

'''simple docstring'''






import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow






@require_torch
class     lowercase_			(unittest.TestCase   ):




  """simple docstring"""




  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   __lowercase													=				'''ylacombe/bark-small'''
   __lowercase													=				tempfile.mkdtemp()
   __lowercase													=				'''en_speaker_1'''
   __lowercase													=				'''This is a test string'''
   __lowercase													=				'''speaker_embeddings_path.json'''
   __lowercase													=				'''speaker_embeddings'''






  def       SCREAMING_SNAKE_CASE   (   self     :  int      ,**lowercase__     :  Union[str, Any]    ):
   return AutoTokenizer.from_pretrained(self.checkpoint      ,**lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   shutil.rmtree(self.tmpdirname    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   __lowercase													=				self.get_tokenizer()

   __lowercase													=				BarkProcessor(tokenizer=lowercase__    )

   processor.save_pretrained(self.tmpdirname    )
   __lowercase													=				BarkProcessor.from_pretrained(self.tmpdirname    )

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






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
   __lowercase													=				BarkProcessor.from_pretrained(
       pretrained_processor_name_or_path=self.checkpoint      ,speaker_embeddings_dict_path=self.speaker_embeddings_dict_path      ,)
   processor.save_pretrained(
       self.tmpdirname      ,speaker_embeddings_dict_path=self.speaker_embeddings_dict_path      ,speaker_embeddings_directory=self.speaker_embeddings_directory      ,)

   __lowercase													=				self.get_tokenizer(bos_token='''(BOS)'''      ,eos_token='''(EOS)'''    )

   __lowercase													=				BarkProcessor.from_pretrained(
       self.tmpdirname      ,self.speaker_embeddings_dict_path      ,bos_token='''(BOS)'''      ,eos_token='''(EOS)'''      ,)

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






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   __lowercase													=				BarkProcessor.from_pretrained(
       pretrained_processor_name_or_path=self.checkpoint      ,speaker_embeddings_dict_path=self.speaker_embeddings_dict_path      ,)

   __lowercase													=				3_5
   __lowercase													=				2
   __lowercase													=				8

   __lowercase													=				{
       '''semantic_prompt''': np.ones(lowercase__    ),
       '''coarse_prompt''': np.ones((nb_codebooks_coarse, seq_len)    ),
       '''fine_prompt''': np.ones((nb_codebooks_total, seq_len)    ),
   }

   # test providing already loaded voice_preset
   __lowercase													=				processor(text=self.input_string      ,voice_preset=lowercase__    )

   __lowercase													=				inputs['''history_prompt''']
   for key in voice_preset:
    self.assertListEqual(voice_preset[key].tolist()      ,processed_voice_preset.get(lowercase__      ,np.array([]    )    ).tolist()    )

   # test loading voice preset from npz file
   __lowercase													=				os.path.join(self.tmpdirname      ,'''file.npz'''    )
   np.savez(lowercase__      ,**lowercase__    )
   __lowercase													=				processor(text=self.input_string      ,voice_preset=lowercase__    )
   __lowercase													=				inputs['''history_prompt''']

   for key in voice_preset:
    self.assertListEqual(voice_preset[key].tolist()      ,processed_voice_preset.get(lowercase__      ,np.array([]    )    ).tolist()    )

   # test loading voice preset from the hub
   __lowercase													=				processor(text=self.input_string      ,voice_preset=self.voice_preset    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   __lowercase													=				self.get_tokenizer()

   __lowercase													=				BarkProcessor(tokenizer=lowercase__    )

   __lowercase													=				processor(text=self.input_string    )

   __lowercase													=				tokenizer(
       self.input_string      ,padding='''max_length'''      ,max_length=2_5_6      ,add_special_tokens=lowercase__      ,return_attention_mask=lowercase__      ,return_token_type_ids=lowercase__      ,)

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




'''simple docstring'''






def 	_A  (					A__   ):






 """simple docstring"""



 if not nums:  # Makes sure that the list is not empty
  raise ValueError('''List is empty'''   )

 __lowercase													=				sum(A__   ) / len(A__   )  # Calculate the average
 return sum(abs(x - average   ) for x in nums   ) / len(A__   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()

