




__magic_name__     :     Dict  							=					{
    """0000""": """0""",
    """0001""": """1""",
    """0010""": """2""",
    """0011""": """3""",
    """0100""": """4""",
    """0101""": """5""",
    """0110""": """6""",
    """0111""": """7""",
    """1000""": """8""",
    """1001""": """9""",
    """1010""": """a""",
    """1011""": """b""",
    """1100""": """c""",
    """1101""": """d""",
    """1110""": """e""",
    """1111""": """f""",
}
def 		a_       (   __lowerCAmelCase							):
  lowerCAmelCase__								=			str(__lowerCAmelCase							).strip()

  # Exceptions
  if not binary_str:
    raise ValueError('''Empty string was passed to the function'''							)
  lowerCAmelCase__								=			binary_str[0] == '''-'''
  lowerCAmelCase__								=			binary_str[1:] if is_negative else binary_str
  if not all(char in '''01''' for char in binary_str							):
    raise ValueError('''Non-binary value was passed to the function'''							)

  lowerCAmelCase__								=			(
      '''0''' * (4 * (divmod(len(__lowerCAmelCase							)      ,   4							)[0] + 1) - len(__lowerCAmelCase							)) + binary_str
  )

  lowerCAmelCase__								=			[]
  for x in range(0      ,   len(__lowerCAmelCase							)      ,   4							):
    hexadecimal.append(BITS_TO_HEX[binary_str[x : x + 4]]							)
  lowerCAmelCase__								=			'''0x''' + ''''''.join(__lowerCAmelCase							)

  return "-" + hexadecimal_str if is_negative else hexadecimal_str


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

      testmod()







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

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

      # drop extra batch for now
      __magic_name__     :     List[str]  							=					range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
      __magic_name__     :     Dict  							=					tr_dataset.shuffle().select(indices)
      __magic_name__     :     Any  							=					range(len(val_dataset) - len(val_dataset) % args.batch_size)
      __magic_name__     :     Dict  							=					val_dataset.shuffle().select(indices)

      if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
            __magic_name__     :     Any  							=					tr_dataset.shuffle().select(range(8_00_00))
            __magic_name__     :     Optional[int]  							=					val_dataset.shuffle().select(range(80_00))

      print(tr_dataset)
      print(val_dataset)

      __magic_name__     :     Union[str, Any]  							=					FlaxBigBirdForNaturalQuestions.from_pretrained(
          args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
      )
      __magic_name__     :     Tuple  							=					BigBirdTokenizerFast.from_pretrained(args.model_id)
      __magic_name__     :     Tuple  							=					DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

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

      __magic_name__     :     List[str]  							=					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,
      )

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

