
"""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__ ( a :					Union[str, Any]=10		):

  """simple docstring"""







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


# Configuration
_a   :      int       =				HfArgumentParser(TokenizerTrainingArguments)
_a   :      Tuple       =				parser.parse_args()

# Base tokenizer
_a   :      Tuple       =				AutoTokenizer.from_pretrained(args.base_tokenizer)
_a   :      List[Any]       =				list(bytes_to_unicode().values())

# Load dataset
_a   :      Tuple       =				load_dataset(args.dataset_name, split="""train""", streaming=True)
_a   :      Optional[Any]       =				iter(dataset)


# Training and saving
_a   :      Optional[Any]       =				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'''







from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments






def 					_SCREAMING_SNAKE_CASE		(						):
	_lowercase							=     HfArgumentParser(snake_case_	)
	try:
		_lowercase							=     parser.parse_args_into_dataclasses()[0]
	except ValueError as e:
		_lowercase							=     """Arg --no_{0} is no longer used, please use --no-{0} instead."""
		_lowercase							=     """ """.join(str(snake_case_	).split(""" """	)[:-1]	)
		_lowercase							=     """"""
		_lowercase							=     eval(str(snake_case_	).split(""" """	)[-1]	)
		_lowercase							=     []
		for arg in depreciated_args:
			# arg[2:] removes '--'
			if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
				# arg[5:] removes '--no_'
				full_error_msg += arg_error_msg.format(arg[5:]	)
			else:
				wrong_args.append(snake_case_	)
		if len(snake_case_	) > 0:
			_lowercase							=     full_error_msg + begin_error_msg + str(snake_case_	)
		raise ValueError(snake_case_	)

	_lowercase							=     PyTorchBenchmark(args=snake_case_	)
	benchmark.run()


if __name__ == "__main__":
							main()




