

"""simple docstring"""




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		:  Dict							):


		'''simple docstring'''







		UpperCAmelCase_    			=     CamembertModel.from_pretrained("camembert-base"							)
		model.to(lowerCAmelCase							)

		UpperCAmelCase_    			=     torch.tensor(
		    [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]						,		device=lowerCAmelCase						,		dtype=torch.long						,		)  # J'aime le camembert !
		with torch.no_grad():
			UpperCAmelCase_    			=     model(lowerCAmelCase							)["last_hidden_state"]
		UpperCAmelCase_    			=     torch.Size((1, 10, 768)							)
		self.assertEqual(output.shape						,		lowerCAmelCase							)
		# compare the actual values for a slice.
		UpperCAmelCase_    			=     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							)							)


from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like



def  __lowerCAmelCase (  A    ,       A=3      ):
	UpperCAmelCase_    			=     int(mask / 2      )
	# copy image size
	UpperCAmelCase_    			=     zeros_like(A      )
	for i in range(A    ,       gray_img.shape[0] - bd      ):
		for j in range(A    ,       gray_img.shape[1] - bd      ):
			# get mask according with mask
			UpperCAmelCase_    			=     ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]      )
			# calculate mask median
			UpperCAmelCase_    			=     sort(A      )[inta(divide((multiply(A    ,       A      ))    ,       2      ) + 1      )]
			UpperCAmelCase_    			=     median
	return median_img


if __name__ == "__main__":
				# read original image
				_a:      Optional[int]								=     imread("""../image_data/lena.jpg""")
				# turn image in gray scale value
				_a:      Dict								=     cvtColor(img, COLOR_BGR2GRAY)

				# get values with two different mask size
				_a:      Tuple								=     median_filter(gray, 3)
				_a:      Dict								=     median_filter(gray, 5)

				# show result images
				imshow("""median filter with 3x3 mask""", medianaxa)
				imshow("""median filter with 5x5 mask""", medianaxa)
				waitKey(0)