




'''simple docstring'''






from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers

class   SCREAMING_SNAKE_CASE_		:



 def __init__(  self   ,		A ):






        '''simple docstring'''




        lowercase  :		int						=      vocab
        lowercase  :		Union[str, Any]						=      normalizers.BertNormalizer(
            clean_text=A   ,		handle_chinese_chars=A   ,		strip_accents=A   ,		lowercase=A   ,		)
        try:
               import rjieba
        except ImportError:
               raise ImportError(
                   '''You need to install rjieba to use RoFormerTokenizer. '''
                   '''See https://pypi.org/project/rjieba/ for installation.''' )
        lowercase  :		List[Any]						=      rjieba



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A ):






        '''simple docstring'''




        lowercase  :		str						=      []

        # this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
        for token, start, end in self.jieba.tokenize(str(A )   ,		hmm=A ):
               if token in self.vocab:
                      splits.append(normalized_string[start:end] )
               else:
                      lowercase  :		Dict						=      self.normalizers.normalize_str(A ).split()
                      for token in token_list:
                             if token:
                                    lowercase  :		Tuple						=      start + len(A )
                                    splits.append(normalized_string[start:end] )
                                    lowercase  :		Any						=      end

        # this code test_alignement_methods can't pass but fast (300ms)
        # for token in self.jieba.cut(str(normalized_string), False):
        #     if token in self.vocab:
        #         splits.append(NormalizedString(token))
        #     else:
        #         token_list = self.normalizers.normalize_str(token).split()
        #         for token in token_list:
        #             if token:
        #                 splits.append(NormalizedString(token))

        return splits





 def    SCREAMING_SNAKE_CASE(  self   ,		A ):






        '''simple docstring'''




        pretok.split(self.jieba_split )











'''simple docstring'''






import numpy as np
from PIL import Image




def 		a_( lowerCamelCase      )				->np.array:






       '''simple docstring'''




       lowercase,     lowercase,     lowercase  :		Union[str, Any]						=      rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
       return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b




def 		a_( lowerCamelCase      )				->np.array:






       '''simple docstring'''




       return (gray > 127) & (gray <= 255)




def 		a_( lowerCamelCase   ,				lowerCamelCase      )				->np.array:






       '''simple docstring'''




       lowercase  :		Optional[Any]						=      np.zeros_like(lowerCamelCase      )
       lowercase  :		Any						=      np.zeros(
           (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)      )

       # Copy image to padded image
       lowercase  :		List[str]						=      image

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


# kernel to be applied
lowercase	:      List[Any]				=       np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

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




