


'''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     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					Optional[Any]        =     k_size // 2
			UpperCAmelCase__   ,       UpperCAmelCase__  :					str        =     mgrid[0 - center : k_size - center, 0 - center : k_size - center]
			UpperCAmelCase__  :					Dict        =     1 / (2 * pi * sigma) * exp(-(square(UpperCamelCase__					) + square(UpperCamelCase__					)) / (2 * square(UpperCamelCase__					))					)
			return g







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__   ,       UpperCAmelCase__  :					str        =     image.shape[0], image.shape[1]
			# dst image height and width
			UpperCAmelCase__  :					Any        =     height - k_size + 1
			UpperCAmelCase__  :					Optional[int]        =     width - k_size + 1

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

			#  turn the kernel into shape(k*k, 1)
			UpperCAmelCase__  :					Tuple        =     gen_gaussian_kernel(UpperCamelCase__ ,      UpperCamelCase__					)
			UpperCAmelCase__  :					int        =     ravel(UpperCamelCase__					)

			# reshape and get the dst image
			UpperCAmelCase__  :					List[str]        =     dot(UpperCamelCase__ ,      UpperCamelCase__					).reshape(UpperCamelCase__ ,      UpperCamelCase__					).astype(UpperCamelCase__					)

			return dst


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

		# get values with two different mask size
		__A	=gaussian_filter(gray, 3, sigma=1)
		__A	=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 AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
		import jax.numpy as jnp

		from transformers import FlaxXLMRobertaModel


@require_sentencepiece
@require_tokenizers
@require_flax
class 		_snake_case					(     unittest.TestCase	):




					@slow
					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Union[str, Any]        =     FlaxXLMRobertaModel.from_pretrained("""xlm-roberta-base""")
								UpperCAmelCase__  :					List[str]        =     AutoTokenizer.from_pretrained("""xlm-roberta-base""")
								UpperCAmelCase__  :					Dict        =     """The dog is cute and lives in the garden house"""
								UpperCAmelCase__  :					Dict        =     jnp.array([tokenizer.encode(_lowerCamelCase)])

								UpperCAmelCase__  :					int        =     (1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
								UpperCAmelCase__  :					Any        =     jnp.array(
								    [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]])

								UpperCAmelCase__  :					Any        =     model(_lowerCamelCase)["""last_hidden_state"""]
								self.assertEqual(output.shape							,							_lowerCamelCase)
								# compare the actual values for a slice of last dim
								self.assertTrue(jnp.allclose(output[:, :, -1]							,							_lowerCamelCase							,							atol=1e-3))