
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=10							):
 for _ in tqdm(range(0,			args.n_examples,			snake_case__							)							):
  yield [next(snake_case__							)[args.text_column] for _ in range(snake_case__							)]


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

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

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


# Training and saving
_lowerCamelCase           =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)



def 					_a      (		lowerCamelCase,			lowerCamelCase							):
 lowerCamelCase	:			Optional[Any]           =   float()
 try:
  lowerCamelCase	:			Tuple           =   mass * acceleration
 except Exception:
  return -0.0
 return force


if __name__ == "__main__":
      import doctest

      # run doctest
      doctest.testmod()

      # demo
      _lowerCamelCase           =12.5
      _lowerCamelCase           =1_0
      _lowerCamelCase           =newtons_second_law_of_motion(mass, acceleration)
      print("""The force is """, force, """N""")




