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

     __magic_name__  :  Union[str, Any]     			=   load_dataset('json', data_files=args.tr_data_path)['train']
     __magic_name__  :  Dict     			=   load_dataset('json', data_files=args.val_data_path)['train']

     # drop extra batch for now
     __magic_name__  :  Any     			=   range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
     __magic_name__  :  Union[str, Any]     			=   tr_dataset.shuffle().select(indices)
     __magic_name__  :  List[Any]     			=   range(len(val_dataset) - len(val_dataset) % args.batch_size)
     __magic_name__  :  Tuple     			=   val_dataset.shuffle().select(indices)

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

     print(tr_dataset)
     print(val_dataset)

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

     __magic_name__  :  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,
     }
     __magic_name__			,	__magic_name__  :  Optional[Any]     			=   build_tx(**tx_args)

     __magic_name__  :  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,
     )

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








import sys

sys.set_int_max_str_digits(0)  # type: ignore





def        lowerCAmelCase       (				snake_case__   :      int )->			bool:
    A_								=					[0] * 11
    A_								=					[0] * 11

    # mark last 9 numbers
    for _ in range(9 ):
        A_								=					1
        A_								=					number // 10
    # flag
    A_								=					True

    # check last 9 numbers for pandigitality

    for x in range(9 ):
        if not check_last[x + 1]:
            A_								=					False
    if not f:
        return f

    # mark first 9 numbers
    A_								=					int(str(snake_case__ )[:9] )

    for _ in range(9 ):
        A_								=					1
        A_								=					number // 10

    # check first 9 numbers for pandigitality

    for x in range(9 ):
        if not check_front[x + 1]:
            A_								=					False
    return f





def        lowerCAmelCase       (				snake_case__   :      int )->			bool:
    A_								=					[0] * 11

    # mark last 9 numbers
    for _ in range(9 ):
        A_								=					1
        A_								=					number // 10
    # flag
    A_								=					True

    # check last 9 numbers for pandigitality

    for x in range(9 ):
        if not check_last[x + 1]:
            A_								=					False
    return f





def        lowerCAmelCase       (				)->			int:
    A_								=					1
    A_								=					1
    A_								=					2
    # temporary Fibonacci numbers

    A_								=					1
    A_								=					1
    A_								=					2
    # temporary Fibonacci numbers mod 1e9

    # mod m=1e9, done for fast optimisation
    A_								=					[0] * 1000000
    A_								=					1000000000

    for x in range(1000000 ):
        A_								=					(aa + ba) % m
        A_								=					ba % m
        A_								=					ca % m
        if checka(snake_case__ ):
            A_								=					1

    for x in range(1000000 ):
        A_								=					a + b
        A_								=					b
        A_								=					c
        # perform check only if in tocheck
        if tocheck[x + 3] and check(snake_case__ ):
            return x + 3  # first 2 already done
    return -1


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






