





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

       snake_case__      :							Any									=  load_dataset('json', data_files=args.tr_data_path)['train']
       snake_case__      :							Optional[Any]									=  load_dataset('json', data_files=args.val_data_path)['train']

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

       if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
              snake_case__      :							List[Any]									=  tr_dataset.shuffle().select(range(80000))
              snake_case__      :							int									=  val_dataset.shuffle().select(range(8000))

       print(tr_dataset)
       print(val_dataset)

       snake_case__      :							int									=  FlaxBigBirdForNaturalQuestions.from_pretrained(
           args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
       )
       snake_case__      :							Optional[int]									=  BigBirdTokenizerFast.from_pretrained(args.model_id)
       snake_case__      :							Any									=  DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

       snake_case__      :							List[str]									=  {
           '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__      :							List[Any]									=  build_tx(**tx_args)

       snake_case__      :							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,
       )

       snake_case__      :							Optional[Any]									=  None
       snake_case__      :							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`')
       snake_case__      :							Union[str, Any]									=  jax_utils.unreplicate(state.params)
       model.save_pretrained(os.path.join(args.base_dir, 'final-weights'), params=params)












import unittest
from timeit import timeit



def _a       (			lowerCamelCase:     int      ,       lowerCamelCase:     int				)			->		int:







      '''simple docstring'''






      __A      =       first_num if first_num >= second_num else second_num
      __A      =       max_num
      while (common_mult % first_num > 0) or (common_mult % second_num > 0):
            common_mult += max_num
      return common_mult



def _a       (			lowerCamelCase:     int      ,       lowerCamelCase:     int				)			->		int:







      '''simple docstring'''






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



def _a       (			lowerCamelCase:     int      ,       lowerCamelCase:     int				)			->		int:







      '''simple docstring'''






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



def _a       (			)			->		Tuple:







      '''simple docstring'''






      __A      =       (
          '''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 A_      (    unittest.TestCase     ):
   lowerCAmelCase__       =				(
       (10, 20),
       (13, 15),
       (4, 31),
       (10, 42),
       (43, 34),
       (5, 12),
       (12, 25),
       (10, 25),
       (6, 9),
   )
   lowerCAmelCase__       =				(20, 195, 124, 210, 1462, 60, 300, 50, 18)




   def _lowerCAmelCase   (self			:List[str]				)->      Union[str, Any]:
         for i, (first_num, second_num) in enumerate(self.test_inputs				):
               __A      =       least_common_multiple_slow(_UpperCamelCase					,     _UpperCamelCase				)
               __A      =       least_common_multiple_fast(_UpperCamelCase					,     _UpperCamelCase				)
               with self.subTest(i=_UpperCamelCase				):
                     self.assertEqual(_UpperCamelCase					,     self.expected_results[i]				)
                     self.assertEqual(_UpperCamelCase					,     self.expected_results[i]				)


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





