


'''simple docstring'''







import cva
import numpy as np







def _UpperCamelCase	(					__A				,      __A				,      __A				,      __A   )							->		int:


      '''simple docstring'''



      try:
            return int(image[x_coordinate][y_coordinate] >= center   )
      except (IndexError, TypeError):
            return 0







def _UpperCamelCase	(					__A				,      __A				,      __A   )							->		int:


      '''simple docstring'''



      UpperCamelCase__  =    image[x_coordinate][y_coordinate]
      UpperCamelCase__  =    [1, 2, 4, 8, 16, 32, 64, 128]

      # skip get_neighbors_pixel if center is null
      if center is None:
            return 0

      # Starting from the top right, assigning value to pixels clockwise
      UpperCamelCase__  =    [
          get_neighbors_pixel(__A				,      x_coordinate - 1				,      y_coordinate + 1				,      __A   ),
          get_neighbors_pixel(__A				,      __A				,      y_coordinate + 1				,      __A   ),
          get_neighbors_pixel(__A				,      x_coordinate - 1				,      __A				,      __A   ),
          get_neighbors_pixel(__A				,      x_coordinate + 1				,      y_coordinate + 1				,      __A   ),
          get_neighbors_pixel(__A				,      x_coordinate + 1				,      __A				,      __A   ),
          get_neighbors_pixel(__A				,      x_coordinate + 1				,      y_coordinate - 1				,      __A   ),
          get_neighbors_pixel(__A				,      __A				,      y_coordinate - 1				,      __A   ),
          get_neighbors_pixel(__A				,      x_coordinate - 1				,      y_coordinate - 1				,      __A   ),
      ]

      # Converting the binary value to decimal.
      return sum(
          binary_value * power for binary_value, power in zip(__A				,      __A   )   )


if __name__ == "__main__":
  # Reading the image and converting it to grayscale.
  a__  : List[Any]				=	cva.imread(
      'digital_image_processing/image_data/lena.jpg', cva.IMREAD_GRAYSCALE
  )

  # Create a numpy array as the same height and width of read image
  a__  : Optional[Any]				=	np.zeros((image.shape[0], image.shape[1]))

  # Iterating through the image and calculating the
  # local binary pattern value for each pixel.
  for i in range(0, image.shape[0]):
    for j in range(0, image.shape[1]):
      a__  : Optional[int]				=	local_binary_value(image, i, j)

  cva.imshow('local binary pattern', lbp_image)
  cva.waitKey(0)
  cva.destroyAllWindows()









'''simple docstring'''







import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
  from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

  from .test_feature_extraction_speechta import floats_list


a__  : int				=	get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')
@require_torch
class  lowercase_					(   unittest.TestCase       ):


    def        __a							(					self   ):
          UpperCamelCase__  =    tempfile.mkdtemp()

          UpperCamelCase__  =    SpeechTaTokenizer(a   )
          tokenizer.save_pretrained(self.tmpdirname   )

          UpperCamelCase__  =    {
              "feature_size": 1,
              "padding_value": 0.0,
              "sampling_rate": 1_60_00,
              "do_normalize": False,
              "num_mel_bins": 80,
              "hop_length": 16,
              "win_length": 64,
              "win_function": "hann_window",
              "fmin": 80,
              "fmax": 76_00,
              "mel_floor": 1e-10,
              "reduction_factor": 2,
              "return_attention_mask": True,
          }

          UpperCamelCase__  =    os.path.join(self.tmpdirname  , a   )
          with open(self.feature_extraction_file  , "w"  , encoding="utf-8"   ) as fp:
                fp.write(json.dumps(a   ) + "\n"   )


    def        __a							(					self  , **a   ):
          return SpeechTaTokenizer.from_pretrained(self.tmpdirname  , **a   )


    def        __a							(					self  , **a   ):
          return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname  , **a   )


    def        __a							(					self   ):
          shutil.rmtree(self.tmpdirname   )


    def        __a							(					self   ):
          UpperCamelCase__  =    self.get_tokenizer()
          UpperCamelCase__  =    self.get_feature_extractor()

          UpperCamelCase__  =    SpeechTaProcessor(tokenizer=a  , feature_extractor=a   )

          processor.save_pretrained(self.tmpdirname   )
          UpperCamelCase__  =    SpeechTaProcessor.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        __a							(					self   ):
          UpperCamelCase__  =    SpeechTaProcessor(tokenizer=self.get_tokenizer()  , feature_extractor=self.get_feature_extractor()   )
          processor.save_pretrained(self.tmpdirname   )

          UpperCamelCase__  =    self.get_tokenizer(bos_token="(BOS)"  , eos_token="(EOS)"   )
          UpperCamelCase__  =    self.get_feature_extractor(do_normalize=a  , padding_value=1.0   )

          UpperCamelCase__  =    SpeechTaProcessor.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        __a							(					self   ):
          UpperCamelCase__  =    self.get_feature_extractor()
          UpperCamelCase__  =    self.get_tokenizer()

          UpperCamelCase__  =    SpeechTaProcessor(tokenizer=a  , feature_extractor=a   )

          UpperCamelCase__  =    floats_list((3, 10_00)   )

          UpperCamelCase__  =    feature_extractor(audio=a  , return_tensors="np"   )
          UpperCamelCase__  =    processor(audio=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        __a							(					self   ):
          UpperCamelCase__  =    self.get_feature_extractor()
          UpperCamelCase__  =    self.get_tokenizer()

          UpperCamelCase__  =    SpeechTaProcessor(tokenizer=a  , feature_extractor=a   )

          UpperCamelCase__  =    floats_list((3, 10_00)   )

          UpperCamelCase__  =    feature_extractor(audio_target=a  , return_tensors="np"   )
          UpperCamelCase__  =    processor(audio_target=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        __a							(					self   ):
          UpperCamelCase__  =    self.get_feature_extractor()
          UpperCamelCase__  =    self.get_tokenizer()

          UpperCamelCase__  =    SpeechTaProcessor(tokenizer=a  , feature_extractor=a   )

          UpperCamelCase__  =    "This is a test string"

          UpperCamelCase__  =    processor(text=a   )
          UpperCamelCase__  =    tokenizer(a   )

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


    def        __a							(					self   ):
          UpperCamelCase__  =    self.get_feature_extractor()
          UpperCamelCase__  =    self.get_tokenizer()

          UpperCamelCase__  =    SpeechTaProcessor(tokenizer=a  , feature_extractor=a   )

          UpperCamelCase__  =    "This is a test string"

          UpperCamelCase__  =    processor(text_target=a   )
          UpperCamelCase__  =    tokenizer(a   )

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


    def        __a							(					self   ):
          UpperCamelCase__  =    self.get_feature_extractor()
          UpperCamelCase__  =    self.get_tokenizer()

          UpperCamelCase__  =    SpeechTaProcessor(tokenizer=a  , feature_extractor=a   )

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

          UpperCamelCase__  =    processor.batch_decode(a   )
          UpperCamelCase__  =    tokenizer.batch_decode(a   )

          self.assertListEqual(a  , a   )


    def        __a							(					self   ):
          UpperCamelCase__  =    self.get_feature_extractor()
          UpperCamelCase__  =    self.get_tokenizer()

          UpperCamelCase__  =    SpeechTaProcessor(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"  , )





