
'''simple docstring'''
import sys

from transformers import AutoTokenizer


__lowerCAmelCase  							= sys.argv[1]
__lowerCAmelCase  							= sys.argv[2]
__lowerCAmelCase  							= int(sys.argv[3])

__lowerCAmelCase  							= 0

__lowerCAmelCase  							= AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, 'rt') as f_p:
				for line in f_p:
								__lowerCAmelCase  							= line.rstrip()

								if not line:
												print(line)
												__lowerCAmelCase  							= 0
												continue

								__lowerCAmelCase  							= line.split()[0]

								__lowerCAmelCase  							= len(tokenizer.tokenize(token))

								# Token contains strange control characters like \x96 or \x95
								# Just filter out the complete line
								if current_subwords_len == 0:
												continue

								if (subword_len_counter + current_subwords_len) > max_len:
												print('')
												print(line)
												__lowerCAmelCase  							= current_subwords_len
												continue

								subword_len_counter += current_subwords_len

								print(line)







'''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
				__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(80_000))
								__lowerCAmelCase  							= val_dataset.shuffle().select(range(8_000))

				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=4_096)

				__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)