
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    _snake_case (						unittest.TestCase				):


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Dict     		=   """ylacombe/bark-small"""
   lowercase__	: Dict     		=   tempfile.mkdtemp()
   lowercase__	: Any     		=   """en_speaker_1"""
   lowercase__	: Optional[int]     		=   """This is a test string"""
   lowercase__	: Tuple     		=   """speaker_embeddings_path.json"""
   lowercase__	: str     		=   """speaker_embeddings"""


 def   lowercase__  (       self       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return AutoTokenizer.from_pretrained(self.checkpoint       ,	**SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   shutil.rmtree(self.tmpdirname)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Dict     		=   self.get_tokenizer()

   lowercase__	: int     		=   BarkProcessor(tokenizer=SCREAMING_SNAKE_CASE_)

   processor.save_pretrained(self.tmpdirname)
   lowercase__	: List[str]     		=   BarkProcessor.from_pretrained(self.tmpdirname)

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


 @slow
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   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__	: Optional[int]     		=   self.get_tokenizer(bos_token="""(BOS)"""       ,	eos_token="""(EOS)""")

   lowercase__	: Any     		=   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   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Dict     		=   BarkProcessor.from_pretrained(
       pretrained_processor_name_or_path=self.checkpoint       ,	speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,	)

   lowercase__	: Optional[int]     		=   35
   lowercase__	: Tuple     		=   2
   lowercase__	: Dict     		=   8

   lowercase__	: Optional[int]     		=   {
       """semantic_prompt""": np.ones(SCREAMING_SNAKE_CASE_),
       """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__	: Tuple     		=   processor(text=self.input_string       ,	voice_preset=SCREAMING_SNAKE_CASE_)

   lowercase__	: Union[str, Any]     		=   inputs["""history_prompt"""]
   for key in voice_preset:
     self.assertListEqual(voice_preset[key].tolist()       ,	processed_voice_preset.get(SCREAMING_SNAKE_CASE_       ,	np.array([])).tolist())

   # test loading voice preset from npz file
   lowercase__	: List[Any]     		=   os.path.join(self.tmpdirname       ,	"""file.npz""")
   np.savez(SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)
   lowercase__	: str     		=   processor(text=self.input_string       ,	voice_preset=SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[Any]     		=   inputs["""history_prompt"""]

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

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



 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Dict     		=   self.get_tokenizer()

   lowercase__	: str     		=   BarkProcessor(tokenizer=SCREAMING_SNAKE_CASE_)

   lowercase__	: List[Any]     		=   processor(text=self.input_string)

   lowercase__	: List[str]     		=   tokenizer(
       self.input_string       ,	padding="""max_length"""       ,	max_length=2_56       ,	add_special_tokens=SCREAMING_SNAKE_CASE_       ,	return_attention_mask=SCREAMING_SNAKE_CASE_       ,	return_token_type_ids=SCREAMING_SNAKE_CASE_       ,	)

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








def 		UpperCamelCase						(   lowercase_ )		->						float:


  '''simple docstring'''






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

  lowercase__	: int     		=   sum(lowercase_ ) / len(lowercase_ )  # Calculate the average
  return sum(abs(x - average ) for x in nums ) / len(lowercase_ )


if __name__ == "__main__":
     import doctest

     doctest.testmod()






