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			:			Union[str, 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			:			List[Any]	  = {
      """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			:			Union[str, Any]	  = sorted(arg_to_scheduler.keys())
  UpperCAmelCase			:			Optional[Any]	  = """{""" + """, """.join(arg_to_scheduler_choices) + """}"""
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					pl.LightningModule):
  
  
  
  
  
  
  
        def __init__(					self	,   lowerCAmelCase__	,   lowerCAmelCase__=None	,   lowerCAmelCase__="base"	,   lowerCAmelCase__=None	,   lowerCAmelCase__=None	,   lowerCAmelCase__=None	,   **lowerCAmelCase__	,   )     ->		List[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          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__							)
          a__			:      List[str]            =0
          a__			:      Dict            =Path(self.hparams.output_dir							)
          a__			:      Dict            =self.hparams.cache_dir if self.hparams.cache_dir else None
          if config is None:
            a__			:      List[Any]            =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:
            a__			:      PretrainedConfig            =config
  
          a__			:      Union[str, Any]            =("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:
            a__			:      Optional[Any]            =AutoTokenizer.from_pretrained(
                self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path	,   cache_dir=lowerCAmelCase__	,   )
          else:
            a__			:      PreTrainedTokenizer            =tokenizer
          a__			:      int            =MODEL_MODES[mode]
          if model is None:
            a__			:      List[Any]            =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:
            a__			:      Optional[Any]            =model
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   *lowerCAmelCase__	,   **lowerCAmelCase__							)     ->		List[str]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      int            =self.model_type.from_pretrained(*lowerCAmelCase__	,   **lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		List[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      str            =arg_to_scheduler[self.hparams.lr_scheduler]
          a__			:      Optional[Any]            =get_schedule_func(
              self.opt	,   num_warmup_steps=self.hparams.warmup_steps	,   num_training_steps=self.total_steps()							)
          a__			:      Optional[int]            ={"scheduler": scheduler, "interval": "step", "frequency": 1}
          return scheduler
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Any:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      List[Any]            =self.model
          a__			:      Any            =["bias", "LayerNorm.weight"]
          a__			:      Dict            =[
              {
                  "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__			:      List[Any]            =Adafactor(
                lowerCAmelCase__	,   lr=self.hparams.learning_rate	,   scale_parameter=lowerCAmelCase__	,   relative_step=lowerCAmelCase__							)
  
          else:
            a__			:      List[str]            =AdamW(
                lowerCAmelCase__	,   lr=self.hparams.learning_rate	,   eps=self.hparams.adam_epsilon							)
          a__			:      int            =optimizer
  
          a__			:      Any            =self.get_lr_scheduler()
  
          return [optimizer], [scheduler]
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__							)     ->		Dict:
  
  
  
          '''simple docstring'''
  
  
  
  
          return self.validation_step(lowerCAmelCase__	,   lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		str:
  
  
  
          '''simple docstring'''
  
  
  
  
          return self.validation_end(lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      List[str]            =max(1	,   self.hparams.gpus							)  # TODO: consider num_tpu_cores
          a__			:      Any            =self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
          return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		Optional[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          if mode == "test":
            a__			:      str            =len(self.test_dataloader().dataset							)
          else:
            a__			:      Tuple            =self.get_dataloader("train"	,   self.hparams.train_batch_size	,   shuffle=lowerCAmelCase__							)
            a__			:      List[Any]            =len(self.train_dataloader().dataset							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__ = False							)     ->		List[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          raise NotImplementedError("You must implement this for your task"							)
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Dict:
  
  
  
          '''simple docstring'''
  
  
  
  
          return self.train_loader
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		str:
  
  
  
          '''simple docstring'''
  
  
  
  
          return self.get_dataloader("dev"	,   self.hparams.eval_batch_size	,   shuffle=lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		str:
  
  
  
          '''simple docstring'''
  
  
  
  
          return self.get_dataloader("test"	,   self.hparams.eval_batch_size	,   shuffle=lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		List[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          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      _lowercase						(					self	,   lowerCAmelCase__							)     ->		None:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      List[str]            =self.output_dir.joinpath("best_tfmr"							)
          a__			:      Optional[Any]            =self.step_count
          self.model.save_pretrained(lowerCAmelCase__							)
          self.tokenizer.save_pretrained(lowerCAmelCase__							)
  
  
  
  
  
  
  
        @staticmethod
        def      _lowercase						(					lowerCAmelCase__	,   lowerCAmelCase__							)     ->		Tuple:
  
  
  
          '''simple docstring'''
  
  
  
  
          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=3_2	,   type=lowerCAmelCase__							)
          parser.add_argument("--eval_batch_size"	,   default=3_2	,   type=lowerCAmelCase__							)
          parser.add_argument("--adafactor"	,   action="store_true"							)
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					pl.Callback):
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__							)     ->		str:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      List[Any]            =trainer.lr_schedulers[0]["scheduler"]
          a__			:      Dict            ={F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()							)}
          pl_module.logger.log_metrics(lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__							)     ->		Any:
  
  
  
          '''simple docstring'''
  
