





'''simple docstring'''






import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


lowerCAmelCase       		=      4
lowerCAmelCase       		=      3
class 		lowerCamelCase  (      _A       ):
	pass






def      __A						(							a_						:  List[str]	):
		for shard in shards:
				for i in range(a_	):
						yield {"i": i, "shard": shard}






def      __A						(							):
		lowerCAmelCase     :    int						=   int(os.environ["RANK"]	)
		lowerCAmelCase     :    Union[str, Any]						=   int(os.environ["WORLD_SIZE"]	)

		lowerCAmelCase     :    Dict						=   ArgumentParser()
		parser.add_argument("--streaming"      ,type=a_	)
		parser.add_argument("--local_rank"      ,type=a_	)
		parser.add_argument("--num_workers"      ,type=a_      ,default=0	)
		lowerCAmelCase     :    str						=   parser.parse_args()
		lowerCAmelCase     :    str						=   args.streaming
		lowerCAmelCase     :    List[str]						=   args.num_workers

		lowerCAmelCase     :    List[str]						=   {"shards": [f'''shard_{shard_idx}''' for shard_idx in range(a_	)]}
		lowerCAmelCase     :    Optional[Any]						=   IterableDataset.from_generator(a_      ,gen_kwargs=a_	)
		if not streaming:
				lowerCAmelCase     :    Dict						=   Dataset.from_list(list(a_	)	)

		lowerCAmelCase     :    Tuple						=   split_dataset_by_node(a_      ,rank=a_      ,world_size=a_	)
		lowerCAmelCase     :    Dict						=   torch.utils.data.DataLoader(a_      ,num_workers=a_	)

		lowerCAmelCase     :    Any						=   NUM_SHARDS * NUM_ITEMS_PER_SHARD
		lowerCAmelCase     :    Dict						=   full_size // world_size
		expected_local_size += int(rank < (full_size % world_size)	)

		lowerCAmelCase     :    int						=   sum(1 for _ in dataloader	)
		if local_size != expected_local_size:
				raise FailedTestError(f'''local_size {local_size} != expected_local_size {expected_local_size}'''	)


if __name__ == "__main__":
					main()










'''simple docstring'''






import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer






def      __A						(							a_						:  List[str]      ,a_						:  Dict      ,a_						:  List[Any]      ,a_						:  Optional[int]=1_0_2_4	):
		lowerCAmelCase	,		lowerCAmelCase     :    Dict						=   [], []

		lowerCAmelCase     :    Optional[Any]						=   list(zip(a_      ,a_	)	)
		lowerCAmelCase	,		lowerCAmelCase     :    Any						=   sorted_examples[0]

		def is_too_big(a_						:  Any	):
				return tok(a_      ,return_tensors="pt"	).input_ids.shape[1] > max_tokens

		for src, tgt in tqdm(sorted_examples[1:]	):
				lowerCAmelCase     :    Optional[int]						=   new_src + " " + src
				lowerCAmelCase     :    Union[str, Any]						=   new_tgt + " " + tgt
				if is_too_big(a_	) or is_too_big(a_	):  # cant fit, finalize example
						finished_src.append(a_	)
						finished_tgt.append(a_	)
						lowerCAmelCase	,		lowerCAmelCase     :    str						=   src, tgt
				else:  # can fit, keep adding
						lowerCAmelCase	,		lowerCAmelCase     :    List[str]						=   cand_src, cand_tgt

    # cleanup
		if new_src:
				assert new_tgt
				finished_src.append(a_	)
				finished_tgt.append(a_	)
		return finished_src, finished_tgt






def      __A						(							a_						:  List[str]      ,a_						:  Path      ,a_						:  Any      ,a_						:  int	):
		lowerCAmelCase     :    Optional[int]						=   Path(a_	)
		save_path.mkdir(exist_ok=a_	)
		for split in ["train"]:
				lowerCAmelCase	,		lowerCAmelCase     :    Tuple						=   data_dir / f'''{split}.source''', data_dir / f'''{split}.target'''
				lowerCAmelCase     :    Tuple						=   [x.rstrip() for x in Path(a_	).open().readlines()]
				lowerCAmelCase     :    str						=   [x.rstrip() for x in Path(a_	).open().readlines()]
				lowerCAmelCase	,		lowerCAmelCase     :    Tuple						=   pack_examples(a_      ,a_      ,a_      ,a_	)
				print(f'''packed {split} split from {len(a_	)} examples -> {len(a_	)}.'''	)
				Path(save_path / f'''{split}.source'''	).open("w"	).write("\n".join(a_	)	)
				Path(save_path / f'''{split}.target'''	).open("w"	).write("\n".join(a_	)	)
		for split in ["val", "test"]:
				lowerCAmelCase	,		lowerCAmelCase     :    Optional[Any]						=   data_dir / f'''{split}.source''', data_dir / f'''{split}.target'''
				shutil.copyfile(a_      ,save_path / f'''{split}.source'''	)
				shutil.copyfile(a_      ,save_path / f'''{split}.target'''	)






def      __A						(							):
		lowerCAmelCase     :    List[str]						=   argparse.ArgumentParser()
		parser.add_argument("--tok_name"      ,type=a_      ,help="like facebook/bart-large-cnn,t5-base, etc."	)
		parser.add_argument("--max_seq_len"      ,type=a_      ,default=1_2_8	)
		parser.add_argument("--data_dir"      ,type=a_	)
		parser.add_argument("--save_path"      ,type=a_	)
		lowerCAmelCase     :    Optional[Any]						=   parser.parse_args()
		lowerCAmelCase     :    List[str]						=   AutoTokenizer.from_pretrained(args.tok_name	)
		return pack_data_dir(a_      ,Path(args.data_dir	)      ,args.max_seq_len      ,args.save_path	)


if __name__ == "__main__":
					packer_cli()



