

import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
       from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

       from .test_feature_extraction_speechta import floats_list


UpperCamelCase__     :					List[str]								=			get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")



@require_torch
class 					__snake_case     (			unittest.TestCase				):

  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  tempfile.mkdtemp()

      SCREAMING_SNAKE_CASE_													=  SpeechTaTokenizer(_A)
      tokenizer.save_pretrained(self.tmpdirname)

      SCREAMING_SNAKE_CASE_													=  {
          'feature_size': 1,
          'padding_value': 0.0,
          'sampling_rate': 16000,
          'do_normalize': False,
          'num_mel_bins': 80,
          'hop_length': 16,
          'win_length': 64,
          'win_function': 'hann_window',
          'fmin': 80,
          'fmax': 7600,
          'mel_floor': 1E-10,
          'reduction_factor': 2,
          'return_attention_mask': True,
      }

      SCREAMING_SNAKE_CASE_													=  os.path.join(self.tmpdirname  ,						_A)
      with open(self.feature_extraction_file  ,						'w'  ,						encoding='utf-8') as fp:
          fp.write(json.dumps(_A) + '\n')

  def   lowerCAmelCase__   (    self  ,						**_A):
      return SpeechTaTokenizer.from_pretrained(self.tmpdirname  ,						**_A)

  def   lowerCAmelCase__   (    self  ,						**_A):
      return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname  ,						**_A)

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

  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  self.get_tokenizer()
      SCREAMING_SNAKE_CASE_													=  self.get_feature_extractor()

      SCREAMING_SNAKE_CASE_													=  SpeechTaProcessor(tokenizer=_A  ,						feature_extractor=_A)

      processor.save_pretrained(self.tmpdirname)
      SCREAMING_SNAKE_CASE_													=  SpeechTaProcessor.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   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  SpeechTaProcessor(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=_A  ,						padding_value=1.0)

      SCREAMING_SNAKE_CASE_													=  SpeechTaProcessor.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   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  self.get_feature_extractor()
      SCREAMING_SNAKE_CASE_													=  self.get_tokenizer()

      SCREAMING_SNAKE_CASE_													=  SpeechTaProcessor(tokenizer=_A  ,						feature_extractor=_A)

      SCREAMING_SNAKE_CASE_													=  floats_list((3, 1000))

      SCREAMING_SNAKE_CASE_													=  feature_extractor(audio=_A  ,						return_tensors='np')
      SCREAMING_SNAKE_CASE_													=  processor(audio=_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   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  self.get_feature_extractor()
      SCREAMING_SNAKE_CASE_													=  self.get_tokenizer()

      SCREAMING_SNAKE_CASE_													=  SpeechTaProcessor(tokenizer=_A  ,						feature_extractor=_A)

      SCREAMING_SNAKE_CASE_													=  floats_list((3, 1000))

      SCREAMING_SNAKE_CASE_													=  feature_extractor(audio_target=_A  ,						return_tensors='np')
      SCREAMING_SNAKE_CASE_													=  processor(audio_target=_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   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  self.get_feature_extractor()
      SCREAMING_SNAKE_CASE_													=  self.get_tokenizer()

      SCREAMING_SNAKE_CASE_													=  SpeechTaProcessor(tokenizer=_A  ,						feature_extractor=_A)

      SCREAMING_SNAKE_CASE_													=  'This is a test string'

      SCREAMING_SNAKE_CASE_													=  processor(text=_A)
      SCREAMING_SNAKE_CASE_													=  tokenizer(_A)

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

  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  self.get_feature_extractor()
      SCREAMING_SNAKE_CASE_													=  self.get_tokenizer()

      SCREAMING_SNAKE_CASE_													=  SpeechTaProcessor(tokenizer=_A  ,						feature_extractor=_A)

      SCREAMING_SNAKE_CASE_													=  'This is a test string'

      SCREAMING_SNAKE_CASE_													=  processor(text_target=_A)
      SCREAMING_SNAKE_CASE_													=  tokenizer(_A)

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

  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  self.get_feature_extractor()
      SCREAMING_SNAKE_CASE_													=  self.get_tokenizer()

      SCREAMING_SNAKE_CASE_													=  SpeechTaProcessor(tokenizer=_A  ,						feature_extractor=_A)

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

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

      self.assertListEqual(_A  ,						_A)


  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  self.get_feature_extractor()
      SCREAMING_SNAKE_CASE_													=  self.get_tokenizer()

      SCREAMING_SNAKE_CASE_													=  SpeechTaProcessor(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     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						str				):





    """simple docstring"""


    return "".join(chr(ord(_SCREAMING_SNAKE_CASE				) + 32				) if 'A' <= char <= 'Z' else char for char in word				)


if __name__ == "__main__":
       from doctest import testmod

       testmod()