  
  
  
          rank_zero_info("***** Validation results *****"							)
          a__			:      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      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__							)     ->		Optional[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          rank_zero_info("***** Test results *****"							)
          a__			:      Dict            =trainer.callback_metrics
          # Log and save results to file
          a__			:      int            =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   _A (	SCREAMING_SNAKE_CASE  :     Union[str, Any] , SCREAMING_SNAKE_CASE  :     str		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    parser.add_argument(
        "--output_dir" , default=SCREAMING_SNAKE_CASE , type=SCREAMING_SNAKE_CASE , required=SCREAMING_SNAKE_CASE , 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=SCREAMING_SNAKE_CASE , 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=SCREAMING_SNAKE_CASE		)
    parser.add_argument("--max_grad_norm" , dest="gradient_clip_val" , default=1.0 , type=SCREAMING_SNAKE_CASE , 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=SCREAMING_SNAKE_CASE , default=1 , help="Number of updates steps to accumulate before performing a backward/update pass." , )
    parser.add_argument("--seed" , type=SCREAMING_SNAKE_CASE , default=42 , help="random seed for initialization"		)
    parser.add_argument(
        "--data_dir" , default=SCREAMING_SNAKE_CASE , type=SCREAMING_SNAKE_CASE , required=SCREAMING_SNAKE_CASE , help="The input data dir. Should contain the training files for the CoNLL-2003 NER task." , )
  
  
  
  
  
  def   _A (	SCREAMING_SNAKE_CASE  :     BaseTransformer , SCREAMING_SNAKE_CASE  :     argparse.Namespace , SCREAMING_SNAKE_CASE  :     Dict=None , SCREAMING_SNAKE_CASE  :     Optional[Any]=True , SCREAMING_SNAKE_CASE  :     Any=[] , SCREAMING_SNAKE_CASE  :     str=None , SCREAMING_SNAKE_CASE  :     str=None , **SCREAMING_SNAKE_CASE  :     Any , ):
  
  
  
  
  
    """simple docstring"""
  
  
  
    pl.seed_everything(args.seed		)
  
    # init model
    a__			:      Dict            =Path(model.hparams.output_dir		)
    odir.mkdir(exist_ok=SCREAMING_SNAKE_CASE		)
  
    # add custom checkpoints
    if checkpoint_callback is None:
      a__			:      Tuple            =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(SCREAMING_SNAKE_CASE		)
    if logging_callback is None:
      a__			:      str            =LoggingCallback()
  
    a__			:      Dict            ={}
  
    # TODO: remove with PyTorch 1.6 since pl uses native amp
    if args.fpaa:
      a__			:      Union[str, Any]            =16
      a__			:      Tuple            =args.fpaa_opt_level
  
    if args.gpus > 1:
      a__			:      Union[str, Any]            ="ddp"
  
    a__			:      Dict            =args.accumulate_grad_batches
    a__			:      Any            =extra_train_kwargs.get("accelerator" , SCREAMING_SNAKE_CASE		)
    a__			:      Optional[int]            =extra_train_kwargs.get("profiler" , SCREAMING_SNAKE_CASE		)
  
    a__			:      int            =pl.Trainer.from_argparse_args(
        SCREAMING_SNAKE_CASE , weights_summary=SCREAMING_SNAKE_CASE , callbacks=[logging_callback] + extra_callbacks , logger=SCREAMING_SNAKE_CASE , checkpoint_callback=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE , )
  
    if args.do_train:
      trainer.fit(SCREAMING_SNAKE_CASE		)
  
    return trainer
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  def   _A (	SCREAMING_SNAKE_CASE  :     int		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    barcode //= 10  # exclude the last digit
    a__			:      int            =False
    a__			:      List[str]            =0
  
    # extract and check each digit
    while barcode != 0:
      a__			:      Optional[Any]            =1 if checker else 3
      s += mult * (barcode % 10)
      barcode //= 10
      a__			:      Union[str, Any]            =not checker
  
    return (10 - (s % 10)) % 10
  def   _A (	SCREAMING_SNAKE_CASE  :     int		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    return len(str(SCREAMING_SNAKE_CASE		)		) == 13 and get_check_digit(SCREAMING_SNAKE_CASE		) == barcode % 10
  def   _A (	SCREAMING_SNAKE_CASE  :     str		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    if str(SCREAMING_SNAKE_CASE		).isalpha():
      a__			:      Tuple            =f'''Barcode \'{barcode}\' has alphabetic characters.'''
      raise ValueError(SCREAMING_SNAKE_CASE		)
    elif int(SCREAMING_SNAKE_CASE		) < 0:
      raise ValueError("The entered barcode has a negative value. Try again."		)
    else:
      return int(SCREAMING_SNAKE_CASE		)
  
  
  if __name__ == "__main__":
       import doctest
  
       doctest.testmod()
       UpperCAmelCase			:			List[str]	  = get_barcode(input("""Barcode: """).strip())
  
       if is_valid(barcode):
            print(F"""'{barcode}' is a valid barcode.""")
       else:
            print(F"""'{barcode}' is NOT a valid barcode.""")
  
  
  
  
  
  
  