


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

				UpperCAmelCase_	:     Tuple							     =	load_dataset('json', data_files=args.tr_data_path)['train']
				UpperCAmelCase_	:     int							     =	load_dataset('json', data_files=args.val_data_path)['train']

				# drop extra batch for now
				UpperCAmelCase_	:     str							     =	range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
				UpperCAmelCase_	:     Optional[int]							     =	tr_dataset.shuffle().select(indices)
				UpperCAmelCase_	:     List[str]							     =	range(len(val_dataset) - len(val_dataset) % args.batch_size)
				UpperCAmelCase_	:     Any							     =	val_dataset.shuffle().select(indices)

				if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
								UpperCAmelCase_	:     List[Any]							     =	tr_dataset.shuffle().select(range(8_0000))
								UpperCAmelCase_	:     str							     =	val_dataset.shuffle().select(range(8000))

				print(tr_dataset)
				print(val_dataset)

				UpperCAmelCase_	:     List[str]							     =	FlaxBigBirdForNaturalQuestions.from_pretrained(
				    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
				)
				UpperCAmelCase_	:     Any							     =	BigBirdTokenizerFast.from_pretrained(args.model_id)
				UpperCAmelCase_	:     Optional[Any]							     =	DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

				UpperCAmelCase_	:     Any							     =	{
				    '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,
				}
				UpperCAmelCase_     , UpperCAmelCase_	:     Tuple							     =	build_tx(**tx_args)

				UpperCAmelCase_	:     str							     =	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,
				)

				UpperCAmelCase_	:     int							     =	None
				UpperCAmelCase_	:     List[str]							     =	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`')
				UpperCAmelCase_	:     Dict							     =	jax_utils.unreplicate(state.params)
				model.save_pretrained(os.path.join(args.base_dir, 'final-weights'), params=params)








'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_	:     Any							     =	{
    'configuration_musicgen': [
        'MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MusicgenConfig',
        'MusicgenDecoderConfig',
    ],
    'processing_musicgen': ['MusicgenProcessor'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_	:     Any							     =	[
				    'MUSICGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MusicgenForConditionalGeneration',
				    'MusicgenForCausalLM',
				    'MusicgenModel',
				    'MusicgenPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_musicgen import (
				    MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    MusicgenConfig,
				    MusicgenDecoderConfig,
				)
				from .processing_musicgen import MusicgenProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_musicgen import (
								    MUSICGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MusicgenForCausalLM,
								    MusicgenForConditionalGeneration,
								    MusicgenModel,
								    MusicgenPreTrainedModel,
								)

else:
				import sys

				UpperCAmelCase_	:     Tuple							     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




