


'''simple docstring'''




from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros





def 	a_					( lowerCamelCase	:     List[str]	, lowerCamelCase	:     int				):
   lowerCAmelCase     =			k_size // 2
   lowerCAmelCase     ,  lowerCAmelCase     =			mgrid[0 - center : k_size - center, 0 - center : k_size - center]
   lowerCAmelCase     =			1 / (2 * pi * sigma) * exp(-(square(lowerCamelCase				) + square(lowerCamelCase				)) / (2 * square(lowerCamelCase				))				)
   return g





def 	a_					( lowerCamelCase	:     Any	, lowerCamelCase	:     Tuple	, lowerCamelCase	:     List[str]				):
   lowerCAmelCase     ,  lowerCAmelCase     =			image.shape[0], image.shape[1]
   # dst image height and width
   lowerCAmelCase     =			height - k_size + 1
   lowerCAmelCase     =			width - k_size + 1

   # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
   lowerCAmelCase     =			zeros((dst_height * dst_width, k_size * k_size)				)
   lowerCAmelCase     =			0
   for i, j in product(range(lowerCamelCase				)	, range(lowerCamelCase				)				):
      lowerCAmelCase     =			ravel(image[i : i + k_size, j : j + k_size]				)
      lowerCAmelCase     =			window
      row += 1

   #  turn the kernel into shape(k*k, 1)
   lowerCAmelCase     =			gen_gaussian_kernel(lowerCamelCase	, lowerCamelCase				)
   lowerCAmelCase     =			ravel(lowerCamelCase				)

   # reshape and get the dst image
   lowerCAmelCase     =			dot(lowerCamelCase	, lowerCamelCase				).reshape(lowerCamelCase	, lowerCamelCase				).astype(lowerCamelCase				)

   return dst


if __name__ == "__main__":
       # read original image
       __snake_case    						=imread(R"""../image_data/lena.jpg""")
       # turn image in gray scale value
       __snake_case    						=cvtColor(img, COLOR_BGR2GRAY)

       # get values with two different mask size
       __snake_case    						=gaussian_filter(gray, 3, sigma=1)
       __snake_case    						=gaussian_filter(gray, 5, sigma=0.8)

       # show result images
       imshow("""gaussian filter with 3x3 mask""", gaussianaxa)
       imshow("""gaussian filter with 5x5 mask""", gaussianaxa)
       waitKey()









'''simple docstring'''




import unittest

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


if is_torch_available():
       import torch

       from transformers import XLMRobertaModel

@require_sentencepiece
@require_tokenizers
@require_torch
class 		UpperCAmelCase_      (  unittest.TestCase		):





     @slow
     def 							__UpperCAmelCase      ( self       :      str    )				->  List[str]:
        lowerCAmelCase     =			XLMRobertaModel.from_pretrained('xlm-roberta-base'    )
        lowerCAmelCase     =			torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]    )
        # The dog is cute and lives in the garden house

        lowerCAmelCase     =			torch.Size((1, 1_2, 7_6_8)    )  # batch_size, sequence_length, embedding_vector_dim
        lowerCAmelCase     =			torch.tensor(
            [[-0.0_101, 0.1_218, -0.0_803, 0.0_801, 0.1_327, 0.0_776, -0.1_215, 0.2_383, 0.3_338, 0.3_106, 0.0_300, 0.0_252]]    )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
           lowerCAmelCase     =			model(UpperCAmelCase__    )['last_hidden_state'].detach()
        self.assertEqual(output.shape    ,      UpperCAmelCase__    )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]    ,      UpperCAmelCase__    ,      atol=1E-3    )    )





     @slow
     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  Tuple:
        lowerCAmelCase     =			XLMRobertaModel.from_pretrained('xlm-roberta-large'    )
        lowerCAmelCase     =			torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]    )
        # The dog is cute and lives in the garden house

        lowerCAmelCase     =			torch.Size((1, 1_2, 1_0_2_4)    )  # batch_size, sequence_length, embedding_vector_dim
        lowerCAmelCase     =			torch.tensor(
            [[-0.0_699, -0.0_318, 0.0_705, -0.1_241, 0.0_999, -0.0_520, 0.1_004, -0.1_838, -0.4_704, 0.1_437, 0.0_821, 0.0_126]]    )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
           lowerCAmelCase     =			model(UpperCAmelCase__    )['last_hidden_state'].detach()
        self.assertEqual(output.shape    ,      UpperCAmelCase__    )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]    ,      UpperCAmelCase__    ,      atol=1E-3    )    )





