




from __future__ import annotations

import collections
import pprint
from pathlib import Path




def     __magic_name__       (				A	:      str     ):






			'''simple docstring'''







			return "".join(sorted(A     )     )




def     __magic_name__       (				A	:      str     ):






			'''simple docstring'''







			return word_by_signature[signature(A     )]


__lowerCAmelCase	:       str     =	Path(__file__).parent.joinpath('words.txt').read_text(encoding='utf-8')
__lowerCAmelCase	:       List[Any]     =	sorted({word.strip().lower() for word in data.splitlines()})

__lowerCAmelCase	:       Dict     =	collections.defaultdict(list)
for word in word_list:
		word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
		__lowerCAmelCase	:       List[Any]     =	{word: anagram(word) for word in word_list if len(anagram(word)) > 1}

		with open('anagrams.txt', 'w') as file:
				file.write('all_anagrams = \n ')
				file.write(pprint.pformat(all_anagrams))












import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser




def     __magic_name__       (				A	:      List[str]     ):






			'''simple docstring'''







			a					=    {}
			a					=    tokenizer(example["content"],						truncation=A     )["input_ids"]
			a					=    len(example["content"]     ) / len(output["input_ids"]     )
			return output


__lowerCAmelCase	:       Dict     =	HfArgumentParser(PretokenizationArguments)
__lowerCAmelCase	:       str     =	parser.parse_args()
if args.num_workers is None:
		__lowerCAmelCase	:       List[Any]     =	multiprocessing.cpu_count()
__lowerCAmelCase	:       str     =	AutoTokenizer.from_pretrained(args.tokenizer_dir)

__lowerCAmelCase	:       List[Any]     =	time.time()
__lowerCAmelCase	:       str     =	load_dataset(args.dataset_name, split='train')
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

__lowerCAmelCase	:       int     =	time.time()
__lowerCAmelCase	:       Optional[int]     =	ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        'repo_name',
        'path',
        'copies',
        'size',
        'content',
        'license',
        'hash',
        'line_mean',
        'line_max',
        'alpha_frac',
        'autogenerated',
    ],
)
print(F'''Dataset tokenized in {time.time()-t_start:.2f}s''')

__lowerCAmelCase	:       Tuple     =	time.time()
ds.push_to_hub(args.tokenized_data_repo)






print(F'''Data pushed to the hub in {time.time()-t_start:.2f}s''')






