

"""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        _UpperCamelCase	(						lowerCamelCase__   ,				unittest.TestCase	):


  """simple docstring"""





  snake_case_        =    MPNetTokenizer
  snake_case_        =    MPNetTokenizerFast
  snake_case_        =    True
  snake_case_        =    True



  def 							_UpperCAmelCase      ( self					: str      )	->			Any:


         '''simple docstring'''





         super().setUp()

         __magic_name__			:		Any        =     [
             '''[UNK]''',
             '''[CLS]''',
             '''[SEP]''',
             '''[PAD]''',
             '''[MASK]''',
             '''want''',
             '''##want''',
             '''##ed''',
             '''wa''',
             '''un''',
             '''runn''',
             '''##ing''',
             ''',''',
             '''low''',
             '''lowest''',
         ]
         __magic_name__			:		Tuple        =     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 							_UpperCAmelCase      ( self					: List[Any]       ,       snake_case					: str      )	->			Any:


         '''simple docstring'''





         __magic_name__			:		Optional[Any]        =     '''UNwant\u00E9d,running'''
         __magic_name__			:		List[Any]        =     '''unwanted, running'''
         return input_text, output_text



  def 							_UpperCAmelCase      ( self					: Dict      )	->			Dict:


         '''simple docstring'''





         __magic_name__			:		Optional[int]        =     self.tokenizer_class(self.vocab_file      )

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




  @slow
  def 							_UpperCAmelCase      ( self					: Any      )	->			Tuple:


         '''simple docstring'''





         __magic_name__			:		Union[str, Any]        =     self.tokenizer_class.from_pretrained('''microsoft/mpnet-base'''      )

         __magic_name__			:		Dict        =     tokenizer.encode('''sequence builders'''       ,       add_special_tokens=snake_case      )
         __magic_name__			:		Dict        =     tokenizer.encode('''multi-sequence build'''       ,       add_special_tokens=snake_case      )

         __magic_name__			:		Any        =     tokenizer.build_inputs_with_special_tokens(snake_case      )
         __magic_name__			:		Optional[int]        =     tokenizer.build_inputs_with_special_tokens(snake_case       ,       snake_case      )

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



"""simple docstring"""
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import array, dot, pad, ravel, uinta, zeros







def       UpperCamelCase_					(     lowerCamelCase							:					Optional[int]     , lowerCamelCase							:					Tuple   )						->     str:



       """simple docstring"""







       __magic_name__       ,							__magic_name__			:		Optional[Any]        =     image.shape
       __magic_name__			:		str        =     cols - block_size[1] + 1
       __magic_name__			:		int        =     rows - block_size[0] + 1
       __magic_name__			:		Optional[Any]        =     zeros((dst_height * dst_width, block_size[1] * block_size[0])   )
       __magic_name__			:		List[Any]        =     0
       for i in range(0     , lowerCamelCase   ):
              for j in range(0     , lowerCamelCase   ):
                     __magic_name__			:		List[Any]        =     ravel(image[i : i + block_size[0], j : j + block_size[1]]   )
                     __magic_name__			:		str        =     window
                     row += 1

       return image_array







def       UpperCamelCase_					(     lowerCamelCase							:					str     , lowerCamelCase							:					List[str]   )						->     Tuple:



       """simple docstring"""







       __magic_name__       ,							__magic_name__			:		List[str]        =     image.shape[0], image.shape[1]
       __magic_name__			:		Any        =     filter_kernel.shape[0]
       __magic_name__			:		Optional[Any]        =     k_size // 2
       # Pads image with the edge values of array.
       __magic_name__			:		Union[str, Any]        =     pad(lowerCamelCase     , lowerCamelCase     , mode='''edge'''   )

       # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
       __magic_name__			:		List[Any]        =     imacol(lowerCamelCase     , (k_size, k_size)   )

       #  turn the kernel into shape(k*k, 1)
       __magic_name__			:		Tuple        =     ravel(lowerCamelCase   )
       # reshape and get the dst image
       __magic_name__			:		int        =     dot(lowerCamelCase     , lowerCamelCase   ).reshape(lowerCamelCase     , lowerCamelCase   )
       return dst


if __name__ == "__main__":
 # read original image
 A									=						imread(R"""../image_data/lena.jpg""")
 # turn image in gray scale value
 A									=						cvtColor(img, COLOR_BGR2GRAY)
 # Laplace operator
 A									=						array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
 A									=						img_convolve(gray, Laplace_kernel).astype(uinta)
 imshow("""Laplacian""", out)
 waitKey(0)
