





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__						(    __a   , __a   ):
	assert isinstance(__a   , __a   )
	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__						(    __a   , __a   , __a   ):
	snake_case_		:			int											=	tmp_path / 'cache'
	snake_case_		:			Optional[int]											=	{'text': 'string'}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		snake_case_		:			Union[str, Any]											=	TextDatasetReader(__a   , cache_dir=__a   , keep_in_memory=__a   ).read()
	_check_text_dataset(__a   , __a   )


@pytest.mark.parametrize(
    'features'   , [
        None,
        {'text': 'string'},
        {'text': 'int32'},
        {'text': 'float32'},
    ]   , )
def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
	snake_case_		:			List[Any]											=	tmp_path / 'cache'
	snake_case_		:			str											=	{'text': 'string'}
	snake_case_		:			List[Any]											=	features.copy() if features else default_expected_features
	snake_case_		:			Tuple											=	(
	    Features({feature: Value(__a   ) for feature, dtype in features.items()}   ) if features is not None else None
	)
	snake_case_		:			Optional[Any]											=	TextDatasetReader(__a   , features=__a   , cache_dir=__a   ).read()
	_check_text_dataset(__a   , __a   )


@pytest.mark.parametrize('split'   , [None, NamedSplit('train'   ), 'train', 'test']   )
def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
	snake_case_		:			List[str]											=	tmp_path / 'cache'
	snake_case_		:			Optional[Any]											=	{'text': 'string'}
	snake_case_		:			str											=	TextDatasetReader(__a   , cache_dir=__a   , split=__a   ).read()
	_check_text_dataset(__a   , __a   )
	assert dataset.split == split if split else "train"


@pytest.mark.parametrize('path_type'   , [str, list]   )
def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
	if issubclass(__a   , __a   ):
		snake_case_		:			str											=	text_path
	elif issubclass(__a   , __a   ):
		snake_case_		:			Any											=	[text_path]
	snake_case_		:			Tuple											=	tmp_path / 'cache'
	snake_case_		:			Optional[int]											=	{'text': 'string'}
	snake_case_		:			int											=	TextDatasetReader(__a   , cache_dir=__a   ).read()
	_check_text_dataset(__a   , __a   )


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a=("train",)   ):
	assert isinstance(__a   , __a   )
	for split in splits:
		snake_case_		:			Optional[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__						(    __a   , __a   , __a   ):
	snake_case_		:			int											=	tmp_path / 'cache'
	snake_case_		:			str											=	{'text': 'string'}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		snake_case_		:			Any											=	TextDatasetReader({'train': text_path}   , cache_dir=__a   , keep_in_memory=__a   ).read()
	_check_text_datasetdict(__a   , __a   )


@pytest.mark.parametrize(
    'features'   , [
        None,
        {'text': 'string'},
        {'text': 'int32'},
        {'text': 'float32'},
    ]   , )
def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
	snake_case_		:			Union[str, Any]											=	tmp_path / 'cache'
	# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
	snake_case_		:			Optional[int]											=	{'text': 'string'}
	snake_case_		:			str											=	features.copy() if features else default_expected_features
	snake_case_		:			Dict											=	(
	    Features({feature: Value(__a   ) for feature, dtype in features.items()}   ) if features is not None else None
	)
	snake_case_		:			List[str]											=	TextDatasetReader({'train': text_path}   , features=__a   , cache_dir=__a   ).read()
	_check_text_datasetdict(__a   , __a   )


@pytest.mark.parametrize('split'   , [None, NamedSplit('train'   ), 'train', 'test']   )
def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
	if split:
		snake_case_		:			Optional[Any]											=	{split: text_path}
	else:
		snake_case_		:			str											=	'train'
		snake_case_		:			Dict											=	{'train': text_path, 'test': text_path}
	snake_case_		:			Optional[int]											=	tmp_path / 'cache'
	snake_case_		:			Union[str, Any]											=	{'text': 'string'}
	snake_case_		:			str											=	TextDatasetReader(__a   , cache_dir=__a   ).read()
	_check_text_datasetdict(__a   , __a   , splits=list(path.keys()   )   )
	assert all(dataset[split].split == split for split in path.keys()   )








from ....utils import logging


_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_						(					snake_case_					):






						def __init__(      self      :      str		,   _A      :      List[Any]		,   _A      :      List[Any]=None		,   _A      :      str=2048	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	config.__dict__
							snake_case_		:			List[str]											=	modal_hidden_size
							if num_labels:
								snake_case_		:			int											=	num_labels

