



"""simple docstring"""




import math
from collections.abc import Generator


def   lowercase__     (			_UpperCAmelCase				)     ->			Generator[int, None, None]:






      '''simple docstring'''


      lowercase  :      Generator					=   (i for i in range(1	,      (max_n + 1)				))
      for i in (n for n in numbers if n > 1):
            for j in range(2	,      _UpperCAmelCase				):
                  if (i % j) == 0:
                        break
            else:
                  yield i


def   lowercase__     (			_UpperCAmelCase				)     ->			Generator[int, None, None]:






      '''simple docstring'''


      lowercase  :      Generator					=   (i for i in range(1	,      (max_n + 1)				))
      for i in (n for n in numbers if n > 1):
            # only need to check for factors up to sqrt(i)
            lowercase  :      Optional[int]					=   int(math.sqrt(_UpperCAmelCase				)				) + 1
            for j in range(2	,      _UpperCAmelCase				):
                  if (i % j) == 0:
                        break
            else:
                  yield i


def   lowercase__     (			_UpperCAmelCase				)     ->			Generator[int, None, None]:






      '''simple docstring'''


      lowercase  :      Generator					=   (i for i in range(1	,      (max_n + 1)	,      2				))
      # It's useless to test even numbers as they will not be prime
      if max_n > 2:
            yield 2  # Because 2 will not be tested, it's necessary to yield it now
      for i in (n for n in numbers if n > 1):
            lowercase  :      List[str]					=   int(math.sqrt(_UpperCAmelCase				)				) + 1
            for j in range(3	,      _UpperCAmelCase	,      2				):
                  # As we removed the even numbers, we don't need them now
                  if (i % j) == 0:
                        break
            else:
                  yield i


def   lowercase__     (			)     ->			Any:






      '''simple docstring'''


      from timeit import timeit

      lowercase  :      Any					=   'from __main__ import slow_primes, primes, fast_primes'
      print(timeit('slow_primes(1_000_000_000_000)'	,      setup=_UpperCAmelCase	,      number=1_00_00_00				)				)
      print(timeit('primes(1_000_000_000_000)'	,      setup=_UpperCAmelCase	,      number=1_00_00_00				)				)
      print(timeit('fast_primes(1_000_000_000_000)'	,      setup=_UpperCAmelCase	,      number=1_00_00_00				)				)


if __name__ == "__main__":
     _UpperCamelCase:				List[Any]    							=  int(input('Calculate primes up to:\n>> ').strip())
     for ret in primes(number):
          print(ret)
     benchmark()





"""simple docstring"""




from arguments import TokenizerTrainingArguments
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpta.tokenization_gpta import bytes_to_unicode


def   lowercase__     (			_UpperCAmelCase=10				)     ->			Any:






      '''simple docstring'''


      for _ in tqdm(range(0	,      args.n_examples	,      _UpperCAmelCase				)				):
            yield [next(_UpperCAmelCase				)[args.text_column] for _ in range(_UpperCAmelCase				)]


# Configuration
_UpperCamelCase:				Union[str, Any]    							=  HfArgumentParser(TokenizerTrainingArguments)
_UpperCamelCase:				Dict    							=  parser.parse_args()

# Base tokenizer
_UpperCamelCase:				List[Any]    							=  AutoTokenizer.from_pretrained(args.base_tokenizer)
_UpperCamelCase:				int    							=  list(bytes_to_unicode().values())

# Load dataset
_UpperCamelCase:				Union[str, Any]    							=  load_dataset(args.dataset_name, split='train', streaming=True)
_UpperCamelCase:				Union[str, Any]    							=  iter(dataset)


# Training and saving
_UpperCamelCase:				Union[str, Any]    							=  tokenizer.train_new_from_iterator(
    batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
)





new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)
