

'''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     __magic_name__       ( A					,   A							)			->		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     __magic_name__       ( A					,   A					,   A							)			->		Union[str, Any]:
	snake_case							 =				tmp_path / 'cache'
	snake_case							 =				{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		snake_case							 =				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     __magic_name__       ( A					,   A					,   A							)			->		Dict:
	snake_case							 =				tmp_path / 'cache'
	# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
	snake_case							 =				{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
	snake_case							 =				features.copy() if features else default_expected_features
	snake_case							 =				(
	    Features({feature: Value(A							) for feature, dtype in features.items()}							) if features is not None else None
	)
	snake_case							 =				CsvDatasetReader(A					,   features=A					,   cache_dir=A							).read()
	_check_csv_dataset(A					,   A							)

@pytest.mark.parametrize('split'					,   [None, NamedSplit('train'							), 'train', 'test']							)
def     __magic_name__       ( A					,   A					,   A							)			->		Tuple:
	snake_case							 =				tmp_path / 'cache'
	snake_case							 =				{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
	snake_case							 =				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     __magic_name__       ( A					,   A					,   A							)			->		Optional[int]:
	if issubclass(A					,   A							):
		snake_case							 =				csv_path
	elif issubclass(A					,   A							):
		snake_case							 =				[csv_path]
	snake_case							 =				tmp_path / 'cache'
	snake_case							 =				{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
	snake_case							 =				CsvDatasetReader(A					,   cache_dir=A							).read()
	_check_csv_dataset(A					,   A							)

def     __magic_name__       ( A					,   A					,   A=("train",)							)			->		Union[str, Any]:
	assert isinstance(A					,   A							)
	for split in splits:
		snake_case							 =				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     __magic_name__       ( A					,   A					,   A							)			->		int:
	snake_case							 =				tmp_path / 'cache'
	snake_case							 =				{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		snake_case							 =				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     __magic_name__       ( A					,   A					,   A							)			->		str:
	snake_case							 =				tmp_path / 'cache'
	# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
	snake_case							 =				{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
	snake_case							 =				features.copy() if features else default_expected_features
	snake_case							 =				(
	    Features({feature: Value(A							) for feature, dtype in features.items()}							) if features is not None else None
	)
	snake_case							 =				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     __magic_name__       ( A					,   A					,   A							)			->		Any:
	if split:
		snake_case							 =				{split: csv_path}
	else:
		snake_case							 =				{'train': csv_path, 'test': csv_path}
	snake_case							 =				tmp_path / 'cache'
	snake_case							 =				{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
	snake_case							 =				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     __magic_name__       ( A							)			->		Union[str, Any]:
	with open(A					,   encoding='utf-8'							) as csvfile:
		yield from csv.reader(A							)

def     __magic_name__       ( A					,   A							)			->		Any:
	snake_case							 =				tmp_path / 'cache'
	snake_case							 =				os.path.join(A					,   'tmp.csv'							)
	snake_case							 =				CsvDatasetReader({'train': csv_path}					,   cache_dir=A							).read()
	CsvDatasetWriter(dataset['train']					,   A					,   num_proc=1							).write()

	snake_case							 =				iter_csv_file(A							)
	snake_case							 =				iter_csv_file(A							)

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

def     __magic_name__       ( A					,   A							)			->		int:
	snake_case							 =				tmp_path / 'cache'
	snake_case							 =				os.path.join(A					,   'tmp.csv'							)
	snake_case							 =				CsvDatasetReader({'train': csv_path}					,   cache_dir=A							).read()
	CsvDatasetWriter(dataset['train']					,   A					,   num_proc=2							).write()

	snake_case							 =				iter_csv_file(A							)
	snake_case							 =				iter_csv_file(A							)

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


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








'''simple docstring'''

from cva import destroyAllWindows, imread, imshow, waitKey

def     __magic_name__       ( A					,   A							)			->		Union[str, Any]:
	snake_case , snake_case							 =				img.shape[0], img.shape[1]

	def to_grayscale(A					,   A					,   A							):
		return 0.2_126 * red + 0.587 * green + 0.114 * blue

	def normalize(A							):
		return min(A					,   2_5_5							)

	for i in range(A							):
		for j in range(A							):
			snake_case							 =				int(to_grayscale(*img[i][j]							)							)
			snake_case							 =				[
			    normalize(A							),
			    normalize(greyscale + factor							),
			    normalize(greyscale + 2 * factor							),
			]

	return img


if __name__ == "__main__":
						# read original image
						lowerCAmelCase_						   =			{
						    percentage: imread("image_data/lena.jpg", 1) for percentage in (1_0, 2_0, 3_0, 4_0)
						}

						for percentage, img in images.items():
												make_sepia(img, percentage)

						for percentage, img in images.items():
												imshow(f"Original image with sepia (factor: {percentage})", img)

						waitKey(0)
						destroyAllWindows()





