





"""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 						lowerCAmelCase       (   pl.LightningModule				):






       def __init__( self     ,	a__     ,	a__=None     ,	a__="base"     ,	a__=None     ,	a__=None     ,	a__=None     ,	**a__     ,	):
              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 				__A			( self     ,	*a__     ,	**a__  ):
              _UpperCAmelCase			     =  self.model_type.from_pretrained(*a__     ,	**a__  )






       def 				__A			( self  ):
              _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 				__A			( self  ):
              _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  )],
                      '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 				__A			( self     ,	a__     ,	a__  ):
              return self.validation_step(a__     ,	a__  )






       def 				__A			( self     ,	a__  ):
              return self.validation_end(a__  )






       def 				__A			( self  ):
              _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 				__A			( self     ,	a__  ):
              if mode == "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 				__A			( self     ,	a__     ,	a__     ,	a__ = False  ):
              raise NotImplementedError('You must implement this for your task'  )






       def 				__A			( self  ):
              return self.train_loader






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






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






       def 				__A			( self     ,	a__  ):
              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 				__A			( self     ,	a__  ):
              _UpperCAmelCase			     =  self.output_dir.joinpath('best_tfmr'  )
              _UpperCAmelCase			     =  self.step_count
              self.model.save_pretrained(a__  )
              self.tokenizer.save_pretrained(a__  )






       @staticmethod
       def 				__A			( a__     ,	a__  ):
              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=''     ,	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 				__A			( self     ,	a__     ,	a__  ):
              _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 				__A			( self     ,	a__     ,	a__  ):
              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 				__A			( self     ,	a__     ,	a__  ):
              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    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							None:






       """simple docstring"""




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


def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE=None,SCREAMING_SNAKE_CASE=True,SCREAMING_SNAKE_CASE=[],SCREAMING_SNAKE_CASE=None,SCREAMING_SNAKE_CASE=None,**SCREAMING_SNAKE_CASE,)		->							Optional[Any]:






       """simple docstring"""




       pl.seed_everything(args.seed				)

       # init model
       _UpperCAmelCase			     =  Path(model.hparams.output_dir				)
       odir.mkdir(exist_ok=SCREAMING_SNAKE_CASE				)

       # 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(SCREAMING_SNAKE_CASE				)
       if logging_callback is None:
              _UpperCAmelCase			     =  LoggingCallback()

       _UpperCAmelCase			     =  {}

       # TODO: remove with PyTorch 1.6 since pl uses native amp
       if args.fpaa:
              _UpperCAmelCase			     =  16
              _UpperCAmelCase			     =  args.fpaa_opt_level

       if args.gpus > 1:
              _UpperCAmelCase			     =  'ddp'

       _UpperCAmelCase			     =  args.accumulate_grad_batches
       _UpperCAmelCase			     =  extra_train_kwargs.get('accelerator',SCREAMING_SNAKE_CASE				)
       _UpperCAmelCase			     =  extra_train_kwargs.get('profiler',SCREAMING_SNAKE_CASE				)

       _UpperCAmelCase			     =  pl.Trainer.from_argparse_args(
           SCREAMING_SNAKE_CASE,weights_summary=SCREAMING_SNAKE_CASE,callbacks=[logging_callback] + extra_callbacks,logger=SCREAMING_SNAKE_CASE,checkpoint_callback=SCREAMING_SNAKE_CASE,**SCREAMING_SNAKE_CASE,)

       if args.do_train:
              trainer.fit(SCREAMING_SNAKE_CASE				)

       return trainer













"""simple docstring"""






import os
from dataclasses import replace

import jax
import wandb
from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
from datasets import load_dataset
from flax import jax_utils

from transformers import BigBirdTokenizerFast


if __name__ == "__main__":
    print('''#################### AVAILABLE DEVICES ####################''')
    print(jax.devices())
    print('''###########################################################''')

    # setup for wandb sweep
    lowerCAmelCase_      =				Args()
    lowerCAmelCase_      =				wandb.init(project='''bigbird-natural-questions''', config=args.__dict__)
    lowerCAmelCase_      =				dict(logger.config)
    del wandb_args["batch_size"]
    lowerCAmelCase_      =				replace(args, **wandb_args)
    lowerCAmelCase_      =				args.base_dir + '''-''' + wandb.run.id
    lowerCAmelCase_      =				replace(args, base_dir=base_dir)
    print(args)

    lowerCAmelCase_      =				load_dataset('''json''', data_files=args.tr_data_path)['''train''']
    lowerCAmelCase_      =				load_dataset('''json''', data_files=args.val_data_path)['''train''']

    # drop extra batch for now
    lowerCAmelCase_      =				range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
    lowerCAmelCase_      =				tr_dataset.shuffle().select(indices)
    lowerCAmelCase_      =				range(len(val_dataset) - len(val_dataset) % args.batch_size)
    lowerCAmelCase_      =				val_dataset.shuffle().select(indices)

    if os.environ.get('''TRAIN_ON_SMALL''', '''false''') == "true":
        lowerCAmelCase_      =				tr_dataset.shuffle().select(range(80_000))
        lowerCAmelCase_      =				val_dataset.shuffle().select(range(8_000))

    print(tr_dataset)
    print(val_dataset)

    lowerCAmelCase_      =				FlaxBigBirdForNaturalQuestions.from_pretrained(
        args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
    )
    lowerCAmelCase_      =				BigBirdTokenizerFast.from_pretrained(args.model_id)
    lowerCAmelCase_      =				DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_096)

    lowerCAmelCase_      =				{
        '''lr''': args.lr,
        '''init_lr''': args.init_lr,
        '''warmup_steps''': args.warmup_steps,
        '''num_train_steps''': args.max_epochs * (len(tr_dataset) // args.batch_size),
        '''weight_decay''': args.weight_decay,
    }
    lowerCAmelCase_      ,					lowerCAmelCase_      =				build_tx(**tx_args)

    lowerCAmelCase_      =				Trainer(
        args=args,
        data_collator=data_collator,
        model_save_fn=model.save_pretrained,
        train_step_fn=train_step,
        val_step_fn=val_step,
        logger=logger,
        scheduler_fn=lr,
    )

    lowerCAmelCase_      =				None
    lowerCAmelCase_      =				trainer.create_state(model, tx, num_train_steps=tx_args['''num_train_steps'''], ckpt_dir=ckpt_dir)
    try:
        trainer.train(state, tr_dataset, val_dataset)
    except KeyboardInterrupt:
        print('''Oooops; TRAINING STOPPED UNFORTUNATELY''')

    print('''SAVING WEIGHTS IN `final-weights`''')
    lowerCAmelCase_      =				jax_utils.unreplicate(state.params)
    model.save_pretrained(os.path.join(args.base_dir, '''final-weights'''), params=params)






