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

 lowerCamelCase_				:							Optional[Any]       =    load_dataset("json", data_files=args.tr_data_path)["""train"""]
 lowerCamelCase_				:							Union[str, Any]       =    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_				:							Tuple       =    tr_dataset.shuffle().select(indices)
 lowerCamelCase_				:							Optional[int]       =    range(len(val_dataset) - len(val_dataset) % args.batch_size)
 lowerCamelCase_				:							str       =    val_dataset.shuffle().select(indices)

 if os.environ.get("TRAIN_ON_SMALL", "false") == "true":
  lowerCamelCase_				:							Optional[int]       =    tr_dataset.shuffle().select(range(80_000))
  lowerCamelCase_				:							Any       =    val_dataset.shuffle().select(range(8_000))

 print(tr_dataset)
 print(val_dataset)

 lowerCamelCase_				:							Any       =    FlaxBigBirdForNaturalQuestions.from_pretrained(
     args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
 )
 lowerCamelCase_				:							int       =    BigBirdTokenizerFast.from_pretrained(args.model_id)
 lowerCamelCase_				:							Union[str, Any]       =    DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_096)

 lowerCamelCase_				:							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,
 }
 lowerCamelCase_				:							str       =    build_tx(**tx_args)

 lowerCamelCase_				:							List[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_				:							Dict       =    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_				:							Any       =    jax_utils.unreplicate(state.params)
 model.save_pretrained(os.path.join(args.base_dir, "final-weights"), params=params)





"""simple docstring"""







import unittest
from timeit import timeit

def 							UpperCamelCase_       (       lowerCAmelCase__ :							int       ,  lowerCAmelCase__ :							int		)		->  int:

  """simple docstring"""




  lowerCAmelCase_  :							Any								=					first_num if first_num >= second_num else second_num
  lowerCAmelCase_  :							Union[str, Any]								=					max_num
  while (common_mult % first_num > 0) or (common_mult % second_num > 0):
    common_mult += max_num
  return common_mult

def 							UpperCamelCase_       (       lowerCAmelCase__ :							int       ,  lowerCAmelCase__ :							int		)		->  int:

  """simple docstring"""




  return b if a == 0 else greatest_common_divisor(b % a       ,  lowerCAmelCase__		)

def 							UpperCamelCase_       (       lowerCAmelCase__ :							int       ,  lowerCAmelCase__ :							int		)		->  int:

  """simple docstring"""




  return first_num // greatest_common_divisor(lowerCAmelCase__       ,  lowerCAmelCase__		) * second_num

def 							UpperCamelCase_       (       )		->  Dict:

  """simple docstring"""




  lowerCAmelCase_  :							str								=					(
      'from __main__ import least_common_multiple_slow, least_common_multiple_fast'
  )
  print(
      'least_common_multiple_slow():'       ,  timeit('least_common_multiple_slow(1000, 999)'       ,  setup=lowerCAmelCase__		)       ,  )
  print(
      'least_common_multiple_fast():'       ,  timeit('least_common_multiple_fast(1000, 999)'       ,  setup=lowerCAmelCase__		)       ,  )







class  UpperCamelCase__  (      unittest.TestCase    ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE    							=	(
          (10, 20),
          (13, 15),
          (4, 31),
          (10, 42),
          (43, 34),
          (5, 12),
          (12, 25),
          (10, 25),
          (6, 9),
      )
      _SCREAMING_SNAKE_CASE    							=	(20, 195, 124, 210, 1462, 60, 300, 50, 18)



      def 	SCREAMING_SNAKE_CASE__					( self							: List[str]			):
        for i, (first_num, second_num) in enumerate(self.test_inputs			):
          lowerCAmelCase_  :							Union[str, Any]								=					least_common_multiple_slow(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			)
          lowerCAmelCase_  :							Any								=					least_common_multiple_fast(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			)
          with self.subTest(i=SCREAMING_SNAKE_CASE_			):
            self.assertEqual(SCREAMING_SNAKE_CASE_       ,     self.expected_results[i]			)
            self.assertEqual(SCREAMING_SNAKE_CASE_       ,     self.expected_results[i]			)


if __name__ == "__main__":
     benchmark()
     unittest.main()





