





"""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		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__="base"		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=None		,**SCREAMING_SNAKE_CASE__		,)      ->				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(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :Dict		    =  0
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  Path(self.hparams.output_dir							)
             __SCREAMING_SNAKE_CASE       :List[str]		    =  self.hparams.cache_dir if self.hparams.cache_dir else None
             if config is None:
                   __SCREAMING_SNAKE_CASE       :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=SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__		,)
             else:
                   __SCREAMING_SNAKE_CASE       :PretrainedConfig		    =  config

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

             if tokenizer is None:
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  AutoTokenizer.from_pretrained(
                       self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path		,cache_dir=SCREAMING_SNAKE_CASE__		,)
             else:
                   __SCREAMING_SNAKE_CASE       :PreTrainedTokenizer		    =  tokenizer
             __SCREAMING_SNAKE_CASE       :Dict		    =  MODEL_MODES[mode]
             if model is None:
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  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=SCREAMING_SNAKE_CASE__		,)
             else:
                   __SCREAMING_SNAKE_CASE       :int		    =  model




       def    _UpperCamelCase					(						self		,*SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)      ->				List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.model_type.from_pretrained(*SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self							)      ->				Any:





             """simple docstring"""






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




       def    _UpperCamelCase					(						self							)      ->				Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  self.model
             __SCREAMING_SNAKE_CASE       :Dict		    =  ['''bias''', '''LayerNorm.weight''']
             __SCREAMING_SNAKE_CASE       :int		    =  [
                 {
                     '''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:
                   __SCREAMING_SNAKE_CASE       :Any		    =  Adafactor(
                       SCREAMING_SNAKE_CASE__		,lr=self.hparams.learning_rate		,scale_parameter=SCREAMING_SNAKE_CASE__		,relative_step=SCREAMING_SNAKE_CASE__							)

             else:
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  AdamW(
                       SCREAMING_SNAKE_CASE__		,lr=self.hparams.learning_rate		,eps=self.hparams.adam_epsilon							)
             __SCREAMING_SNAKE_CASE       :Tuple		    =  optimizer

             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.get_lr_scheduler()

             return [optimizer], [scheduler]




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





             """simple docstring"""






             return self.validation_step(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				str:





             """simple docstring"""






             return self.validation_end(SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  max(1		,self.hparams.gpus							)  # TODO: consider num_tpu_cores
             __SCREAMING_SNAKE_CASE       :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    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				str:





             """simple docstring"""






             if stage == "test":
                   __SCREAMING_SNAKE_CASE       :Dict		    =  len(self.test_dataloader().dataset							)
             else:
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  self.get_dataloader('''train'''		,self.hparams.train_batch_size		,shuffle=SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE       :Any		    =  len(self.train_dataloader().dataset							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = False							)      ->				Tuple:





             """simple docstring"""






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




       def    _UpperCamelCase					(						self							)      ->				Any:





             """simple docstring"""






             return self.train_loader




       def    _UpperCamelCase					(						self							)      ->				Any:





             """simple docstring"""






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




       def    _UpperCamelCase					(						self							)      ->				Tuple:





             """simple docstring"""






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




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				Optional[int]:





             """simple docstring"""






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




       @pl.utilities.rank_zero_only
       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				None:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[str]		    =  self.output_dir.joinpath('''best_tfmr'''							)
             __SCREAMING_SNAKE_CASE       :List[str]		    =  self.step_count
             self.model.save_pretrained(SCREAMING_SNAKE_CASE__							)
             self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__							)




       @staticmethod
       def    _UpperCamelCase					(						SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Optional[int]:





             """simple docstring"""






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






class  _SCREAMING_SNAKE_CASE(       pl.Callback  ):




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Dict:





             """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  _SCREAMING_SNAKE_CASE(       pl.Callback  ):




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Any:





             """simple docstring"""






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






class  _SCREAMING_SNAKE_CASE(       pl.Callback  ):




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Dict		    =  trainer.lr_schedulers[0]['''scheduler''']
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  {f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()							)}
             pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE__							)




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





             """simple docstring"""






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




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Optional[Any]:





             """simple docstring"""






             rank_zero_info('''***** Test results *****'''							)
             __SCREAMING_SNAKE_CASE       :Any		    =  trainer.callback_metrics
             # Log and save results to file
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  os.path.join(pl_module.hparams.output_dir		,'''test_results.txt'''							)
             with open(SCREAMING_SNAKE_CASE__		,'''w'''							) as writer:
                   for key in sorted(SCREAMING_SNAKE_CASE__							):
                         if key not in ["log", "progress_bar"]:
                               rank_zero_info('''{} = {}\n'''.format(SCREAMING_SNAKE_CASE__		,str(metrics[key]							)							)							)
                               writer.write('''{} = {}\n'''.format(SCREAMING_SNAKE_CASE__		,str(metrics[key]							)							)							)




def 					__lowerCamelCase       (							a_					:		Union[str, Any]			,       a_					:		List[Any]							)      ->				None:
      #  To allow all pl args uncomment the following line
      #  parser = pl.Trainer.add_argparse_args(parser)
      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 					__lowerCamelCase       (							a_					:		BaseTransformer			,       a_					:		argparse.Namespace			,       a_					:		Union[str, Any]=None			,       a_					:		Tuple=True			,       a_					:		Optional[int]=[]			,       a_					:		Any=None			,       a_					:		List[Any]=None			,       **a_					:		Optional[int]			,       )      ->				List[str]:
      pl.seed_everything(args.seed							)

      # init model
      __SCREAMING_SNAKE_CASE       :int		    =  Path(model.hparams.output_dir							)
      odir.mkdir(exist_ok=a_							)

      # add custom checkpoints
      if checkpoint_callback is None:
            __SCREAMING_SNAKE_CASE       :List[str]		    =  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:
            __SCREAMING_SNAKE_CASE       :Any		    =  LoggingCallback()

      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  {}

      if args.fpaa:
            __SCREAMING_SNAKE_CASE       :int		    =  16

      if args.gpus > 1:
            __SCREAMING_SNAKE_CASE       :Tuple		    =  '''auto'''
            __SCREAMING_SNAKE_CASE       :List[str]		    =  '''ddp'''

      __SCREAMING_SNAKE_CASE       :Any		    =  args.accumulate_grad_batches
      __SCREAMING_SNAKE_CASE       :Dict		    =  None
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  '''auto'''

      __SCREAMING_SNAKE_CASE       :List[Any]		    =  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"""
from torch import nn






class  _SCREAMING_SNAKE_CASE(       nn.Module  ):




       def __init__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				List[str]:





             """simple docstring"""






             super().__init__()
             __SCREAMING_SNAKE_CASE       :Tuple		    =  class_size
             __SCREAMING_SNAKE_CASE       :str		    =  embed_size
             # self.mlp1 = nn.Linear(embed_size, embed_size)
             # self.mlp2 = (nn.Linear(embed_size, class_size))
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  nn.Linear(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				Optional[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.mlp(SCREAMING_SNAKE_CASE__							)
             return logits