


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     )    ->Optional[int]:







      """simple docstring"""

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


# Configuration
__a   				=      HfArgumentParser(TokenizerTrainingArguments)
__a   				=      parser.parse_args()

# Base tokenizer
__a   				=      AutoTokenizer.from_pretrained(args.base_tokenizer)
__a   				=      list(bytes_to_unicode().values())

# Load dataset
__a   				=      load_dataset(args.dataset_name, split='''train''', streaming=True)
__a   				=      iter(dataset)


# Training and saving
__a   				=      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)





import math




def 			__lowercase	(						_UpperCamelCase     )    ->Union[str, Any]:







      """simple docstring"""

      lowercase			:   List[Any]					   =		0
      if n % 2 == 0:
            count += 1
            while n % 2 == 0:
                  lowercase			:   Any					   =		int(n / 2     )
    # the n input value must be odd so that
    # we can skip one element (ie i += 2)

      lowercase			:   Optional[int]					   =		3

      while i <= int(math.sqrt(_UpperCamelCase     )     ):
            if n % i == 0:
                  count += 1
                  while n % i == 0:
                        lowercase			:   Optional[Any]					   =		int(n / i     )
            lowercase			:   str					   =		i + 2

      # this condition checks the prime
      # number n is greater than 2

      if n > 2:
            count += 1
      return count


if __name__ == "__main__":
  __a   				=      51_24_21_83
  print(F'''The number of distinct prime factors is/are {exact_prime_factor_count(n)}''')
  print(F'''The value of log(log(n)) is {math.log(math.log(n)):.4f}''')

