



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    (				SCREAMING_SNAKE_CASE__    :		Dict=10			)					->       int:
 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
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)





def  lowercase    (				SCREAMING_SNAKE_CASE__    :		float			,    SCREAMING_SNAKE_CASE__    :		float			)					->       float:
 _snake_case      :					str					=   float()
 try:
  _snake_case      :					str					=   mass * acceleration
 except Exception:
  return -0.0
 return force


if __name__ == "__main__":
  import doctest

  # run doctest
  doctest.testmod()

  # demo
  a__      =		12.5
  a__      =		10
  a__      =		newtons_second_law_of_motion(mass, acceleration)
  print("""The force is """, force, """N""")
