


import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

__snake_case     =		1_8_0






def 						_A						(						_lowercase			,       _lowercase				)			->				Tuple:


     """simple docstring"""


     __UpperCamelCase				=	k_size // 2
     __UpperCamelCase,  __UpperCamelCase				=	np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
     __UpperCamelCase				=	(
         1
         / (2 * np.pi * sigma)
         * np.exp(-(np.square(_lowercase				) + np.square(_lowercase				)) / (2 * np.square(_lowercase				))				)
     )
     return g






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				Tuple:


     """simple docstring"""


     __UpperCamelCase				=	np.zeros(_lowercase				)

     for row in range(1			,       image_shape[0] - 1				):
          for col in range(1			,       image_shape[1] - 1				):
               __UpperCamelCase				=	gradient_direction[row, col]

               if (
                   0 <= direction < PI / 8
                   or 15 * PI / 8 <= direction <= 2 * PI
                   or 7 * PI / 8 <= direction <= 9 * PI / 8
               ):
                    __UpperCamelCase				=	sobel_grad[row, col - 1]
                    __UpperCamelCase				=	sobel_grad[row, col + 1]
                    if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                         __UpperCamelCase				=	sobel_grad[row, col]

               elif (
                   PI / 8 <= direction < 3 * PI / 8
                   or 9 * PI / 8 <= direction < 11 * PI / 8
               ):
                    __UpperCamelCase				=	sobel_grad[row + 1, col - 1]
                    __UpperCamelCase				=	sobel_grad[row - 1, col + 1]
                    if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
                         __UpperCamelCase				=	sobel_grad[row, col]

               elif (
                   3 * PI / 8 <= direction < 5 * PI / 8
                   or 11 * PI / 8 <= direction < 13 * PI / 8
               ):
                    __UpperCamelCase				=	sobel_grad[row - 1, col]
                    __UpperCamelCase				=	sobel_grad[row + 1, col]
                    if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
                         __UpperCamelCase				=	sobel_grad[row, col]

               elif (
                   5 * PI / 8 <= direction < 7 * PI / 8
                   or 13 * PI / 8 <= direction < 15 * PI / 8
               ):
                    __UpperCamelCase				=	sobel_grad[row - 1, col - 1]
                    __UpperCamelCase				=	sobel_grad[row + 1, col + 1]
                    if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
                         __UpperCamelCase				=	sobel_grad[row, col]

     return destination






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)			->				str:


     """simple docstring"""


     for row in range(1			,       image_shape[0] - 1				):
          for col in range(1			,       image_shape[1] - 1				):
               if destination[row, col] >= threshold_high:
                    __UpperCamelCase				=	strong
               elif destination[row, col] <= threshold_low:
                    __UpperCamelCase				=	0
               else:
                    __UpperCamelCase				=	weak






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)			->				int:


     """simple docstring"""


     for row in range(1			,       image_shape[0]				):
          for col in range(1			,       image_shape[1]				):
               if destination[row, col] == weak:
                    if 2_55 in (
                        destination[row, col + 1],
                        destination[row, col - 1],
                        destination[row - 1, col],
                        destination[row + 1, col],
                        destination[row - 1, col - 1],
                        destination[row + 1, col - 1],
                        destination[row - 1, col + 1],
                        destination[row + 1, col + 1],
                    ):
                         __UpperCamelCase				=	strong
                    else:
                         __UpperCamelCase				=	0






def 						_A						(						_lowercase			,       _lowercase=15			,       _lowercase=30			,       _lowercase=1_28			,       _lowercase=2_55				)			->				Optional[Any]:


     """simple docstring"""


     __UpperCamelCase				=	img_convolve(_lowercase			,       gen_gaussian_kernel(9			,       sigma=1.4				)				)
     # get the gradient and degree by sobel_filter
     __UpperCamelCase,  __UpperCamelCase				=	sobel_filter(_lowercase				)
     __UpperCamelCase				=	PI + np.radadeg(_lowercase				)

