






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

  A__       :			Dict=						load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
  A__       :			Tuple=						load_dataset("""json""", data_files=args.val_data_path)["""train"""]

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

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

  print(tr_dataset)
  print(val_dataset)

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

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









from __future__ import annotations

from random import randrange
def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						list					)->list:
      if len(_SCREAMING_SNAKE_CASE					) < 2:
            return collection
      _lowerCAmelCase			      =	randrange(len(_SCREAMING_SNAKE_CASE					)					)  # Use random element as pivot
      _lowerCAmelCase			      =	collection[pivot_index]
      _lowerCAmelCase			      =	[]  # All elements greater than pivot
      _lowerCAmelCase			      =	[]  # All elements less than or equal to pivot

      for element in collection[:pivot_index]:
            (greater if element > pivot else lesser).append(_SCREAMING_SNAKE_CASE					)

      for element in collection[pivot_index + 1 :]:
            (greater if element > pivot else lesser).append(_SCREAMING_SNAKE_CASE					)

      return [*quick_sort(_SCREAMING_SNAKE_CASE					), pivot, *quick_sort(_SCREAMING_SNAKE_CASE					)]


if __name__ == "__main__":
    UpperCAmelCase_          =				input("Enter numbers separated by a comma:\n").strip()
    UpperCAmelCase_          =				[int(item) for item in user_input.split(",")]
    print(quick_sort(unsorted))