
'''simple docstring'''






import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		assert isinstance(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  )
		assert dataset.num_rows == 4
		assert dataset.num_columns == 3
		assert dataset.column_names == ["col_1", "col_2", "col_3"]
		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__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		lowerCAmelCase_   :       Any    							=tmp_path / '''cache'''
		lowerCAmelCase_   :       int    							={'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				lowerCAmelCase_   :       List[Any]    							=CsvDatasetReader(_SCREAMING_SNAKE_CASE  ,					cache_dir=_SCREAMING_SNAKE_CASE  ,					keep_in_memory=_SCREAMING_SNAKE_CASE  ).read()
		_check_csv_dataset(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  )







@pytest.mark.parametrize(
    '''features'''  ,					[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]  ,					)
def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		lowerCAmelCase_   :       str    							=tmp_path / '''cache'''
		# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
		lowerCAmelCase_   :       List[str]    							={'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
		lowerCAmelCase_   :       Optional[Any]    							=features.copy() if features else default_expected_features
		lowerCAmelCase_   :       Union[str, Any]    							=(
		    Features({feature: Value(_SCREAMING_SNAKE_CASE  ) for feature, dtype in features.items()}  ) if features is not None else None
		)
		lowerCAmelCase_   :       int    							=CsvDatasetReader(_SCREAMING_SNAKE_CASE  ,					features=_SCREAMING_SNAKE_CASE  ,					cache_dir=_SCREAMING_SNAKE_CASE  ).read()
		_check_csv_dataset(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  )







@pytest.mark.parametrize('''split'''  ,					[None, NamedSplit('''train'''  ), '''train''', '''test''']  )
def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		lowerCAmelCase_   :       List[str]    							=tmp_path / '''cache'''
		lowerCAmelCase_   :       str    							={'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
		lowerCAmelCase_   :       Any    							=CsvDatasetReader(_SCREAMING_SNAKE_CASE  ,					cache_dir=_SCREAMING_SNAKE_CASE  ,					split=_SCREAMING_SNAKE_CASE  ).read()
		_check_csv_dataset(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  )
		assert dataset.split == split if split else "train"







@pytest.mark.parametrize('''path_type'''  ,					[str, list]  )
def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		if issubclass(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
				lowerCAmelCase_   :       List[str]    							=csv_path
		elif issubclass(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
				lowerCAmelCase_   :       Union[str, Any]    							=[csv_path]
		lowerCAmelCase_   :       Union[str, Any]    							=tmp_path / '''cache'''
		lowerCAmelCase_   :       Optional[Any]    							={'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
		lowerCAmelCase_   :       int    							=CsvDatasetReader(_SCREAMING_SNAKE_CASE  ,					cache_dir=_SCREAMING_SNAKE_CASE  ).read()
		_check_csv_dataset(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  )







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE=("train",)  ):
		assert isinstance(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  )
		for split in splits:
				lowerCAmelCase_   :       List[str]    							=dataset_dict[split]
				assert dataset.num_rows == 4
				assert dataset.num_columns == 3
				assert dataset.column_names == ["col_1", "col_2", "col_3"]
				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__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		lowerCAmelCase_   :       Any    							=tmp_path / '''cache'''
		lowerCAmelCase_   :       Tuple    							={'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				lowerCAmelCase_   :       List[Any]    							=CsvDatasetReader({'''train''': csv_path}  ,					cache_dir=_SCREAMING_SNAKE_CASE  ,					keep_in_memory=_SCREAMING_SNAKE_CASE  ).read()
		_check_csv_datasetdict(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  )







@pytest.mark.parametrize(
    '''features'''  ,					[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]  ,					)
def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		lowerCAmelCase_   :       List[Any]    							=tmp_path / '''cache'''
		# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
		lowerCAmelCase_   :       int    							={'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
		lowerCAmelCase_   :       Any    							=features.copy() if features else default_expected_features
		lowerCAmelCase_   :       Tuple    							=(
		    Features({feature: Value(_SCREAMING_SNAKE_CASE  ) for feature, dtype in features.items()}  ) if features is not None else None
		)
		lowerCAmelCase_   :       List[Any]    							=CsvDatasetReader({'''train''': csv_path}  ,					features=_SCREAMING_SNAKE_CASE  ,					cache_dir=_SCREAMING_SNAKE_CASE  ).read()
		_check_csv_datasetdict(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  )







