






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    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					assert isinstance(_lowercase    ,	_lowercase       )
					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    __A    (   _lowercase    ,	_lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       tmp_path / '''cache'''
					_A									=       {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										_A									=       CsvDatasetReader(_lowercase    ,	cache_dir=_lowercase    ,	keep_in_memory=_lowercase       ).read()
					_check_csv_dataset(_lowercase    ,	_lowercase       )




@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    __A    (   _lowercase    ,	_lowercase    ,	_lowercase       ):

					'''simple docstring'''







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




@pytest.mark.parametrize('''split'''    ,	[None, NamedSplit('''train'''       ), '''train''', '''test''']       )
def    __A    (   _lowercase    ,	_lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       tmp_path / '''cache'''
					_A									=       {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					_A									=       CsvDatasetReader(_lowercase    ,	cache_dir=_lowercase    ,	split=_lowercase       ).read()
					_check_csv_dataset(_lowercase    ,	_lowercase       )
					assert dataset.split == split if split else "train"




@pytest.mark.parametrize('''path_type'''    ,	[str, list]       )
def    __A    (   _lowercase    ,	_lowercase    ,	_lowercase       ):

					'''simple docstring'''







					if issubclass(_lowercase    ,	_lowercase       ):
										_A									=       csv_path
					elif issubclass(_lowercase    ,	_lowercase       ):
										_A									=       [csv_path]
					_A									=       tmp_path / '''cache'''
					_A									=       {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					_A									=       CsvDatasetReader(_lowercase    ,	cache_dir=_lowercase       ).read()
					_check_csv_dataset(_lowercase    ,	_lowercase       )




def    __A    (   _lowercase    ,	_lowercase    ,	_lowercase=("train",)       ):

					'''simple docstring'''







					assert isinstance(_lowercase    ,	_lowercase       )
					for split in splits:
										_A									=       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    __A    (   _lowercase    ,	_lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       tmp_path / '''cache'''
					_A									=       {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										_A									=       CsvDatasetReader({'''train''': csv_path}    ,	cache_dir=_lowercase    ,	keep_in_memory=_lowercase       ).read()
					_check_csv_datasetdict(_lowercase    ,	_lowercase       )




@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    __A    (   _lowercase    ,	_lowercase    ,	_lowercase       ):

					'''simple docstring'''







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




@pytest.mark.parametrize('''split'''    ,	[None, NamedSplit('''train'''       ), '''train''', '''test''']       )
def    __A    (   _lowercase    ,	_lowercase    ,	_lowercase       ):

					'''simple docstring'''







					if split:
										_A									=       {split: csv_path}
					else:
										_A									=       {'''train''': csv_path, '''test''': csv_path}
					_A									=       tmp_path / '''cache'''
					_A									=       {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					_A									=       CsvDatasetReader(_lowercase    ,	cache_dir=_lowercase       ).read()
					_check_csv_datasetdict(_lowercase    ,	_lowercase    ,	splits=list(path.keys()       )       )
					assert all(dataset[split].split == split for split in path.keys()       )




def    __A    (   _lowercase       ):

					'''simple docstring'''







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




def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       tmp_path / '''cache'''
					_A									=       os.path.join(_lowercase    ,	'''tmp.csv'''       )
					_A									=       CsvDatasetReader({'''train''': csv_path}    ,	cache_dir=_lowercase       ).read()
					CsvDatasetWriter(dataset['''train''']    ,	_lowercase    ,	num_proc=1       ).write()

					_A									=       iter_csv_file(_lowercase       )
					_A									=       iter_csv_file(_lowercase       )

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




def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       tmp_path / '''cache'''
					_A									=       os.path.join(_lowercase    ,	'''tmp.csv'''       )
					_A									=       CsvDatasetReader({'''train''': csv_path}    ,	cache_dir=_lowercase       ).read()
					CsvDatasetWriter(dataset['''train''']    ,	_lowercase    ,	num_proc=2       ).write()

					_A									=       iter_csv_file(_lowercase       )
					_A									=       iter_csv_file(_lowercase       )

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




def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       tmp_path / '''cache'''
					_A									=       os.path.join(_lowercase    ,	'''tmp.csv'''       )
					_A									=       CsvDatasetReader({'''train''': csv_path}    ,	cache_dir=_lowercase       ).read()
					with pytest.raises(_lowercase       ):
										CsvDatasetWriter(dataset['''train''']    ,	_lowercase    ,	num_proc=0       )











from math import factorial




def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					return factorial(_lowercase       ) / (factorial(_lowercase       ) * factorial(n - r       ))




def    __A    (   ):

					'''simple docstring'''







					_A									=       0

					for i in range(1    ,	1_01       ):
										for j in range(1    ,	i + 1       ):
															if combinations(_lowercase    ,	_lowercase       ) > 1e6:
																				total += 1
					return total


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



