






'''simple docstring'''







import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


_A     :      str   					=logging.get_logger(__name__)

class _lowercase     :
							a    				=	None





@experimental
def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)				->	Dict:
							if ParallelBackendConfig.backend_name is None:
														return _map_with_multiprocessing_pool(
														    UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)

							return _map_with_joblib(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)				->	int:
							lowerCamelCase__ :     Any	  =	num_proc if num_proc <= len(UpperCamelCase				) else len(UpperCamelCase				)
							lowerCamelCase__ :     int	  =	[]  # We organize the splits ourselve (contiguous splits)
							for index in range(UpperCamelCase				):
														lowerCamelCase__ :     List[str]	  =	len(UpperCamelCase				) // num_proc
														lowerCamelCase__ :     Optional[Any]	  =	len(UpperCamelCase				) % num_proc
														lowerCamelCase__ :     List[Any]	  =	div * index + min(UpperCamelCase    ,      UpperCamelCase				)
														lowerCamelCase__ :     Union[str, Any]	  =	start + div + (1 if index < mod else 0)
														split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)				)

							if len(UpperCamelCase				) != sum(len(i[1]				) for i in split_kwds				):
														raise ValueError(
														    f'''Error dividing inputs iterable among processes. '''
														    f'''Total number of objects {len(UpperCamelCase				)}, '''
														    f'''length: {sum(len(i[1]				) for i in split_kwds				)}'''				)

							logger.info(
							    f'''Spawning {num_proc} processes for {len(UpperCamelCase				)} objects in slices of {[len(i[1]				) for i in split_kwds]}'''				)
							lowerCamelCase__  ,		lowerCamelCase__ :     Dict	  =	None, None
							if not disable_tqdm:
														lowerCamelCase__  ,		lowerCamelCase__ :     Tuple	  =	(RLock(),), tqdm.set_lock
							with Pool(UpperCamelCase    ,      initargs=UpperCamelCase    ,      initializer=UpperCamelCase				) as pool:
														lowerCamelCase__ :     int	  =	pool.map(UpperCamelCase    ,      UpperCamelCase				)
							logger.info(f'''Finished {num_proc} processes'''				)
							lowerCamelCase__ :     Optional[Any]	  =	[obj for proc_res in mapped for obj in proc_res]
							logger.info(f'''Unpacked {len(UpperCamelCase				)} objects'''				)

							return mapped





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)				->	Union[str, Any]:
							# progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
							# and it requires monkey-patching joblib internal classes which is subject to change
							import joblib

							with joblib.parallel_backend(ParallelBackendConfig.backend_name    ,      n_jobs=UpperCamelCase				):
														return joblib.Parallel()(
														    joblib.delayed(UpperCamelCase				)((function, obj, types, None, True, None)				) for obj in iterable				)





@experimental
@contextlib.contextmanager
def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	List[str]:
							lowerCamelCase__ :     List[Any]	  =	backend_name

							if backend_name == "spark":
														from joblibspark import register_spark

														register_spark()

														# TODO: call create_cache_and_write_probe if "download" in steps
														# TODO: raise NotImplementedError when Dataset.map etc is called

							try:
														yield
							finally:
														lowerCamelCase__ :     int	  =	None









'''simple docstring'''







import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

_A     :      int   					=imread(r'''digital_image_processing/image_data/lena_small.jpg''')
_A     :      Optional[Any]   					=cvtColor(img, COLOR_BGR2GRAY)





def 			SCREAMING_SNAKE_CASE_	()				->	Any:
							lowerCamelCase__ :     int	  =	cn.convert_to_negative(UpperCamelCase				)
							# assert negative_img array for at least one True
							assert negative_img.any()





def 			SCREAMING_SNAKE_CASE_	()				->	Optional[int]:
							with Image.open("""digital_image_processing/image_data/lena_small.jpg"""				) as img:
														# Work around assertion for response
														assert str(cc.change_contrast(UpperCamelCase    ,      110				)				).startswith(
														    """<PIL.Image.Image image mode=RGB size=100x100 at"""				)





def 			SCREAMING_SNAKE_CASE_	()				->	Union[str, Any]:
							lowerCamelCase__ :     Optional[int]	  =	canny.gen_gaussian_kernel(9    ,      sigma=1.4				)
							# Assert ambiguous array
							assert resp.all()





def 			SCREAMING_SNAKE_CASE_	()				->	str:
							lowerCamelCase__ :     int	  =	imread("""digital_image_processing/image_data/lena_small.jpg"""    ,      0				)
							# assert ambiguous array for all == True
							assert canny_img.all()
							lowerCamelCase__ :     Union[str, Any]	  =	canny.canny(UpperCamelCase				)
							# assert canny array for at least one True
							assert canny_array.any()





def 			SCREAMING_SNAKE_CASE_	()				->	str:
							assert gg.gaussian_filter(UpperCamelCase    ,      5    ,      sigma=0.9				).all()





def 			SCREAMING_SNAKE_CASE_	()				->	int:
							# laplace diagonals
							lowerCamelCase__ :     int	  =	array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]				)
							lowerCamelCase__ :     int	  =	conv.img_convolve(UpperCamelCase    ,      UpperCamelCase				).astype(UpperCamelCase				)
							assert res.any()





def 			SCREAMING_SNAKE_CASE_	()				->	Any:
							assert med.median_filter(UpperCamelCase    ,      3				).any()





def 			SCREAMING_SNAKE_CASE_	()				->	Any:
							lowerCamelCase__  ,		lowerCamelCase__ :     Optional[Any]	  =	sob.sobel_filter(UpperCamelCase				)
							assert grad.any() and theta.any()





def 			SCREAMING_SNAKE_CASE_	()				->	Tuple:
							lowerCamelCase__ :     Union[str, Any]	  =	sp.make_sepia(UpperCamelCase    ,      20				)
							assert sepia.all()





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"				)				->	Optional[Any]:
							lowerCamelCase__ :     Union[str, Any]	  =	bs.Burkes(imread(UpperCamelCase    ,      1				)    ,      120				)
							burkes.process()
							assert burkes.output_img.any()





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"    ,      )				->	Optional[Any]:
							lowerCamelCase__ :     int	  =	rs.NearestNeighbour(imread(UpperCamelCase    ,      1				)    ,      400    ,      200				)
							nn.process()
							assert nn.output.any()





def 			SCREAMING_SNAKE_CASE_	()				->	Optional[int]:
							lowerCamelCase__ :     Union[str, Any]	  =	"""digital_image_processing/image_data/lena.jpg"""

							# Reading the image and converting it to grayscale.
							lowerCamelCase__ :     Tuple	  =	imread(UpperCamelCase    ,      0				)

							# Test for get_neighbors_pixel function() return not None
							lowerCamelCase__ :     int	  =	0
							lowerCamelCase__ :     Dict	  =	0
							lowerCamelCase__ :     Optional[Any]	  =	image[x_coordinate][y_coordinate]

							lowerCamelCase__ :     str	  =	lbp.get_neighbors_pixel(
							    UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)

							assert neighbors_pixels is not None

							# Test for local_binary_pattern function()
							# Create a numpy array as the same height and width of read image
							lowerCamelCase__ :     List[str]	  =	np.zeros((image.shape[0], image.shape[1])				)

							# Iterating through the image and calculating the local binary pattern value
							# for each pixel.
							for i in range(0    ,      image.shape[0]				):
														for j in range(0    ,      image.shape[1]				):
																					lowerCamelCase__ :     List[str]	  =	lbp.local_binary_value(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)

							assert lbp_image.any()

