


"""simple docstring"""


import numpy as np
from PIL import Image


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






						lowerCAmelCase      :str				  = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
						return 0.2_989 * r + 0.5_870 * g + 0.1_140 * b


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






						return (gray > 1_27) & (gray <= 2_55)


def 					UpperCAmelCase			(				a__    ,			a__   ):





						'''simple docstring'''






						lowerCAmelCase      :Tuple				  = np.zeros_like(a__   )
						lowerCAmelCase      :int				  = np.zeros(
						    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)   )

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

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


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

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







"""simple docstring"""


import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir




@dataclass
class 	__UpperCamelCase    :
					lowercase_						:							str




class 	__UpperCamelCase    (    unittest.TestCase	):
					def 					UpperCAmelCase__   (       self       :  Union[str, Any]      )  ->					Optional[Any]:
											lowerCAmelCase      :List[str]				  = get_tests_dir('fixtures/test_sentencepiece.model'      )
											lowerCAmelCase      :Tuple				  = get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model'      )

											lowerCAmelCase      :List[str]				  = FakeOriginalTokenizer(vocab_file=UpperCAmelCase      )

											with warnings.catch_warnings(record=UpperCAmelCase      ) as w:
																	lowerCAmelCase      :Dict				  = SpmConverter(UpperCAmelCase      )
											self.assertEqual(len(UpperCAmelCase      )    ,   0      )

											lowerCAmelCase      :int				  = FakeOriginalTokenizer(vocab_file=UpperCAmelCase      )

											with warnings.catch_warnings(record=UpperCAmelCase      ) as w:
																	lowerCAmelCase      :List[str]				  = SpmConverter(UpperCAmelCase      )
											self.assertEqual(len(UpperCAmelCase      )    ,   1      )

											self.assertIn(
											    'The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option'
											    ' which is not implemented in the fast tokenizers.'    ,   str(w[0].message      )    ,   )