




'''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						:   Union[str, Any]      						=							logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

lowercase						:   Tuple      						=							{
    '''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						:   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
}
lowercase						:   List[Any]      						=							sorted(arg_to_scheduler.keys())
lowercase						:   Union[str, Any]      						=							'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''

class 	SCREAMING_SNAKE_CASE_		(							pl.LightningModule					):



 def __init__(	self ,		A ,		A=None ,		A="base" ,		A=None ,		A=None ,		A=None ,		**A ,		):






        '''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(A			)
        lowercase	:		Dict    =   0
        lowercase	:		Union[str, Any]    =   Path(self.hparams.output_dir			)
        lowercase	:		List[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=A ,		**A ,		)
        else:
               lowercase	:		PretrainedConfig    =   config

        lowercase	:		Tuple    =   ('''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:
               lowercase	:		str    =   AutoTokenizer.from_pretrained(
                   self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path ,		cache_dir=A ,		)
        else:
               lowercase	:		PreTrainedTokenizer    =   tokenizer
        lowercase	:		str    =   MODEL_MODES[mode]
        if model is None:
               lowercase	:		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=A ,		)
        else:
               lowercase	:		str    =   model



 def  SCREAMING_SNAKE_CASE(	self ,		*A ,		**A			):






        '''simple docstring'''




        lowercase	:		Dict    =   self.model_type.from_pretrained(*A ,		**A			)



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		Union[str, Any]    =   arg_to_scheduler[self.hparams.lr_scheduler]
        lowercase	:		List[str]    =   get_schedule_func(
            self.opt ,		num_warmup_steps=self.hparams.warmup_steps ,		num_training_steps=self.total_steps()			)
        lowercase	:		Tuple    =   {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
        return scheduler



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		Union[str, Any]    =   self.model
        lowercase	:		int    =   ['''bias''', '''LayerNorm.weight''']
        lowercase	:		Union[str, 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	:		List[Any]    =   Adafactor(
                   A ,		lr=self.hparams.learning_rate ,		scale_parameter=A ,		relative_step=A			)

        else:
               lowercase	:		Dict    =   AdamW(
                   A ,		lr=self.hparams.learning_rate ,		eps=self.hparams.adam_epsilon			)
        lowercase	:		Optional[Any]    =   optimizer

        lowercase	:		Tuple    =   self.get_lr_scheduler()

        return [optimizer], [scheduler]



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A			):






        '''simple docstring'''




        return self.validation_step(A ,		A			)



 def  SCREAMING_SNAKE_CASE(	self ,		A			):






        '''simple docstring'''




        return self.validation_end(A			)



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		List[str]    =   max(1 ,		self.hparams.gpus			)  # TODO: consider num_tpu_cores
        lowercase	:		List[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  SCREAMING_SNAKE_CASE(	self ,		A			):






        '''simple docstring'''




        if mode == "test":
               lowercase	:		Dict    =   len(self.test_dataloader().dataset			)
        else:
               lowercase	:		str    =   self.get_dataloader('''train''' ,		self.hparams.train_batch_size ,		shuffle=A			)
               lowercase	:		Optional[Any]    =   len(self.train_dataloader().dataset			)



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A ,		A = False			):






        '''simple docstring'''




        raise NotImplementedError('''You must implement this for your task'''			)



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        return self.train_loader



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        return self.get_dataloader('''dev''' ,		self.hparams.eval_batch_size ,		shuffle=A			)



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        return self.get_dataloader('''test''' ,		self.hparams.eval_batch_size ,		shuffle=A			)



 def  SCREAMING_SNAKE_CASE(	self ,		A			):






        '''simple docstring'''




        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  SCREAMING_SNAKE_CASE(	self ,		A			):






        '''simple docstring'''




        lowercase	:		Dict    =   self.output_dir.joinpath('''best_tfmr'''			)
        lowercase	:		str    =   self.step_count
        self.model.save_pretrained(A			)
        self.tokenizer.save_pretrained(A			)





 @staticmethod
 def  SCREAMING_SNAKE_CASE(	A ,		A			):






        '''simple docstring'''




        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 	SCREAMING_SNAKE_CASE_		(							pl.Callback					):



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A			):






        '''simple docstring'''




        lowercase	:		Tuple    =   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(A			)



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A			):






        '''simple docstring'''




        rank_zero_info('''***** Validation results *****'''			)
        lowercase	:		str    =   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  SCREAMING_SNAKE_CASE(	self ,		A ,		A			):






        '''simple docstring'''




        rank_zero_info('''***** Test results *****'''			)
        lowercase	:		List[Any]    =   trainer.callback_metrics
        # Log and save results to file
        lowercase	:		str    =   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 		a_(			lowerCamelCase ,					lowerCamelCase   )					->None:






       '''simple docstring'''




       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 		a_(			lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase=None ,					lowerCamelCase=True ,					lowerCamelCase=[] ,					lowerCamelCase=None ,					lowerCamelCase=None ,					**lowerCamelCase ,					)					->str:






       '''simple docstring'''




       pl.seed_everything(args.seed   )

       # init model
       lowercase	:		Optional[int]    =   Path(model.hparams.output_dir   )
       odir.mkdir(exist_ok=lowerCamelCase   )

       # add custom checkpoints
       if checkpoint_callback is None:
              lowercase	:		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(lowerCamelCase   )
       if logging_callback is None:
              lowercase	:		Tuple    =   LoggingCallback()

       lowercase	:		Tuple    =   {}

       # TODO: remove with PyTorch 1.6 since pl uses native amp
       if args.fpaa:
              lowercase	:		str    =   16
              lowercase	:		Union[str, Any]    =   args.fpaa_opt_level

       if args.gpus > 1:
              lowercase	:		Optional[Any]    =   '''ddp'''

       lowercase	:		Optional[Any]    =   args.accumulate_grad_batches
       lowercase	:		Any    =   extra_train_kwargs.get('''accelerator''' ,					lowerCamelCase   )
       lowercase	:		str    =   extra_train_kwargs.get('''profiler''' ,					lowerCamelCase   )

       lowercase	:		List[str]    =   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










'''simple docstring'''






def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->int:






       '''simple docstring'''




       if len(lowerCamelCase   ) < len(lowerCamelCase   ):
              return levenshtein_distance(lowerCamelCase ,					lowerCamelCase   )

       if len(lowerCamelCase   ) == 0:
              return len(lowerCamelCase   )

       lowercase	:		List[Any]    =   list(range(len(lowerCamelCase   ) + 1   )   )

       for i, ca in enumerate(lowerCamelCase   ):
              lowercase	:		Any    =   [i + 1]

              for j, ca in enumerate(lowerCamelCase   ):
                     # Calculate insertions, deletions and substitutions
                     lowercase	:		List[Any]    =   previous_row[j + 1] + 1
                     lowercase	:		str    =   current_row[j] + 1
                     lowercase	:		List[Any]    =   previous_row[j] + (ca != ca)

                     # Get the minimum to append to the current row
                     current_row.append(min(lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )   )

              # Store the previous row
              lowercase	:		List[str]    =   current_row

       # Returns the last element (distance)
       return previous_row[-1]


if __name__ == "__main__":
      lowercase						:   Dict      						=							input('''Enter the first word:\n''').strip()
      lowercase						:   Tuple      						=							input('''Enter the second word:\n''').strip()

      lowercase						:   Tuple      						=							levenshtein_distance(first_word, second_word)
      print(f'Levenshtein distance between {first_word} and {second_word} is {result}')




