
'''simple docstring'''

import numpy as np
from PIL import Image







def __A  (    lowerCAmelCase_       ):
     _UpperCAmelCase  ,     _UpperCAmelCase  ,     _UpperCAmelCase					:  Tuple         =    rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
     return 0.2989 * r + 0.5870 * g + 0.1140 * b







def __A  (    lowerCAmelCase_       ):
     return (gray > 127) & (gray <= 255)







def __A  (    lowerCAmelCase_ ,       lowerCAmelCase_       ):
     _UpperCAmelCase					:  Optional[int]         =    np.zeros_like(lowerCAmelCase_       )
     _UpperCAmelCase					:  Tuple         =    np.zeros(
         (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)       )

     # Copy image to padded image
     _UpperCAmelCase					:  Tuple         =    image

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


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

if __name__ == "__main__":
   # read original image
   lowerCAmelCase_			:			Tuple			   =     np.array(Image.open(r'''..\image_data\lena.jpg'''))
   # Apply erosion operation to a binary image
   lowerCAmelCase_			:			List[Any]			   =     erosion(grayabinary(rgbagray(image)), structuring_element)
   # Save the output image
   lowerCAmelCase_			:			Optional[int]			   =     Image.fromarray(output).convert('''RGB''')
   pil_img.save('''result_erosion.png''')




'''simple docstring'''

import unittest

from transformers import is_torch_available, load_tool

from .test_tools_common import ToolTesterMixin


if is_torch_available():
   import torch







class 	__lowerCAmelCase    (  unittest.TestCase       ,					__a				):



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Tuple         =    load_tool("""speech-to-text"""	)
          self.tool.setup()



     def     snake_case_ (self	):
          _UpperCAmelCase					:  str         =    self.tool(torch.ones(3_0_0_0	)	)
          self.assertEqual(lowerCAmelCase__						,	""" you"""	)



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Union[str, Any]         =    self.tool(audio=torch.ones(3_0_0_0	)	)
          self.assertEqual(lowerCAmelCase__						,	""" you"""	)


