




'''simple docstring'''






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


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

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



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Optional[Any]						=      tempfile.mkdtemp()

        lowercase  :		Union[str, Any]						=      ['''<s>''', '''<pad>''', '''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
        lowercase  :		Optional[int]						=      dict(zip(A   ,		range(len(A ) ) ) )
        lowercase  :		int						=      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'''] )

        lowercase  :		List[str]						=      SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
        tokenizer.save_pretrained(self.tmpdirname )

        lowercase  :		List[Any]						=      {
            '''feature_size''': 24,
            '''num_mel_bins''': 24,
            '''padding_value''': 0.0,
            '''sampling_rate''': 16000,
            '''return_attention_mask''': False,
            '''do_normalize''': True,
        }
        save_json(A   ,		save_dir / FEATURE_EXTRACTOR_NAME )



 def    SCREAMING_SNAKE_CASE(  self   ,		**A ):






        '''simple docstring'''




        return SpeechaTextTokenizer.from_pretrained(self.tmpdirname   ,		**A )



 def    SCREAMING_SNAKE_CASE(  self   ,		**A ):






        '''simple docstring'''




        return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname   ,		**A )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        shutil.rmtree(self.tmpdirname )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Union[str, Any]						=      self.get_tokenizer()
        lowercase  :		Optional[Any]						=      self.get_feature_extractor()

        lowercase  :		Tuple						=      SpeechaTextProcessor(tokenizer=A   ,		feature_extractor=A )

        processor.save_pretrained(self.tmpdirname )
        lowercase  :		Optional[int]						=      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    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		List[Any]						=      SpeechaTextProcessor(
            tokenizer=self.get_tokenizer()   ,		feature_extractor=self.get_feature_extractor() )
        processor.save_pretrained(self.tmpdirname )

        lowercase  :		Any						=      self.get_tokenizer(bos_token='''(BOS)'''   ,		eos_token='''(EOS)''' )
        lowercase  :		int						=      self.get_feature_extractor(do_normalize=A   ,		padding_value=1.0 )

        lowercase  :		Tuple						=      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    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		int						=      self.get_feature_extractor()
        lowercase  :		List[str]						=      self.get_tokenizer()

        lowercase  :		str						=      SpeechaTextProcessor(tokenizer=A   ,		feature_extractor=A )

        lowercase  :		Optional[Any]						=      floats_list((3, 1000) )

        lowercase  :		Tuple						=      feature_extractor(A   ,		return_tensors='''np''' )
        lowercase  :		List[Any]						=      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    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		str						=      self.get_feature_extractor()
        lowercase  :		List[str]						=      self.get_tokenizer()

        lowercase  :		List[str]						=      SpeechaTextProcessor(tokenizer=A   ,		feature_extractor=A )

        lowercase  :		List[str]						=      '''This is a test string'''

        lowercase  :		Tuple						=      processor(text=A )

        lowercase  :		str						=      tokenizer(A )

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



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Optional[Any]						=      self.get_feature_extractor()
        lowercase  :		List[str]						=      self.get_tokenizer()

        lowercase  :		str						=      SpeechaTextProcessor(tokenizer=A   ,		feature_extractor=A )

        lowercase  :		Dict						=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        lowercase  :		Tuple						=      processor.batch_decode(A )
        lowercase  :		Dict						=      tokenizer.batch_decode(A )

        self.assertListEqual(A   ,		A )





 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		int						=      self.get_feature_extractor()
        lowercase  :		Tuple						=      self.get_tokenizer()

        lowercase  :		Union[str, Any]						=      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'''   ,		)










'''simple docstring'''






import numpy as np
from PIL import Image




def 		a_( lowerCamelCase      )				->np.array:






       '''simple docstring'''




       lowercase,     lowercase,     lowercase  :		Union[str, Any]						=      rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
       return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b




def 		a_( lowerCamelCase      )				->np.array:






       '''simple docstring'''




       return (gray > 127) & (gray <= 255)




def 		a_( lowerCamelCase   ,				lowerCamelCase      )				->np.array:






       '''simple docstring'''




       lowercase  :		Optional[Any]						=      np.zeros_like(lowerCamelCase      )
       lowercase  :		Any						=      np.zeros(
           (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)      )

       # Copy image to padded image
       lowercase  :		List[str]						=      image

       # Iterate over image & apply kernel
       for x in range(image.shape[1]      ):
              for y in range(image.shape[0]      ):
                     lowercase  :		Union[str, Any]						=      (
                         kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
                     ).sum()
                     lowercase  :		Any						=      int(summation == 5      )
       return output


# kernel to be applied
lowercase	:      List[Any]				=       np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
      # read original image
      lowercase	:      Dict				=       np.array(Image.open(r'''..\image_data\lena.jpg'''))
      # Apply erosion operation to a binary image
      lowercase	:      List[str]				=       erosion(grayabinary(rgbagray(image)), structuring_element)
      # Save the output image
      lowercase	:      Optional[Any]				=       Image.fromarray(output).convert('''RGB''')
      pil_img.save('''result_erosion.png''')




