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

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

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

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

				print(tr_dataset)
				print(val_dataset)

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

				lowerCAmelCase__					:       List[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,
				}
				lowerCAmelCase__,					lowerCAmelCase__					:       Any		=       build_tx(**tx_args)

				lowerCAmelCase__					:       List[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,
				)

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



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


lowerCAmelCase__					:       Optional[int]		=       {}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					:       Tuple		=       ['''MBart50Tokenizer''']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					:       Tuple		=       ['''MBart50TokenizerFast''']


if TYPE_CHECKING:
				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mbartaa import MBartaaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
				import sys

				lowerCAmelCase__					:       Optional[int]		=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


