
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp


def     __lowerCAmelCase			(			)       ->  Optional[Any]:
				__a										=    ArgumentParser(
				    description=(
				        '''PyTorch TPU distributed training launch '''
				        '''helper utility that will spawn up '''
				        '''multiple distributed processes'''
				    )  )

				# Optional arguments for the launch helper
				parser.add_argument('''--num_cores'''						,    type=a__						,    default=1						,    help='''Number of TPU cores to use (1 or 8).'''  )

				# positional
				parser.add_argument(
				    '''training_script'''						,    type=a__						,    help=(
				        '''The full path to the single TPU training '''
				        '''program/script to be launched in parallel, '''
				        '''followed by all the arguments for the '''
				        '''training script'''
				    )						,    )

				# rest from the training program
				parser.add_argument('''training_script_args'''						,    nargs=a__  )

				return parser.parse_args()


def     __lowerCAmelCase			(			)       ->  Optional[Any]:
				__a										=    parse_args()

				# Import training_script as a module.
				__a										=    Path(args.training_script  )
				sys.path.append(str(script_fpath.parent.resolve()  )  )
				__a										=    script_fpath.stem
				__a										=    importlib.import_module(a__  )

				# Patch sys.argv
				__a										=    [args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores  )]
				xmp.spawn(mod._mp_fn						,    args=()						,    nprocs=args.num_cores  )


if __name__ == "__main__":
					main()

import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save


def     __lowerCAmelCase			(			a__						,    a__						,    a__=1024						,    a__=1024						,    a__=False						,    **a__  )       ->  Optional[Any]:
				__a										=    AutoTokenizer.from_pretrained(a__  )
				__a										=    SeqaSeqDataset(a__						,    a__						,    a__						,    a__						,    type_path='''train'''						,    **a__  )
				__a										=    tok.pad_token_id

				def get_lens(a__  ):
								__a										=    tqdm(
								    DataLoader(a__						,    batch_size=512						,    num_workers=8						,    shuffle=a__						,    collate_fn=ds.collate_fn  )						,    desc=str(ds.len_file  )						,    )
								__a										=    []
								for batch in dl:
												__a										=    batch['''input_ids'''].ne(a__  ).sum(1  ).tolist()
												__a										=    batch['''labels'''].ne(a__  ).sum(1  ).tolist()
												if consider_target:
																for src, tgt in zip(a__						,    a__  ):
																				max_lens.append(max(a__						,    a__  )  )
												else:
																max_lens.extend(a__  )
								return max_lens

				__a										=    get_lens(a__  )
				__a										=    SeqaSeqDataset(a__						,    a__						,    a__						,    a__						,    type_path='''val'''						,    **a__  )
				__a										=    get_lens(a__  )
				pickle_save(a__						,    train_ds.len_file  )
				pickle_save(a__						,    val_ds.len_file  )


if __name__ == "__main__":
					fire.Fire(save_len_file)