



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    UpperCAmelCase__(				unittest.TestCase						):

   '''simple docstring'''




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




         """simple docstring"""

         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       UpperCAmelCase		(    self	:      Optional[int]      ,					**lowerCAmelCase	:      Union[str, Any])						->					int:




         """simple docstring"""

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


   def       UpperCAmelCase		(    self	:      List[str])						->					List[str]:




         """simple docstring"""

         shutil.rmtree(self.tmpdirname)


   def       UpperCAmelCase		(    self	:      List[Any])						->					List[Any]:




         """simple docstring"""

         lowercase__  =	self.get_tokenizer()

         lowercase__  =	BarkProcessor(tokenizer=lowerCAmelCase)

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

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


   @slow
   def       UpperCAmelCase		(    self	:      Optional[Any])						->					List[Any]:




         """simple docstring"""

         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       UpperCAmelCase		(    self	:      Optional[int])						->					str:




         """simple docstring"""

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

         lowercase__  =	35
         lowercase__  =	2
         lowercase__  =	8

         lowercase__  =	{
             '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
         lowercase__  =	processor(text=self.input_string      ,					voice_preset=lowerCAmelCase)

         lowercase__  =	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
         lowercase__  =	os.path.join(self.tmpdirname      ,					'file.npz')
         np.savez(lowerCAmelCase      ,					**lowerCAmelCase)
         lowercase__  =	processor(text=self.input_string      ,					voice_preset=lowerCAmelCase)
         lowercase__  =	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
         lowercase__  =	processor(text=self.input_string      ,					voice_preset=self.voice_preset)



   def       UpperCAmelCase		(    self	:      Tuple)						->					Optional[int]:




         """simple docstring"""

         lowercase__  =	self.get_tokenizer()

         lowercase__  =	BarkProcessor(tokenizer=lowerCAmelCase)

         lowercase__  =	processor(text=self.input_string)

         lowercase__  =	tokenizer(
             self.input_string      ,					padding='max_length'      ,					max_length=2_56      ,					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 	_lowerCAmelCase (    A__ = 50_000_000						):
      lowercase__  =	set()
      lowercase__  =	int((limit - 24) ** (1 / 2)						)

      lowercase__  =	set(range(3    ,	prime_square_limit + 1    ,	2						)						)
      primes.add(2						)
      for p in range(3    ,	prime_square_limit + 1    ,	2						):
            if p not in primes:
                  continue
            primes.difference_update(set(range(p * p    ,	prime_square_limit + 1    ,	A__						)						)						)

      for primea in primes:
            lowercase__  =	primea * primea
            for primea in primes:
                  lowercase__  =	primea * primea * primea
                  if square + cube >= limit - 16:
                        break
                  for primea in primes:
                        lowercase__  =	primea * primea * primea * primea
                        lowercase__  =	square + cube + tetr
                        if total >= limit:
                              break
                        ret.add(A__						)

      return len(A__						)


if __name__ == "__main__":
       print(F'''{solution() = }''')
