

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


snake_case_    						=				get_tests_dir('''fixtures/test_sentencepiece.model''')


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





    def    snake_case_       (   self):
         lowercase__					:     Optional[Any]   = tempfile.mkdtemp()

         lowercase__					:     Any   = ['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
         lowercase__					:     List[Any]   = dict(zip(a		,     range(len(a))))
         lowercase__					:     List[str]   = 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__					:     Dict   = SpeechaTextTokenizer.from_pretrained(self.tmpdirname)
         tokenizer.save_pretrained(self.tmpdirname)

         lowercase__					:     Union[str, Any]   = {
             'feature_size': 24,
             'num_mel_bins': 24,
             'padding_value': 0.0,
             'sampling_rate': 1_6000,
             'return_attention_mask': False,
             'do_normalize': True,
         }
         save_json(a		,     save_dir / FEATURE_EXTRACTOR_NAME)





    def    snake_case_       (   self		,     **a):
         return SpeechaTextTokenizer.from_pretrained(self.tmpdirname		,     **a)





    def    snake_case_       (   self		,     **a):
         return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname		,     **a)





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





    def    snake_case_       (   self):
         lowercase__					:     Union[str, Any]   = self.get_tokenizer()
         lowercase__					:     Any   = self.get_feature_extractor()

         lowercase__					:     Dict   = SpeechaTextProcessor(tokenizer=a		,     feature_extractor=a)

         processor.save_pretrained(self.tmpdirname)
         lowercase__					:     Any   = 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    snake_case_       (   self):
         lowercase__					:     Tuple   = SpeechaTextProcessor(
             tokenizer=self.get_tokenizer()		,     feature_extractor=self.get_feature_extractor())
         processor.save_pretrained(self.tmpdirname)

         lowercase__					:     int   = self.get_tokenizer(bos_token='(BOS)'		,     eos_token='(EOS)')
         lowercase__					:     Optional[int]   = self.get_feature_extractor(do_normalize=a		,     padding_value=1.0)

         lowercase__					:     Any   = 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    snake_case_       (   self):
         lowercase__					:     Tuple   = self.get_feature_extractor()
         lowercase__					:     List[Any]   = self.get_tokenizer()

         lowercase__					:     Tuple   = SpeechaTextProcessor(tokenizer=a		,     feature_extractor=a)

         lowercase__					:     Dict   = floats_list((3, 1000))

         lowercase__					:     List[str]   = feature_extractor(a		,     return_tensors='np')
         lowercase__					:     Union[str, 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    snake_case_       (   self):
         lowercase__					:     int   = self.get_feature_extractor()
         lowercase__					:     str   = self.get_tokenizer()

         lowercase__					:     int   = SpeechaTextProcessor(tokenizer=a		,     feature_extractor=a)

         lowercase__					:     str   = 'This is a test string'

         lowercase__					:     Tuple   = processor(text=a)

         lowercase__					:     List[str]   = tokenizer(a)

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





    def    snake_case_       (   self):
         lowercase__					:     Tuple   = self.get_feature_extractor()
         lowercase__					:     Tuple   = self.get_tokenizer()

         lowercase__					:     List[str]   = SpeechaTextProcessor(tokenizer=a		,     feature_extractor=a)

         lowercase__					:     Any   = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         lowercase__					:     Dict   = processor.batch_decode(a)
         lowercase__					:     int   = tokenizer.batch_decode(a)

         self.assertListEqual(a		,     a)





    def    snake_case_       (   self):
         lowercase__					:     Optional[Any]   = self.get_feature_extractor()
         lowercase__					:     Optional[Any]   = 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'		,     )




from cva import destroyAllWindows, imread, imshow, waitKey

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					int			,  SCREAMING_SNAKE_CASE_	:					int    ):



     '''simple docstring'''





     lowercase__   ,				lowercase__					:     Optional[int]   = img.shape[0], img.shape[1]

     def to_grayscale(SCREAMING_SNAKE_CASE_	:					Union[str, Any]			,  SCREAMING_SNAKE_CASE_	:					List[Any]			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]    ):
          return 0.2126 * red + 0.587 * green + 0.114 * blue

     def normalize(SCREAMING_SNAKE_CASE_	:					int    ):
          return min(SCREAMING_SNAKE_CASE_			,  255    )

     for i in range(SCREAMING_SNAKE_CASE_    ):
          for j in range(SCREAMING_SNAKE_CASE_    ):
               lowercase__					:     str   = int(to_grayscale(*img[i][j]    )    )
               lowercase__					:     Dict   = [
                   normalize(SCREAMING_SNAKE_CASE_    ),
                   normalize(greyscale + factor    ),
                   normalize(greyscale + 2 * factor    ),
               ]

     return img


if __name__ == "__main__":
       # read original image
       snake_case_    						=				{
           percentage: imread('''image_data/lena.jpg''', 1) for percentage in (10, 20, 30, 40)
       }

       for percentage, img in images.items():
              make_sepia(img, percentage)

       for percentage, img in images.items():
              imshow(F'''Original image with sepia (factor: {percentage})''', img)

       waitKey(0)
       destroyAllWindows()

