


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


__snake_case   							=						logging.getLogger(__name__)

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

__snake_case   							=						{
    '''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
__snake_case   							=						{
    '''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
}
__snake_case   							=						sorted(arg_to_scheduler.keys())
__snake_case   							=						'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''



class 	__lowerCamelCase			(pl.LightningModule						):







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

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







       def 		snake_case_	(			self: str,*A_: Dict,**A_: Any					):






            '''simple docstring'''




            __UpperCamelCase =			self.model_type.from_pretrained(*A_,**A_					)







       def 		snake_case_	(			self: str					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




            __UpperCamelCase =			self.model
            __UpperCamelCase =			['bias', 'LayerNorm.weight']
            __UpperCamelCase =			[
                {
                    '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:
                 __UpperCamelCase =			Adafactor(
                     A_,lr=self.hparams.learning_rate,scale_parameter=A_,relative_step=A_					)

            else:
                 __UpperCamelCase =			AdamW(
                     A_,lr=self.hparams.learning_rate,eps=self.hparams.adam_epsilon					)
            __UpperCamelCase =			optimizer

            __UpperCamelCase =			self.get_lr_scheduler()

            return [optimizer], [scheduler]







       def 		snake_case_	(			self: int,A_: int,A_: str					):






            '''simple docstring'''




            return self.validation_step(A_,A_					)







       def 		snake_case_	(			self: Any,A_: Any					):






            '''simple docstring'''




            return self.validation_end(A_					)







       def 		snake_case_	(			self: Optional[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			max(1,self.hparams.gpus					)  # TODO: consider num_tpu_cores
            __UpperCamelCase =			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: List[str],A_: Optional[Any]					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: Optional[Any],A_: str,A_: int,A_: bool = False					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            return self.train_loader







       def 		snake_case_	(			self: Optional[Any]					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: List[str],A_: Dict					):






            '''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 		snake_case_	(			self: Optional[Any],A_: Dict[str, Any]					):






            '''simple docstring'''




            __UpperCamelCase =			self.output_dir.joinpath('best_tfmr'					)
            __UpperCamelCase =			self.step_count
            self.model.save_pretrained(A_					)
            self.tokenizer.save_pretrained(A_					)







       @staticmethod
       def 		snake_case_	(			A_: List[Any],A_: Tuple					):






            '''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=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 	__lowerCamelCase			(pl.Callback						):







       def 		snake_case_	(			self: Optional[int],A_: Tuple,A_: Optional[Any]					):






            '''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 	__lowerCamelCase			(pl.Callback						):







       def 		snake_case_	(			self: Optional[int],A_: Union[str, Any],A_: int					):






            '''simple docstring'''




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



class 	__lowerCamelCase			(pl.Callback						):







       def 		snake_case_	(			self: str,A_: List[str],A_: List[str]					):






            '''simple docstring'''




            __UpperCamelCase =			trainer.lr_schedulers[0]['scheduler']
            __UpperCamelCase =			{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()					)}
            pl_module.logger.log_metrics(A_					)







       def 		snake_case_	(			self: Any,A_: pl.Trainer,A_: pl.LightningModule					):






            '''simple docstring'''




            rank_zero_info('***** Validation results *****'					)
            __UpperCamelCase =			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: Dict,A_: pl.Trainer,A_: pl.LightningModule					):






            '''simple docstring'''




            rank_zero_info('***** Test results *****'					)
            __UpperCamelCase =			trainer.callback_metrics
            # Log and save results to file
            __UpperCamelCase =			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		(		_lowercase					,  _lowercase )					-> None:


     """simple docstring"""


     parser.add_argument(
         '--output_dir'					,  default=str(Path(_lowercase ).parent / 'test_run' / 'model_checkpoints' )					,  type=_lowercase					,  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=_lowercase					,  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=_lowercase )
     parser.add_argument('--max_grad_norm'					,  dest='gradient_clip_val'					,  default=1.0					,  type=_lowercase					,  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=_lowercase					,  default=1					,  help='Number of updates steps to accumulate before performing a backward/update pass.'					,  )
     parser.add_argument('--seed'					,  type=_lowercase					,  default=42					,  help='random seed for initialization' )
     parser.add_argument(
         '--data_dir'					,  default=str(Path(_lowercase ).parent / 'test_run' / 'dummy-train-data' )					,  type=_lowercase					,  help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'					,  )






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase=None					,  _lowercase=True					,  _lowercase=[]					,  _lowercase=None					,  _lowercase=None					,  **_lowercase					,  )					-> List[str]:


