"""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     a_		(   lowerCamelCase=1_0			):

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


# Configuration
lowerCAmelCase__    :    int			=      HfArgumentParser(TokenizerTrainingArguments)
lowerCAmelCase__    :    Optional[int]			=      parser.parse_args()

# Base tokenizer
lowerCAmelCase__    :    Dict			=      AutoTokenizer.from_pretrained(args.base_tokenizer)
lowerCAmelCase__    :    Optional[int]			=      list(bytes_to_unicode().values())

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


# Training and saving
lowerCAmelCase__    :    str			=      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)





_A       =					{
    "0": 1,
    "1": 1,
    "2": 2,
    "3": 6,
    "4": 24,
    "5": 1_20,
    "6": 7_20,
    "7": 50_40,
    "8": 4_03_20,
    "9": 36_28_80,
}

_A       =					{1_45: 1_45}

_A       =					{
    1_45: 0,
    1_69: 3,
    3_63_01: 3,
    14_54: 3,
    8_71: 2,
    4_53_61: 2,
    8_72: 2,
}



def 		lowercase_     (  A__						)				->				int:
    """simple docstring"""

    if n in CACHE_SUM_DIGIT_FACTORIALS:
        return CACHE_SUM_DIGIT_FACTORIALS[n]
    snake_case						=   sum(DIGIT_FACTORIALS[let] for let in str(A__						)						)
    snake_case						=   ret
    return ret



def 		lowercase_     (  A__					,						A__ = None						)				->				int:
    """simple docstring"""

    snake_case						=   previous or set()
    if n in CHAIN_LENGTH_CACHE:
        return CHAIN_LENGTH_CACHE[n]
    snake_case						=   sum_digit_factorials(A__						)
    if next_number in previous:
        snake_case						=   0
        return 0
    else:
        previous.add(A__						)
        snake_case						=   1 + chain_length(A__					,						A__						)
        snake_case						=   ret
        return ret



def 		lowercase_     (  A__ = 60					,						A__ = 100_0000						)				->				int:
    """simple docstring"""

    return sum(1 for i in range(1					,						A__						) if chain_length(A__						) == num_terms						)


if __name__ == "__main__":
   print(f"{solution() = }")

