'''simple docstring'''

import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 							lowercase_    (     __UpperCAmelCase ,  unittest.TestCase						):
				a_             =   MPNetTokenizer
				a_             =   MPNetTokenizerFast
				a_             =   True
				a_             =   True







				def   lowerCamelCase_    (	self				)				->       Optional[Any]:





					"""simple docstring"""

					super().setUp()

					UpperCAmelCase_				       =					[
					    "[UNK]",
					    "[CLS]",
					    "[SEP]",
					    "[PAD]",
					    "[MASK]",
					    "want",
					    "##want",
					    "##ed",
					    "wa",
					    "un",
					    "runn",
					    "##ing",
					    ",",
					    "low",
					    "lowest",
					]
					UpperCAmelCase_				       =					os.path.join(self.tmpdirname       ,     VOCAB_FILES_NAMES["vocab_file"]				)
					with open(self.vocab_file       ,     "w"       ,     encoding="utf-8"				) as vocab_writer:
						vocab_writer.write("".join([x + "\n" for x in vocab_tokens]				)				)







				def   lowerCamelCase_    (	self       ,     UpperCamelCase__				)				->       int:





					"""simple docstring"""

					UpperCAmelCase_				       =					"UNwant\u00E9d,running"
					UpperCAmelCase_				       =					"unwanted, running"
					return input_text, output_text







				def   lowerCamelCase_    (	self				)				->       Any:





					"""simple docstring"""

					UpperCAmelCase_				       =					self.tokenizer_class(self.vocab_file				)

					UpperCAmelCase_				       =					tokenizer.tokenize("UNwant\u00E9d,running"				)
					self.assertListEqual(lowerCamelCase__       ,     ["un", "##want", "##ed", ",", "runn", "##ing"]				)
					self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__				)       ,     [9, 6, 7, 1_2, 1_0, 1_1]				)







				@slow
				def   lowerCamelCase_    (	self				)				->       Optional[int]:





					"""simple docstring"""

					UpperCAmelCase_				       =					self.tokenizer_class.from_pretrained("microsoft/mpnet-base"				)

					UpperCAmelCase_				       =					tokenizer.encode("sequence builders"       ,     add_special_tokens=lowerCamelCase__				)
					UpperCAmelCase_				       =					tokenizer.encode("multi-sequence build"       ,     add_special_tokens=lowerCamelCase__				)

					UpperCAmelCase_				       =					tokenizer.build_inputs_with_special_tokens(lowerCamelCase__				)
					UpperCAmelCase_				       =					tokenizer.build_inputs_with_special_tokens(lowerCamelCase__       ,     lowerCamelCase__				)

					assert encoded_sentence == [0] + text + [2]
					assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]

'''simple docstring'''

import numpy as np
from PIL import Image



def lowerCamelCase__						(       A_       ):
 UpperCAmelCase_       ,       UpperCAmelCase_       ,       UpperCAmelCase_				       =					rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
 return 0.2989 * r + 0.5870 * g + 0.1140 * b



def lowerCamelCase__						(       A_       ):
 return (gray > 127) & (gray <= 255)



def lowerCamelCase__						(       A_       ,			A_       ):
 UpperCAmelCase_				       =					np.zeros_like(A_       )
 UpperCAmelCase_				       =					np.zeros(
     (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)       )

 # Copy image to padded image
 UpperCAmelCase_				       =					image

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


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

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