


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

							__magic_name__       					=      load_dataset('''json''', data_files=args.tr_data_path)['''train''']
							__magic_name__       					=      load_dataset('''json''', data_files=args.val_data_path)['''train''']

							# drop extra batch for now
							__magic_name__       					=      range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
							__magic_name__       					=      tr_dataset.shuffle().select(indices)
							__magic_name__       					=      range(len(val_dataset) - len(val_dataset) % args.batch_size)
							__magic_name__       					=      val_dataset.shuffle().select(indices)

							if os.environ.get('''TRAIN_ON_SMALL''', '''false''') == "true":
														__magic_name__       					=      tr_dataset.shuffle().select(range(8_0_0_0_0))
														__magic_name__       					=      val_dataset.shuffle().select(range(8_0_0_0))

							print(tr_dataset)
							print(val_dataset)

							__magic_name__       					=      FlaxBigBirdForNaturalQuestions.from_pretrained(
							    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
							)
							__magic_name__       					=      BigBirdTokenizerFast.from_pretrained(args.model_id)
							__magic_name__       					=      DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_0_9_6)

							__magic_name__       					=      {
							    '''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,
							}
							__magic_name__					,      __magic_name__       					=      build_tx(**tx_args)

							__magic_name__       					=      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,
							)

							__magic_name__       					=      None
							__magic_name__       					=      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`''')
							__magic_name__       					=      jax_utils.unreplicate(state.params)
							model.save_pretrained(os.path.join(args.base_dir, '''final-weights'''), params=params)





__magic_name__       					=      {
    '''0''': 1,
    '''1''': 1,
    '''2''': 2,
    '''3''': 6,
    '''4''': 2_4,
    '''5''': 1_2_0,
    '''6''': 7_2_0,
    '''7''': 5_0_4_0,
    '''8''': 4_0_3_2_0,
    '''9''': 3_6_2_8_8_0,
}

__magic_name__       					=      {1_4_5: 1_4_5}

__magic_name__       					=      {
    1_4_5: 0,
    1_6_9: 3,
    3_6_3_0_1: 3,
    1_4_5_4: 3,
    8_7_1: 2,
    4_5_3_6_1: 2,
    8_7_2: 2,
}







def     __magic_name__  (      lowerCAmelCase_):
	'''simple docstring'''


	if n in CACHE_SUM_DIGIT_FACTORIALS:
		return CACHE_SUM_DIGIT_FACTORIALS[n]
	lowerCamelCase_			:							List[str]      =       sum(DIGIT_FACTORIALS[let] for let in str(lowerCAmelCase_))
	lowerCamelCase_			:							List[str]      =       ret
	return ret







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_ = None):
	'''simple docstring'''


	lowerCamelCase_			:							List[str]      =       previous or set()
	if n in CHAIN_LENGTH_CACHE:
		return CHAIN_LENGTH_CACHE[n]
	lowerCamelCase_			:							str      =       sum_digit_factorials(lowerCAmelCase_)
	if next_number in previous:
		lowerCamelCase_			:							List[str]      =       0
		return 0
	else:
		previous.add(lowerCAmelCase_)
		lowerCamelCase_			:							List[Any]      =       1 + chain_length(lowerCAmelCase_      ,					lowerCAmelCase_)
		lowerCamelCase_			:							Optional[int]      =       ret
		return ret







def     __magic_name__  (      lowerCAmelCase_ = 60      ,					lowerCAmelCase_ = 100_0000):
	'''simple docstring'''


	return sum(1 for i in range(1      ,					lowerCAmelCase_) if chain_length(lowerCAmelCase_) == num_terms)


if __name__ == "__main__":
							print(f'''{solution() = }''')

