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     :					int				  =						get_tests_dir('''fixtures/test_sentencepiece.model''')







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




   def       __a		(       self     :							Union[str, Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= tempfile.mkdtemp()

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

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

     SCREAMING_SNAKE_CASE__     				= {
         """feature_size""": 24,
         """num_mel_bins""": 24,
         """padding_value""": 0.0,
         """sampling_rate""": 1_60_00,
         """return_attention_mask""": False,
         """do_normalize""": True,
     }
     save_json(_lowercase			,      save_dir / FEATURE_EXTRACTOR_NAME )




   def       __a		(       self     :							Dict			,      **_lowercase     :							List[str] ):



     """simple docstring"""






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




   def       __a		(       self     :							Any			,      **_lowercase     :							Union[str, Any] ):



     """simple docstring"""






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




   def       __a		(       self     :							str ):



     """simple docstring"""






     shutil.rmtree(self.tmpdirname )




   def       __a		(       self     :							Any ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.get_tokenizer()
     SCREAMING_SNAKE_CASE__     				= self.get_feature_extractor()

     SCREAMING_SNAKE_CASE__     				= SpeechaTextProcessor(tokenizer=_lowercase			,      feature_extractor=_lowercase )

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

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

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




   def       __a		(       self     :							Optional[int] ):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__     				= self.get_tokenizer(bos_token="""(BOS)"""			,      eos_token="""(EOS)""" )
     SCREAMING_SNAKE_CASE__     				= self.get_feature_extractor(do_normalize=_lowercase			,      padding_value=1.0 )

     SCREAMING_SNAKE_CASE__     				= SpeechaTextProcessor.from_pretrained(
         self.tmpdirname			,      bos_token="""(BOS)"""			,      eos_token="""(EOS)"""			,      do_normalize=_lowercase			,      padding_value=1.0 )

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

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




   def       __a		(       self     :							Dict ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.get_feature_extractor()
     SCREAMING_SNAKE_CASE__     				= self.get_tokenizer()

     SCREAMING_SNAKE_CASE__     				= SpeechaTextProcessor(tokenizer=_lowercase			,      feature_extractor=_lowercase )

     SCREAMING_SNAKE_CASE__     				= floats_list((3, 10_00) )

     SCREAMING_SNAKE_CASE__     				= feature_extractor(_lowercase			,      return_tensors="""np""" )
     SCREAMING_SNAKE_CASE__     				= processor(_lowercase			,      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     :							Dict ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.get_feature_extractor()
     SCREAMING_SNAKE_CASE__     				= self.get_tokenizer()

     SCREAMING_SNAKE_CASE__     				= SpeechaTextProcessor(tokenizer=_lowercase			,      feature_extractor=_lowercase )

     SCREAMING_SNAKE_CASE__     				= """This is a test string"""

     SCREAMING_SNAKE_CASE__     				= processor(text=_lowercase )

     SCREAMING_SNAKE_CASE__     				= tokenizer(_lowercase )

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




   def       __a		(       self     :							Optional[int] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.get_feature_extractor()
     SCREAMING_SNAKE_CASE__     				= self.get_tokenizer()

     SCREAMING_SNAKE_CASE__     				= SpeechaTextProcessor(tokenizer=_lowercase			,      feature_extractor=_lowercase )

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

     SCREAMING_SNAKE_CASE__     				= processor.batch_decode(_lowercase )
     SCREAMING_SNAKE_CASE__     				= tokenizer.batch_decode(_lowercase )

     self.assertListEqual(_lowercase			,      _lowercase )




   def       __a		(       self     :							Union[str, Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.get_feature_extractor()
     SCREAMING_SNAKE_CASE__     				= self.get_tokenizer()

     SCREAMING_SNAKE_CASE__     				= SpeechaTextProcessor(tokenizer=_lowercase			,      feature_extractor=_lowercase )

     self.assertListEqual(
         processor.model_input_names			,      feature_extractor.model_input_names			,      msg="""`processor` and `feature_extractor` model input names do not match"""			,      )

import unittest

from transformers import is_torch_available, load_tool

from .test_tools_common import ToolTesterMixin


if is_torch_available():
   import torch







class __snake_case							(		unittest.TestCase  ,						lowerCamelCase_						):




   def       __a		(       self     :							Dict ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= load_tool("""speech-to-text""" )
     self.tool.setup()




   def       __a		(       self     :							Tuple ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.tool(torch.ones(30_00 ) )
     self.assertEqual(_lowercase			,      """ you""" )




   def       __a		(       self     :							Any ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.tool(audio=torch.ones(30_00 ) )
     self.assertEqual(_lowercase			,      """ you""" )