     """simple docstring"""


     pl.seed_everything(args.seed )

     # init model
     __UpperCamelCase =			Path(model.hparams.output_dir )
     odir.mkdir(exist_ok=_lowercase )

     # add custom checkpoints
     if checkpoint_callback is None:
          __UpperCamelCase =			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(_lowercase )
     if logging_callback is None:
          __UpperCamelCase =			LoggingCallback()

     __UpperCamelCase =			{}

     if args.fpaa:
          __UpperCamelCase =			16

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

     __UpperCamelCase =			args.accumulate_grad_batches
     __UpperCamelCase =			None
     __UpperCamelCase =			'auto'

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

     if args.do_train:
          trainer.fit(_lowercase )

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






from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
    import datasets



class 	__lowerCamelCase			(_a						):







       @slow
       @require_torch
       def 		snake_case_	(			self: Union[str, Any]					):






            '''simple docstring'''




            __UpperCamelCase =			EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny','prajjwal1/bert-tiny'					)
            __UpperCamelCase =			BertTokenizer.from_pretrained('bert-base-uncased'					)

            __UpperCamelCase =			bertabert.config.encoder.vocab_size
            __UpperCamelCase =			tokenizer.sep_token_id
            __UpperCamelCase =			tokenizer.cls_token_id
            __UpperCamelCase =			128

            __UpperCamelCase =			datasets.load_dataset('cnn_dailymail','3.0.0',split='train[:1%]'					)
            __UpperCamelCase =			datasets.load_dataset('cnn_dailymail','3.0.0',split='validation[:1%]'					)

            __UpperCamelCase =			train_dataset.select(range(32					)					)
            __UpperCamelCase =			val_dataset.select(range(16					)					)

            __UpperCamelCase =			4

            def _map_to_encoder_decoder_inputs(A_: Dict					):
                 # Tokenizer will automatically set [BOS] <text> [EOS]
                 __UpperCamelCase =			tokenizer(batch['article'],padding='max_length',truncation=A_,max_length=512					)
                 __UpperCamelCase =			tokenizer(batch['highlights'],padding='max_length',truncation=A_,max_length=128					)
                 __UpperCamelCase =			inputs.input_ids
                 __UpperCamelCase =			inputs.attention_mask

                 __UpperCamelCase =			outputs.input_ids
                 __UpperCamelCase =			outputs.input_ids.copy()
                 __UpperCamelCase =			[
                     [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
                 ]
                 __UpperCamelCase =			outputs.attention_mask

                 assert all(len(A_					) == 512 for x in inputs.input_ids					)
                 assert all(len(A_					) == 128 for x in outputs.input_ids					)

                 return batch

            def _compute_metrics(A_: str					):
                 __UpperCamelCase =			pred.label_ids
                 __UpperCamelCase =			pred.predictions

                 # all unnecessary tokens are removed
                 __UpperCamelCase =			tokenizer.batch_decode(A_,skip_special_tokens=A_					)
                 __UpperCamelCase =			tokenizer.batch_decode(A_,skip_special_tokens=A_					)

                 __UpperCamelCase =			sum([int(pred_str[i] == label_str[i]					) for i in range(len(A_					)					)]					) / len(A_					)

                 return {"accuracy": accuracy}

            # map train dataset
            __UpperCamelCase =			train_dataset.map(
                _map_to_encoder_decoder_inputs,batched=A_,batch_size=A_,remove_columns=['article', 'highlights'],)
            train_dataset.set_format(
                type='torch',columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels'],)

            # same for validation dataset
            __UpperCamelCase =			val_dataset.map(
                _map_to_encoder_decoder_inputs,batched=A_,batch_size=A_,remove_columns=['article', 'highlights'],)
            val_dataset.set_format(
                type='torch',columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels'],)

            __UpperCamelCase =			self.get_auto_remove_tmp_dir()

            __UpperCamelCase =			SeqaSeqTrainingArguments(
                output_dir=A_,per_device_train_batch_size=A_,per_device_eval_batch_size=A_,predict_with_generate=A_,evaluation_strategy='steps',do_train=A_,do_eval=A_,warmup_steps=0,eval_steps=2,logging_steps=2,)

            # instantiate trainer
            __UpperCamelCase =			SeqaSeqTrainer(
                model=A_,args=A_,compute_metrics=_compute_metrics,train_dataset=A_,eval_dataset=A_,tokenizer=A_,)

            # start training
            trainer.train()


