





'''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 lowerCamelCase						(unittest.TestCase					):







  def 				UpperCAmelCase_				(		self		)		->						Optional[Any]:






        """simple docstring"""





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







  def 				UpperCAmelCase_				(		self     ,     **lowercase__		)		->						Optional[int]:






        """simple docstring"""





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







  def 				UpperCAmelCase_				(		self		)		->						Union[str, Any]:






        """simple docstring"""





        shutil.rmtree(self.tmpdirname		)







  def 				UpperCAmelCase_				(		self		)		->						Tuple:






        """simple docstring"""





        _snake_case		:	Any					     =     self.get_tokenizer()

        _snake_case		:	Any					     =     BarkProcessor(tokenizer=lowercase__		)

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

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







  @slow
  def 				UpperCAmelCase_				(		self		)		->						Union[str, Any]:






        """simple docstring"""





        _snake_case		:	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     ,     )

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

        _snake_case		:	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 				UpperCAmelCase_				(		self		)		->						Optional[int]:






        """simple docstring"""





        _snake_case		:	Dict					     =     BarkProcessor.from_pretrained(
            pretrained_processor_name_or_path=self.checkpoint     ,     speaker_embeddings_dict_path=self.speaker_embeddings_dict_path     ,     )

        _snake_case		:	Optional[Any]					     =     35
        _snake_case		:	List[str]					     =     2
        _snake_case		:	Any					     =     8

        _snake_case		:	Any					     =     {
            '''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
        _snake_case		:	Dict					     =     processor(text=self.input_string     ,     voice_preset=lowercase__		)

        _snake_case		:	str					     =     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
        _snake_case		:	List[Any]					     =     os.path.join(self.tmpdirname     ,     '''file.npz'''		)
        np.savez(lowercase__     ,     **lowercase__		)
        _snake_case		:	Any					     =     processor(text=self.input_string     ,     voice_preset=lowercase__		)
        _snake_case		:	List[str]					     =     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
        _snake_case		:	str					     =     processor(text=self.input_string     ,     voice_preset=self.voice_preset		)







  def 				UpperCAmelCase_				(		self		)		->						Dict:






        """simple docstring"""





        _snake_case		:	List[str]					     =     self.get_tokenizer()

        _snake_case		:	int					     =     BarkProcessor(tokenizer=lowercase__		)

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

        _snake_case		:	str					     =     tokenizer(
            self.input_string     ,     padding='''max_length'''     ,     max_length=256     ,     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 ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
   from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline


