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




def  _lowerCAmelCase		(		):






  """simple docstring"""



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

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

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

  _lowercase:			Optional[int]       =     []
  _lowercase:			Tuple       =     0
  _lowercase:			Union[str, Any]       =     10_000
  _lowercase:			Tuple       =     time.time()
  for text in data:
    _lowercase:			int       =     f'''{bos} {text.strip()} {sep}'''
    _lowercase:			Union[str, Any]       =     tokenizer.encode(_lowercase ,				add_special_tokens=_lowercase						)
    rslt.append(_lowercase						)

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

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


if __name__ == "__main__":
      main()


"""simple docstring"""




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  if num <= 0:
    raise ValueError('''Input must be a positive integer'''						)

  _lowercase:			Tuple       =     [True] * (num + 1)

  _lowercase:			List[str]       =     2
  while p * p <= num:
    if primes[p]:
      for i in range(p * p ,				num + 1 ,				_UpperCamelCase						):
        _lowercase:			List[str]       =     False
    p += 1

  return [prime for prime in range(2 ,				num + 1						) if primes[prime]]


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  A__   :					List[Any]							=   int(input('Enter a positive integer: ').strip())
  print(prime_sieve_eratosthenes(user_num))
