





import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows







def   __lowerCamelCase   (				__a  :Union[str, Any]       )						->							Tuple:  # picklable for multiprocessing






		"""simple docstring"""

		return i + 1







@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def   __lowerCamelCase   (				)						->							Dict:






		"""simple docstring"""

		with parallel_backend("""spark"""       ):
				assert ParallelBackendConfig.backend_name == "spark"

		A__     =  [1, 2, 3]
		with pytest.raises(__a       ):
				with parallel_backend("""unsupported backend"""       ):
						map_nested(__a       ,	__a       ,	num_proc=2       )

		with pytest.raises(__a       ):
				with parallel_backend("""unsupported backend"""       ):
						map_nested(__a       ,	__a       ,	num_proc=-1       )







@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("""num_proc"""       ,	[2, -1]       )
def   __lowerCamelCase   (				__a  :Tuple       )						->							Union[str, Any]:






		"""simple docstring"""

		A__     =  [1, 2]
		A__     =  {"""a""": 1, """b""": 2}
		A__     =  {"""a""": [1, 2], """b""": [3, 4]}
		A__     =  {"""a""": {"""1""": 1}, """b""": 2}
		A__     =  {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
		A__     =  [2, 3]
		A__     =  {"""a""": 2, """b""": 3}
		A__     =  {"""a""": [2, 3], """b""": [4, 5]}
		A__     =  {"""a""": {"""1""": 2}, """b""": 3}
		A__     =  {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}

		with parallel_backend("""spark"""       ):
				assert map_nested(__a       ,	__a       ,	num_proc=__a       ) == expected_map_nested_sa
				assert map_nested(__a       ,	__a       ,	num_proc=__a       ) == expected_map_nested_sa
				assert map_nested(__a       ,	__a       ,	num_proc=__a       ) == expected_map_nested_sa
				assert map_nested(__a       ,	__a       ,	num_proc=__a       ) == expected_map_nested_sa
				assert map_nested(__a       ,	__a       ,	num_proc=__a       ) == expected_map_nested_sa










import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser







def   __lowerCamelCase   (				__a  :Optional[int]       )						->							str:






		"""simple docstring"""

		A__     =  {}
		A__     =  tokenizer(example["""content"""]       ,	truncation=__a       )["""input_ids"""]
		A__     =  len(example["""content"""]       ) / len(output["""input_ids"""]       )
		return output


A       :   Union[str, Any]					= HfArgumentParser(PretokenizationArguments)
A       :   str					= parser.parse_args()
if args.num_workers is None:
					A       :   Any					= multiprocessing.cpu_count()
A       :   int					= AutoTokenizer.from_pretrained(args.tokenizer_dir)

A       :   Optional[int]					= time.time()
A       :   int					= load_dataset(args.dataset_name, split='''train''')
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

A       :   Optional[Any]					= time.time()
A       :   int					= 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''')



