






import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	assert isinstance(lowerCamelCase				,  lowerCamelCase					)
	assert dataset.num_rows == 4
	assert dataset.num_columns == 1
	assert dataset.column_names == ["text"]
	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  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  tmp_path / """cache"""
	__lowercase             =  {"""text""": """string"""}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		__lowercase             =  TextDatasetReader(lowerCamelCase				,  cache_dir=lowerCamelCase				,  keep_in_memory=lowerCamelCase					).read()
	_check_text_dataset(lowerCamelCase				,  lowerCamelCase					)





@pytest.mark.parametrize(
    """features"""				,  [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]				,  )
def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  tmp_path / """cache"""
	__lowercase             =  {"""text""": """string"""}
	__lowercase             =  features.copy() if features else default_expected_features
	__lowercase             =  (
	    Features({feature: Value(lowerCamelCase					) for feature, dtype in features.items()}					) if features is not None else None
	)
	__lowercase             =  TextDatasetReader(lowerCamelCase				,  features=lowerCamelCase				,  cache_dir=lowerCamelCase					).read()
	_check_text_dataset(lowerCamelCase				,  lowerCamelCase					)





@pytest.mark.parametrize("""split"""				,  [None, NamedSplit("""train"""					), """train""", """test"""]					)
def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  tmp_path / """cache"""
	__lowercase             =  {"""text""": """string"""}
	__lowercase             =  TextDatasetReader(lowerCamelCase				,  cache_dir=lowerCamelCase				,  split=lowerCamelCase					).read()
	_check_text_dataset(lowerCamelCase				,  lowerCamelCase					)
	assert dataset.split == split if split else "train"





@pytest.mark.parametrize("""path_type"""				,  [str, list]					)
def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	if issubclass(lowerCamelCase				,  lowerCamelCase					):
		__lowercase             =  text_path
	elif issubclass(lowerCamelCase				,  lowerCamelCase					):
		__lowercase             =  [text_path]
	__lowercase             =  tmp_path / """cache"""
	__lowercase             =  {"""text""": """string"""}
	__lowercase             =  TextDatasetReader(lowerCamelCase				,  cache_dir=lowerCamelCase					).read()
	_check_text_dataset(lowerCamelCase				,  lowerCamelCase					)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase=("train",)					):




	'''simple docstring'''







	assert isinstance(lowerCamelCase				,  lowerCamelCase					)
	for split in splits:
		__lowercase             =  dataset_dict[split]
		assert dataset.num_rows == 4
		assert dataset.num_columns == 1
		assert dataset.column_names == ["text"]
		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  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  tmp_path / """cache"""
	__lowercase             =  {"""text""": """string"""}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		__lowercase             =  TextDatasetReader({"""train""": text_path}				,  cache_dir=lowerCamelCase				,  keep_in_memory=lowerCamelCase					).read()
	_check_text_datasetdict(lowerCamelCase				,  lowerCamelCase					)





@pytest.mark.parametrize(
    """features"""				,  [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]				,  )
def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  tmp_path / """cache"""
	# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
	__lowercase             =  {"""text""": """string"""}
	__lowercase             =  features.copy() if features else default_expected_features
	__lowercase             =  (
	    Features({feature: Value(lowerCamelCase					) for feature, dtype in features.items()}					) if features is not None else None
	)
	__lowercase             =  TextDatasetReader({"""train""": text_path}				,  features=lowerCamelCase				,  cache_dir=lowerCamelCase					).read()
	_check_text_datasetdict(lowerCamelCase				,  lowerCamelCase					)





@pytest.mark.parametrize("""split"""				,  [None, NamedSplit("""train"""					), """train""", """test"""]					)
def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	if split:
		__lowercase             =  {split: text_path}
	else:
		__lowercase             =  """train"""
		__lowercase             =  {"""train""": text_path, """test""": text_path}
	__lowercase             =  tmp_path / """cache"""
	__lowercase             =  {"""text""": """string"""}
	__lowercase             =  TextDatasetReader(lowerCamelCase				,  cache_dir=lowerCamelCase					).read()
	_check_text_datasetdict(lowerCamelCase				,  lowerCamelCase				,  splits=list(path.keys()					)					)
	assert all(dataset[split].split == split for split in path.keys()					)









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





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







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

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

			# degree to radiant
			__lowercase             =  theta / 180 * np.pi
			__lowercase             =  np.cos(_theta					)
			__lowercase             =  np.sin(_theta					)

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

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

			# fill kernel
			__lowercase             =  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
						__UpperCamelCase      :      List[Any]     						=						imread("""../image_data/lena.jpg""")
						# turn image in gray scale value
						__UpperCamelCase      :      Union[str, Any]     						=						cvtColor(img, COLOR_BGR2GRAY)

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

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

						waitKey(0)

