


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
)
_SCREAMING_SNAKE_CASE 		=						logging.getLogger(__name__)


def 							SCREAMING_SNAKE_CASE__  (      ):
	snake_case_						:     int     =		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.'				)
	snake_case_						:     Union[str, Any]     =		parser.parse_args()

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

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

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

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

	snake_case_						:     List[Any]     =		f"""{args.dump_file}.{args.tokenizer_name}.pickle"""
	snake_case_						:     int     =		tokenizer.vocab_size
	if vocab_size < (1 << 16):
		snake_case_						:     Any     =		[np.uintaa(__a				) for d in rslt]
	else:
		snake_case_						:     Optional[Any]     =		[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()








def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a = False				):

	if not isinstance(__a				,	__a				):
		snake_case_						:     str     =		f"""Expected string as input, found {type(__a				)}"""
		raise ValueError(__a				)
	if not isinstance(__a				,	__a				):
		snake_case_						:     int     =		f"""Expected boolean as use_pascal parameter, found {type(__a				)}"""
		raise ValueError(__a				)

	snake_case_						:     Union[str, Any]     =		input_str.split('_'				)

	snake_case_						:     int     =		0 if use_pascal else 1

	snake_case_						:     List[Any]     =		words[start_index:]

	snake_case_						:     str     =		[word[0].upper() + word[1:] for word in words_to_capitalize]

	snake_case_						:     Optional[Any]     =		'' if use_pascal else words[0]

	return "".join([initial_word, *capitalized_words]				)


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

						testmod()

