code: 
  
  
  
  import shutil
  import tempfile
  import unittest
  from pathlib import Path
  from shutil import copyfile
  
  from transformers import SpeechaTextTokenizer, is_speech_available
  from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
  from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
  from transformers.utils import FEATURE_EXTRACTOR_NAME
  
  from .test_feature_extraction_speech_to_text import floats_list
  
  
  if is_speech_available():
      from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor
  
  
  __lowerCamelCase  				=					get_tests_dir("""fixtures/test_sentencepiece.model""")
  
  
  
  @require_torch
  @require_torchaudio
  @require_sentencepiece
  class 		_snake_case      (			unittest.TestCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   def lowercase_		(						self					)	->							Optional[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    tempfile.mkdtemp()
  
     _A           =    ['''<s>''', '''<pad>''', '''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
     _A           =    dict(zip(a	,    range(len(a					)					)					)					)
     _A           =    Path(self.tmpdirname					)
     save_json(a	,    save_dir / VOCAB_FILES_NAMES['''vocab_file''']					)
     if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
       copyfile(a	,    save_dir / VOCAB_FILES_NAMES['''spm_file''']					)
  
     _A           =    SpeechaTextTokenizer.from_pretrained(self.tmpdirname					)
     tokenizer.save_pretrained(self.tmpdirname					)
  
     _A           =    {
         '''feature_size''': 2_4,
         '''num_mel_bins''': 2_4,
         '''padding_value''': 0.0,
         '''sampling_rate''': 1_6_0_0_0,
         '''return_attention_mask''': False,
         '''do_normalize''': True,
     }
     save_json(a	,    save_dir / FEATURE_EXTRACTOR_NAME					)
   def lowercase_		(						self	,    **a					)	->							Dict:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return SpeechaTextTokenizer.from_pretrained(self.tmpdirname	,    **a					)
   def lowercase_		(						self	,    **a					)	->							List[str]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname	,    **a					)
   def lowercase_		(						self					)	->							int:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     shutil.rmtree(self.tmpdirname					)
   def lowercase_		(						self					)	->							Optional[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    self.get_tokenizer()
     _A           =    self.get_feature_extractor()
  
     _A           =    SpeechaTextProcessor(tokenizer=a	,    feature_extractor=a					)
  
     processor.save_pretrained(self.tmpdirname					)
     _A           =    SpeechaTextProcessor.from_pretrained(self.tmpdirname					)
  
     self.assertEqual(processor.tokenizer.get_vocab()	,    tokenizer.get_vocab()					)
     self.assertIsInstance(processor.tokenizer	,    a					)
  
     self.assertEqual(processor.feature_extractor.to_json_string()	,    feature_extractor.to_json_string()					)
     self.assertIsInstance(processor.feature_extractor	,    a					)
   def lowercase_		(						self					)	->							List[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    SpeechaTextProcessor(
         tokenizer=self.get_tokenizer()	,    feature_extractor=self.get_feature_extractor()					)
     processor.save_pretrained(self.tmpdirname					)
  
     _A           =    self.get_tokenizer(bos_token='''(BOS)'''	,    eos_token='''(EOS)'''					)
     _A           =    self.get_feature_extractor(do_normalize=a	,    padding_value=1.0					)
  
     _A           =    SpeechaTextProcessor.from_pretrained(
         self.tmpdirname	,    bos_token='''(BOS)'''	,    eos_token='''(EOS)'''	,    do_normalize=a	,    padding_value=1.0					)
  
     self.assertEqual(processor.tokenizer.get_vocab()	,    tokenizer_add_kwargs.get_vocab()					)
     self.assertIsInstance(processor.tokenizer	,    a					)
  
     self.assertEqual(processor.feature_extractor.to_json_string()	,    feature_extractor_add_kwargs.to_json_string()					)
     self.assertIsInstance(processor.feature_extractor	,    a					)
   def lowercase_		(						self					)	->							str:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    self.get_feature_extractor()
     _A           =    self.get_tokenizer()
  
     _A           =    SpeechaTextProcessor(tokenizer=a	,    feature_extractor=a					)
  
     _A           =    floats_list((3, 1_0_0_0)					)
  
     _A           =    feature_extractor(a	,    return_tensors='''np'''					)
     _A           =    processor(a	,    return_tensors='''np'''					)
  
     for key in input_feat_extract.keys():
       self.assertAlmostEqual(input_feat_extract[key].sum()	,    input_processor[key].sum()	,    delta=1e-2					)
   def lowercase_		(						self					)	->							List[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    self.get_feature_extractor()
     _A           =    self.get_tokenizer()
  
     _A           =    SpeechaTextProcessor(tokenizer=a	,    feature_extractor=a					)
  
     _A           =    '''This is a test string'''
  
