





"""simple docstring"""




import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[int]					, _lowercase		:					List[Any]      )				->Optional[Any]:






	'''simple docstring'''






	assert isinstance(_lowercase					, _lowercase      )
	assert dataset.num_rows == 4
	assert dataset.num_columns == 1
	assert dataset.column_names == ["text"]
	for feature, expected_dtype in expected_features.items():
		assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize("keep_in_memory"					, [False, True]      )
def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Dict					, _lowercase		:					int					, _lowercase		:					Optional[Any]      )				->List[Any]:






	'''simple docstring'''






	a   :	Optional[int]			=       tmp_path / "cache"
	a   :	List[Any]			=       {"text": "string"}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		a   :	Dict			=       TextDatasetReader(_lowercase					, cache_dir=_lowercase					, keep_in_memory=_lowercase      ).read()
	_check_text_dataset(_lowercase					, _lowercase      )






@pytest.mark.parametrize(
    "features"					, [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]					, )
def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Dict					, _lowercase		:					str					, _lowercase		:					Dict      )				->Union[str, Any]:






	'''simple docstring'''






	a   :	Tuple			=       tmp_path / "cache"
	a   :	List[Any]			=       {"text": "string"}
	a   :	Tuple			=       features.copy() if features else default_expected_features
	a   :	List[str]			=       (
	    Features({feature: Value(_lowercase      ) for feature, dtype in features.items()}      ) if features is not None else None
	)
	a   :	Any			=       TextDatasetReader(_lowercase					, features=_lowercase					, cache_dir=_lowercase      ).read()
	_check_text_dataset(_lowercase					, _lowercase      )






@pytest.mark.parametrize("split"					, [None, NamedSplit("train"      ), "train", "test"]      )
def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Tuple					, _lowercase		:					str					, _lowercase		:					List[str]      )				->str:






	'''simple docstring'''






	a   :	List[Any]			=       tmp_path / "cache"
	a   :	Optional[int]			=       {"text": "string"}
	a   :	Tuple			=       TextDatasetReader(_lowercase					, cache_dir=_lowercase					, split=_lowercase      ).read()
	_check_text_dataset(_lowercase					, _lowercase      )
	assert dataset.split == split if split else "train"






@pytest.mark.parametrize("path_type"					, [str, list]      )
def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Any					, _lowercase		:					Tuple					, _lowercase		:					List[Any]      )				->List[Any]:






	'''simple docstring'''






	if issubclass(_lowercase					, _lowercase      ):
		a   :	Any			=       text_path
	elif issubclass(_lowercase					, _lowercase      ):
		a   :	Optional[Any]			=       [text_path]
	a   :	int			=       tmp_path / "cache"
	a   :	int			=       {"text": "string"}
	a   :	Dict			=       TextDatasetReader(_lowercase					, cache_dir=_lowercase      ).read()
	_check_text_dataset(_lowercase					, _lowercase      )






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Union[str, Any]					, _lowercase		:					Dict					, _lowercase		:					Any=("train",)      )				->List[str]:






	'''simple docstring'''






	assert isinstance(_lowercase					, _lowercase      )
	for split in splits:
		a   :	int			=       dataset_dict[split]
		assert dataset.num_rows == 4
		assert dataset.num_columns == 1
		assert dataset.column_names == ["text"]
		for feature, expected_dtype in expected_features.items():
			assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize("keep_in_memory"					, [False, True]      )
def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					str					, _lowercase		:					List[str]					, _lowercase		:					Union[str, Any]      )				->str:






	'''simple docstring'''






	a   :	Dict			=       tmp_path / "cache"
	a   :	List[str]			=       {"text": "string"}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		a   :	Dict			=       TextDatasetReader({"train": text_path}					, cache_dir=_lowercase					, keep_in_memory=_lowercase      ).read()
	_check_text_datasetdict(_lowercase					, _lowercase      )






@pytest.mark.parametrize(
    "features"					, [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]					, )
def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					str					, _lowercase		:					Dict					, _lowercase		:					str      )				->Dict:






	'''simple docstring'''






	a   :	List[str]			=       tmp_path / "cache"
	# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
	a   :	Any			=       {"text": "string"}
	a   :	Union[str, Any]			=       features.copy() if features else default_expected_features
	a   :	List[Any]			=       (
	    Features({feature: Value(_lowercase      ) for feature, dtype in features.items()}      ) if features is not None else None
	)
	a   :	List[str]			=       TextDatasetReader({"train": text_path}					, features=_lowercase					, cache_dir=_lowercase      ).read()
	_check_text_datasetdict(_lowercase					, _lowercase      )







@pytest.mark.parametrize("split"					, [None, NamedSplit("train"      ), "train", "test"]      )
def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[Any]					, _lowercase		:					Any					, _lowercase		:					str      )				->Union[str, Any]:






	'''simple docstring'''






	if split:
		a   :	Optional[Any]			=       {split: text_path}
	else:
		a   :	Any			=       "train"
		a   :	int			=       {"train": text_path, "test": text_path}
	a   :	Optional[int]			=       tmp_path / "cache"
	a   :	Any			=       {"text": "string"}
	a   :	Optional[Any]			=       TextDatasetReader(_lowercase					, cache_dir=_lowercase      ).read()
	_check_text_datasetdict(_lowercase					, _lowercase					, splits=list(path.keys()      )      )
	assert all(dataset[split].split == split for split in path.keys()      )









"""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''')


