

"""simple docstring"""






import numpy as np
from PIL import Image
def 		A_	(   __UpperCamelCase     :				np.array				):
		lowercase										=							rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
		return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b
def 		A_	(   __UpperCamelCase     :				np.array				):
		return (gray > 1_27) & (gray <= 2_55)
def 		A_	(   __UpperCamelCase     :				np.array       ,	__UpperCamelCase     :				np.array				):
		lowercase										=							np.zeros_like(snake_case_				)
		lowercase										=							np.zeros(
		    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)				)

		# Copy image to padded image
		lowercase										=							image

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


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

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

'''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 		_a     (							SCREAMING_SNAKE_CASE			,    unittest.TestCase  ):







   """simple docstring"""




   A		= MPNetTokenizer
   A		= MPNetTokenizerFast
   A		= True
   A		= True






   def 					__a			(       self      ):
    super().setUp()

    SCREAMING_SNAKE_CASE  :  Tuple								=		[
        '[UNK]',
        '[CLS]',
        '[SEP]',
        '[PAD]',
        '[MASK]',
        'want',
        '##want',
        '##ed',
        'wa',
        'un',
        'runn',
        '##ing',
        ',',
        'low',
        'lowest',
    ]
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		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 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  List[str]								=		'UNwant\u00E9d,running'
    SCREAMING_SNAKE_CASE  :  Any								=		'unwanted, running'
    return input_text, output_text






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  int								=		self.tokenizer_class(self.vocab_file      )

    SCREAMING_SNAKE_CASE  :  List[str]								=		tokenizer.tokenize('UNwant\u00E9d,running'      )
    self.assertListEqual(__SCREAMING_SNAKE_CASE    ,['un', '##want', '##ed', ',', 'runn', '##ing']      )
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE      )    ,[9, 6, 7, 12, 10, 11]      )






   @slow
   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Tuple								=		self.tokenizer_class.from_pretrained('microsoft/mpnet-base'      )

    SCREAMING_SNAKE_CASE  :  Dict								=		tokenizer.encode('sequence builders'    ,add_special_tokens=__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  List[Any]								=		tokenizer.encode('multi-sequence build'    ,add_special_tokens=__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Tuple								=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )

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


