


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

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

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

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

					print(tr_dataset)
					print(val_dataset)

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

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

					lowercase  		=			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,
					)

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











'''simple docstring'''
import os
def 							snake_case_    (  ):



    """simple docstring"""




    __lowercase     							=		os.path.dirname(os.path.realpath(a__     )     )
    __lowercase     							=		os.path.join(a__       ,"""triangle.txt"""     )

    with open(a__     ) as f:
        __lowercase     							=		f.readlines()

    __lowercase     							=		[[int(a__     ) for y in x.rstrip("""\r\n"""     ).split(""" """     )] for x in triangle]

    for i in range(1       ,len(a__     )     ):
        for j in range(len(a[i]     )     ):
            __lowercase     							=		a[i - 1][j] if j != len(a[i - 1]     ) else 0
            __lowercase     							=		a[i - 1][j - 1] if j > 0 else 0
            a[i][j] += max(a__       ,a__     )
    return max(a[-1]     )


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




