




import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

__lowerCAmelCase		:    Dict	      =		180




def       __magic_name__    (			A		: Union[str, Any],  A		: Tuple       ):






			'''simple docstring'''







			a   =      k_size // 2
			a   ,		a   =      np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
			a   =      (
			    1
			    / (2 * np.pi * sigma)
			    * np.exp(-(np.square(A       ) + np.square(A       )) / (2 * np.square(A       ))       )
			)
			return g




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






			'''simple docstring'''







			a   =      np.zeros(A       )

			for row in range(1,  image_shape[0] - 1       ):
						for col in range(1,  image_shape[1] - 1       ):
									a   =      gradient_direction[row, col]

									if (
									    0 <= direction < PI / 8
									    or 15 * PI / 8 <= direction <= 2 * PI
									    or 7 * PI / 8 <= direction <= 9 * PI / 8
									):
												a   =      sobel_grad[row, col - 1]
												a   =      sobel_grad[row, col + 1]
												if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
															a   =      sobel_grad[row, col]

									elif (
									    PI / 8 <= direction < 3 * PI / 8
									    or 9 * PI / 8 <= direction < 11 * PI / 8
									):
												a   =      sobel_grad[row + 1, col - 1]
												a   =      sobel_grad[row - 1, col + 1]
												if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
															a   =      sobel_grad[row, col]

									elif (
									    3 * PI / 8 <= direction < 5 * PI / 8
									    or 11 * PI / 8 <= direction < 13 * PI / 8
									):
												a   =      sobel_grad[row - 1, col]
												a   =      sobel_grad[row + 1, col]
												if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
															a   =      sobel_grad[row, col]

									elif (
									    5 * PI / 8 <= direction < 7 * PI / 8
									    or 13 * PI / 8 <= direction < 15 * PI / 8
									):
												a   =      sobel_grad[row - 1, col - 1]
												a   =      sobel_grad[row + 1, col + 1]
												if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
															a   =      sobel_grad[row, col]

			return destination




def       __magic_name__    (			A		: int,  A		: int,  A		: Optional[int],  A		: str,  A		: Union[str, Any],  A		: Union[str, Any]       ):






			'''simple docstring'''







			for row in range(1,  image_shape[0] - 1       ):
						for col in range(1,  image_shape[1] - 1       ):
									if destination[row, col] >= threshold_high:
												a   =      strong
									elif destination[row, col] <= threshold_low:
												a   =      0
									else:
												a   =      weak




def       __magic_name__    (			A		: Union[str, Any],  A		: Dict,  A		: int,  A		: Union[str, Any]       ):






			'''simple docstring'''







			for row in range(1,  image_shape[0]       ):
						for col in range(1,  image_shape[1]       ):
									if destination[row, col] == weak:
												if 255 in (
												    destination[row, col + 1],
												    destination[row, col - 1],
												    destination[row - 1, col],
												    destination[row + 1, col],
												    destination[row - 1, col - 1],
												    destination[row + 1, col - 1],
												    destination[row - 1, col + 1],
												    destination[row + 1, col + 1],
												):
															a   =      strong
												else:
															a   =      0




def       __magic_name__    (			A		: List[str],  A		: Tuple=15,  A		: Optional[Any]=30,  A		: int=128,  A		: Optional[Any]=255       ):






			'''simple docstring'''







			a   =      img_convolve(A,  gen_gaussian_kernel(9,  sigma=1.4       )       )
			# get the gradient and degree by sobel_filter
			a   ,		a   =      sobel_filter(A       )
			a   =      PI + np.radadeg(A       )

			a   =      suppress_non_maximum(image.shape,  A,  A       )

			detect_high_low_threshold(
			    image.shape,  A,  A,  A,  A,  A       )

			track_edge(image.shape,  A,  A,  A       )

			return destination


if __name__ == "__main__":
		# read original image in gray mode
		__lowerCAmelCase		:    List[str]	      =		cva.imread(r'../image_data/lena.jpg', 0)
		# canny edge detection
		__lowerCAmelCase		:    Dict	      =		canny(lena)
		cva.imshow('canny', canny_destination)
		cva.waitKey(0)












import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
		import torch

		from transformers import CamembertModel




@require_torch
@require_sentencepiece
@require_tokenizers
class    snake_case__							(unittest.TestCase       ):
				"""simple docstring"""



				@slow
				def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					List[str]:
							a   =      CamembertModel.from_pretrained("camembert-base"							)
							model.to(__lowerCamelCase							)

							a   =      torch.tensor(
							    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]] ,				device=__lowerCamelCase ,				dtype=torch.long ,				)  # J'aime le camembert !
							with torch.no_grad():
										a   =      model(__lowerCamelCase							)["last_hidden_state"]
							a   =      torch.Size((1, 10, 7_68)							)
							self.assertEqual(output.shape ,				__lowerCamelCase							)
							# compare the actual values for a slice.
							a   =      torch.tensor(
							    [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]] ,				device=__lowerCamelCase ,				dtype=torch.float ,				)
							# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
							# camembert.eval()
							# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

							self.assertTrue(torch.allclose(output[:, :3, :3] ,				__lowerCamelCase ,				atol=1e-4							)							)






