


"""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 							A_      (       unittest.TestCase ):


  def 							_lowercase					(			self:						List[str]    ):







     '''simple docstring'''







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


  def 							_lowercase					(			self:						str							,**__lowerCAmelCase:						int    ):







     '''simple docstring'''







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


  def 							_lowercase					(			self:						Any    ):







     '''simple docstring'''







     shutil.rmtree(self.tmpdirname    )


  def 							_lowercase					(			self:						str    ):







     '''simple docstring'''







     _lowerCamelCase   :				Optional[Any]							=     self.get_tokenizer()

     _lowerCamelCase   :				List[str]							=     BarkProcessor(tokenizer=__lowerCAmelCase    )

     processor.save_pretrained(self.tmpdirname    )
     _lowerCamelCase   :				Union[str, Any]							=     BarkProcessor.from_pretrained(self.tmpdirname    )

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


  @slow
  def 							_lowercase					(			self:						Optional[Any]    ):







     '''simple docstring'''







     _lowerCamelCase   :				List[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							,)

     _lowerCamelCase   :				Any							=     self.get_tokenizer(bos_token="(BOS)"							,eos_token="(EOS)"    )

     _lowerCamelCase   :				List[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 							_lowercase					(			self:						Dict    ):







     '''simple docstring'''







     _lowerCamelCase   :				Any							=     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   :				Optional[Any]							=     2
     _lowerCamelCase   :				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   :				Union[str, Any]							=     processor(text=self.input_string							,voice_preset=__lowerCAmelCase    )

     _lowerCamelCase   :				List[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[int]							=     os.path.join(self.tmpdirname							,"file.npz"    )
     np.savez(__lowerCAmelCase							,**__lowerCAmelCase    )
     _lowerCamelCase   :				str							=     processor(text=self.input_string							,voice_preset=__lowerCAmelCase    )
     _lowerCamelCase   :				Optional[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 the hub
     _lowerCamelCase   :				Optional[Any]							=     processor(text=self.input_string							,voice_preset=self.voice_preset    )






  def 							_lowercase					(			self:						Optional[int]    ):







     '''simple docstring'''







     _lowerCamelCase   :				List[Any]							=     self.get_tokenizer()

     _lowerCamelCase   :				Optional[Any]							=     BarkProcessor(tokenizer=__lowerCAmelCase    )

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

     _lowerCamelCase   :				Union[str, 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 	__a     (      A__			:  int = 1000       ):
    SCREAMING_SNAKE_CASE										=   3
    SCREAMING_SNAKE_CASE										=   0
    while a < n:
        if a % 3 == 0 or a % 5 == 0:
            result += a
        elif a % 15 == 0:
            result -= a
        a += 1
    return result


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