





"""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
       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(80000))
              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_				,    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)











"""simple docstring"""



from __future__ import annotations

import sys







def 	__lowercase  (     snake_case_						:						int       ,snake_case_						:						int		)     ->tuple[int, int]:



    '''simple docstring'''







    if abs(snake_case_		) == 1:
        return a, 0
    elif abs(snake_case_		) == 1:
        return 0, b

    __A				, __A					:		int						=  a, b
    __A				, __A					:		Any						=  1, 0
    __A				, __A					:		int						=  0, 1

    while remainder != 0:
        __A					:		List[Any]						=  old_remainder // remainder
        __A				, __A					:		int						=  remainder, old_remainder - quotient * remainder
        __A				, __A					:		List[Any]						=  coeff_a, old_coeff_a - quotient * coeff_a
        __A				, __A					:		Union[str, Any]						=  coeff_b, old_coeff_b - quotient * coeff_b

    # sign correction for negative numbers
    if a < 0:
        __A					:		Optional[Any]						=  -old_coeff_a
    if b < 0:
        __A					:		List[Any]						=  -old_coeff_b

    return old_coeff_a, old_coeff_b







def 	__lowercase  (     )     ->List[str]:



    '''simple docstring'''







    if len(sys.argv		) < 3:
        print('''2 integer arguments required'''		)
        return 1
    __A					:		Optional[int]						=  int(sys.argv[1]		)
    __A					:		Tuple						=  int(sys.argv[2]		)
    print(extended_euclidean_algorithm(snake_case_       ,snake_case_		)		)
    return 0


if __name__ == "__main__":
       raise SystemExit(main())




