


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   __lowerCamelCase (unittest.TestCase	):







       def 		snake_case_  ( self:   List[str]				):






            '''simple docstring'''




            __UpperCamelCase   = tempfile.mkdtemp()

            __UpperCamelCase   = ['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
            __UpperCamelCase   = dict(zip(A_,range(len(A_				)				)				)				)
            __UpperCamelCase   = 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']				)

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

            __UpperCamelCase   = {
                '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:   Tuple,**A_:   Optional[int]				):






            '''simple docstring'''




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







       def 		snake_case_  ( self:   Union[str, Any],**A_:   List[Any]				):






            '''simple docstring'''




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







       def 		snake_case_  ( self:   List[Any]				):






            '''simple docstring'''




            shutil.rmtree(self.tmpdirname				)







       def 		snake_case_  ( self:   Dict				):






            '''simple docstring'''




            __UpperCamelCase   = self.get_tokenizer()
            __UpperCamelCase   = self.get_feature_extractor()

            __UpperCamelCase   = SpeechaTextProcessor(tokenizer=A_,feature_extractor=A_				)

            processor.save_pretrained(self.tmpdirname				)
            __UpperCamelCase   = 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:   Tuple				):






            '''simple docstring'''




            __UpperCamelCase   = SpeechaTextProcessor(
                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   = 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:   str				):






            '''simple docstring'''




            __UpperCamelCase   = self.get_feature_extractor()
            __UpperCamelCase   = self.get_tokenizer()

            __UpperCamelCase   = SpeechaTextProcessor(tokenizer=A_,feature_extractor=A_				)

            __UpperCamelCase   = floats_list((3, 1000)				)

            __UpperCamelCase   = feature_extractor(A_,return_tensors='np'				)
            __UpperCamelCase   = 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:   str				):






            '''simple docstring'''




            __UpperCamelCase   = self.get_feature_extractor()
            __UpperCamelCase   = self.get_tokenizer()

            __UpperCamelCase   = SpeechaTextProcessor(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 		snake_case_  ( self:   Union[str, Any]				):






            '''simple docstring'''




            __UpperCamelCase   = self.get_feature_extractor()
            __UpperCamelCase   = self.get_tokenizer()

            __UpperCamelCase   = SpeechaTextProcessor(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 		snake_case_  ( self:   List[str]				):






            '''simple docstring'''




            __UpperCamelCase   = self.get_feature_extractor()
            __UpperCamelCase   = self.get_tokenizer()

            __UpperCamelCase   = 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',)






def 		_A		(		_lowercase   )				->   bool:


     """simple docstring"""


     if not isinstance(_lowercase				,     _lowercase   ):
          raise ValueError('Input series is not valid, valid series - [1, 2/3, 2]'   )
     if len(_lowercase   ) == 0:
          raise ValueError('Input list must be a non empty list'   )
     if len(_lowercase   ) == 1 and series[0] != 0:
          return True
     __UpperCamelCase   = []
     __UpperCamelCase   = len(_lowercase   )
     for i in range(0				,     _lowercase   ):
          if series[i] == 0:
               raise ValueError('Input series cannot have 0 as an element'   )
          rec_series.append(1 / series[i]   )
     __UpperCamelCase   = rec_series[1] - rec_series[0]
     for index in range(2				,     _lowercase   ):
          if rec_series[index] - rec_series[index - 1] != common_diff:
               return False
     return True






def 		_A		(		_lowercase   )				->   float:


     """simple docstring"""


     if not isinstance(_lowercase				,     _lowercase   ):
          raise ValueError('Input series is not valid, valid series - [2, 4, 6]'   )
     if len(_lowercase   ) == 0:
          raise ValueError('Input list must be a non empty list'   )
     __UpperCamelCase   = 0
     for val in series:
          answer += 1 / val
     return len(_lowercase   ) / answer


if __name__ == "__main__":
    import doctest

    doctest.testmod()


