
import numpy as np
from PIL import Image




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE  )   ->      np.array:

							"""simple docstring"""






							_UpperCAmelCase       ,_UpperCAmelCase       ,_UpperCAmelCase  :			Optional[Any]	       =		rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
							return 0.2989 * r + 0.5870 * g + 0.1140 * b




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE  )   ->      np.array:

							"""simple docstring"""






							return (gray > 1_2_7) & (gray <= 2_5_5)




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )   ->      np.array:

							"""simple docstring"""






							_UpperCAmelCase  :			Dict	       =		np.zeros_like(_SCREAMING_SNAKE_CASE  )
							_UpperCAmelCase  :			Optional[Any]	       =		np.zeros(
							    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)  )

							# Copy image to padded image
							_UpperCAmelCase  :			Dict	       =		image

							# Iterate over image & apply kernel
							for x in range(image.shape[1]  ):
														for y in range(image.shape[0]  ):
																					_UpperCAmelCase  :			Any	       =		(
																					    kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
																					).sum()
																					_UpperCAmelCase  :			Union[str, Any]	       =		int(summation == 5  )
							return output


# kernel to be applied
__lowerCamelCase								=					np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
				# read original image
				__lowerCamelCase								=					np.array(Image.open(R'..\image_data\lena.jpg'))
				# Apply erosion operation to a binary image
				__lowerCamelCase								=					erosion(grayabinary(rgbagray(image)), structuring_element)
				# Save the output image
				__lowerCamelCase								=					Image.fromarray(output).convert('RGB')
				pil_img.save('result_erosion.png')






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    _UpperCamelCase(   unittest.TestCase				):

						@slow
						def 						a__ (					self		:			Optional[Any]			):
													_UpperCAmelCase  :			Tuple	       =		CamembertModel.from_pretrained("camembert-base"			)
													model.to(_lowerCamelCase			)

													_UpperCAmelCase  :			List[Any]	       =		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():
																				_UpperCAmelCase  :			Dict	       =		model(_lowerCamelCase			)["last_hidden_state"]
													_UpperCAmelCase  :			Optional[int]	       =		torch.Size((1, 10, 7_68)			)
													self.assertEqual(output.shape	,						_lowerCamelCase			)
													# compare the actual values for a slice.
													_UpperCAmelCase  :			List[str]	       =		torch.tensor(
													    [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]	,						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			)			)




