





"""simple docstring"""




import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset






@pytest.mark.parametrize("dataset_size"					, [None, 400 * 2**20, 600 * 2**20]      )
@pytest.mark.parametrize("input_in_memory_max_size"					, ["default", 0, 100 * 2**20, 900 * 2**20]      )
def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[Any]					, _lowercase		:					Any					, _lowercase		:					Dict      )				->Optional[int]:






	'''simple docstring'''






	if input_in_memory_max_size != "default":
		monkeypatch.setattr(datasets.config					, "IN_MEMORY_MAX_SIZE"					, _lowercase      )
	a   :	Optional[int]			=       datasets.config.IN_MEMORY_MAX_SIZE
	if input_in_memory_max_size == "default":
		assert in_memory_max_size == 0
	else:
		assert in_memory_max_size == input_in_memory_max_size
	if dataset_size and in_memory_max_size:
		a   :	List[str]			=       dataset_size < in_memory_max_size
	else:
		a   :	str			=       False
	a   :	Optional[int]			=       is_small_dataset(_lowercase      )
	assert result == expected









"""simple docstring"""




import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					List[str]      )				->int:






	'''simple docstring'''






	a   :	int			=       {}
	a   :	Union[str, Any]			=       tokenizer(example["content"]					, truncation=_lowercase      )["input_ids"]
	a   :	Any			=       len(example["content"]      ) / len(output["input_ids"]      )
	return output


a		:				int  			=    HfArgumentParser(PretokenizationArguments)
a		:				Optional[int]  			=    parser.parse_args()
if args.num_workers is None:
		a		:				Tuple  			=    multiprocessing.cpu_count()
a		:				Optional[int]  			=    AutoTokenizer.from_pretrained(args.tokenizer_dir)

a		:				Dict  			=    time.time()
a		:				Tuple  			=    load_dataset(args.dataset_name, split='''train''')
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

a		:				Dict  			=    time.time()
a		:				Tuple  			=    ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        '''repo_name''',
        '''path''',
        '''copies''',
        '''size''',
        '''content''',
        '''license''',
        '''hash''',
        '''line_mean''',
        '''line_max''',
        '''alpha_frac''',
        '''autogenerated''',
    ],
)
print(F'''Dataset tokenized in {time.time()-t_start:.2f}s''')

a		:				Tuple  			=    time.time()
ds.push_to_hub(args.tokenized_data_repo)







print(F'''Data pushed to the hub in {time.time()-t_start:.2f}s''')


