
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


_UpperCamelCase:					str     =get_tests_dir('fixtures/test_sentencepiece.model')





@require_torch
@require_torchaudio
@require_sentencepiece
class 						__lowercase(     unittest.TestCase   ):


       """simple docstring"""







       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   Any:
         _lowerCAmelCase						=		tempfile.mkdtemp()

         _lowerCAmelCase						=		['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
         _lowerCAmelCase						=		dict(zip(_lowerCAmelCase  ,      range(len(_lowerCAmelCase       )       )       )       )
         _lowerCAmelCase						=		Path(self.tmpdirname       )
         save_json(_lowerCAmelCase  ,      save_dir / VOCAB_FILES_NAMES['vocab_file']       )
         if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
           copyfile(_lowerCAmelCase  ,      save_dir / VOCAB_FILES_NAMES['spm_file']       )

         _lowerCAmelCase						=		SpeechaTextTokenizer.from_pretrained(self.tmpdirname       )
         tokenizer.save_pretrained(self.tmpdirname       )

         _lowerCAmelCase						=		{
             'feature_size': 24,
             'num_mel_bins': 24,
             'padding_value': 0.0,
             'sampling_rate': 1_6000,
             'return_attention_mask': False,
             'do_normalize': True,
         }
         save_json(_lowerCAmelCase  ,      save_dir / FEATURE_EXTRACTOR_NAME       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]  ,      **_lowerCAmelCase					:			Optional[Any]       )					->   List[str]:
         return SpeechaTextTokenizer.from_pretrained(self.tmpdirname  ,      **_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]  ,      **_lowerCAmelCase					:			Dict       )					->   Tuple:
         return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname  ,      **_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]       )					->   int:
         shutil.rmtree(self.tmpdirname       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   Any:
         _lowerCAmelCase						=		self.get_tokenizer()
         _lowerCAmelCase						=		self.get_feature_extractor()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         processor.save_pretrained(self.tmpdirname       )
         _lowerCAmelCase						=		SpeechaTextProcessor.from_pretrained(self.tmpdirname       )

         self.assertEqual(processor.tokenizer.get_vocab()  ,      tokenizer.get_vocab()       )
         self.assertIsInstance(processor.tokenizer  ,      _lowerCAmelCase       )

         self.assertEqual(processor.feature_extractor.to_json_string()  ,      feature_extractor.to_json_string()       )
         self.assertIsInstance(processor.feature_extractor  ,      _lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   int:
         _lowerCAmelCase						=		SpeechaTextProcessor(
             tokenizer=self.get_tokenizer()  ,      feature_extractor=self.get_feature_extractor()       )
         processor.save_pretrained(self.tmpdirname       )

         _lowerCAmelCase						=		self.get_tokenizer(bos_token='(BOS)'  ,      eos_token='(EOS)'       )
         _lowerCAmelCase						=		self.get_feature_extractor(do_normalize=_lowerCAmelCase  ,      padding_value=1.0       )

         _lowerCAmelCase						=		SpeechaTextProcessor.from_pretrained(
             self.tmpdirname  ,      bos_token='(BOS)'  ,      eos_token='(EOS)'  ,      do_normalize=_lowerCAmelCase  ,      padding_value=1.0       )

         self.assertEqual(processor.tokenizer.get_vocab()  ,      tokenizer_add_kwargs.get_vocab()       )
         self.assertIsInstance(processor.tokenizer  ,      _lowerCAmelCase       )

         self.assertEqual(processor.feature_extractor.to_json_string()  ,      feature_extractor_add_kwargs.to_json_string()       )
         self.assertIsInstance(processor.feature_extractor  ,      _lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Union[str, Any]:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         _lowerCAmelCase						=		floats_list((3, 1000)       )

         _lowerCAmelCase						=		feature_extractor(_lowerCAmelCase  ,      return_tensors='np'       )
         _lowerCAmelCase						=		processor(_lowerCAmelCase  ,      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 SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Any:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         _lowerCAmelCase						=		'This is a test string'

         _lowerCAmelCase						=		processor(text=_lowerCAmelCase       )

         _lowerCAmelCase						=		tokenizer(_lowerCAmelCase       )

         for key in encoded_tok.keys():
           self.assertListEqual(encoded_tok[key]  ,      encoded_processor[key]       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple       )					->   Tuple:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         _lowerCAmelCase						=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         _lowerCAmelCase						=		processor.batch_decode(_lowerCAmelCase       )
         _lowerCAmelCase						=		tokenizer.batch_decode(_lowerCAmelCase       )

         self.assertListEqual(_lowerCAmelCase  ,      _lowerCAmelCase       )






       def SCREAMING_SNAKE_CASE_				(     self					:			Any       )					->   Union[str, Any]:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         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

_UpperCamelCase:					Tuple     =180



def 	_a  (					__SCREAMING_SNAKE_CASE					:  List[Any]		,				__SCREAMING_SNAKE_CASE					:  int    ):






  """simple docstring"""




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



def 	_a  (					__SCREAMING_SNAKE_CASE					:  List[str]		,				__SCREAMING_SNAKE_CASE					:  Tuple		,				__SCREAMING_SNAKE_CASE					:  Optional[Any]    ):






  """simple docstring"""




  _lowerCAmelCase						=		np.zeros(__SCREAMING_SNAKE_CASE    )

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

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

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

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

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

  return destination



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Tuple		,				__SCREAMING_SNAKE_CASE					:  List[str]		,				__SCREAMING_SNAKE_CASE					:  Tuple		,				__SCREAMING_SNAKE_CASE					:  Union[str, Any]		,				__SCREAMING_SNAKE_CASE					:  Dict		,				__SCREAMING_SNAKE_CASE					:  List[Any]    ):






  """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:
        _lowerCAmelCase						=		strong
      elif destination[row, col] <= threshold_low:
        _lowerCAmelCase						=		0
      else:
        _lowerCAmelCase						=		weak



def 	_a  (					__SCREAMING_SNAKE_CASE					:  List[Any]		,				__SCREAMING_SNAKE_CASE					:  Optional[int]		,				__SCREAMING_SNAKE_CASE					:  Optional[int]		,				__SCREAMING_SNAKE_CASE					:  Union[str, Any]    ):






  """simple docstring"""




  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],
        ):
          _lowerCAmelCase						=		strong
        else:
          _lowerCAmelCase						=		0



def 	_a  (					__SCREAMING_SNAKE_CASE					:  List[str]		,				__SCREAMING_SNAKE_CASE					:  int=15		,				__SCREAMING_SNAKE_CASE					:  int=30		,				__SCREAMING_SNAKE_CASE					:  Tuple=128		,				__SCREAMING_SNAKE_CASE					:  List[Any]=255    ):






  """simple docstring"""




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

  _lowerCAmelCase						=		suppress_non_maximum(image.shape		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )

  detect_high_low_threshold(
      image.shape		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )

  track_edge(image.shape		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )

  return destination


if __name__ == "__main__":
       # read original image in gray mode
       _UpperCamelCase:					Optional[Any]     =cva.imread(R'../image_data/lena.jpg', 0)
       # canny edge detection
       _UpperCamelCase:					Tuple     =canny(lena)
       cva.imshow('canny', canny_destination)
       cva.waitKey(0)