     _A           =    processor(text=a					)
  
     _A           =    tokenizer(a					)
  
     for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key]	,    encoded_processor[key]					)
   def lowercase_		(						self					)	->							Union[str, Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    self.get_feature_extractor()
     _A           =    self.get_tokenizer()
  
     _A           =    SpeechaTextProcessor(tokenizer=a	,    feature_extractor=a					)
  
     _A           =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
     _A           =    processor.batch_decode(a					)
     _A           =    tokenizer.batch_decode(a					)
  
     self.assertListEqual(a	,    a					)
  
  
  
   def lowercase_		(						self					)	->							Union[str, Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    self.get_feature_extractor()
     _A           =    self.get_tokenizer()
  
     _A           =    SpeechaTextProcessor(tokenizer=a	,    feature_extractor=a					)
  
     self.assertListEqual(
         processor.model_input_names	,    feature_extractor.model_input_names	,    msg='''`processor` and `feature_extractor` model input names do not match'''	,    )
style_context: 
  
  
  
  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
  
  __lowerCamelCase  				=					1_8_0
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case							)   -> Dict:
    _A           =    k_size // 2
    _A    ,   _A           =    np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
    _A           =    (
        1
        / (2 * np.pi * sigma)
        * np.exp(-(np.square(__snake_case							) + np.square(__snake_case							)) / (2 * np.square(__snake_case							))							)
    )
    return g
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case							)   -> int:
    _A           =    np.zeros(__snake_case							)
  
    for row in range(1						,				image_shape[0] - 1							):
      for col in range(1						,				image_shape[1] - 1							):
        _A           =    gradient_direction[row, col]
  
        if (
            0 <= direction < PI / 8
            or 15 * PI / 8 <= direction <= 2 * PI
            or 7 * PI / 8 <= direction <= 9 * PI / 8
        ):
          _A           =    sobel_grad[row, col - 1]
          _A           =    sobel_grad[row, col + 1]
          if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
            _A           =    sobel_grad[row, col]
  
        elif (
            PI / 8 <= direction < 3 * PI / 8
            or 9 * PI / 8 <= direction < 11 * PI / 8
        ):
          _A           =    sobel_grad[row + 1, col - 1]
          _A           =    sobel_grad[row - 1, col + 1]
          if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
            _A           =    sobel_grad[row, col]
  
        elif (
            3 * PI / 8 <= direction < 5 * PI / 8
            or 11 * PI / 8 <= direction < 13 * PI / 8
        ):
          _A           =    sobel_grad[row - 1, col]
          _A           =    sobel_grad[row + 1, col]
          if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
            _A           =    sobel_grad[row, col]
  
        elif (
            5 * PI / 8 <= direction < 7 * PI / 8
            or 13 * PI / 8 <= direction < 15 * PI / 8
        ):
          _A           =    sobel_grad[row - 1, col - 1]
          _A           =    sobel_grad[row + 1, col + 1]
          if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
            _A           =    sobel_grad[row, col]
  
    return destination
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case						,				__snake_case						,				__snake_case						,				__snake_case							)   -> Optional[int]:
    for row in range(1						,				image_shape[0] - 1							):
      for col in range(1						,				image_shape[1] - 1							):
        if destination[row, col] >= threshold_high:
          _A           =    strong
        elif destination[row, col] <= threshold_low:
          _A           =    0
        else:
          _A           =    weak
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case						,				__snake_case							)   -> List[Any]:
    for row in range(1						,				image_shape[0]							):
      for col in range(1						,				image_shape[1]							):
        if destination[row, col] == weak:
          if 255 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],
          ):
            _A           =    strong
          else:
            _A           =    0
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case=15						,				__snake_case=30						,				__snake_case=128						,				__snake_case=255							)   -> List[Any]:
    # gaussian_filter
    _A           =    img_convolve(__snake_case						,				gen_gaussian_kernel(9						,				sigma=1.4							)							)
    # get the gradient and degree by sobel_filter
    _A    ,   _A           =    sobel_filter(__snake_case							)
    _A           =    PI + np.radadeg(__snake_case							)
  
    _A           =    suppress_non_maximum(image.shape						,				__snake_case						,				__snake_case							)
  
    detect_high_low_threshold(
        image.shape						,				__snake_case						,				__snake_case						,				__snake_case						,				__snake_case						,				__snake_case							)
  
    track_edge(image.shape						,				__snake_case						,				__snake_case						,				__snake_case							)
  
    return destination
  
  
  if __name__ == "__main__":
      # read original image in gray mode
      __lowerCamelCase  				=					cva.imread(R"""../image_data/lena.jpg""", 0)
      # canny edge detection
      __lowerCamelCase  				=					canny(lena)
      cva.imshow("""canny""", canny_destination)
      cva.waitKey(0)