


"""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
)
__SCREAMING_SNAKE_CASE							:      str						=      logging.getLogger(__name__)







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

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

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

							snake_case_          =						[]
							snake_case_          =						0
							snake_case_          =						10_000
							snake_case_          =						time.time()
							for text in data:
														snake_case_          =						f"""{bos} {text.strip()} {sep}"""
														snake_case_          =						tokenizer.encode(_SCREAMING_SNAKE_CASE			,	add_special_tokens=_SCREAMING_SNAKE_CASE			)
														rslt.append(_SCREAMING_SNAKE_CASE			)

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

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


if __name__ == "__main__":
				main()







"""simple docstring"""
def   _a			( _SCREAMING_SNAKE_CASE			)      ->			int:
							assert column_title.isupper()
							snake_case_          =						0
							snake_case_          =						len(_SCREAMING_SNAKE_CASE			) - 1
							snake_case_          =						0

							while index >= 0:
														snake_case_          =						(ord(column_title[index]			) - 64) * pow(26			,	_SCREAMING_SNAKE_CASE			)
														answer += value
														power += 1
														index -= 1

							return answer


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

				testmod()



