





"""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"""



def 	__lowercase  (     snake_case_						:						Union[str, Any]		)     ->Optional[int]:



    '''simple docstring'''







    __A					:		Dict						=  [701, 301, 132, 57, 23, 10, 4, 1]
    for gap in gaps:
        for i in range(snake_case_       ,len(snake_case_		)		):
            __A					:		Tuple						=  collection[i]
            __A					:		Union[str, Any]						=  i
            while j >= gap and collection[j - gap] > insert_value:
                __A					:		Optional[int]						=  collection[j - gap]
                j -= gap
            if j != i:
                __A					:		Optional[int]						=  insert_value
    return collection


if __name__ == "__main__":
       from doctest import testmod

       testmod()
       a_				=    input("""Enter numbers separated by a comma:\n""").strip()
       a_				=    [int(item) for item in user_input.split(""",""")]
       print(shell_sort(unsorted))




