




"""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_00_00))
														_lowercase						   =     val_dataset.shuffle().select(range(80_00))

							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=40_96)

							_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   ,_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"""





_lowercase						   =     [0.4_85, 0.4_56, 0.4_06]
_lowercase						   =     [0.2_29, 0.2_24, 0.2_25]
_lowercase						   =     [0.5, 0.5, 0.5]
_lowercase						   =     [0.5, 0.5, 0.5]
_lowercase						   =     [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
_lowercase						   =     [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]


