






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

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

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

 if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
  __snake_case							 =  tr_dataset.shuffle().select(range(8_00_00))
  __snake_case							 =  val_dataset.shuffle().select(range(80_00))

 print(tr_dataset)
 print(val_dataset)

 __snake_case							 =  FlaxBigBirdForNaturalQuestions.from_pretrained(
     args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
 )
 __snake_case							 =  BigBirdTokenizerFast.from_pretrained(args.model_id)
 __snake_case							 =  DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

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

 __snake_case							 =  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,
 )

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













def   _A       (    SCREAMING_SNAKE_CASE__  :     int	):
    UpperCamelCase	:Optional[Any]     							=       9

    while num > 0:
        if num % 10 != digit:
            return False
        num //= 100
        digit -= 1

    return True


def   _A       (    ):
    UpperCamelCase	:List[str]     							=       138902663

    while not is_square_form(num * num	):
        if num % 10 == 3:
            num -= 6  # (3 - 6) % 10 = 7
        else:
            num -= 4  # (7 - 4) % 10 = 3

    return num * 10


if __name__ == "__main__":
 print(f'''{solution() = }''')





