

"""simple docstring"""


import math
import sys

import cva
import numpy as np




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase							)       ->				np.ndarray:
      # For applying gaussian function for each element in matrix.
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	math.sqrt(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	1 / (sigma * math.sqrt(2 * math.pi							))
      return cons * np.exp(-((img / sigma) ** 2) * 0.5							)




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				np.ndarray:
      SCREAMING_SNAKE_CASE__   :      str      		=	kernel_size // 2
      return img[x - half : x + half + 1, y - half : y + half + 1]




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase							)       ->				np.ndarray:
      # Creates a gaussian kernel of given dimension.
      SCREAMING_SNAKE_CASE__   :      int      		=	np.zeros((kernel_size, kernel_size)							)
      for i in range(0  ,					__lowerCAmelCase							):
            for j in range(0  ,					__lowerCAmelCase							):
                  SCREAMING_SNAKE_CASE__   :      Tuple      		=	math.sqrt(
                      abs(i - kernel_size // 2							) ** 2 + abs(j - kernel_size // 2							) ** 2							)
      return vec_gaussian(__lowerCAmelCase  ,					__lowerCAmelCase							)




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					)       ->				np.ndarray:
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	np.zeros(img.shape							)
      SCREAMING_SNAKE_CASE__   :      Any      		=	get_gauss_kernel(__lowerCAmelCase  ,					__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Any      		=	img.shape
      for i in range(kernel_size // 2  ,					size_x - kernel_size // 2							):
            for j in range(kernel_size // 2  ,					size_y - kernel_size // 2							):
                  SCREAMING_SNAKE_CASE__   :      str      		=	get_slice(__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)
                  SCREAMING_SNAKE_CASE__   :      int      		=	img_s - img_s[kernel_size // 2, kernel_size // 2]
                  SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	vec_gaussian(__lowerCAmelCase  ,					__lowerCAmelCase							)
                  SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	np.multiply(__lowerCAmelCase  ,					__lowerCAmelCase							)
                  SCREAMING_SNAKE_CASE__   :      List[str]      		=	np.multiply(__lowerCAmelCase  ,					__lowerCAmelCase							)
                  SCREAMING_SNAKE_CASE__   :      Dict      		=	np.sum(__lowerCAmelCase							) / np.sum(__lowerCAmelCase							)
                  SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	val
      return imga




def  _lowercase					(    __lowerCAmelCase							)       ->				tuple:
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	args[1] if args[1:] else """../image_data/lena.jpg"""
      SCREAMING_SNAKE_CASE__   :      Any      		=	float(args[2]							) if args[2:] else 1.0
      SCREAMING_SNAKE_CASE__   :      Any      		=	float(args[3]							) if args[3:] else 1.0
      if args[4:]:
            SCREAMING_SNAKE_CASE__   :      int      		=	int(args[4]							)
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	kernel_size + abs(kernel_size % 2 - 1							)
      else:
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	5
      return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
  a    ,a    ,a    ,a					:int    = parse_args(sys.argv)
  a					:List[Any]    = cva.imread(filename, 0)
  cva.imshow("input image", img)

  a					:int    = img / 255
  a					:Tuple    = out.astype("float32")
  a					:Dict    = bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
  a					:Any    = out * 255
  a					:Any    = np.uinta(out)
  cva.imshow("output image", out)
  cva.waitKey(0)
  cva.destroyAllWindows()







"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
  import numpy as np
  import tensorflow as tf

  from transformers import TFCamembertModel

@require_tf
@require_sentencepiece
@require_tokenizers
class      __a							(unittest.TestCase):






     '''simple docstring'''

     @slow
     def 					_a			(						self  )  ->      Union[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	tf.convert_to_tensor(
               [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]	,    dtype=tf.intaa	,    )  # J'aime le camembert !"

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	model(_a  )["""last_hidden_state"""]
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	tf.TensorShape((1, 10, 768)  )
           self.assertEqual(output.shape	,    _a  )
           # compare the actual values for a slice.
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	tf.convert_to_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]]]	,    dtype=tf.floataa	,    )
           # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
           # camembert.eval()
           # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

           self.assertTrue(np.allclose(output[:, :3, :3].numpy()	,    expected_slice.numpy()	,    atol=1E-4  )  )




