

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

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

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

 if os.environ.get('''TRAIN_ON_SMALL''', '''false''') == "true":
  UpperCAmelCase     						=			tr_dataset.shuffle().select(range(8_00_00))
  UpperCAmelCase     						=			val_dataset.shuffle().select(range(80_00))

 print(tr_dataset)
 print(val_dataset)

 UpperCAmelCase     						=			FlaxBigBirdForNaturalQuestions.from_pretrained(
     args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
 )
 UpperCAmelCase     						=			BigBirdTokenizerFast.from_pretrained(args.model_id)
 UpperCAmelCase     						=			DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

 UpperCAmelCase     						=			{
     '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,
 }
 UpperCAmelCase     						=			build_tx(**tx_args)

 UpperCAmelCase     						=			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,
 )

 UpperCAmelCase     						=			None
 UpperCAmelCase     						=			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`''')
 UpperCAmelCase     						=			jax_utils.unreplicate(state.params)
 model.save_pretrained(os.path.join(args.base_dir, '''final-weights'''), params=params)




def SCREAMING_SNAKE_CASE			(       lowercase_:		int			):
       if not isinstance(lowercase_						,  lowercase_			):
              _UpperCamelCase       :    Optional[int]					  =					(
                  'operation can not be conducted on a object of type '
                  F"{type(lowercase_			).__name__}"
              )
              raise TypeError(lowercase_			)
       _UpperCamelCase       :    List[Any]					  =					''
       for _ in range(0						,  32			):
              bit_string += str(number % 2			)
              _UpperCamelCase       :    Dict					  =					number >> 1
       return bit_string


def SCREAMING_SNAKE_CASE			(       lowercase_:		int			):
       if number < 0:
              raise ValueError('the value of input must be positive'			)
       elif isinstance(lowercase_						,  lowercase_			):
              raise TypeError('Input value must be a \'int\' type'			)
       elif isinstance(lowercase_						,  lowercase_			):
              raise TypeError('\'<\' not supported between instances of \'str\' and \'int\''			)
       _UpperCamelCase       :    List[Any]					  =					0
       # iterator over [1 to 32],since we are dealing with 32 bit integer
       for _ in range(1						,  33			):
              # left shift the bits by unity
              _UpperCamelCase       :    Tuple					  =					result << 1
              # get the end bit
              _UpperCamelCase       :    List[Any]					  =					number % 2
              # right shift the bits by unity
              _UpperCamelCase       :    Dict					  =					number >> 1
              # add that bit to our ans
              _UpperCamelCase       :    int					  =					result | end_bit
       return get_reverse_bit_string(lowercase_			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()



