





'''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 			SCREAMING_SNAKE_CASE   (unittest.TestCase							):


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			List[Any]								=	'ylacombe/bark-small'
          __A						:			List[str]								=	tempfile.mkdtemp()
          __A						:			int								=	'en_speaker_1'
          __A						:			Tuple								=	'This is a test string'
          __A						:			Any								=	'speaker_embeddings_path.json'
          __A						:			str								=	'speaker_embeddings'


     def 							SCREAMING_SNAKE_CASE  (						self						,     **_UpperCAmelCase):

          '''simple docstring'''






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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          shutil.rmtree(self.tmpdirname)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Union[str, Any]								=	self.get_tokenizer()

          __A						:			List[Any]								=	BarkProcessor(tokenizer=_UpperCAmelCase)

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

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


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			str								=	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						:			int								=	self.get_tokenizer(bos_token='(BOS)'						,     eos_token='(EOS)')

          __A						:			Union[str, 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 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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

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

          __A						:			List[Any]								=	{
              'semantic_prompt': np.ones(_UpperCAmelCase),
              '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						:			Dict								=	processor(text=self.input_string						,     voice_preset=_UpperCAmelCase)

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

          # test loading voice preset from npz file
          __A						:			Optional[int]								=	os.path.join(self.tmpdirname						,     'file.npz')
          np.savez(_UpperCAmelCase						,     **_UpperCAmelCase)
          __A						:			Union[str, Any]								=	processor(text=self.input_string						,     voice_preset=_UpperCAmelCase)
          __A						:			str								=	inputs['history_prompt']

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

          # test loading voice preset from the hub
          __A						:			List[Any]								=	processor(text=self.input_string						,     voice_preset=self.voice_preset)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[int]								=	self.get_tokenizer()

          __A						:			Tuple								=	BarkProcessor(tokenizer=_UpperCAmelCase)

          __A						:			int								=	processor(text=self.input_string)

          __A						:			Any								=	tokenizer(
              self.input_string						,     padding='max_length'						,     max_length=256						,     add_special_tokens=_UpperCAmelCase						,     return_attention_mask=_UpperCAmelCase						,     return_token_type_ids=_UpperCAmelCase						,     )

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






'''simple docstring'''




import math






def 	_lowerCAmelCase      (  __snake_case  :			int      )					->					int:

     if not isinstance(__snake_case			,							__snake_case      ):
          __A						:			List[Any]								=	f'Input value of [number={number}] must be an integer'
          raise TypeError(__snake_case      )

     if number < 1:
          __A						:			Union[str, Any]								=	f'Input value of [number={number}] must be > 0'
          raise ValueError(__snake_case      )
     elif number == 1:
          return 3
     elif number == 2:
          return 5
     else:
          __A						:			Optional[Any]								=	int(math.log(number // 3			,							2      )      ) + 2

          __A						:			Union[str, Any]								=	[3, 5]
          __A						:			List[Any]								=	2
          __A						:			Optional[Any]								=	3
          for block in range(1			,							__snake_case      ):
               for _ in range(__snake_case      ):
                    proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1]      )
                    proth_index += 1
               increment *= 2

     return proth_list[number - 1]


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  for number in range(11):
    lowercase__   :				str      =   0
    try:
      lowercase__   :				List[str]      =   proth(number)
    except ValueError:
      print(f"""ValueError: there is no {number}th Proth number""")
      continue

    print(f"""The {number}th Proth number: {value}""")