

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


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







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

   '''simple docstring'''




   def 					_UpperCAmelCase					(							self			)				->       List[str]:






     """simple docstring"""






     a__    =tempfile.mkdtemp()

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

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

     a__    ={
         'feature_size': 24,
         'num_mel_bins': 24,
         'padding_value': 0.0,
         'sampling_rate': 16000,
         'return_attention_mask': False,
         'do_normalize': True,
     }
     save_json(SCREAMING_SNAKE_CASE_,       save_dir / FEATURE_EXTRACTOR_NAME			)



   def 					_UpperCAmelCase					(							self,       **lowercase_			)				->       str:






     """simple docstring"""






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



   def 					_UpperCAmelCase					(							self,       **lowercase_			)				->       Optional[Any]:






     """simple docstring"""






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



   def 					_UpperCAmelCase					(							self			)				->       Dict:






     """simple docstring"""






     shutil.rmtree(self.tmpdirname			)



   def 					_UpperCAmelCase					(							self			)				->       Any:






     """simple docstring"""






     a__    =self.get_tokenizer()
     a__    =self.get_feature_extractor()

     a__    =SpeechaTextProcessor(tokenizer=SCREAMING_SNAKE_CASE_,       feature_extractor=SCREAMING_SNAKE_CASE_			)

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

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

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



   def 					_UpperCAmelCase					(							self			)				->       List[str]:






     """simple docstring"""






     a__    =SpeechaTextProcessor(
         tokenizer=self.get_tokenizer(),       feature_extractor=self.get_feature_extractor()			)
     processor.save_pretrained(self.tmpdirname			)

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

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

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

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



   def 					_UpperCAmelCase					(							self			)				->       int:






     """simple docstring"""






     a__    =self.get_feature_extractor()
     a__    =self.get_tokenizer()

     a__    =SpeechaTextProcessor(tokenizer=SCREAMING_SNAKE_CASE_,       feature_extractor=SCREAMING_SNAKE_CASE_			)

     a__    =floats_list((3, 1000)			)

     a__    =feature_extractor(SCREAMING_SNAKE_CASE_,       return_tensors='''np'''			)
     a__    =processor(SCREAMING_SNAKE_CASE_,       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 					_UpperCAmelCase					(							self			)				->       List[Any]:






     """simple docstring"""






     a__    =self.get_feature_extractor()
     a__    =self.get_tokenizer()

     a__    =SpeechaTextProcessor(tokenizer=SCREAMING_SNAKE_CASE_,       feature_extractor=SCREAMING_SNAKE_CASE_			)

     a__    ='This is a test string'

     a__    =processor(text=SCREAMING_SNAKE_CASE_			)

     a__    =tokenizer(SCREAMING_SNAKE_CASE_			)

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



   def 					_UpperCAmelCase					(							self			)				->       Dict:






     """simple docstring"""






     a__    =self.get_feature_extractor()
     a__    =self.get_tokenizer()

     a__    =SpeechaTextProcessor(tokenizer=SCREAMING_SNAKE_CASE_,       feature_extractor=SCREAMING_SNAKE_CASE_			)

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

     a__    =processor.batch_decode(SCREAMING_SNAKE_CASE_			)
     a__    =tokenizer.batch_decode(SCREAMING_SNAKE_CASE_			)

     self.assertListEqual(SCREAMING_SNAKE_CASE_,       SCREAMING_SNAKE_CASE_			)



   def 					_UpperCAmelCase					(							self			)				->       List[Any]:






     """simple docstring"""






     a__    =self.get_feature_extractor()
     a__    =self.get_tokenizer()

     a__    =SpeechaTextProcessor(tokenizer=SCREAMING_SNAKE_CASE_,       feature_extractor=SCREAMING_SNAKE_CASE_			)

     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 pytest

from datasets.utils.version import Version

@pytest.mark.parametrize(
    'other, expected_equality'       ,  [
        (Version('1.0.0'		), True),
        ('1.0.0', True),
        (Version('2.0.0'		), False),
        ('2.0.0', False),
        ('1', False),
        ('a', False),
        (1, False),
        (None, False),
    ]       ,  )
def 							UpperCamelCase_       (       lowerCAmelCase__ :							List[Any]       ,  lowerCAmelCase__ :							List[Any]		)		->  Tuple:

  """simple docstring"""




  lowerCAmelCase_  :							List[str]								=					Version('1.0.0'		)
  assert (version == other) is expected_equality
  assert (version != other) is not expected_equality
  assert (hash(lowerCAmelCase__		) == hash(lowerCAmelCase__		)) is expected_equality





