


'''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 				a_							(    lowercase=10     )      ->Optional[int]:






				'''simple docstring'''

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


# Configuration
__lowercase	:   Optional[int]   		=					HfArgumentParser(TokenizerTrainingArguments)
__lowercase	:   Dict   		=					parser.parse_args()

# Base tokenizer
__lowercase	:   Optional[Any]   		=					AutoTokenizer.from_pretrained(args.base_tokenizer)
__lowercase	:   Dict   		=					list(bytes_to_unicode().values())

# Load dataset
__lowercase	:   List[Any]   		=					load_dataset(args.dataset_name, split="""train""", streaming=True)
__lowercase	:   Union[str, Any]   		=					iter(dataset)


# Training and saving
__lowercase	:   List[str]   		=					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 				a_							(    lowercase				,    lowercase     )      ->list:






				'''simple docstring'''

				__lowerCamelCase		:   int           =       len(lowercase     )
				__lowerCamelCase		:   int           =       len(lowercase     )
				__lowerCamelCase		:   int           =       (
				    first_input_list_length
				    if first_input_list_length > second_input_list_length
				    else second_input_list_length
				)
				__lowerCamelCase		:   list           =       []
				for char_count in range(lowercase     ):
								if char_count < first_input_list_length:
												output_result_list.append(first_input_list[char_count]     )
								if char_count < second_input_list_length:
												output_result_list.append(second_input_list[char_count]     )

				return output_result_list


if __name__ == "__main__":
				print(alternative_list_arrange(["""A""", """B""", """C"""], [1, 2, 3, 4, 5]), end=""" """)





