






'''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   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__=10		):
 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
SCREAMING_SNAKE_CASE_				      =							HfArgumentParser(TokenizerTrainingArguments)
SCREAMING_SNAKE_CASE_				      =							parser.parse_args()

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

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


# Training and saving
SCREAMING_SNAKE_CASE_				      =							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)












'''simple docstring'''


def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__ = 30		):
 __a    :	Tuple									=						exponent + 2
 __a    :	str									=						(1 + 5**0.5) / 2
 __a    :	Any									=						(phi**fibonacci_index - (phi - 1) ** fibonacci_index) / 5**0.5

 return int(SCREAMING_SNAKE_CASE__		)


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




