






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 __UpperCAmelCase (			A			:	List[str]	,   A			:	List[Any]						)  -> List[Any]:
			assert isinstance(A	,   A						)
			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 __UpperCAmelCase (			A			:	Dict	,   A			:	Any	,   A			:	int						)  -> int:
			UpperCAmelCase_     :   Dict       =   tmp_path / '''cache'''
			UpperCAmelCase_     :   List[Any]       =   {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						UpperCAmelCase_     :   Union[str, Any]       =   CsvDatasetReader(A	,   cache_dir=A	,   keep_in_memory=A						).read()
			_check_csv_dataset(A	,   A						)
@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 __UpperCAmelCase (			A			:	List[str]	,   A			:	List[Any]	,   A			:	Optional[Any]						)  -> List[Any]:
			UpperCAmelCase_     :   Union[str, Any]       =   tmp_path / '''cache'''
			# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
			UpperCAmelCase_     :   Optional[int]       =   {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
			UpperCAmelCase_     :   Optional[int]       =   features.copy() if features else default_expected_features
			UpperCAmelCase_     :   int       =   (
			    Features({feature: Value(A						) for feature, dtype in features.items()}						) if features is not None else None
			)
			UpperCAmelCase_     :   str       =   CsvDatasetReader(A	,   features=A	,   cache_dir=A						).read()
			_check_csv_dataset(A	,   A						)
@pytest.mark.parametrize('''split'''	,   [None, NamedSplit('''train'''						), '''train''', '''test''']						)
def __UpperCAmelCase (			A			:	List[str]	,   A			:	Dict	,   A			:	Optional[Any]						)  -> List[str]:
			UpperCAmelCase_     :   Dict       =   tmp_path / '''cache'''
			UpperCAmelCase_     :   int       =   {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
			UpperCAmelCase_     :   Dict       =   CsvDatasetReader(A	,   cache_dir=A	,   split=A						).read()
			_check_csv_dataset(A	,   A						)
			assert dataset.split == split if split else "train"
@pytest.mark.parametrize('''path_type'''	,   [str, list]						)
def __UpperCAmelCase (			A			:	int	,   A			:	Dict	,   A			:	Union[str, Any]						)  -> int:




			if issubclass(A	,   A						):
						UpperCAmelCase_     :   Dict       =   csv_path
			elif issubclass(A	,   A						):
						UpperCAmelCase_     :   Tuple       =   [csv_path]
			UpperCAmelCase_     :   str       =   tmp_path / '''cache'''
			UpperCAmelCase_     :   Any       =   {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
			UpperCAmelCase_     :   Dict       =   CsvDatasetReader(A	,   cache_dir=A						).read()
			_check_csv_dataset(A	,   A						)
def __UpperCAmelCase (			A			:	Dict	,   A			:	str	,   A			:	Union[str, Any]=("train",)						)  -> Optional[Any]:
			assert isinstance(A	,   A						)
			for split in splits:
						UpperCAmelCase_     :   Any       =   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 __UpperCAmelCase (			A			:	Union[str, Any]	,   A			:	Union[str, Any]	,   A			:	int						)  -> Any:
			UpperCAmelCase_     :   Optional[int]       =   tmp_path / '''cache'''
			UpperCAmelCase_     :   Dict       =   {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						UpperCAmelCase_     :   str       =   CsvDatasetReader({'''train''': csv_path}	,   cache_dir=A	,   keep_in_memory=A						).read()
			_check_csv_datasetdict(A	,   A						)
@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 __UpperCAmelCase (			A			:	Optional[int]	,   A			:	Optional[Any]	,   A			:	Optional[int]						)  -> List[Any]:
			UpperCAmelCase_     :   List[str]       =   tmp_path / '''cache'''
			# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
			UpperCAmelCase_     :   int       =   {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
			UpperCAmelCase_     :   int       =   features.copy() if features else default_expected_features
			UpperCAmelCase_     :   Union[str, Any]       =   (
			    Features({feature: Value(A						) for feature, dtype in features.items()}						) if features is not None else None
			)
			UpperCAmelCase_     :   Dict       =   CsvDatasetReader({'''train''': csv_path}	,   features=A	,   cache_dir=A						).read()
			_check_csv_datasetdict(A	,   A						)
@pytest.mark.parametrize('''split'''	,   [None, NamedSplit('''train'''						), '''train''', '''test''']						)
def __UpperCAmelCase (			A			:	int	,   A			:	List[Any]	,   A			:	int						)  -> Optional[int]:




			if split:
						UpperCAmelCase_     :   List[str]       =   {split: csv_path}
			else:
						UpperCAmelCase_     :   Any       =   {'''train''': csv_path, '''test''': csv_path}
			UpperCAmelCase_     :   Optional[Any]       =   tmp_path / '''cache'''
			UpperCAmelCase_     :   Optional[Any]       =   {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
			UpperCAmelCase_     :   List[str]       =   CsvDatasetReader(A	,   cache_dir=A						).read()
			_check_csv_datasetdict(A	,   A	,   splits=list(path.keys()						)						)
			assert all(dataset[split].split == split for split in path.keys()						)
def __UpperCAmelCase (			A			:	Dict						)  -> int:




