




'''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 		snake_case_    (UpperCamelCase       :  Dict ,						UpperCamelCase       :  List[str]      ):





							'''simple docstring'''


							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 		snake_case_    (UpperCamelCase       :  Any ,						UpperCamelCase       :  Optional[int] ,						UpperCamelCase       :  List[Any]      ):





							'''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(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 		snake_case_    (UpperCamelCase       :  List[str] ,						UpperCamelCase       :  List[str] ,						UpperCamelCase       :  Optional[int]      ):





							'''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(UpperCamelCase      ) for feature, dtype in features.items()}      ) if features is not None else None
							)
							_a	     =				CsvDatasetReader(UpperCamelCase ,						features=UpperCamelCase ,						cache_dir=UpperCamelCase      ).read()
							_check_csv_dataset(UpperCamelCase ,						UpperCamelCase      )
@pytest.mark.parametrize('''split''' ,						[None, NamedSplit('''train'''      ), '''train''', '''test''']      )
def 		snake_case_    (UpperCamelCase       :  str ,						UpperCamelCase       :  Any ,						UpperCamelCase       :  int      ):





							'''simple docstring'''


							_a	     =				tmp_path / '''cache'''
							_a	     =				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
							_a	     =				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 		snake_case_    (UpperCamelCase       :  List[Any] ,						UpperCamelCase       :  Union[str, Any] ,						UpperCamelCase       :  List[Any]      ):





							'''simple docstring'''


							if issubclass(UpperCamelCase ,						UpperCamelCase      ):
														_a	     =				csv_path
							elif issubclass(UpperCamelCase ,						UpperCamelCase      ):
														_a	     =				[csv_path]
							_a	     =				tmp_path / '''cache'''
							_a	     =				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
							_a	     =				CsvDatasetReader(UpperCamelCase ,						cache_dir=UpperCamelCase      ).read()
							_check_csv_dataset(UpperCamelCase ,						UpperCamelCase      )
def 		snake_case_    (UpperCamelCase       :  str ,						UpperCamelCase       :  Optional[Any] ,						UpperCamelCase       :  int=("train",)      ):





							'''simple docstring'''


							assert isinstance(UpperCamelCase ,						UpperCamelCase      )
							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 		snake_case_    (UpperCamelCase       :  Any ,						UpperCamelCase       :  Tuple ,						UpperCamelCase       :  Dict      ):





							'''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=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 		snake_case_    (UpperCamelCase       :  Optional[int] ,						UpperCamelCase       :  List[Any] ,						UpperCamelCase       :  int      ):





							'''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(UpperCamelCase      ) for feature, dtype in features.items()}      ) if features is not None else None
							)
							_a	     =				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 		snake_case_    (UpperCamelCase       :  Any ,						UpperCamelCase       :  Optional[Any] ,						UpperCamelCase       :  Optional[Any]      ):





							'''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(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 		snake_case_    (UpperCamelCase       :  int      ):





							'''simple docstring'''


							with open(UpperCamelCase ,						encoding='''utf-8'''      ) as csvfile:
														yield from csv.reader(UpperCamelCase      )
def 		snake_case_    (UpperCamelCase       :  Tuple ,						UpperCamelCase       :  List[str]      ):





							'''simple docstring'''


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

							_a	     =				iter_csv_file(UpperCamelCase      )
							_a	     =				iter_csv_file(UpperCamelCase      )

							for rowa, rowa in zip(UpperCamelCase ,						UpperCamelCase      ):
														assert rowa == rowa
def 		snake_case_    (UpperCamelCase       :  Optional[Any] ,						UpperCamelCase       :  Dict      ):





							'''simple docstring'''


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

							_a	     =				iter_csv_file(UpperCamelCase      )
							_a	     =				iter_csv_file(UpperCamelCase      )

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

def 		snake_case_    (UpperCamelCase       :  Dict ,						UpperCamelCase       :  Union[str, Any]      ):





							'''simple docstring'''


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







'''simple docstring'''


from __future__ import annotations
def 		snake_case_    (UpperCamelCase       :  int      ):





							'''simple docstring'''


							_a	     =				[0] * max_one

							for i in range(0 ,						UpperCamelCase      ):
														_a	     =				i

							for i in range(2 ,						UpperCamelCase      ):
														if totients[i] == i:
																					for j in range(UpperCamelCase ,						UpperCamelCase ,						UpperCamelCase      ):
																												totients[j] -= totients[j] // i

							return totients
def 		snake_case_    (UpperCamelCase       :  int ,						UpperCamelCase       :  int      ):





							'''simple docstring'''


							return sorted(str(UpperCamelCase      )      ) == sorted(str(UpperCamelCase      )      )
def 		snake_case_    (UpperCamelCase       :  int = 1000_0000      ):





							'''simple docstring'''


							_a	     =				1  # i
							_a	     =				0  # φ(i)
							_a	     =				get_totients(max_n + 1      )

							for i in range(2 ,						max_n + 1      ):
														_a	     =				totients[i]

														if i * min_denominator < min_numerator * t and has_same_digits(UpperCamelCase ,						UpperCamelCase      ):
																					_a	     =				i
																					_a	     =				t

							return min_numerator


if __name__ == "__main__":
				print(F'''{solution() = }''')

