






def    UpperCamelCase						(	_A			):






						"""simple docstring"""







						return "".join([hex(_A			)[2:].zfill(2			).upper() for byte in list(_A			)]			)

def    UpperCamelCase						(	_A			):






						"""simple docstring"""







						if (len(_A			) % 2) != 0:
												raise ValueError(
												    """Base16 encoded data is invalid:
Data does not have an even number of hex digits."""			)
						# Check the character set - the standard base16 alphabet
						# is uppercase according to RFC3548 section 6
						if not set(_A			) <= set("""0123456789ABCDEF"""			):
												raise ValueError(
												    """Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters."""			)
						# For every two hexadecimal digits (= a byte), turn it into an integer.
						# Then, string the result together into bytes, and return it.
						return bytes(int(data[i] + data[i + 1],   16			) for i in range(0,   len(_A			),   2			)			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()








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
)
__magic_name__:					str							=     logging.getLogger(__name__)

def    UpperCamelCase						(	):






						"""simple docstring"""







						__magic_name__      :       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."""			)
						__magic_name__      :       Dict       						= parser.parse_args()

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

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

						__magic_name__      :       List[Any]       						= []
						__magic_name__      :       str       						= 0
						__magic_name__      :       str       						= 10000
						__magic_name__      :       Dict       						= time.time()
						for text in data:
												__magic_name__      :       Tuple       						= f'{bos} {text.strip()} {sep}'
												__magic_name__      :       Optional[int]       						= tokenizer.encode(_A,   add_special_tokens=_A			)
												rslt.append(_A			)

												iter += 1
												if iter % interval == 0:
																		__magic_name__      :       Union[str, Any]       						= time.time()
																		logger.info(f'{iter} examples processed. - {(end-start):.2f}s/{interval}expl'			)
																		__magic_name__      :       Any       						= time.time()
						logger.info("""Finished binarization"""			)
						logger.info(f'{len(_A			)} examples processed.'			)

						__magic_name__      :       Tuple       						= f'{args.dump_file}.{args.tokenizer_name}.pickle'
						__magic_name__      :       Tuple       						= tokenizer.vocab_size
						if vocab_size < (1 << 16):
												__magic_name__      :       Optional[int]       						= [np.uintaa(_A			) for d in rslt]
						else:
												__magic_name__      :       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()
