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




    def      _a    (    self       ,						**lowerCamelCase__ ):
       return AutoTokenizer.from_pretrained(self.checkpoint       ,						**lowerCamelCase__ )




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




    def      _a    (    self ):
       lowerCAmelCase_:							str       =	self.get_tokenizer()

       lowerCAmelCase_:							Any       =	BarkProcessor(tokenizer=lowerCamelCase__ )

       processor.save_pretrained(self.tmpdirname )
       lowerCAmelCase_:							Dict       =	BarkProcessor.from_pretrained(self.tmpdirname )

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




    @slow
    def      _a    (    self ):
       lowerCAmelCase_:							int       =	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       ,						)

       lowerCAmelCase_:							Optional[Any]       =	self.get_tokenizer(bos_token="(BOS)"       ,						eos_token="(EOS)" )

       lowerCAmelCase_:							Dict       =	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      _a    (    self ):
       lowerCAmelCase_:							int       =	BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint       ,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,						)

       lowerCAmelCase_:							Union[str, Any]       =	35
       lowerCAmelCase_:							List[Any]       =	2
       lowerCAmelCase_:							Optional[int]       =	8

       lowerCAmelCase_:							Union[str, Any]       =	{
           "semantic_prompt": np.ones(lowerCamelCase__ ),
           "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len) ),
           "fine_prompt": np.ones((nb_codebooks_total, seq_len) ),
       }

       # test providing already loaded voice_preset
       lowerCAmelCase_:							Optional[Any]       =	processor(text=self.input_string       ,						voice_preset=lowerCamelCase__ )

       lowerCAmelCase_:							Any       =	inputs["history_prompt"]
       for key in voice_preset:
          self.assertListEqual(voice_preset[key].tolist()       ,						processed_voice_preset.get(lowerCamelCase__       ,						np.array([] ) ).tolist() )

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

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

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




    def      _a    (    self ):
       lowerCAmelCase_:							List[Any]       =	self.get_tokenizer()

       lowerCAmelCase_:							Tuple       =	BarkProcessor(tokenizer=lowerCamelCase__ )

       lowerCAmelCase_:							Tuple       =	processor(text=self.input_string )

       lowerCAmelCase_:							List[Any]       =	tokenizer(
           self.input_string       ,						padding="max_length"       ,						max_length=256       ,						add_special_tokens=lowerCamelCase__       ,						return_attention_mask=lowerCamelCase__       ,						return_token_type_ids=lowerCamelCase__       ,						)

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

   if density <= 0:
      raise ValueError("Impossible fluid density"					)
   if bulk_modulus <= 0:
      raise ValueError("Impossible bulk modulus"					)

   return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
 import doctest

 doctest.testmod()