


from pathlib import Path

import numpy as np
from PIL import Image
def      snake_case_						( __lowercase       ):
   UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_							:      Optional[int]						=   rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
   return 0.2_9_8_9 * r + 0.5_8_7_0 * g + 0.1_1_4_0 * b
def      snake_case_						( __lowercase       ):
   return (gray > 1_2_7) & (gray <= 2_5_5)
def      snake_case_						( __lowercase			,       __lowercase       ):
   UpperCAmelCase_							:      Union[str, Any]						=   np.zeros_like(__lowercase       )
   UpperCAmelCase_							:      Dict						=   np.zeros(
       (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)       )

   # Copy image to padded image
   UpperCAmelCase_							:      Optional[int]						=   image

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


if __name__ == "__main__":
       # read original image
       __UpperCamelCase				:  int      					=							Path(__file__).resolve().parent / 'image_data' / 'lena.jpg'
       __UpperCamelCase				:  Optional[Any]      					=							np.array(Image.open(lena_path))
       # kernel to be applied
       __UpperCamelCase				:  Optional[Any]      					=							np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
       __UpperCamelCase				:  Any      					=							dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
       # Save the output image
       __UpperCamelCase				:  Union[str, Any]      					=							Image.fromarray(output).convert('RGB')
       pil_img.save('result_dilation.png')



from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def      snake_case_						( ):
   UpperCAmelCase_							:      str						=   HfArgumentParser(__lowercase       )
   UpperCAmelCase_							:      Optional[Any]						=   parser.parse_args_into_dataclasses()[0]
   UpperCAmelCase_							:      Optional[int]						=   TensorFlowBenchmark(args=__lowercase       )
   try:
      UpperCAmelCase_							:      List[Any]						=   parser.parse_args_into_dataclasses()[0]
   except ValueError as e:
      UpperCAmelCase_							:      List[Any]						=   '''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
      UpperCAmelCase_							:      List[str]						=   ''' '''.join(str(__lowercase       ).split(''' '''       )[:-1]       )
      UpperCAmelCase_							:      Optional[int]						=   ''''''
      UpperCAmelCase_							:      Dict						=   eval(str(__lowercase       ).split(''' '''       )[-1]       )
      UpperCAmelCase_							:      int						=   []
      for arg in depreciated_args:
         # arg[2:] removes '--'
         if arg[2:] in TensorFlowBenchmark.deprecated_args:
            # arg[5:] removes '--no_'
            full_error_msg += arg_error_msg.format(arg[5:]       )
         else:
            wrong_args.append(__lowercase       )
      if len(__lowercase       ) > 0:
         UpperCAmelCase_							:      Tuple						=   full_error_msg + begin_error_msg + str(__lowercase       )
      raise ValueError(__lowercase       )
   benchmark.run()


if __name__ == "__main__":
       main()