



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







    def     lowercase_ (   self				:						Dict		,    **__snake_case				:						Optional[Any]			):
      return AutoTokenizer.from_pretrained(self.checkpoint		,    **__snake_case			)







    def     lowercase_ (   self				:						List[Any]			):
      shutil.rmtree(self.tmpdirname			)







    def     lowercase_ (   self				:						Optional[int]			):
      a		:      Optional[Any]       =  self.get_tokenizer()

      a		:      Optional[int]       =  BarkProcessor(tokenizer=__snake_case			)

      processor.save_pretrained(self.tmpdirname			)
      a		:      Any       =  BarkProcessor.from_pretrained(self.tmpdirname			)

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







    @slow
    def     lowercase_ (   self				:						int			):
      a		:      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		,    )

      a		:      List[str]       =  self.get_tokenizer(bos_token='(BOS)'		,    eos_token='(EOS)'			)

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

      a		:      List[str]       =  35
      a		:      Any       =  2
      a		:      Optional[int]       =  8

      a		:      Any       =  {
          'semantic_prompt': np.ones(__snake_case			),
          '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		:      Any       =  processor(text=self.input_string		,    voice_preset=__snake_case			)

      a		:      List[Any]       =  inputs['history_prompt']
      for key in voice_preset:
        self.assertListEqual(voice_preset[key].tolist()		,    processed_voice_preset.get(__snake_case		,    np.array([]			)			).tolist()			)

      # test loading voice preset from npz file
      a		:      Tuple       =  os.path.join(self.tmpdirname		,    'file.npz'			)
      np.savez(__snake_case		,    **__snake_case			)
      a		:      Dict       =  processor(text=self.input_string		,    voice_preset=__snake_case			)
      a		:      List[str]       =  inputs['history_prompt']

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

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







    def     lowercase_ (   self				:						int			):
      a		:      Dict       =  self.get_tokenizer()

      a		:      Union[str, Any]       =  BarkProcessor(tokenizer=__snake_case			)

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

      a		:      Dict       =  tokenizer(
          self.input_string		,    padding='max_length'		,    max_length=2_56		,    add_special_tokens=__snake_case		,    return_attention_mask=__snake_case		,    return_token_type_ids=__snake_case		,    )

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




'''simple docstring'''






from ..utils import DummyObject, requires_backends

class 							a__(      metaclass=lowerCamelCase__			):
    lowercase__				=					["""torch""", """torchsde"""]







    def __init__(   self				:						Any		,    *__snake_case				:						List[str]		,    **__snake_case				:						Tuple			):
      requires_backends(self		,    ['torch', 'torchsde']			)







    @classmethod
    def     lowercase_ (   cls				:						List[Any]		,    *__snake_case				:						str		,    **__snake_case				:						List[Any]			):
      requires_backends(cls		,    ['torch', 'torchsde']			)







    @classmethod
    def     lowercase_ (   cls				:						Dict		,    *__snake_case				:						Optional[Any]		,    **__snake_case				:						str			):
      requires_backends(cls		,    ['torch', 'torchsde']			)