






"""simple docstring"""






import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
UpperCAmelCase_   : List[str]   			= logging.getLogger(__name__)



def 					_A			() ->       Optional[int]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  str    						=		argparse.ArgumentParser(
        description='''Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'''							)
    parser.add_argument('''--file_path'''    ,				type=__a    ,				default='''data/dump.txt'''    ,				help='''The path to the data.'''							)
    parser.add_argument('''--tokenizer_type'''    ,				type=__a    ,				default='''bert'''    ,				choices=['''bert''', '''roberta''', '''gpt2''']							)
    parser.add_argument('''--tokenizer_name'''    ,				type=__a    ,				default='''bert-base-uncased'''    ,				help='''The tokenizer to use.'''							)
    parser.add_argument('''--dump_file'''    ,				type=__a    ,				default='''data/dump'''    ,				help='''The dump file prefix.'''							)
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		parser.parse_args()

    logger.info(f'Loading Tokenizer ({args.tokenizer_name})'							)
    if args.tokenizer_type == "bert":
        SCREAMING_SNAKE_CASE_      :  Dict    						=		BertTokenizer.from_pretrained(args.tokenizer_name							)
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		tokenizer.special_tokens_map['''cls_token''']  # `[CLS]`
        SCREAMING_SNAKE_CASE_      :  str    						=		tokenizer.special_tokens_map['''sep_token''']  # `[SEP]`
    elif args.tokenizer_type == "roberta":
        SCREAMING_SNAKE_CASE_      :  List[str]    						=		RobertaTokenizer.from_pretrained(args.tokenizer_name							)
        SCREAMING_SNAKE_CASE_      :  int    						=		tokenizer.special_tokens_map['''cls_token''']  # `<s>`
        SCREAMING_SNAKE_CASE_      :  Dict    						=		tokenizer.special_tokens_map['''sep_token''']  # `</s>`
    elif args.tokenizer_type == "gpt2":
        SCREAMING_SNAKE_CASE_      :  Any    						=		GPTaTokenizer.from_pretrained(args.tokenizer_name							)
        SCREAMING_SNAKE_CASE_      :  List[str]    						=		tokenizer.special_tokens_map['''bos_token''']  # `<|endoftext|>`
        SCREAMING_SNAKE_CASE_      :  str    						=		tokenizer.special_tokens_map['''eos_token''']  # `<|endoftext|>`

    logger.info(f'Loading text from {args.file_path}'							)
    with open(args.file_path    ,				'''r'''    ,				encoding='''utf8'''							) as fp:
        SCREAMING_SNAKE_CASE_      :  Any    						=		fp.readlines()

    logger.info('''Start encoding'''							)
    logger.info(f'{len(__a							)} examples to process.'							)

    SCREAMING_SNAKE_CASE_      :  int    						=		[]
    SCREAMING_SNAKE_CASE_      :  int    						=		0
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		1_00_00
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		time.time()
    for text in data:
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		f'{bos} {text.strip()} {sep}'
        SCREAMING_SNAKE_CASE_      :  List[Any]    						=		tokenizer.encode(__a    ,				add_special_tokens=__a							)
        rslt.append(__a							)

        iter += 1
        if iter % interval == 0:
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		time.time()
            logger.info(f'{iter} examples processed. - {(end-start):.2f}s/{interval}expl'							)
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		time.time()
    logger.info('''Finished binarization'''							)
    logger.info(f'{len(__a							)} examples processed.'							)

    SCREAMING_SNAKE_CASE_      :  Any    						=		f'{args.dump_file}.{args.tokenizer_name}.pickle'
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		tokenizer.vocab_size
    if vocab_size < (1 << 16):
        SCREAMING_SNAKE_CASE_      :  Any    						=		[np.uintaa(__a							) for d in rslt]
    else:
        SCREAMING_SNAKE_CASE_      :  List[str]    						=		[np.intaa(__a							) for d in rslt]
    random.shuffle(rslt_							)
    logger.info(f'Dump to {dp_file}'							)
    with open(__a    ,				'''wb'''							) as handle:
        pickle.dump(rslt_    ,				__a    ,				protocol=pickle.HIGHEST_PROTOCOL							)


if __name__ == "__main__":
  main()










from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat


def __A    (      )->  tuple[list[int], int]:





						"""simple docstring"""



						_UpperCAmelCase									=			[randint(-1_000 ,		1_000	) for i in range(10	)]
						_UpperCAmelCase									=			randint(-5_000 ,		5_000	)
						return (arr, r)


_a      	=  make_dataset()


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  tuple[int, ...]:





						"""simple docstring"""



						for triplet in permutations(__lowerCAmelCase ,		3	):
												if sum(__lowerCAmelCase	) == target:
																		return tuple(sorted(__lowerCAmelCase	)	)
						return (0, 0, 0)


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  tuple[int, int, int]:





						"""simple docstring"""



						arr.sort()
						_UpperCAmelCase									=			len(__lowerCAmelCase	)
						for i in range(n - 1	):
												_UpperCAmelCase					, _UpperCAmelCase									=			i + 1, n - 1
												while left < right:
																		if arr[i] + arr[left] + arr[right] == target:
																								return (arr[i], arr[left], arr[right])
																		elif arr[i] + arr[left] + arr[right] < target:
																								left += 1
																		elif arr[i] + arr[left] + arr[right] > target:
																								right -= 1
						return (0, 0, 0)


def __A    (      )->  tuple[float, float]:





						"""simple docstring"""



						_UpperCAmelCase									=			'\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n'
						_UpperCAmelCase									=			'\ntriplet_sum1(*dataset)\n'
						_UpperCAmelCase									=			'\ntriplet_sum2(*dataset)\n'
						_UpperCAmelCase									=			repeat(setup=__lowerCAmelCase ,		stmt=__lowerCAmelCase ,		repeat=5 ,		number=10_000	)
						_UpperCAmelCase									=			repeat(setup=__lowerCAmelCase ,		stmt=__lowerCAmelCase ,		repeat=5 ,		number=10_000	)
						return (min(__lowerCAmelCase	), min(__lowerCAmelCase	))


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		_a      	=  solution_times()
		print(F'''The time for naive implementation is {times[0]}.''')
		print(F'''The time for optimized implementation is {times[1]}.''')
