code: 
  
  
  
  
  
  '''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      :				int											=					Args()
  	_a      :				Dict											=					wandb.init(project="""bigbird-natural-questions""", config=args.__dict__)
  	_a      :				Optional[int]											=					dict(logger.config)
  	del wandb_args["batch_size"]
  	_a      :				List[Any]											=					replace(args, **wandb_args)
  	_a      :				int											=					args.base_dir + """-""" + wandb.run.id
  	_a      :				Optional[int]											=					replace(args, base_dir=base_dir)
  	print(args)
  
  	_a      :				Tuple											=					load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
  	_a      :				List[str]											=					load_dataset("""json""", data_files=args.val_data_path)["""train"""]
  
  	# drop extra batch for now
  	_a      :				List[str]											=					range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
  	_a      :				str											=					tr_dataset.shuffle().select(indices)
  	_a      :				Optional[int]											=					range(len(val_dataset) - len(val_dataset) % args.batch_size)
  	_a      :				Any											=					val_dataset.shuffle().select(indices)
  
  	if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
  		_a      :				Tuple											=					tr_dataset.shuffle().select(range(8_0_0_0_0))
  		_a      :				str											=					val_dataset.shuffle().select(range(8_0_0_0))
  
  	print(tr_dataset)
  	print(val_dataset)
  
  	_a      :				Optional[Any]											=					FlaxBigBirdForNaturalQuestions.from_pretrained(
  	    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
  	)
  	_a      :				Optional[int]											=					BigBirdTokenizerFast.from_pretrained(args.model_id)
  	_a      :				Tuple											=					DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_0_9_6)
  
  	_a      :				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,
  	}
  	_a					,_a      :				int											=					build_tx(**tx_args)
  
  	_a      :				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      :				str											=					None
  	_a      :				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`""")
  	_a      :				List[Any]											=					jax_utils.unreplicate(state.params)
  	model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase = 1000			)      ->      int:
  			__lowerCAmelCase      		=       []
  			__lowerCAmelCase      		=       []
  			__lowerCAmelCase      		=       3
  			__lowerCAmelCase      		=       5
  			__lowerCAmelCase      		=       1
  			while True:
  						__lowerCAmelCase      		=       z * temp
  						if result < n:
  									zmulti.append(lowercase			)
  									temp += 1
  						else:
  									__lowerCAmelCase      		=       1
  									break
  			while True:
  						__lowerCAmelCase      		=       x * temp
  						if result < n:
  									xmulti.append(lowercase			)
  									temp += 1
  						else:
  									break
  			__lowerCAmelCase      		=       list(set(xmulti + zmulti			)			)
  			return sum(lowercase			)
  
  
  if __name__ == "__main__":
  	print(f'{solution() = }')
  
  