





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   :							Dict     =	logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

_UpperCamelCase   :							Optional[int]     =	{
    """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   :							Dict     =	{
    """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   :							str     =	sorted(arg_to_scheduler.keys())
_UpperCamelCase   :							List[Any]     =	"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""


class 						_lowerCAmelCase(						pl.LightningModule):

  """simple docstring"""







  def __init__( self  ,    UpperCAmelCase  ,    UpperCAmelCase=None  ,    UpperCAmelCase="base"  ,    UpperCAmelCase=None  ,    UpperCAmelCase=None  ,    UpperCAmelCase=None  ,    **UpperCAmelCase  ,    )-> Union[str, Any]:
     super().__init__()
     # TODO: move to self.save_hyperparameters()
     # self.save_hyperparameters()
     # can also expand arguments into trainer signature for easier reading

     self.save_hyperparameters(UpperCAmelCase     )
     __A				 =       0
     __A				 =       Path(self.hparams.output_dir     )
     __A				 =       self.hparams.cache_dir if self.hparams.cache_dir else None
     if config is None:
        __A				 =       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=UpperCAmelCase  ,    **UpperCAmelCase  ,    )
     else:
        __A				 =       config

     __A				 =       ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
     for p in extra_model_params:
        if getattr(self.hparams  ,    UpperCAmelCase  ,    UpperCAmelCase     ):
           assert hasattr(self.config  ,    UpperCAmelCase     ), f"model config doesn't have a `{p}` attribute"
           setattr(self.config  ,    UpperCAmelCase  ,    getattr(self.hparams  ,    UpperCAmelCase     )     )

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

  def 		SCREAMING_SNAKE_CASE__	( self  ,    *UpperCAmelCase  ,    **UpperCAmelCase     )-> Any:
     __A				 =       self.model_type.from_pretrained(*UpperCAmelCase  ,    **UpperCAmelCase     )

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

  def 		SCREAMING_SNAKE_CASE__	( self     )-> int:
     __A				 =       self.model
     __A				 =       ['''bias''', '''LayerNorm.weight''']
     __A				 =       [
         {
             '''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				 =       Adafactor(
            UpperCAmelCase  ,    lr=self.hparams.learning_rate  ,    scale_parameter=UpperCAmelCase  ,    relative_step=UpperCAmelCase     )

     else:
        __A				 =       AdamW(
            UpperCAmelCase  ,    lr=self.hparams.learning_rate  ,    eps=self.hparams.adam_epsilon     )
     __A				 =       optimizer

     __A				 =       self.get_lr_scheduler()

     return [optimizer], [scheduler]

  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase  ,    UpperCAmelCase     )-> int:
     return self.validation_step(UpperCAmelCase  ,    UpperCAmelCase     )

  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase     )-> Union[str, Any]:
     return self.validation_end(UpperCAmelCase     )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> int:
     __A				 =       max(1  ,    self.hparams.gpus     )  # TODO: consider num_tpu_cores
     __A				 =       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  ,    UpperCAmelCase     )-> Optional[Any]:
     if stage == "test":
        __A				 =       len(self.test_dataloader().dataset     )
     else:
        __A				 =       self.get_dataloader('''train'''  ,    self.hparams.train_batch_size  ,    shuffle=UpperCAmelCase     )
        __A				 =       len(self.train_dataloader().dataset     )

  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase = False     )-> List[str]:
     raise NotImplementedError('''You must implement this for your task'''     )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Union[str, Any]:
     return self.train_loader

  def 		SCREAMING_SNAKE_CASE__	( self     )-> str:
     return self.get_dataloader('''dev'''  ,    self.hparams.eval_batch_size  ,    shuffle=UpperCAmelCase     )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Dict:
     return self.get_dataloader('''test'''  ,    self.hparams.eval_batch_size  ,    shuffle=UpperCAmelCase     )

  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase     )-> Optional[int]:
     return os.path.join(
         self.hparams.data_dir  ,    '''cached_{}_{}_{}'''.format(
             UpperCAmelCase  ,    list(filter(UpperCAmelCase  ,    self.hparams.model_name_or_path.split('''/'''     )     )     ).pop()  ,    str(self.hparams.max_seq_length     )  ,    )  ,    )

