



"""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
    lowerCamelCase				:     Any								=  Args()
    lowerCamelCase				:     Optional[int]								=  wandb.init(project="""bigbird-natural-questions""", config=args.__dict__)
    lowerCamelCase				:     int								=  dict(logger.config)
    del wandb_args["batch_size"]
    lowerCamelCase				:     List[Any]								=  replace(args, **wandb_args)
    lowerCamelCase				:     Union[str, Any]								=  args.base_dir + """-""" + wandb.run.id
    lowerCamelCase				:     Optional[Any]								=  replace(args, base_dir=base_dir)
    print(args)

    lowerCamelCase				:     List[str]								=  load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
    lowerCamelCase				:     str								=  load_dataset("""json""", data_files=args.val_data_path)["""train"""]

    # drop extra batch for now
    lowerCamelCase				:     Tuple								=  range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
    lowerCamelCase				:     Dict								=  tr_dataset.shuffle().select(indices)
    lowerCamelCase				:     int								=  range(len(val_dataset) - len(val_dataset) % args.batch_size)
    lowerCamelCase				:     int								=  val_dataset.shuffle().select(indices)

    if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
        lowerCamelCase				:     Optional[int]								=  tr_dataset.shuffle().select(range(8_0_0_0_0))
        lowerCamelCase				:     Any								=  val_dataset.shuffle().select(range(8_0_0_0))

    print(tr_dataset)
    print(val_dataset)

    lowerCamelCase				:     List[Any]								=  FlaxBigBirdForNaturalQuestions.from_pretrained(
        args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
    )
    lowerCamelCase				:     Optional[int]								=  BigBirdTokenizerFast.from_pretrained(args.model_id)
    lowerCamelCase				:     Optional[int]								=  DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_0_9_6)

    lowerCamelCase				:     Optional[Any]								=  {
        """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,
    }
    lowerCamelCase			,						lowerCamelCase				:     int								=  build_tx(**tx_args)

    lowerCamelCase				:     Union[str, Any]								=  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,
    )

    lowerCamelCase				:     Optional[Any]								=  None
    lowerCamelCase				:     Optional[int]								=  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`""")
    lowerCamelCase				:     List[str]								=  jax_utils.unreplicate(state.params)
    model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)







"""simple docstring"""






def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE				)		->							Dict:






       """simple docstring"""




       _UpperCAmelCase			     =  [701, 301, 132, 57, 23, 10, 4, 1]
       for gap in gaps:
              for i in range(SCREAMING_SNAKE_CASE,len(SCREAMING_SNAKE_CASE				)				):
                     _UpperCAmelCase			     =  collection[i]
                     _UpperCAmelCase			     =  i
                     while j >= gap and collection[j - gap] > insert_value:
                            _UpperCAmelCase			     =  collection[j - gap]
                            j -= gap
                     if j != i:
                            _UpperCAmelCase			     =  insert_value
       return collection


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

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






