



def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		float       )						->     float:





     '''simple docstring'''

     return -num if num < 0 else num

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list[int]       )						->     int:





     '''simple docstring'''

     if len(SCREAMING_SNAKE_CASE__       ) == 0:
          raise ValueError("abs_min() arg is an empty sequence"       )
     _UpperCAmelCase    :			int              =					x[0]
     for i in x:
          if abs_val(SCREAMING_SNAKE_CASE__       ) < abs_val(SCREAMING_SNAKE_CASE__       ):
               _UpperCAmelCase    :			List[str]              =					i
     return j

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list[int]       )						->     int:





     '''simple docstring'''

     if len(SCREAMING_SNAKE_CASE__       ) == 0:
          raise ValueError("abs_max() arg is an empty sequence"       )
     _UpperCAmelCase    :			int              =					x[0]
     for i in x:
          if abs(SCREAMING_SNAKE_CASE__       ) > abs(SCREAMING_SNAKE_CASE__       ):
               _UpperCAmelCase    :			Tuple              =					i
     return j

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list[int]       )						->     int:





     '''simple docstring'''

     if len(SCREAMING_SNAKE_CASE__       ) == 0:
          raise ValueError("abs_max_sort() arg is an empty sequence"       )
     return sorted(SCREAMING_SNAKE_CASE__		,						key=SCREAMING_SNAKE_CASE__       )[-1]

def 		__snake_case		(   )						->     Optional[Any]:





     '''simple docstring'''

     assert abs_val(0       ) == 0
     assert abs_val(34       ) == 34
     assert abs_val(-100_000_000_000       ) == 100_000_000_000

     _UpperCAmelCase    :			List[str]              =					[-3, -1, 2, -11]
     assert abs_max(SCREAMING_SNAKE_CASE__       ) == -11
     assert abs_max_sort(SCREAMING_SNAKE_CASE__       ) == -11
     assert abs_min(SCREAMING_SNAKE_CASE__       ) == -1


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 test_abs_val()
 print(abs_val(-34))  # --> 34





"""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 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		str=10       )						->     List[Any]:





     '''simple docstring'''

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


# Configuration
_lowerCAmelCase       :      Optional[Any]							=							HfArgumentParser(TokenizerTrainingArguments)
_lowerCAmelCase       :      Dict							=							parser.parse_args()

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

# Load dataset
_lowerCAmelCase       :      Dict							=							load_dataset(args.dataset_name, split="train", streaming=True)
_lowerCAmelCase       :      List[Any]							=							iter(dataset)


# Training and saving
_lowerCAmelCase       :      List[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)
