





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












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.""")






