




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

     a_   :	Optional[int]							   = load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
     a_   :	List[Any]							   = load_dataset("""json""", data_files=args.val_data_path)["""train"""]

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

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

     print(tr_dataset)
     print(val_dataset)

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

     a_   :	List[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,
     }
     a_   , a_   :	Tuple							   = build_tx(**tx_args)

     a_   :	Optional[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,
     )

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










'''simple docstring'''




from .hash_table import HashTable


class 						__UpperCamelCase (				lowerCamelCase__       ):






       def __init__(							self,	*lowerCAmelCase,	**lowerCAmelCase						):



              """simple docstring"""







              super().__init__(*lowerCAmelCase,	**lowerCAmelCase						)






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase=None						):



              """simple docstring"""







              lowerCamelCase_							=1
              lowerCamelCase_							=self.hash_function(key + i * i						)

              while self.values[new_key] is not None and self.values[new_key] != key:
                     i += 1
                     lowerCamelCase_							=(
                         self.hash_function(key + i * i						)
                         if not self.balanced_factor() >= self.lim_charge
                         else None
                     )

                     if new_key is None:
                            break

              return new_key




