


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

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

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

    if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
        lowerCAmelCase__						=			tr_dataset.shuffle().select(range(8_00_00))
        lowerCAmelCase__						=			val_dataset.shuffle().select(range(80_00))

    print(tr_dataset)
    print(val_dataset)

    lowerCAmelCase__						=			FlaxBigBirdForNaturalQuestions.from_pretrained(
        args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
    )
    lowerCAmelCase__						=			BigBirdTokenizerFast.from_pretrained(args.model_id)
    lowerCAmelCase__						=			DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

    lowerCAmelCase__						=			{
        '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__						=			build_tx(**tx_args)

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




import warnings

from .generation import FlaxGenerationMixin







class 					lowerCAmelCase__	(    a):




     '''simple docstring'''
     # warning at import time
     warnings.warn(
         "Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will "
         "be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead."					,   a					,   )
