


'''simple docstring'''



import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases


def 				lowerCamelCase__				(	A       :      str				,       A       :      str					):




						'''simple docstring'''






						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 				lowerCamelCase__				(	A       :      Optional[int]				,       A       :      Optional[int]				,       A       :      Tuple					):




						'''simple docstring'''






						UpperCAmelCase					    =     tmp_path / '''cache'''
						UpperCAmelCase					    =     {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												UpperCAmelCase					    =     ParquetDatasetReader(A				,       cache_dir=A				,       keep_in_memory=A					).read()
						_check_parquet_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 				lowerCamelCase__				(	A       :      Optional[int]				,       A       :      Optional[Any]				,       A       :      Dict					):




						'''simple docstring'''






						UpperCAmelCase					    =     tmp_path / '''cache'''
						UpperCAmelCase					    =     {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						UpperCAmelCase					    =     features.copy() if features else default_expected_features
						UpperCAmelCase					    =     (
						    Features({feature: Value(A					) for feature, dtype in features.items()}					) if features is not None else None
						)
						UpperCAmelCase					    =     ParquetDatasetReader(A				,       features=A				,       cache_dir=A					).read()
						_check_parquet_dataset(A				,       A					)


@pytest.mark.parametrize('''split'''				,       [None, NamedSplit('''train'''					), '''train''', '''test''']					)
def 				lowerCamelCase__				(	A       :      Dict				,       A       :      Optional[Any]				,       A       :      List[str]					):




						'''simple docstring'''






						UpperCAmelCase					    =     tmp_path / '''cache'''
						UpperCAmelCase					    =     {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						UpperCAmelCase					    =     ParquetDatasetReader(A				,       cache_dir=A				,       split=A					).read()
						_check_parquet_dataset(A				,       A					)
						assert dataset.split == split if split else "train"


@pytest.mark.parametrize('''path_type'''				,       [str, list]					)
def 				lowerCamelCase__				(	A       :      List[Any]				,       A       :      Optional[int]				,       A       :      List[Any]					):




						'''simple docstring'''






						if issubclass(A				,       A					):
												UpperCAmelCase					    =     parquet_path
						elif issubclass(A				,       A					):
												UpperCAmelCase					    =     [parquet_path]
						UpperCAmelCase					    =     tmp_path / '''cache'''
						UpperCAmelCase					    =     {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						UpperCAmelCase					    =     ParquetDatasetReader(A				,       cache_dir=A					).read()
						_check_parquet_dataset(A				,       A					)


def 				lowerCamelCase__				(	A       :      Optional[Any]				,       A       :      Tuple				,       A       :      str=("train",)					):




						'''simple docstring'''






						assert isinstance(A				,       A					)
						for split in splits:
												UpperCAmelCase					    =     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__				(	A       :      List[Any]				,       A       :      List[str]				,       A       :      List[Any]					):




						'''simple docstring'''






						UpperCAmelCase					    =     tmp_path / '''cache'''
						UpperCAmelCase					    =     {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												UpperCAmelCase					    =     ParquetDatasetReader(
												    {'''train''': parquet_path}				,       cache_dir=A				,       keep_in_memory=A					).read()
						_check_parquet_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 				lowerCamelCase__				(	A       :      Dict				,       A       :      Any				,       A       :      Dict					):




						'''simple docstring'''






						UpperCAmelCase					    =     tmp_path / '''cache'''
						UpperCAmelCase					    =     {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						UpperCAmelCase					    =     features.copy() if features else default_expected_features
						UpperCAmelCase					    =     (
						    Features({feature: Value(A					) for feature, dtype in features.items()}					) if features is not None else None
						)
						UpperCAmelCase					    =     ParquetDatasetReader({'''train''': parquet_path}				,       features=A				,       cache_dir=A					).read()
						_check_parquet_datasetdict(A				,       A					)


@pytest.mark.parametrize('''split'''				,       [None, NamedSplit('''train'''					), '''train''', '''test''']					)
def 				lowerCamelCase__				(	A       :      Dict				,       A       :      Any				,       A       :      List[str]					):




