




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_:							List[str]=1_0 )     -> Dict:




				'''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__						=	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    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							list		,    SCREAMING_SNAKE_CASE_:							int		,    SCREAMING_SNAKE_CASE_:							int		,    SCREAMING_SNAKE_CASE_:							int )     -> int:




				'''simple docstring'''





				A__            =     int((lower_bound + upper_bound) // 2 )
				if array[r] == value:
								return r
				if lower_bound >= upper_bound:
								return -1
				if array[r] < value:
								return binary_search(SCREAMING_SNAKE_CASE_		,    r + 1		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_ )
				else:
								return binary_search(SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    r - 1		,    SCREAMING_SNAKE_CASE_ )



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							int		,    SCREAMING_SNAKE_CASE_:							list )     -> list:




				'''simple docstring'''





				A__            =     0
				if matrix[index][0] == value:
								return [index, 0]
				while index < len(SCREAMING_SNAKE_CASE_ ) and matrix[index][0] < value:
								A__            =     binary_search(matrix[index]		,    0		,    len(matrix[index] ) - 1		,    SCREAMING_SNAKE_CASE_ )
								if r != -1:
												return [index, r]
								index += 1
				return [-1, -1]


if __name__ == "__main__":
	import doctest

	doctest.testmod()