			with open(A	,   encoding='''utf-8'''						) as csvfile:
						yield from csv.reader(A						)
def __UpperCAmelCase (			A			:	Dict	,   A			:	Optional[Any]						)  -> Optional[int]:
			UpperCAmelCase_     :   Tuple       =   tmp_path / '''cache'''
			UpperCAmelCase_     :   Union[str, Any]       =   os.path.join(A	,   '''tmp.csv'''						)
			UpperCAmelCase_     :   Optional[Any]       =   CsvDatasetReader({'''train''': csv_path}	,   cache_dir=A						).read()
			CsvDatasetWriter(dataset['''train''']	,   A	,   num_proc=1						).write()

			UpperCAmelCase_     :   List[Any]       =   iter_csv_file(A						)
			UpperCAmelCase_     :   List[str]       =   iter_csv_file(A						)

			for rowa, rowa in zip(A	,   A						):
						assert rowa == rowa
def __UpperCAmelCase (			A			:	Any	,   A			:	Optional[int]						)  -> str:
			UpperCAmelCase_     :   Optional[Any]       =   tmp_path / '''cache'''
			UpperCAmelCase_     :   int       =   os.path.join(A	,   '''tmp.csv'''						)
			UpperCAmelCase_     :   List[Any]       =   CsvDatasetReader({'''train''': csv_path}	,   cache_dir=A						).read()
			CsvDatasetWriter(dataset['''train''']	,   A	,   num_proc=2						).write()

			UpperCAmelCase_     :   Tuple       =   iter_csv_file(A						)
			UpperCAmelCase_     :   Union[str, Any]       =   iter_csv_file(A						)

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







def __UpperCAmelCase (			A			:	int	,   A			:	Optional[int]						)  -> Dict:
			UpperCAmelCase_     :   Optional[Any]       =   tmp_path / '''cache'''
			UpperCAmelCase_     :   int       =   os.path.join(A	,   '''tmp.csv'''						)
			UpperCAmelCase_     :   Optional[Any]       =   CsvDatasetReader({'''train''': csv_path}	,   cache_dir=A						).read()
			with pytest.raises(A						):
						CsvDatasetWriter(dataset['''train''']	,   A	,   num_proc=0						)










'''simple docstring'''





def __UpperCAmelCase (			A			:	list[list[int]]						)  -> bool:
			# must be
			UpperCAmelCase_     :   int       =   [list(A						) for row in matrix]
			if matrix and isinstance(A	,   A						):
						if isinstance(matrix[0]	,   A						):
									UpperCAmelCase_     :   Optional[Any]       =   0
									for row in matrix:
												if prev_len == 0:
															UpperCAmelCase_     :   List[str]       =   len(A						)
															UpperCAmelCase_     :   Any       =   True
												else:
															UpperCAmelCase_     :   Tuple       =   prev_len == len(A						)
						else:
									UpperCAmelCase_     :   Any       =   True
			else:
						UpperCAmelCase_     :   Dict       =   False

			return result
def __UpperCAmelCase (			A			:	list[list[int]]						)  -> None:
			if check_matrix(A						) and len(A						) > 0:
						UpperCAmelCase_     :   Dict       =   [list(A						) for row in a]
						UpperCAmelCase_     :   List[Any]       =   len(A						)
						if isinstance(a[0]	,   A						):
									UpperCAmelCase_     :   Any       =   len(a[0]						)
						else:
									for dat in a:
												print(A						)
									return

						# horizotal printing increasing
						for i in range(0	,   A						):
									print(a[0][i]						)
						# vertical printing down
						for i in range(1	,   A						):
									print(a[i][mat_col - 1]						)
						# horizotal printing decreasing
						if mat_row > 1:
									for i in range(mat_col - 2	,   -1	,   -1						):
												print(a[mat_row - 1][i]						)
        # vertical printing up
						for i in range(mat_row - 2	,   0	,   -1						):
									print(a[i][0]						)
						UpperCAmelCase_     :   Union[str, Any]       =   [row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
						if len(A						) > 0:
									spiral_print_clockwise(A						)
						else:
									return
			else:
						print('''Not a valid matrix'''						)
						return
def __UpperCAmelCase (			A			:	list[list]						)  -> list[int]:
			if matrix:
						return list(matrix.pop(0						)						) + spiral_traversal(list(zip(*A						)						)[::-1]						)
			else:
						return []


# driver code
if __name__ == "__main__":
						import doctest

						doctest.testmod()

						_UpperCamelCase		:		List[Any]								=			[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
						spiral_print_clockwise(a)