  @pl.utilities.rank_zero_only
  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase     )-> None:
     __A				 =       self.output_dir.joinpath('''best_tfmr'''     )
     __A				 =       self.step_count
     self.model.save_pretrained(UpperCAmelCase     )
     self.tokenizer.save_pretrained(UpperCAmelCase     )





  @staticmethod
  def 		SCREAMING_SNAKE_CASE__	( UpperCAmelCase  ,    UpperCAmelCase     )-> Tuple:
     parser.add_argument(
         '''--model_name_or_path'''  ,    default=UpperCAmelCase  ,    type=UpperCAmelCase  ,    required=UpperCAmelCase  ,    help='''Path to pretrained model or model identifier from huggingface.co/models'''  ,    )
     parser.add_argument(
         '''--config_name'''  ,    default=''''''  ,    type=UpperCAmelCase  ,    help='''Pretrained config name or path if not the same as model_name'''     )
     parser.add_argument(
         '''--tokenizer_name'''  ,    default=UpperCAmelCase  ,    type=UpperCAmelCase  ,    help='''Pretrained tokenizer name or path if not the same as model_name'''  ,    )
     parser.add_argument(
         '''--cache_dir'''  ,    default=''''''  ,    type=UpperCAmelCase  ,    help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''  ,    )
     parser.add_argument(
         '''--encoder_layerdrop'''  ,    type=UpperCAmelCase  ,    help='''Encoder layer dropout probability (Optional). Goes into model.config'''  ,    )
     parser.add_argument(
         '''--decoder_layerdrop'''  ,    type=UpperCAmelCase  ,    help='''Decoder layer dropout probability (Optional). Goes into model.config'''  ,    )
     parser.add_argument(
         '''--dropout'''  ,    type=UpperCAmelCase  ,    help='''Dropout probability (Optional). Goes into model.config'''  ,    )
     parser.add_argument(
         '''--attention_dropout'''  ,    type=UpperCAmelCase  ,    help='''Attention dropout probability (Optional). Goes into model.config'''  ,    )
     parser.add_argument('''--learning_rate'''  ,    default=5e-5  ,    type=UpperCAmelCase  ,    help='''The initial learning rate for Adam.'''     )
     parser.add_argument(
         '''--lr_scheduler'''  ,    default='''linear'''  ,    choices=UpperCAmelCase  ,    metavar=UpperCAmelCase  ,    type=UpperCAmelCase  ,    help='''Learning rate scheduler'''  ,    )
     parser.add_argument('''--weight_decay'''  ,    default=0.0  ,    type=UpperCAmelCase  ,    help='''Weight decay if we apply some.'''     )
     parser.add_argument('''--adam_epsilon'''  ,    default=1e-8  ,    type=UpperCAmelCase  ,    help='''Epsilon for Adam optimizer.'''     )
     parser.add_argument('''--warmup_steps'''  ,    default=0  ,    type=UpperCAmelCase  ,    help='''Linear warmup over warmup_steps.'''     )
     parser.add_argument('''--num_workers'''  ,    default=4  ,    type=UpperCAmelCase  ,    help='''kwarg passed to DataLoader'''     )
     parser.add_argument('''--num_train_epochs'''  ,    dest='''max_epochs'''  ,    default=3  ,    type=UpperCAmelCase     )
     parser.add_argument('''--train_batch_size'''  ,    default=32  ,    type=UpperCAmelCase     )
     parser.add_argument('''--eval_batch_size'''  ,    default=32  ,    type=UpperCAmelCase     )
     parser.add_argument('''--adafactor'''  ,    action='''store_true'''     )


class 						_lowerCAmelCase(						pl.Callback):

  """simple docstring"""







  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase  ,    UpperCAmelCase     )-> 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 						_lowerCAmelCase(						pl.Callback):

  """simple docstring"""







  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase  ,    UpperCAmelCase     )-> List[str]:
     __A				 =       trainer.lr_schedulers[0]['''scheduler''']
     __A				 =       {f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()     )}
     pl_module.logger.log_metrics(UpperCAmelCase     )

  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase  ,    UpperCAmelCase     )-> str:
     rank_zero_info('''***** Validation results *****'''     )
     __A				 =       trainer.callback_metrics
     # Log results
     for key in sorted(UpperCAmelCase     ):
        if key not in ["log", "progress_bar"]:
           rank_zero_info('''{} = {}\n'''.format(UpperCAmelCase  ,    str(metrics[key]     )     )     )





