



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'''	,    )




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)