






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 							_lowerCAmelCase(			unittest.TestCase   ):





       """simple docstring"""







       def      _a       (   self				):
         UpperCamelCase_:   Union[str, Any]        =							'ylacombe/bark-small'
         UpperCamelCase_:   Optional[int]        =							tempfile.mkdtemp()
         UpperCamelCase_:   Dict        =							'en_speaker_1'
         UpperCamelCase_:   List[Any]        =							'This is a test string'
         UpperCamelCase_:   Tuple        =							'speaker_embeddings_path.json'
         UpperCamelCase_:   Tuple        =							'speaker_embeddings'





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





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





       def      _a       (   self				):
         UpperCamelCase_:   int        =							self.get_tokenizer()

         UpperCamelCase_:   Dict        =							BarkProcessor(tokenizer=_lowerCamelCase				)

         processor.save_pretrained(self.tmpdirname				)
         UpperCamelCase_:   Optional[Any]        =							BarkProcessor.from_pretrained(self.tmpdirname				)

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





       @slow
       def      _a       (   self				):
         UpperCamelCase_:   Dict        =							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    ,     )

         UpperCamelCase_:   Optional[int]        =							self.get_tokenizer(bos_token='(BOS)'    ,     eos_token='(EOS)'				)

         UpperCamelCase_:   Optional[int]        =							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				):
         UpperCamelCase_:   Union[str, Any]        =							BarkProcessor.from_pretrained(
             pretrained_processor_name_or_path=self.checkpoint    ,     speaker_embeddings_dict_path=self.speaker_embeddings_dict_path    ,     )

         UpperCamelCase_:   int        =							3_5
         UpperCamelCase_:   Optional[int]        =							2
         UpperCamelCase_:   int        =							8

         UpperCamelCase_:   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
         UpperCamelCase_:   Dict        =							processor(text=self.input_string    ,     voice_preset=_lowerCamelCase				)

         UpperCamelCase_:   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 npz file
         UpperCamelCase_:   Tuple        =							os.path.join(self.tmpdirname    ,     'file.npz'				)
         np.savez(_lowerCamelCase    ,     **_lowerCamelCase				)
         UpperCamelCase_:   List[Any]        =							processor(text=self.input_string    ,     voice_preset=_lowerCamelCase				)
         UpperCamelCase_:   Tuple        =							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
         UpperCamelCase_:   int        =							processor(text=self.input_string    ,     voice_preset=self.voice_preset				)





       def      _a       (   self				):
         UpperCamelCase_:   Tuple        =							self.get_tokenizer()

         UpperCamelCase_:   str        =							BarkProcessor(tokenizer=_lowerCamelCase				)

         UpperCamelCase_:   List[str]        =							processor(text=self.input_string				)

         UpperCamelCase_:   List[Any]        =							tokenizer(
             self.input_string    ,     padding='max_length'    ,     max_length=2_5_6    ,     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()				)



'''simple docstring'''





from __future__ import annotations

from functools import lru_cache
from math import ceil

UpperCAmelCase__  			=				1_0_0

UpperCAmelCase__  			=				set(range(3, NUM_PRIMES, 2))
primes.add(2)
UpperCAmelCase__  			=				42

for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
					if prime not in primes:
										continue
					primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))






@lru_cache(maxsize=100 )
def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int ):






			"""simple docstring"""




			if number_to_partition < 0:
						return set()
			elif number_to_partition == 0:
						return {1}

			__A=	set()
			__A=	42
			__A=	42

			for prime in primes:
						if prime > number_to_partition:
									continue
						for sub in partition(number_to_partition - prime ):
									ret.add(sub * prime )

			return ret






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int = 5000 ):






			"""simple docstring"""




			for number_to_partition in range(1,_SCREAMING_SNAKE_CASE ):
						if len(partition(_SCREAMING_SNAKE_CASE ) ) > number_unique_partitions:
									return number_to_partition
			return None


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



