

'''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


_lowerCAmelCase         =						logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

_lowerCAmelCase         =						{
    "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
_lowerCAmelCase         =						{
    "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
}
_lowerCAmelCase         =						sorted(arg_to_scheduler.keys())
_lowerCAmelCase         =						"{" + ", ".join(arg_to_scheduler_choices) + "}"






class   _SCREAMING_SNAKE_CASE   (   pl.LightningModule     ):



 def __init__(   self		:   List[Any]			,				a__		:   argparse.Namespace			,				a__		:   Any=None			,				a__		:   Tuple="base"			,				a__		:   int=None			,				a__		:   List[str]=None			,				a__		:   Any=None			,				**a__		:   List[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(a__      )
      __magic_name__						       =    0
      __magic_name__						       =    Path(self.hparams.output_dir      )
      __magic_name__						       =    self.hparams.cache_dir if self.hparams.cache_dir else None
      if config is None:
           __magic_name__						       =    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:
           __magic_name__						       =    config

      __magic_name__						       =    ('''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:
           __magic_name__						       =    AutoTokenizer.from_pretrained(
               self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path			,				cache_dir=a__			,				)
      else:
           __magic_name__						       =    tokenizer
      __magic_name__						       =    MODEL_MODES[mode]
      if model is None:
           __magic_name__						       =    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:
           __magic_name__						       =    model



 def 			snake_case__  (   self		:   Any			,				*a__		:   Optional[Any]			,				**a__		:   Any      ):
      __magic_name__						       =    self.model_type.from_pretrained(*a__			,				**a__      )



 def 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    arg_to_scheduler[self.hparams.lr_scheduler]
      __magic_name__						       =    get_schedule_func(
          self.opt			,				num_warmup_steps=self.hparams.warmup_steps			,				num_training_steps=self.total_steps()      )
      __magic_name__						       =    {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
      return scheduler



 def 			snake_case__  (   self		:   str      ):
      __magic_name__						       =    self.model
      __magic_name__						       =    ['''bias''', '''LayerNorm.weight''']
      __magic_name__						       =    [
          {
              '''params''': [
                  p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay      )
              ],  # check this named paramters
              '''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:
           __magic_name__						       =    Adafactor(
               a__			,				lr=self.hparams.learning_rate			,				scale_parameter=a__			,				relative_step=a__      )

      else:
           __magic_name__						       =    AdamW(
               a__			,				lr=self.hparams.learning_rate			,				eps=self.hparams.adam_epsilon      )
      __magic_name__						       =    optimizer

      __magic_name__						       =    self.get_lr_scheduler()

      return [optimizer], [scheduler]



 def 			snake_case__  (   self		:   List[Any]			,				a__		:   List[str]			,				a__		:   List[str]      ):
      return self.validation_step(a__			,				a__      )



 def 			snake_case__  (   self		:   str			,				a__		:   Any      ):
      return self.validation_end(a__      )



 def 			snake_case__  (   self		:   List[str]      ):
      __magic_name__						       =    max(1			,				self.hparams.gpus      )  # TODO: consider num_tpu_cores
      __magic_name__						       =    self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
      return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs



 def 			snake_case__  (   self		:   Union[str, Any]			,				a__		:   List[Any]      ):
      if stage == "test":
           __magic_name__						       =    len(self.test_dataloader().dataset      )
      else:
           __magic_name__						       =    self.get_dataloader('''train'''			,				self.hparams.train_batch_size			,				shuffle=a__      )
           __magic_name__						       =    len(self.train_dataloader().dataset      )



 def 			snake_case__  (   self		:   List[str]			,				a__		:   str			,				a__		:   int			,				a__		:   bool = False      ):
      raise NotImplementedError('''You must implement this for your task'''      )



 def 			snake_case__  (   self		:   Any      ):
      return self.train_loader



 def 			snake_case__  (   self		:   str      ):
      return self.get_dataloader('''dev'''			,				self.hparams.eval_batch_size			,				shuffle=a__      )



 def 			snake_case__  (   self		:   Dict      ):
      return self.get_dataloader('''test'''			,				self.hparams.eval_batch_size			,				shuffle=a__      )



 def 			snake_case__  (   self		:   str			,				a__		:   Any      ):
      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 			snake_case__  (   self		:   Optional[int]			,				a__		:   Dict[str, Any]      ):
      __magic_name__						       =    self.output_dir.joinpath('''best_tfmr'''      )
      __magic_name__						       =    self.step_count
      self.model.save_pretrained(a__      )
      self.tokenizer.save_pretrained(a__      )