@pytest.mark.parametrize('''split'''  ,					[None, NamedSplit('''train'''  ), '''train''', '''test''']  )
def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		if split:
				lowerCAmelCase_   :       Union[str, Any]    							={split: csv_path}
		else:
				lowerCAmelCase_   :       List[str]    							={'''train''': csv_path, '''test''': csv_path}
		lowerCAmelCase_   :       Any    							=tmp_path / '''cache'''
		lowerCAmelCase_   :       Any    							={'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
		lowerCAmelCase_   :       Tuple    							=CsvDatasetReader(_SCREAMING_SNAKE_CASE  ,					cache_dir=_SCREAMING_SNAKE_CASE  ).read()
		_check_csv_datasetdict(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					splits=list(path.keys()  )  )
		assert all(dataset[split].split == split for split in path.keys()  )







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ):
		with open(_SCREAMING_SNAKE_CASE  ,					encoding='''utf-8'''  ) as csvfile:
				yield from csv.reader(_SCREAMING_SNAKE_CASE  )







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		lowerCAmelCase_   :       Tuple    							=tmp_path / '''cache'''
		lowerCAmelCase_   :       Any    							=os.path.join(_SCREAMING_SNAKE_CASE  ,					'''tmp.csv'''  )
		lowerCAmelCase_   :       Any    							=CsvDatasetReader({'''train''': csv_path}  ,					cache_dir=_SCREAMING_SNAKE_CASE  ).read()
		CsvDatasetWriter(dataset['''train''']  ,					_SCREAMING_SNAKE_CASE  ,					num_proc=1  ).write()

		lowerCAmelCase_   :       Tuple    							=iter_csv_file(_SCREAMING_SNAKE_CASE  )
		lowerCAmelCase_   :       List[Any]    							=iter_csv_file(_SCREAMING_SNAKE_CASE  )

		for rowa, rowa in zip(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
				assert rowa == rowa







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		lowerCAmelCase_   :       Any    							=tmp_path / '''cache'''
		lowerCAmelCase_   :       Tuple    							=os.path.join(_SCREAMING_SNAKE_CASE  ,					'''tmp.csv'''  )
		lowerCAmelCase_   :       int    							=CsvDatasetReader({'''train''': csv_path}  ,					cache_dir=_SCREAMING_SNAKE_CASE  ).read()
		CsvDatasetWriter(dataset['''train''']  ,					_SCREAMING_SNAKE_CASE  ,					num_proc=2  ).write()

		lowerCAmelCase_   :       Union[str, Any]    							=iter_csv_file(_SCREAMING_SNAKE_CASE  )
		lowerCAmelCase_   :       int    							=iter_csv_file(_SCREAMING_SNAKE_CASE  )

		for rowa, rowa in zip(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
				assert rowa == rowa







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		lowerCAmelCase_   :       Union[str, Any]    							=tmp_path / '''cache'''
		lowerCAmelCase_   :       List[str]    							=os.path.join(_SCREAMING_SNAKE_CASE  ,					'''tmp.csv'''  )
		lowerCAmelCase_   :       str    							=CsvDatasetReader({'''train''': csv_path}  ,					cache_dir=_SCREAMING_SNAKE_CASE  ).read()
		with pytest.raises(_SCREAMING_SNAKE_CASE  ):
				CsvDatasetWriter(dataset['''train''']  ,					_SCREAMING_SNAKE_CASE  ,					num_proc=0  )














def   snake_case		(				UpperCAmelCase						:							set,    UpperCAmelCase						:							set ):
				assert isinstance(UpperCAmelCase,    UpperCAmelCase ), f'The input value of [set_a={set_a}] is not a set'
				assert isinstance(UpperCAmelCase,    UpperCAmelCase ), f'The input value of [set_b={set_b}] is not a set'

				return {a + b for a in set_a for b in set_b}


if __name__ == "__main__":
					from doctest import testmod

					testmod()