     __UpperCamelCase				=	suppress_non_maximum(image.shape			,       _lowercase			,       _lowercase				)

     detect_high_low_threshold(
         image.shape			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)

     track_edge(image.shape			,       _lowercase			,       _lowercase			,       _lowercase				)

     return destination


if __name__ == "__main__":
    # read original image in gray mode
    __snake_case     =		cva.imread(r'''../image_data/lena.jpg''', 0)
    # canny edge detection
    __snake_case     =		canny(lena)
    cva.imshow('''canny''', canny_destination)
    cva.waitKey(0)






import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin



class 					__lowerCamelCase	(_a							,     unittest.TestCase		):
       _lowercase    							=      SpeechaTextaTokenizer
       _lowercase    							=      False







       def 						snake_case_					(	self:				str			):






            '''simple docstring'''




            super().setUp()

            __UpperCamelCase				=	'<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'.split(' '			)
            __UpperCamelCase				=	['he re</w> 123', 'here a 1456']
            __UpperCamelCase				=	dict(zip(A_,range(len(A_			)			)			)			)

            __UpperCamelCase				=	{'pad_token': '<pad>', 'unk_token': '<unk>', 'bos_token': '<s>', 'eos_token': '</s>'}

            __UpperCamelCase				=	tempfile.mkdtemp()
            __UpperCamelCase				=	os.path.join(self.tmpdirname,VOCAB_FILES_NAMES['vocab_file']			)
            __UpperCamelCase				=	os.path.join(self.tmpdirname,VOCAB_FILES_NAMES['merges_file']			)

            with open(self.vocab_file,'w',encoding='utf-8'			) as fp:
                 fp.write(json.dumps(A_			) + '\n'			)

            with open(self.merges_file,'w'			) as fp:
                 fp.write('\n'.join(A_			)			)







       def 						snake_case_					(	self:				Optional[int]			):






            '''simple docstring'''




            __UpperCamelCase				=	list(self.get_tokenizer().get_vocab().keys()			)

            self.assertEqual(vocab_keys[0],'<s>'			)
            self.assertEqual(vocab_keys[1],'<pad>'			)
            self.assertEqual(vocab_keys[-1],'vocab'			)
            self.assertEqual(len(A_			),14			)







       def 						snake_case_					(	self:				str			):






            '''simple docstring'''




            self.assertEqual(self.get_tokenizer().vocab_size,14			)







       def 						snake_case_					(	self:				Dict			):






            '''simple docstring'''




            __UpperCamelCase				=	SpeechaTextaTokenizer.from_pretrained(self.tmpdirname			)

            # make sure @@ is correctly concatenated
            __UpperCamelCase				=	[4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
            __UpperCamelCase				=	tokenizer.decode(A_			)

            self.assertTrue(output_string == 'herecouple words ofthe'			)







       def 						snake_case_					(	self:				int			):






            '''simple docstring'''




            __UpperCamelCase				=	SpeechaTextaTokenizer.from_pretrained(self.tmpdirname			)

            with tempfile.TemporaryDirectory() as tmp_dirname:
                 tokenizer.save_pretrained(A_			)
                 os.remove(os.path.join(A_,'merges.txt'			)			)

                 # load tokenizer without merges file should not throw an error
                 __UpperCamelCase				=	SpeechaTextaTokenizer.from_pretrained(A_			)

            with tempfile.TemporaryDirectory() as tmp_dirname:
                 # save tokenizer and load again
                 tokenizer.save_pretrained(A_			)
                 __UpperCamelCase				=	SpeechaTextaTokenizer.from_pretrained(A_			)

            self.assertIsNotNone(A_			)







       def 						snake_case_					(	self:				str			):






            '''simple docstring'''




            if not self.test_slow_tokenizer:
                 return

            __UpperCamelCase				=	inspect.signature(self.tokenizer_class.__init__			)
            __UpperCamelCase				=	self.get_tokenizer()

            for parameter_name, parameter in signature.parameters.items():
                 if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
                      self.assertIn(A_,tokenizer.init_kwargs			)


