


import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
   import librosa  # noqa: E402

   SCREAMING_SNAKE_CASE	:int  		=     True
   SCREAMING_SNAKE_CASE	:List[str]  		=     ''''''
except Exception as e:
   SCREAMING_SNAKE_CASE	:Dict  		=     False
   SCREAMING_SNAKE_CASE	:int  		=     (
       F'''Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it.'''
   )


from PIL import Image  # noqa: E402


class     __lowerCAmelCase     ( a ,  a					):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		    =       'mel_config.json'



  @register_to_config
  def __init__(     self       :       Dict    ,				_lowerCAmelCase       :       int = 2_5_6    ,				_lowerCAmelCase       :       int = 2_5_6    ,				_lowerCAmelCase       :       int = 2_2_0_5_0    ,				_lowerCAmelCase       :       int = 2_0_4_8    ,				_lowerCAmelCase       :       int = 5_1_2    ,				_lowerCAmelCase       :       int = 8_0    ,				_lowerCAmelCase       :       int = 3_2    ,				)   -> List[str]:




     """simple docstring"""
     snake_case_					    =					hop_length
     snake_case_					    =					sample_rate
     snake_case_					    =					n_fft
     snake_case_					    =					top_db
     snake_case_					    =					n_iter
     self.set_resolution(_lowerCAmelCase    ,				_lowerCAmelCase  )
     snake_case_					    =					None

     if not _librosa_can_be_imported:
        raise ValueError(_import_error  )



  def 					lowerCAmelCase__  (     self       :       Optional[Any]    ,				_lowerCAmelCase       :       int    ,				_lowerCAmelCase       :       int  )   -> int:




     """simple docstring"""
     snake_case_					    =					x_res
     snake_case_					    =					y_res
     snake_case_					    =					self.y_res
     snake_case_					    =					self.x_res * self.hop_length - 1



  def 					lowerCAmelCase__  (     self       :       Optional[int]    ,				_lowerCAmelCase       :       str = None    ,				_lowerCAmelCase       :       np.ndarray = None  )   -> int:




     """simple docstring"""
     if audio_file is not None:
        snake_case_    ,    snake_case_					    =					librosa.load(_lowerCAmelCase    ,				mono=_lowerCAmelCase    ,				sr=self.sr  )
     else:
        snake_case_					    =					raw_audio

     # Pad with silence if necessary.
     if len(self.audio  ) < self.x_res * self.hop_length:
        snake_case_					    =					np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio  ),)  )]  )



  def 					lowerCAmelCase__  (     self       :       Tuple  )   -> int:




     """simple docstring"""
     return len(self.audio  ) // self.slice_size



  def 					lowerCAmelCase__  (     self       :       List[Any]    ,				_lowerCAmelCase       :       int = 0  )   -> np.ndarray:




     """simple docstring"""
     return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]



  def 					lowerCAmelCase__  (     self       :       Union[str, Any]  )   -> int:




     """simple docstring"""
     return self.sr



  def 					lowerCAmelCase__  (     self       :       int    ,				_lowerCAmelCase       :       int  )   -> Image.Image:




     """simple docstring"""
     snake_case_					    =					librosa.feature.melspectrogram(
         y=self.get_audio_slice(_lowerCAmelCase  )    ,				sr=self.sr    ,				n_fft=self.n_fft    ,				hop_length=self.hop_length    ,				n_mels=self.n_mels  )
     snake_case_					    =					librosa.power_to_db(_lowerCAmelCase    ,				ref=np.max    ,				top_db=self.top_db  )
     snake_case_					    =					(((log_S + self.top_db) * 2_5_5 / self.top_db).clip(0    ,				2_5_5  ) + 0.5).astype(np.uinta  )
     snake_case_					    =					Image.fromarray(_lowerCAmelCase  )
     return image



  def 					lowerCAmelCase__  (     self       :       Optional[int]    ,				_lowerCAmelCase       :       Image.Image  )   -> np.ndarray:




     """simple docstring"""
     snake_case_					    =					np.frombuffer(image.tobytes()    ,				dtype="uint8"  ).reshape((image.height, image.width)  )
     snake_case_					    =					bytedata.astype("float"  ) * self.top_db / 2_5_5 - self.top_db
     snake_case_					    =					librosa.db_to_power(_lowerCAmelCase  )
     snake_case_					    =					librosa.feature.inverse.mel_to_audio(
         _lowerCAmelCase    ,				sr=self.sr    ,				n_fft=self.n_fft    ,				hop_length=self.hop_length    ,				n_iter=self.n_iter  )
     return audio








import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
   pass

if is_speech_available():
   from transformers import EncodecFeatureExtractor, MusicgenProcessor


SCREAMING_SNAKE_CASE	:Union[str, Any]  		=     random.Random()



def 			_lowerCAmelCase						(							lowerCAmelCase_							:Union[str, Any]		,						lowerCAmelCase_							:Optional[int]=1.0		,						lowerCAmelCase_							:List[str]=None		,						lowerCAmelCase_							:str=None							)->Union[str, Any]:
   '''simple docstring'''






   if rng is None:
      snake_case_					    =					global_rng

   snake_case_					    =					[]
   for batch_idx in range(shape[0]							):
      values.append([]							)
      for _ in range(shape[1]							):
         values[-1].append(rng.random() * scale							)

   return values



@require_torch
@require_sentencepiece
class     __lowerCAmelCase     ( unittest.TestCase					):






  """simple docstring"""





  def 					lowerCAmelCase__  (     self       :       List[str]  )   -> List[Any]:




