
"""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     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase       )				-> Any:
	assert isinstance(__UpperCamelCase    , __UpperCamelCase       )
	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     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )				-> int:
	_lowerCAmelCase								=tmp_path / """cache"""
	_lowerCAmelCase								={"""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								=CsvDatasetReader(__UpperCamelCase    , cache_dir=__UpperCamelCase    , keep_in_memory=__UpperCamelCase       ).read()
	_check_csv_dataset(__UpperCamelCase    , __UpperCamelCase       )



@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     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )				-> Tuple:
	_lowerCAmelCase								=tmp_path / """cache"""
	# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
	_lowerCAmelCase								={"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
	_lowerCAmelCase								=features.copy() if features else default_expected_features
	_lowerCAmelCase								=(
	    Features({feature: Value(__UpperCamelCase       ) for feature, dtype in features.items()}       ) if features is not None else None
	)
	_lowerCAmelCase								=CsvDatasetReader(__UpperCamelCase    , features=__UpperCamelCase    , cache_dir=__UpperCamelCase       ).read()
	_check_csv_dataset(__UpperCamelCase    , __UpperCamelCase       )



@pytest.mark.parametrize("""split"""    , [None, NamedSplit("""train"""       ), """train""", """test"""]       )
def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )				-> Optional[Any]:
	_lowerCAmelCase								=tmp_path / """cache"""
	_lowerCAmelCase								={"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
	_lowerCAmelCase								=CsvDatasetReader(__UpperCamelCase    , cache_dir=__UpperCamelCase    , split=__UpperCamelCase       ).read()
	_check_csv_dataset(__UpperCamelCase    , __UpperCamelCase       )
	assert dataset.split == split if split else "train"



@pytest.mark.parametrize("""path_type"""    , [str, list]       )
def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )				-> List[Any]:
	if issubclass(__UpperCamelCase    , __UpperCamelCase       ):
		_lowerCAmelCase								=csv_path
	elif issubclass(__UpperCamelCase    , __UpperCamelCase       ):
		_lowerCAmelCase								=[csv_path]
	_lowerCAmelCase								=tmp_path / """cache"""
	_lowerCAmelCase								={"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
	_lowerCAmelCase								=CsvDatasetReader(__UpperCamelCase    , cache_dir=__UpperCamelCase       ).read()
	_check_csv_dataset(__UpperCamelCase    , __UpperCamelCase       )



def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase=("train",)       )				-> int:
	assert isinstance(__UpperCamelCase    , __UpperCamelCase       )
	for split in splits:
		_lowerCAmelCase								=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     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )				-> Optional[Any]:
	_lowerCAmelCase								=tmp_path / """cache"""
	_lowerCAmelCase								={"""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								=CsvDatasetReader({"""train""": csv_path}    , cache_dir=__UpperCamelCase    , keep_in_memory=__UpperCamelCase       ).read()
	_check_csv_datasetdict(__UpperCamelCase    , __UpperCamelCase       )



@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     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )				-> int:
	_lowerCAmelCase								=tmp_path / """cache"""
	# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
	_lowerCAmelCase								={"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
	_lowerCAmelCase								=features.copy() if features else default_expected_features
	_lowerCAmelCase								=(
	    Features({feature: Value(__UpperCamelCase       ) for feature, dtype in features.items()}       ) if features is not None else None
	)
	_lowerCAmelCase								=CsvDatasetReader({"""train""": csv_path}    , features=__UpperCamelCase    , cache_dir=__UpperCamelCase       ).read()
	_check_csv_datasetdict(__UpperCamelCase    , __UpperCamelCase       )



@pytest.mark.parametrize("""split"""    , [None, NamedSplit("""train"""       ), """train""", """test"""]       )
def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )				-> Dict:
	if split:
		_lowerCAmelCase								={split: csv_path}
	else:
		_lowerCAmelCase								={"""train""": csv_path, """test""": csv_path}
	_lowerCAmelCase								=tmp_path / """cache"""
	_lowerCAmelCase								={"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
	_lowerCAmelCase								=CsvDatasetReader(__UpperCamelCase    , cache_dir=__UpperCamelCase       ).read()
	_check_csv_datasetdict(__UpperCamelCase    , __UpperCamelCase    , splits=list(path.keys()       )       )
	assert all(dataset[split].split == split for split in path.keys()       )



def     _lowerCamelCase(__UpperCamelCase       )				-> Optional[int]:
	with open(__UpperCamelCase    , encoding="""utf-8"""       ) as csvfile:
		yield from csv.reader(__UpperCamelCase       )



def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase       )				-> str:
	_lowerCAmelCase								=tmp_path / """cache"""
	_lowerCAmelCase								=os.path.join(__UpperCamelCase    , """tmp.csv"""       )
	_lowerCAmelCase								=CsvDatasetReader({"""train""": csv_path}    , cache_dir=__UpperCamelCase       ).read()
	CsvDatasetWriter(dataset["""train"""]    , __UpperCamelCase    , num_proc=1       ).write()

	_lowerCAmelCase								=iter_csv_file(__UpperCamelCase       )
	_lowerCAmelCase								=iter_csv_file(__UpperCamelCase       )

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



def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase       )				-> str:
	_lowerCAmelCase								=tmp_path / """cache"""
	_lowerCAmelCase								=os.path.join(__UpperCamelCase    , """tmp.csv"""       )
	_lowerCAmelCase								=CsvDatasetReader({"""train""": csv_path}    , cache_dir=__UpperCamelCase       ).read()
	CsvDatasetWriter(dataset["""train"""]    , __UpperCamelCase    , num_proc=2       ).write()

	_lowerCAmelCase								=iter_csv_file(__UpperCamelCase       )
	_lowerCAmelCase								=iter_csv_file(__UpperCamelCase       )

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



def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase       )				-> List[Any]:
	_lowerCAmelCase								=tmp_path / """cache"""
	_lowerCAmelCase								=os.path.join(__UpperCamelCase    , """tmp.csv"""       )
	_lowerCAmelCase								=CsvDatasetReader({"""train""": csv_path}    , cache_dir=__UpperCamelCase       ).read()
	with pytest.raises(__UpperCamelCase       ):
		CsvDatasetWriter(dataset["""train"""]    , __UpperCamelCase    , num_proc=0       )





"""simple docstring"""


import itertools



def     _lowerCamelCase(__UpperCamelCase       )				-> List[str]:
	return (
	    int("""""".join(combination[0:2]       )       ) * int("""""".join(combination[2:5]       )       )
	    == int("""""".join(combination[5:9]       )       )
	) or (
	    int("""""".join(combination[0]       )       ) * int("""""".join(combination[1:5]       )       )
	    == int("""""".join(combination[5:9]       )       )
	)



def     _lowerCamelCase()				-> Dict:
	return sum(
	    {
	        int("""""".join(pandigital[5:9]       )       )
	        for pandigital in itertools.permutations("""123456789"""       )
	        if is_combination_valid(__UpperCamelCase       )
	    }       )


if __name__ == "__main__":
			print(solution())