						'''simple docstring'''






						if split:
												UpperCAmelCase					    =     {split: parquet_path}
						else:
												UpperCAmelCase					    =     '''train'''
												UpperCAmelCase					    =     {'''train''': parquet_path, '''test''': parquet_path}
						UpperCAmelCase					    =     tmp_path / '''cache'''
						UpperCAmelCase					    =     {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						UpperCAmelCase					    =     ParquetDatasetReader(A				,       cache_dir=A					).read()
						_check_parquet_datasetdict(A				,       A				,       splits=list(path.keys()					)					)
						assert all(dataset[split].split == split for split in path.keys()					)


def 				lowerCamelCase__				(	A       :      Optional[int]				,       A       :      List[Any]					):




						'''simple docstring'''






						UpperCAmelCase					    =     ParquetDatasetWriter(A				,       tmp_path / '''foo.parquet'''					)
						assert writer.write() > 0
						UpperCAmelCase					    =     pq.ParquetFile(tmp_path / '''foo.parquet'''					)
						UpperCAmelCase					    =     pf.read()
						assert dataset.data.table == output_table


def 				lowerCamelCase__				(	A       :      str				,       A       :      List[Any]					):




						'''simple docstring'''






						UpperCAmelCase					    =     str(shared_datadir / '''test_image_rgb.jpg'''					)
						UpperCAmelCase					    =     {'''image''': [image_path]}
						UpperCAmelCase					    =     Features({'''image''': Image()}					)
						UpperCAmelCase					    =     Dataset.from_dict(A				,       features=A					)
						UpperCAmelCase					    =     ParquetDatasetWriter(A				,       tmp_path / '''foo.parquet'''					)
						assert writer.write() > 0

						UpperCAmelCase					    =     Dataset.from_parquet(str(tmp_path / '''foo.parquet'''					)					)
						assert dataset.features == reloaded_dataset.features

						UpperCAmelCase					    =     ParquetDatasetReader(str(tmp_path / '''foo.parquet'''					)				,       streaming=A					).read()
						assert dataset.features == reloaded_iterable_dataset.features




@pytest.mark.parametrize(
    '''feature, expected'''				,       [
        (Features({'''foo''': Value('''int32'''					)}					), None),
        (Features({'''image''': Image(), '''foo''': Value('''int32'''					)}					), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({'''nested''': Sequence(Audio()					)}					), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]				,       )
def 				lowerCamelCase__				(	A       :      int				,       A       :      List[Any]					):




						'''simple docstring'''






						assert get_writer_batch_size(A					) == expected









'''simple docstring'''



import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey


def 				lowerCamelCase__				(	A       :      int				,       A       :      int				,       A       :      int				,       A       :      int				,       A       :      int				,       A       :      int					):




						'''simple docstring'''






						if (ksize % 2) == 0:
												UpperCAmelCase					    =     ksize + 1
						UpperCAmelCase					    =     np.zeros((ksize, ksize)				,       dtype=np.floataa					)

						# each value
						for y in range(A					):
												for x in range(A					):
																		# distance from center
																		UpperCAmelCase					    =     x - ksize // 2
																		UpperCAmelCase					    =     y - ksize // 2

																		# degree to radiant
																		UpperCAmelCase					    =     theta / 1_80 * np.pi
																		UpperCAmelCase					    =     np.cos(_theta					)
																		UpperCAmelCase					    =     np.sin(_theta					)

																		# get kernel x
																		UpperCAmelCase					    =     cos_theta * px + sin_theta * py

																		# get kernel y
																		UpperCAmelCase					    =     -sin_theta * px + cos_theta * py

																		# fill kernel
																		UpperCAmelCase					    =     np.exp(
																		    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)					) * np.cos(2 * np.pi * _x / lambd + psi					)

						return gabor


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	# read original image
	_lowercase      :      Tuple            =					imread("""../image_data/lena.jpg""")
	# turn image in gray scale value
	_lowercase      :      int            =					cvtColor(img, COLOR_BGR2GRAY)

	# Apply multiple Kernel to detect edges
	_lowercase      :      List[str]            =					np.zeros(gray.shape[:2])
	for theta in [0, 30, 60, 90, 120, 150]:
		_lowercase      :      List[Any]            =					gabor_filter_kernel(10, 8, theta, 10, 0, 0)
		out += filteraD(gray, CV_8UC3, kernel_aa)
	_lowercase      :      Optional[int]            =					out / out.max() * 255
	_lowercase      :      Optional[int]            =					out.astype(np.uinta)

	imshow("""Original""", gray)
	imshow("""Gabor filter with 20x20 mask and 6 directions""", out)

	waitKey(0)





