
import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import DonutImageProcessor



class    a_ (			unittest.TestCase    ):






			def __init__(      self				,			__UpperCamelCase				,			__UpperCamelCase=7				,			__UpperCamelCase=3				,			__UpperCamelCase=18				,			__UpperCamelCase=30				,			__UpperCamelCase=400				,			__UpperCamelCase=True				,			__UpperCamelCase=None				,			__UpperCamelCase=True				,			__UpperCamelCase=False				,			__UpperCamelCase=True				,			__UpperCamelCase=True				,			__UpperCamelCase=[0.5, 0.5, 0.5]				,			__UpperCamelCase=[0.5, 0.5, 0.5]				,			):
									_lowercase					       =					parent
									_lowercase					       =					batch_size
									_lowercase					       =					num_channels
									_lowercase					       =					image_size
									_lowercase					       =					min_resolution
									_lowercase					       =					max_resolution
									_lowercase					       =					do_resize
									_lowercase					       =					size if size is not None else {"""height""": 18, """width""": 20}
									_lowercase					       =					do_thumbnail
									_lowercase					       =					do_align_axis
									_lowercase					       =					do_pad
									_lowercase					       =					do_normalize
									_lowercase					       =					image_mean
									_lowercase					       =					image_std






			def    UpperCamelCase_				(      self  ):
									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_thumbnail": self.do_thumbnail,
									    "do_align_long_axis": self.do_align_axis,
									    "do_pad": self.do_pad,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									}







@require_torch
@require_vision
class    a_ (			_a					,			unittest.TestCase    ):
			a	:			List[str]					   =							DonutImageProcessor if is_vision_available() else None






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					DonutImageProcessingTester(self  )






			@property
			def    UpperCamelCase_				(      self  ):
									return self.image_processor_tester.prepare_image_processor_dict()






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.image_processing_class(**self.image_processor_dict  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""do_resize"""  )  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""size"""  )  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""do_thumbnail"""  )  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""do_align_long_axis"""  )  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""do_pad"""  )  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""do_normalize"""  )  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""image_mean"""  )  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""image_std"""  )  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.image_processing_class.from_dict(self.image_processor_dict  )
									self.assertEqual(image_processor.size				,			{"""height""": 18, """width""": 20}  )

									_lowercase					       =					self.image_processing_class.from_dict(self.image_processor_dict				,			size=42  )
									self.assertEqual(image_processor.size				,			{"""height""": 42, """width""": 42}  )

									# Previous config had dimensions in (width, height) order
									_lowercase					       =					self.image_processing_class.from_dict(self.image_processor_dict				,			size=(42, 84)  )
									self.assertEqual(image_processor.size				,			{"""height""": 84, """width""": 42}  )






			def    UpperCamelCase_				(      self  ):
									pass






			@is_flaky()
			def    UpperCamelCase_				(      self  ):
									# Initialize image_processing
									_lowercase					       =					self.image_processing_class(**self.image_processor_dict  )
									# create random PIL images
									_lowercase					       =					prepare_image_inputs(self.image_processor_tester				,			equal_resolution=__UpperCamelCase  )
									for image in image_inputs:
															self.assertIsInstance(__UpperCamelCase				,			Image.Image  )

									# Test not batched input
									_lowercase					       =					image_processing(image_inputs[0]				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_images.shape				,			(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )				,			)

									# Test batched
									_lowercase					       =					image_processing(__UpperCamelCase				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_images.shape				,			(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )				,			)






			@is_flaky()
			def    UpperCamelCase_				(      self  ):
									# Initialize image_processing
									_lowercase					       =					self.image_processing_class(**self.image_processor_dict  )
									# create random numpy tensors
									_lowercase					       =					prepare_image_inputs(self.image_processor_tester				,			equal_resolution=__UpperCamelCase				,			numpify=__UpperCamelCase  )
									for image in image_inputs:
															self.assertIsInstance(__UpperCamelCase				,			np.ndarray  )

									# Test not batched input
									_lowercase					       =					image_processing(image_inputs[0]				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_images.shape				,			(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )				,			)

									# Test batched
									_lowercase					       =					image_processing(__UpperCamelCase				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_images.shape				,			(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )				,			)






			@is_flaky()
			def    UpperCamelCase_				(      self  ):
									# Initialize image_processing
									_lowercase					       =					self.image_processing_class(**self.image_processor_dict  )
									# create random PyTorch tensors
									_lowercase					       =					prepare_image_inputs(self.image_processor_tester				,			equal_resolution=__UpperCamelCase				,			torchify=__UpperCamelCase  )
									for image in image_inputs:
															self.assertIsInstance(__UpperCamelCase				,			torch.Tensor  )

									# Test not batched input
									_lowercase					       =					image_processing(image_inputs[0]				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_images.shape				,			(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )				,			)

									# Test batched
									_lowercase					       =					image_processing(__UpperCamelCase				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_images.shape				,			(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )				,			)

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




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int       )  ->     np.ndarray:

						# prepare kernel
						# the kernel size have to be odd
						if (ksize % 2) == 0:
												_lowercase					       =					ksize + 1
						_lowercase					       =					np.zeros((ksize, ksize)		,   dtype=np.floataa       )

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

																		# degree to radiant
																		_lowercase					       =					theta / 1_80 * 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
		A		: Optional[Any]								=     imread('''../image_data/lena.jpg''')
		# turn image in gray scale value
		A		: Optional[int]								=     cvtColor(img, COLOR_BGR2GRAY)

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

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

		waitKey(0)