  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase  ,    UpperCAmelCase     )-> Tuple:
     rank_zero_info('''***** Test results *****'''     )
     __A				 =       trainer.callback_metrics
     # Log and save results to file
     __A				 =       os.path.join(pl_module.hparams.output_dir  ,    '''test_results.txt'''     )
     with open(UpperCAmelCase  ,    '''w'''     ) as writer:
        for key in sorted(UpperCAmelCase     ):
           if key not in ["log", "progress_bar"]:
              rank_zero_info('''{} = {}\n'''.format(UpperCAmelCase  ,    str(metrics[key]     )     )     )
              writer.write('''{} = {}\n'''.format(UpperCAmelCase  ,    str(metrics[key]     )     )     )
def 				__UpperCamelCase  (		snake_case    ,	snake_case     )  ->						None:


   '''simple docstring'''







   parser.add_argument(
       '''--output_dir'''    ,	default=snake_case    ,	type=snake_case    ,	required=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=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=snake_case     )
   parser.add_argument('''--max_grad_norm'''    ,	dest='''gradient_clip_val'''    ,	default=1.0    ,	type=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=snake_case    ,	default=1    ,	help='''Number of updates steps to accumulate before performing a backward/update pass.'''    ,	)
   parser.add_argument('''--seed'''    ,	type=snake_case    ,	default=4_2    ,	help='''random seed for initialization'''     )
   parser.add_argument(
       '''--data_dir'''    ,	default=snake_case    ,	type=snake_case    ,	required=snake_case    ,	help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''    ,	)
def 				__UpperCamelCase  (		snake_case    ,	snake_case    ,	snake_case=None    ,	snake_case=True    ,	snake_case=None    ,	snake_case=[]    ,	snake_case=None    ,	snake_case=None    ,	**snake_case    ,	)  ->						str:


   '''simple docstring'''







   pl.seed_everything(args.seed     )

   # init model
   __A				 =       Path(model.hparams.output_dir     )
   odir.mkdir(exist_ok=snake_case     )

   # add custom checkpoints
   if checkpoint_callback is None:
      __A				 =       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(snake_case     )
   if logging_callback is None:
      __A				 =       LoggingCallback()

   __A				 =       {}

   # TODO: remove with PyTorch 1.6 since pl uses native amp
   if args.fpaa:
      __A				 =       1_6
      # train_params["amp_level"] = args.fp16_opt_level

   if args.gpus > 1:
      __A				 =       '''auto'''  # "ddp"
      __A				 =       '''ddp'''

   __A				 =       args.accumulate_grad_batches
   __A				 =       None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
   __A				 =       '''auto'''

   __A				 =       pl.Trainer.from_argparse_args(
       snake_case    ,	weights_summary=snake_case    ,	callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]    ,	logger=snake_case    ,	**snake_case    ,	)

   if args.do_train:
      trainer.fit(snake_case     )

   return trainer








from __future__ import annotations

import sys
def 				__UpperCamelCase  (		snake_case    ,	snake_case     )  ->						tuple[int, int]:


   '''simple docstring'''







   if abs(snake_case     ) == 1:
      return a, 0
   elif abs(snake_case     ) == 1:
      return 0, b

   __A ,						__A				 =       a, b
   __A ,						__A				 =       1, 0
   __A ,						__A				 =       0, 1

   while remainder != 0:
      __A				 =       old_remainder // remainder
      __A ,						__A				 =       remainder, old_remainder - quotient * remainder
      __A ,						__A				 =       coeff_a, old_coeff_a - quotient * coeff_a
      __A ,						__A				 =       coeff_b, old_coeff_b - quotient * coeff_b

   # sign correction for negative numbers
   if a < 0:
      __A				 =       -old_coeff_a
   if b < 0:
      __A				 =       -old_coeff_b

   return old_coeff_a, old_coeff_b
def 				__UpperCamelCase  (		)  ->						Tuple:


   '''simple docstring'''







   if len(sys.argv     ) < 3:
      print('''2 integer arguments required'''     )
      return 1
   __A				 =       int(sys.argv[1]     )
   __A				 =       int(sys.argv[2]     )
   print(extended_euclidean_algorithm(snake_case    ,	snake_case     )     )
   return 0


if __name__ == "__main__":
    raise SystemExit(main())

