'''simple docstring'''
def 				__UpperCamelCase (					__UpperCAmelCase  :					str			)							->	str:
  lowercase				:							List[Any]									=   word.upper()
  lowercase				:							Optional[int]									=   0
  lowercase				:							Optional[Any]									=   {}
  lowercase				:							Any									=   []

  for letter in word:
    if letter not in letter_nums:
      lowercase				:							Tuple									=   str(__UpperCAmelCase			)
      next_num += 1
    word_pattern.append(letter_nums[letter]			)
  return ".".join(__UpperCAmelCase			)


if __name__ == "__main__":
 import pprint
 import time

 _A		:      Dict						   =time.time()
 with open('''dictionary.txt''') as in_file:
  _A		:      str						   =in_file.read().splitlines()

 _A		:      dict						   ={}
 for word in word_list:
  _A		:      str						   =get_word_pattern(word)
  if pattern in all_patterns:
   all_patterns[pattern].append(word)
  else:
   _A		:      Dict						   =[word]

 with open('''word_patterns.txt''', '''w''') as out_file:
  out_file.write(pprint.pformat(all_patterns))

 _A		:      Optional[Any]						   =round(time.time() - start_time, 2)
 print(f"""Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.""")
 # Done!  9,581 word patterns found in 0.58 seconds.
'''simple docstring'''
import numpy as np
from PIL import Image







def 				__UpperCamelCase (					__UpperCAmelCase  :					np.array			)							->	np.array:
  lowercase				,      lowercase				,      lowercase				:							Dict									=   rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
  return 0.2989 * r + 0.5870 * g + 0.1140 * b







def 				__UpperCamelCase (					__UpperCAmelCase  :					np.array			)							->	np.array:
  return (gray > 127) & (gray <= 255)







def 				__UpperCamelCase (					__UpperCAmelCase  :					np.array ,   __UpperCAmelCase  :					np.array			)							->	np.array:
  lowercase				:							str									=   np.zeros_like(__UpperCAmelCase			)
  lowercase				:							Tuple									=   np.zeros(
      (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)			)

  # Copy image to padded image
  lowercase				:							int									=   image

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


# kernel to be applied
_A		:      Union[str, Any]						   =np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

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