     """simple docstring"""
     snake_case_					    =					"facebook/musicgen-small"
     snake_case_					    =					tempfile.mkdtemp()



  def 					lowerCAmelCase__  (     self       :       Tuple    ,				**_lowerCAmelCase       :       Tuple  )   -> Optional[int]:




     """simple docstring"""
     return TaTokenizer.from_pretrained(self.checkpoint    ,				**_lowerCAmelCase  )



  def 					lowerCAmelCase__  (     self       :       Union[str, Any]    ,				**_lowerCAmelCase       :       Union[str, Any]  )   -> int:




     """simple docstring"""
     return EncodecFeatureExtractor.from_pretrained(self.checkpoint    ,				**_lowerCAmelCase  )



  def 					lowerCAmelCase__  (     self       :       Optional[Any]  )   -> Optional[int]:




     """simple docstring"""
     shutil.rmtree(self.tmpdirname  )



  def 					lowerCAmelCase__  (     self       :       int  )   -> List[Any]:




     """simple docstring"""
     snake_case_					    =					self.get_tokenizer()
     snake_case_					    =					self.get_feature_extractor()

     snake_case_					    =					MusicgenProcessor(tokenizer=_lowerCAmelCase    ,				feature_extractor=_lowerCAmelCase  )

     processor.save_pretrained(self.tmpdirname  )
     snake_case_					    =					MusicgenProcessor.from_pretrained(self.tmpdirname  )

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

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



  def 					lowerCAmelCase__  (     self       :       Optional[Any]  )   -> Tuple:




     """simple docstring"""
     snake_case_					    =					MusicgenProcessor(tokenizer=self.get_tokenizer()    ,				feature_extractor=self.get_feature_extractor()  )
     processor.save_pretrained(self.tmpdirname  )

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

     snake_case_					    =					MusicgenProcessor.from_pretrained(
         self.tmpdirname    ,				bos_token="(BOS)"    ,				eos_token="(EOS)"    ,				do_normalize=_lowerCAmelCase    ,				padding_value=1.0  )

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

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



  def 					lowerCAmelCase__  (     self       :       List[str]  )   -> Optional[Any]:




     """simple docstring"""
     snake_case_					    =					self.get_feature_extractor()
     snake_case_					    =					self.get_tokenizer()

     snake_case_					    =					MusicgenProcessor(tokenizer=_lowerCAmelCase    ,				feature_extractor=_lowerCAmelCase  )

     snake_case_					    =					floats_list((3, 1_0_0_0)  )

     snake_case_					    =					feature_extractor(_lowerCAmelCase    ,				return_tensors="np"  )
     snake_case_					    =					processor(_lowerCAmelCase    ,				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       :       Tuple  )   -> List[Any]:




     """simple docstring"""
     snake_case_					    =					self.get_feature_extractor()
     snake_case_					    =					self.get_tokenizer()

     snake_case_					    =					MusicgenProcessor(tokenizer=_lowerCAmelCase    ,				feature_extractor=_lowerCAmelCase  )

     snake_case_					    =					"This is a test string"

     snake_case_					    =					processor(text=_lowerCAmelCase  )

     snake_case_					    =					tokenizer(_lowerCAmelCase  )

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



  def 					lowerCAmelCase__  (     self       :       Any  )   -> str:




     """simple docstring"""
     snake_case_					    =					self.get_feature_extractor()
     snake_case_					    =					self.get_tokenizer()

     snake_case_					    =					MusicgenProcessor(tokenizer=_lowerCAmelCase    ,				feature_extractor=_lowerCAmelCase  )

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

     snake_case_					    =					processor.batch_decode(sequences=_lowerCAmelCase  )
     snake_case_					    =					tokenizer.batch_decode(_lowerCAmelCase  )

     self.assertListEqual(_lowerCAmelCase    ,				_lowerCAmelCase  )



  def 					lowerCAmelCase__  (     self       :       Optional[int]  )   -> Dict:




     """simple docstring"""
     snake_case_					    =					self.get_feature_extractor()
     snake_case_					    =					self.get_tokenizer()

     snake_case_					    =					MusicgenProcessor(tokenizer=_lowerCAmelCase    ,				feature_extractor=_lowerCAmelCase  )

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



  def 					lowerCAmelCase__  (     self       :       Tuple  )   -> int:




     """simple docstring"""
     snake_case_					    =					self.get_feature_extractor(padding_side="left"  )
     snake_case_					    =					self.get_tokenizer()

     snake_case_					    =					MusicgenProcessor(tokenizer=_lowerCAmelCase    ,				feature_extractor=_lowerCAmelCase  )

     snake_case_					    =					[floats_list((1, x)  )[0] for x in range(5    ,				2_0    ,				5  )]
     snake_case_					    =					processor(_lowerCAmelCase  ).padding_mask

     snake_case_					    =					np.asarray(floats_list((3, 2_0)  )  )[:, None, :]
     snake_case_					    =					processor.batch_decode(_lowerCAmelCase    ,				padding_mask=_lowerCAmelCase  )

     self.assertIsInstance(_lowerCAmelCase    ,				_lowerCAmelCase  )

     for audio in decoded_audios:
        self.assertIsInstance(_lowerCAmelCase    ,				np.ndarray  )

     self.assertTrue(decoded_audios[0].shape == (1, 1_0)  )
     self.assertTrue(decoded_audios[1].shape == (1, 1_5)  )
     self.assertTrue(decoded_audios[2].shape == (1, 2_0)  )




