

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 						__magic_name__			(							pl.LightningModule							):

   '''simple docstring'''




   def __init__(				self,    lowercase_,    lowercase_=None,    lowercase_="base",    lowercase_=None,    lowercase_=None,    lowercase_=None,    **lowercase_,    )							->    Tuple:






     """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(lowercase_       )
     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=lowercase_,    **lowercase_,    )
     else:
       a__  =config

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

     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=lowercase_,    )
     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=lowercase_,    )
     else:
       a__  =model



   def  _UpperCAmelCase (				self,    *lowercase_,    **lowercase_       )							->    List[str]:






     """simple docstring"""






     a__  =self.model_type.from_pretrained(*lowercase_,    **lowercase_       )



   def  _UpperCAmelCase (				self       )							->    Union[str, Any]:






     """simple docstring"""






     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  _UpperCAmelCase (				self       )							->    Optional[int]:






     """simple docstring"""






     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       )
             ],  # 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:
       a__  =Adafactor(
           lowercase_,    lr=self.hparams.learning_rate,    scale_parameter=lowercase_,    relative_step=lowercase_       )

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

     a__  =self.get_lr_scheduler()

     return [optimizer], [scheduler]



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_       )							->    int:






     """simple docstring"""






     return self.validation_step(lowercase_,    lowercase_       )



   def  _UpperCAmelCase (				self,    lowercase_       )							->    Union[str, Any]:






     """simple docstring"""






     return self.validation_end(lowercase_       )



   def  _UpperCAmelCase (				self       )							->    int:






     """simple docstring"""






     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  _UpperCAmelCase (				self,    lowercase_       )							->    Union[str, Any]:






     """simple docstring"""






     if stage == "test":
       a__  =len(self.test_dataloader().dataset       )
     else:
       a__  =self.get_dataloader('''train''',    self.hparams.train_batch_size,    shuffle=lowercase_       )
       a__  =len(self.train_dataloader().dataset       )



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_,    lowercase_ = False       )							->    int:






     """simple docstring"""






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



   def  _UpperCAmelCase (				self       )							->    str:






     """simple docstring"""






     return self.train_loader



   def  _UpperCAmelCase (				self       )							->    Optional[Any]:






     """simple docstring"""






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



   def  _UpperCAmelCase (				self       )							->    Tuple:






     """simple docstring"""






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



   def  _UpperCAmelCase (				self,    lowercase_       )							->    Any:






     """simple docstring"""






     return os.path.join(
         self.hparams.data_dir,    '''cached_{}_{}_{}'''.format(
             lowercase_,    list(filter(lowercase_,    self.hparams.model_name_or_path.split('''/'''       )       )       ).pop(),    str(self.hparams.max_seq_length       ),    ),    )



   @pl.utilities.rank_zero_only
   def  _UpperCAmelCase (				self,    lowercase_       )							->    None:






     """simple docstring"""






     a__  =self.output_dir.joinpath('''best_tfmr'''       )
     a__  =self.step_count
     self.model.save_pretrained(lowercase_       )
     self.tokenizer.save_pretrained(lowercase_       )



   @staticmethod
   def  _UpperCAmelCase (				lowercase_,    lowercase_       )							->    Any:






     """simple docstring"""






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







class 						__magic_name__			(							pl.Callback							):

   '''simple docstring'''




   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_       )							->    Tuple:






     """simple docstring"""






     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 						__magic_name__			(							pl.Callback							):

   '''simple docstring'''




   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_       )							->    Tuple:






     """simple docstring"""






     for name, param in pl_module.model.rag.named_parameters():
       if param.grad is None:
         print(lowercase_       )







class 						__magic_name__			(							pl.Callback							):

   '''simple docstring'''




   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_       )							->    Dict:






     """simple docstring"""






     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(lowercase_       )



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_       )							->    str:






     """simple docstring"""






     rank_zero_info('''***** Validation results *****'''       )
     a__  =trainer.callback_metrics
     # Log results
     for key in sorted(lowercase_       ):
       if key not in ["log", "progress_bar"]:
         rank_zero_info('''{} = {}\n'''.format(lowercase_,    str(metrics[key]       )       )       )



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_       )							->    List[str]:






     """simple docstring"""






     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(lowercase_,    '''w'''       ) as writer:
       for key in sorted(lowercase_       ):
         if key not in ["log", "progress_bar"]:
           rank_zero_info('''{} = {}\n'''.format(lowercase_,    str(metrics[key]       )       )       )
           writer.write('''{} = {}\n'''.format(lowercase_,    str(metrics[key]       )       )       )





def 			UpperCAmelCase__				(			_A      : int	,      _A      : Tuple							):





  '''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      : BaseTransformer	,      _A      : argparse.Namespace	,      _A      : List[Any]=None	,      _A      : str=True	,      _A      : str=[]	,      _A      : str=None	,      _A      : Optional[Any]=None	,      **_A      : List[str]	,      ):





  '''simple docstring'''







  pl.seed_everything(args.seed							)

  # init model
  a__  =Path(model.hparams.output_dir							)
  odir.mkdir(exist_ok=_A							)

  # 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(_A							)
  if logging_callback is None:
    a__  =LoggingCallback()

  a__  ={}

  if args.fpaa:
    a__  =16

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

  a__  =args.accumulate_grad_batches
  a__  =None
  a__  ='''auto'''

  a__  =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



def 			UpperCAmelCase__				(			):





  '''simple docstring'''







  return [
      a * b * (10_00 - a - b)
      for a in range(1	,      9_99							)
      for b in range(_A	,      9_99							)
      if (a * a + b * b == (10_00 - a - b) ** 2)
  ][0]


if __name__ == "__main__":
       print(f"""{solution() = }""")
