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    A_ (       unittest.TestCase ):

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

  def 		_UpperCAmelCase   (		self :      Any     ,							**__SCREAMING_SNAKE_CASE :      Dict       ):
   return AutoTokenizer.from_pretrained(self.checkpoint     ,							**__lowerCAmelCase       )

  def 		_UpperCAmelCase   (		self :      int       ):
   shutil.rmtree(self.tmpdirname       )

  def 		_UpperCAmelCase   (		self :      Any       ):
   __a       =  self.get_tokenizer()

   __a       =  BarkProcessor(tokenizer=__lowerCAmelCase       )

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

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

  @slow
  def 		_UpperCAmelCase   (		self :      List[str]       ):
   __a       =  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     ,							)

   __a       =  self.get_tokenizer(bos_token="(BOS)"     ,							eos_token="(EOS)"       )

   __a       =  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[Any]       ):
   __a       =  BarkProcessor.from_pretrained(
       pretrained_processor_name_or_path=self.checkpoint     ,							speaker_embeddings_dict_path=self.speaker_embeddings_dict_path     ,							)

   __a       =  35
   __a       =  2
   __a       =  8

   __a       =  {
       "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
   __a       =  processor(text=self.input_string     ,							voice_preset=__lowerCAmelCase       )

   __a       =  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
   __a       =  os.path.join(self.tmpdirname     ,							"file.npz"       )
   np.savez(__lowerCAmelCase     ,							**__lowerCAmelCase       )
   __a       =  processor(text=self.input_string     ,							voice_preset=__lowerCAmelCase       )
   __a       =  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
   __a       =  processor(text=self.input_string     ,							voice_preset=self.voice_preset       )

  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   __a       =  self.get_tokenizer()

   __a       =  BarkProcessor(tokenizer=__lowerCAmelCase       )

   __a       =  processor(text=self.input_string       )

   __a       =  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()       )



from __future__ import annotations



def   __A		(			_A					):



 """simple docstring"""


 __a       =  [True] * limit
 __a       =  False
 __a       =  False
 __a       =  True

 for i in range(3							,				int(limit**0.5 + 1					)							,				2					):
  __a       =  i * 2
  while index < limit:
   __a       =  False
   __a       =  index + i

 __a       =  [2]

 for i in range(3							,				_A							,				2					):
  if is_prime[i]:
   primes.append(_A					)

 return primes



def   __A		(			_A = 100_0000					):



 """simple docstring"""


 __a       =  prime_sieve(_A					)
 __a       =  0
 __a       =  0

 for i in range(len(_A					)					):
  for j in range(i + length							,				len(_A					)					):
   __a       =  sum(primes[i:j]					)
   if sol >= ceiling:
    break

   if sol in primes:
    __a       =  j - i
    __a       =  sol

 return largest


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


