






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(      lowerCAmelCase__					:		Optional[int]=10   ):





				'''simple docstring'''







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


# 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)



'''simple docstring'''




import os





def 				a_							(    )      ->int:






				'''simple docstring'''

				__lowerCamelCase		:   Dict           =       os.path.dirname(os.path.realpath(lowercase     )     )
				__lowerCamelCase		:   Any           =       os.path.join(lowercase				,    "triangle.txt"     )

				with open(lowercase     ) as in_file:
								__lowerCamelCase		:   List[str]           =       [[int(lowercase     ) for i in line.split()] for line in in_file]

				while len(lowercase     ) != 1:
								__lowerCamelCase		:   List[str]           =       triangle.pop()
								__lowerCamelCase		:   Union[str, Any]           =       triangle[-1]
								for j in range(len(lowercase     ) - 1     ):
												curr_row[j] += max(last_row[j]				,    last_row[j + 1]     )
				return triangle[0][0]


if __name__ == "__main__":
				print(solution())