 @staticmethod
 def 			snake_case__  (   a__		:   List[Any]			,				a__		:   Any      ):
      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=str(Path(a__      ).parent / '''test_run''' / '''cache'''      )			,				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 			snake_case__  (   self		:   str			,				a__		:   Dict			,				a__		:   List[Any]      ):
      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   _SCREAMING_SNAKE_CASE   (   pl.Callback     ):



 def 			snake_case__  (   self		:   Optional[int]			,				a__		:   Optional[int]			,				a__		:   Tuple      ):
      # print(pl_module.model.rag)
      for name, param in pl_module.model.rag.named_parameters():
           if param.grad is None:
                print(a__      )






class   _SCREAMING_SNAKE_CASE   (   pl.Callback     ):



 def 			snake_case__  (   self		:   Tuple			,				a__		:   Dict			,				a__		:   Optional[Any]      ):
      __magic_name__						       =    trainer.lr_schedulers[0]['''scheduler''']
      __magic_name__						       =    {F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()      )}
      pl_module.logger.log_metrics(a__      )



 def 			snake_case__  (   self		:   Optional[int]			,				a__		:   pl.Trainer			,				a__		:   pl.LightningModule      ):
      rank_zero_info('''***** Validation results *****'''      )
      __magic_name__						       =    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 			snake_case__  (   self		:   Union[str, Any]			,				a__		:   pl.Trainer			,				a__		:   pl.LightningModule      ):
      rank_zero_info('''***** Test results *****'''      )
      __magic_name__						       =    trainer.callback_metrics
      # Log and save results to file
      __magic_name__						       =    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 						UpperCamelCase	(			a		,						a			)				->     None:






     '''simple docstring'''
     parser.add_argument(
         '''--output_dir'''		,						default=str(Path(a			).parent / '''test_run''' / '''model_checkpoints'''			)		,						type=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=str(Path(a			).parent / '''test_run''' / '''dummy-train-data'''			)		,						type=a		,						help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''		,						)




def 						UpperCamelCase	(			a		,						a		,						a=None		,						a=True		,						a=[]		,						a=None		,						a=None		,						**a		,						)				->     Union[str, Any]:






     '''simple docstring'''
     pl.seed_everything(args.seed			)

     # init model
     __magic_name__						       =    Path(model.hparams.output_dir			)
     odir.mkdir(exist_ok=a			)

     # add custom checkpoints
     if checkpoint_callback is None:
          __magic_name__						       =    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:
          __magic_name__						       =    LoggingCallback()

     __magic_name__						       =    {}

     if args.fpaa:
          __magic_name__						       =    16

     if args.gpus > 1:
          __magic_name__						       =    '''auto'''
          __magic_name__						       =    '''ddp'''

     __magic_name__						       =    args.accumulate_grad_batches
     __magic_name__						       =    None
     __magic_name__						       =    '''auto'''

     __magic_name__						       =    pl.Trainer.from_argparse_args(
         a		,						weights_summary=a		,						callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]		,						logger=a		,						val_check_interval=1		,						num_sanity_val_steps=2		,						**a		,						)

     if args.do_train:
          trainer.fit(a			)

     else:
          print('''RAG modeling tests with new set functions successfuly executed!'''			)
     return trainer









'''simple docstring'''




import functools




def 						UpperCamelCase	(			a		,						a			)				->     int:






     '''simple docstring'''
     __magic_name__						       =    len(a			)
     __magic_name__						       =    len(a			)

     @functools.cache
     def min_distance(a		,						a			) -> int:
          # if first word index is overflow - delete all from the second word
          if indexa >= len_worda:
               return len_worda - indexa
          # if second word index is overflow - delete all from the first word
          if indexa >= len_worda:
               return len_worda - indexa
          __magic_name__						       =    int(worda[indexa] != worda[indexa]			)  # current letters not identical
          return min(
              1 + min_distance(indexa + 1		,						a			)		,						1 + min_distance(a		,						indexa + 1			)		,						diff + min_distance(indexa + 1		,						indexa + 1			)		,						)

     return min_distance(0		,						0			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()






