




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

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

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

 if os.environ.get('''TRAIN_ON_SMALL''', '''false''') == "true":
  a__		    =    tr_dataset.shuffle().select(range(8_0000))
  a__		    =    val_dataset.shuffle().select(range(8000))

 print(tr_dataset)
 print(val_dataset)

 a__		    =    FlaxBigBirdForNaturalQuestions.from_pretrained(
     args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
 )
 a__		    =    BigBirdTokenizerFast.from_pretrained(args.model_id)
 a__		    =    DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

 a__		    =    {
     """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,
 }
 a__		    =    build_tx(**tx_args)

 a__		    =    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,
 )

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












import os





def 							A__   ()    ->	int:
					__UpperCamelCase				:  int								=			os.path.dirname(os.path.realpath(snake_case							)							)
					__UpperCamelCase				:  List[str]								=			os.path.join(snake_case		,  """triangle.txt"""							)

					with open(snake_case							) as in_file:
										__UpperCamelCase				:  Optional[Any]								=			[[int(snake_case							) for i in line.split()] for line in in_file]

					while len(snake_case							) != 1:
										__UpperCamelCase				:  Union[str, Any]								=			triangle.pop()
										__UpperCamelCase				:  List[str]								=			triangle[-1]
										for j in range(len(snake_case							) - 1							):
															curr_row[j] += max(last_row[j]		,  last_row[j + 1]							)
					return triangle[0][0]


if __name__ == "__main__":
	print(solution())






