"""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


lowerCamelCase      : Optional[int]       =get_tests_dir('''fixtures/test_sentencepiece.model''')






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


    '''simple docstring'''





    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					str								=		tempfile.mkdtemp()

         __A					:					Dict								=		["""<s>""", """<pad>""", """</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est"""]
         __A					:					Tuple								=		dict(zip(a_					,       range(len(a_	)	)	)	)
         __A					:					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']	)

         __A					:					Dict								=		SpeechaTextTokenizer.from_pretrained(self.tmpdirname	)
         tokenizer.save_pretrained(self.tmpdirname	)

         __A					:					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 						_a							(  self					,       **__lowerCamelCase	):




         '''simple docstring'''



         return SpeechaTextTokenizer.from_pretrained(self.tmpdirname					,       **a_	)
    def 						_a							(  self					,       **__lowerCamelCase	):




         '''simple docstring'''



         return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname					,       **a_	)
    def 						_a							(  self	):




         '''simple docstring'''



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




         '''simple docstring'''



         __A					:					Optional[int]								=		self.get_tokenizer()
         __A					:					Tuple								=		self.get_feature_extractor()

         __A					:					Dict								=		SpeechaTextProcessor(tokenizer=a_					,       feature_extractor=a_	)

         processor.save_pretrained(self.tmpdirname	)
         __A					:					Tuple								=		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 						_a							(  self	):




         '''simple docstring'''



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

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

         __A					:					List[str]								=		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 						_a							(  self	):




         '''simple docstring'''



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

         __A					:					Optional[int]								=		SpeechaTextProcessor(tokenizer=a_					,       feature_extractor=a_	)

         __A					:					Optional[int]								=		floats_list((3, 1000)	)

         __A					:					Any								=		feature_extractor(a_					,       return_tensors='np'	)
         __A					:					Dict								=		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 						_a							(  self	):




         '''simple docstring'''



         __A					:					Dict								=		self.get_feature_extractor()
         __A					:					str								=		self.get_tokenizer()

         __A					:					int								=		SpeechaTextProcessor(tokenizer=a_					,       feature_extractor=a_	)

         __A					:					Dict								=		"""This is a test string"""

         __A					:					Tuple								=		processor(text=a_	)

         __A					:					List[Any]								=		tokenizer(a_	)

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




         '''simple docstring'''



         __A					:					Tuple								=		self.get_feature_extractor()
         __A					:					Dict								=		self.get_tokenizer()

         __A					:					int								=		SpeechaTextProcessor(tokenizer=a_					,       feature_extractor=a_	)

         __A					:					Union[str, Any]								=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         __A					:					str								=		processor.batch_decode(a_	)
         __A					:					str								=		tokenizer.batch_decode(a_	)

         self.assertListEqual(a_					,       a_	)

    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					List[Any]								=		self.get_feature_extractor()
         __A					:					Any								=		self.get_tokenizer()

         __A					:					int								=		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'''






def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_						)->						float:





       '''simple docstring'''


       return a + b


if __name__ == "__main__":
   A_				:					Tuple							=      5
   A_				:					Tuple							=      6
   print(f"""The sum of {a} + {b} is {add(a, b)}""")



