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

 __lowerCAmelCase   :     List[str]      =						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   :     str      =						range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
 __lowerCAmelCase   :     List[str]      =						tr_dataset.shuffle().select(indices)
 __lowerCAmelCase   :     Any      =						range(len(val_dataset) - len(val_dataset) % args.batch_size)
 __lowerCAmelCase   :     List[Any]      =						val_dataset.shuffle().select(indices)

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

 print(tr_dataset)
 print(val_dataset)

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

 __lowerCAmelCase   :     Optional[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,
 }
 __lowerCAmelCase ,				__lowerCAmelCase   :     Union[str, Any]      =						build_tx(**tx_args)

 __lowerCAmelCase   :     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   :     str      =						None
 __lowerCAmelCase   :     Any      =						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   :     List[Any]      =						jax_utils.unreplicate(state.params)
 model.save_pretrained(os.path.join(args.base_dir, "final-weights"), params=params)




from __future__ import annotations

from collections import Counter


def       UpperCAmelCase_					(  __lowerCAmelCase		)	->  list[int]:
    __lowercase						:     Any		      =    [True] * n
    __lowercase						:     str		      =    False
    __lowercase						:     int		      =    False
    __lowercase						:     List[Any]		      =    True

    for i in range(3		,   int(n**0.5 + 1		)		,   2		):
        __lowercase						:     List[str]		      =    i * 2
        while index < n:
            __lowercase						:     List[Any]		      =    False
            __lowercase						:     Dict		      =    index + i

    __lowercase						:     Tuple		      =    [2]

    for i in range(3		,   __lowerCAmelCase		,   2		):
        if is_prime[i]:
            primes.append(__lowerCAmelCase		)

    return primes


def       UpperCAmelCase_					(  __lowerCAmelCase		)	->  list[list[int]]:
    __lowercase						:     Optional[int]		      =    str(__lowerCAmelCase		)
    __lowercase						:     List[str]		      =    []
    __lowercase						:     int		      =    ['''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''']

    for duplicate in Counter(__lowerCAmelCase		) - Counter(set(__lowerCAmelCase		)		):
        __lowercase						:     Union[str, Any]		      =    [int(number_str.replace(__lowerCAmelCase		,   __lowerCAmelCase		)		) for digit in digits]
        replacements.append(__lowerCAmelCase		)

    return replacements


def       UpperCAmelCase_					(  __lowerCAmelCase = 8		)	->  int:
    __lowercase						:     Tuple		      =    set()

    # Filter primes with less than 3 replaceable digits
    __lowercase						:     int		      =    {
        x for x in set(prime_sieve(1_000_000		)		) if len(str(__lowerCAmelCase		)		) - len(set(str(__lowerCAmelCase		)		)		) >= 3
    }

    for prime in primes:
        if prime in numbers_checked:
            continue

        __lowercase						:     str		      =    digit_replacements(__lowerCAmelCase		)

        for family in replacements:
            numbers_checked.update(__lowerCAmelCase		)
            __lowercase						:     List[Any]		      =    primes.intersection(__lowerCAmelCase		)

            if len(__lowerCAmelCase		) != family_length:
                continue

            return min(__lowerCAmelCase		)

    return -1


if __name__ == "__main__":
 print(solution())


