





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
)
_a     :   Optional[int]						=   logging.getLogger(__name__)





def lowerCAmelCase__()       ->   Tuple:







	'''simple docstring'''







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

	logger.info(F'Loading Tokenizer ({args.tokenizer_name})'							)
	if args.tokenizer_type == "bert":
		lowerCamelCase__              =	BertTokenizer.from_pretrained(args.tokenizer_name							)
		lowerCamelCase__              =	tokenizer.special_tokens_map['''cls_token''']  # `[CLS]`
		lowerCamelCase__              =	tokenizer.special_tokens_map['''sep_token''']  # `[SEP]`
	elif args.tokenizer_type == "roberta":
		lowerCamelCase__              =	RobertaTokenizer.from_pretrained(args.tokenizer_name							)
		lowerCamelCase__              =	tokenizer.special_tokens_map['''cls_token''']  # `<s>`
		lowerCamelCase__              =	tokenizer.special_tokens_map['''sep_token''']  # `</s>`
	elif args.tokenizer_type == "gpt2":
		lowerCamelCase__              =	GPTaTokenizer.from_pretrained(args.tokenizer_name							)
		lowerCamelCase__              =	tokenizer.special_tokens_map['''bos_token''']  # `<|endoftext|>`
		lowerCamelCase__              =	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:
		lowerCamelCase__              =	fp.readlines()

	logger.info('''Start encoding'''							)
	logger.info(F'{len(__snake_case							)} examples to process.'							)

	lowerCamelCase__              =	[]
	lowerCamelCase__              =	0
	lowerCamelCase__              =	10000
	lowerCamelCase__              =	time.time()
	for text in data:
		lowerCamelCase__              =	F'{bos} {text.strip()} {sep}'
		lowerCamelCase__              =	tokenizer.encode(__snake_case ,add_special_tokens=__snake_case							)
		rslt.append(__snake_case							)

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

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


if __name__ == "__main__":
			main()










_a						=   "\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

_a						=   [{"type": "code", "content": INSTALL_CONTENT}]
_a						=   {
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}



