

'''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     lowercase_			(unittest.TestCase   ):




  """simple docstring"""




  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   __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       SCREAMING_SNAKE_CASE   (   self     :  int      ,**lowercase__     :  Union[str, Any]    ):
   return AutoTokenizer.from_pretrained(self.checkpoint      ,**lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   shutil.rmtree(self.tmpdirname    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   __lowercase													=				self.get_tokenizer()

   __lowercase													=				BarkProcessor(tokenizer=lowercase__    )

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

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






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
   __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       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   __lowercase													=				BarkProcessor.from_pretrained(
       pretrained_processor_name_or_path=self.checkpoint      ,speaker_embeddings_dict_path=self.speaker_embeddings_dict_path      ,)

   __lowercase													=				3_5
   __lowercase													=				2
   __lowercase													=				8

   __lowercase													=				{
       '''semantic_prompt''': np.ones(lowercase__    ),
       '''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=lowercase__    )

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

   # test loading voice preset from npz file
   __lowercase													=				os.path.join(self.tmpdirname      ,'''file.npz'''    )
   np.savez(lowercase__      ,**lowercase__    )
   __lowercase													=				processor(text=self.input_string      ,voice_preset=lowercase__    )
   __lowercase													=				inputs['''history_prompt''']

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

   # test loading voice preset from the hub
   __lowercase													=				processor(text=self.input_string      ,voice_preset=self.voice_preset    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   __lowercase													=				self.get_tokenizer()

   __lowercase													=				BarkProcessor(tokenizer=lowercase__    )

   __lowercase													=				processor(text=self.input_string    )

   __lowercase													=				tokenizer(
       self.input_string      ,padding='''max_length'''      ,max_length=2_5_6      ,add_special_tokens=lowercase__      ,return_attention_mask=lowercase__      ,return_token_type_ids=lowercase__      ,)

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








'''simple docstring'''






from argparse import ArgumentParser

from . import BaseTransformersCLICommand


def 	_A  (					A__   ):






 """simple docstring"""



 return DownloadCommand(args.model  ,       args.cache_dir  ,       args.force  ,       args.trust_remote_code   )






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  @staticmethod
  def       SCREAMING_SNAKE_CASE   (   lowercase__     :  ArgumentParser    ):
   __lowercase													=				parser.add_parser('''download'''    )
   download_parser.add_argument(
       '''--cache-dir'''      ,type=lowercase__      ,default=lowercase__      ,help='''Path to location to store the models'''    )
   download_parser.add_argument(
       '''--force'''      ,action='''store_true'''      ,help='''Force the model to be download even if already in cache-dir'''    )
   download_parser.add_argument(
       '''--trust-remote-code'''      ,action='''store_true'''      ,help='''Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine'''      ,)
   download_parser.add_argument('''model'''      ,type=lowercase__      ,help='''Name of the model to download'''    )
   download_parser.set_defaults(func=lowercase__    )






  def __init__(   self     :  str      ,lowercase__     :  str      ,lowercase__     :  str      ,lowercase__     :  bool      ,lowercase__     :  bool    ):
   __lowercase													=				model
   __lowercase													=				cache
   __lowercase													=				force
   __lowercase													=				trust_remote_code






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   from ..models.auto import AutoModel, AutoTokenizer

   AutoModel.from_pretrained(
       self._model      ,cache_dir=self._cache      ,force_download=self._force      ,trust_remote_code=self._trust_remote_code    )
   AutoTokenizer.from_pretrained(
       self._model      ,cache_dir=self._cache      ,force_download=self._force      ,trust_remote_code=self._trust_remote_code    )

