model
        _lowercase					:  List[str]    			=      Path(model.hparams.output_dir     )
        odir.mkdir(exist_ok=a_     )
  
        # add custom checkpoints
        if checkpoint_callback is None:
              _lowercase					:  Dict    			=      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:
              _lowercase					:  Any    			=      LoggingCallback()
  
        _lowercase					:  List[str]    			=      {}
  
        # TODO: remove with PyTorch 1.6 since pl uses native amp
        if args.fpaa:
              _lowercase					:  Optional[int]    			=      16
              # train_params["amp_level"] = args.fp16_opt_level
  
        if args.gpus > 1:
              _lowercase					:  int    			=      "auto"  # "ddp"
              _lowercase					:  int    			=      "ddp"
  
        _lowercase					:  Dict    			=      args.accumulate_grad_batches
        _lowercase					:  Union[str, Any]    			=      None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
        _lowercase					:  Optional[int]    			=      "auto"
  
        _lowercase					:  int    			=      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: 
  import os
  import unittest
  
  from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer
  
  from ...test_tokenization_common import TokenizerTesterMixin
  
  
  
  
  class      __lowercase	(	__snake_case      ,   unittest.TestCase					):
  	_A   			=      BertweetTokenizer
  	_A   			=      False
  
  
  
  
  
  
  	def 			_a(self							:   Tuple						)   -> Any:
  							super().setUp()
  
  							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
  							_lowercase					:  Tuple    			=      ["I", "m", "V@@", "R@@", "r", "e@@"]
  							_lowercase					:  Tuple    			=      dict(zip(snake_case			,  range(len(snake_case						)						)						)						)
  							_lowercase					:  Tuple    			=      ["#version: 0.2", "a m</w>"]
  							_lowercase					:  Optional[Any]    			=      {"unk_token": "<unk>"}
  
  							_lowercase					:  int    			=      os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES["vocab_file"]						)
  							_lowercase					:  Optional[int]    			=      os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES["merges_file"]						)
  							with open(self.vocab_file			,  "w"			,  encoding="utf-8"						) as fp:
  													for token in vocab_tokens:
  																			fp.write(F"""{token} {vocab_tokens[token]}\n"""						)
  							with open(self.merges_file			,  "w"			,  encoding="utf-8"						) as fp:
  													fp.write("\n".join(snake_case						)						)
  
  
  
  
  
  
  	def 			_a(self							:   List[Any]			,  **snake_case							:   Tuple						)   -> Union[str, Any]:
  							kwargs.update(self.special_tokens_map						)
  							return BertweetTokenizer.from_pretrained(self.tmpdirname			,  **snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   Any			,  snake_case							:   Any						)   -> List[Any]:
  							_lowercase					:  Tuple    			=      "I am VinAI Research"
  							_lowercase					:  Optional[int]    			=      "I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
  							return input_text, output_text
  
  
  
  
  
  
  	def 			_a(self							:   str						)   -> Optional[int]:
  							_lowercase					:  str    			=      BertweetTokenizer(self.vocab_file			,  self.merges_file			,  **self.special_tokens_map						)
  							_lowercase					:  Optional[Any]    			=      "I am VinAI Research"
  							_lowercase					:  str    			=      "I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
  							_lowercase					:  Tuple    			=      tokenizer.tokenize(snake_case						)
  							self.assertListEqual(snake_case			,  snake_case						)
  
  							_lowercase					:  str    			=      tokens + [tokenizer.unk_token]
  
  							_lowercase					:  str    			=      [4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
  							self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case						)			,  snake_case						)
  
  
  
  
  