



'''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						):



 '''simple docstring'''







 def 					a__  (self       )    ->     Tuple:


      """simple docstring"""



      _a      =  '''ylacombe/bark-small'''
      _a      =  tempfile.mkdtemp()
      _a      =  '''en_speaker_1'''
      _a      =  '''This is a test string'''
      _a      =  '''speaker_embeddings_path.json'''
      _a      =  '''speaker_embeddings'''



 def 					a__  (self ,   **A       )    ->     Any:


      """simple docstring"""



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



 def 					a__  (self       )    ->     int:


      """simple docstring"""



      shutil.rmtree(self.tmpdirname       )



 def 					a__  (self       )    ->     str:


      """simple docstring"""



      _a      =  self.get_tokenizer()

      _a      =  BarkProcessor(tokenizer=lowerCAmelCase__       )

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

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



 @slow
 def 					a__  (self       )    ->     List[Any]:


      """simple docstring"""



      _a      =  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      =  self.get_tokenizer(bos_token='''(BOS)''' ,   eos_token='''(EOS)'''       )

      _a      =  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       )    ->     Tuple:


      """simple docstring"""



      _a      =  BarkProcessor.from_pretrained(
          pretrained_processor_name_or_path=self.checkpoint ,   speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,   )

      _a      =  35
      _a      =  2
      _a      =  8

      _a      =  {
          '''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
      _a      =  processor(text=self.input_string ,   voice_preset=lowerCAmelCase__       )

      _a      =  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
      _a      =  os.path.join(self.tmpdirname ,   '''file.npz'''       )
      np.savez(lowerCAmelCase__ ,   **lowerCAmelCase__       )
      _a      =  processor(text=self.input_string ,   voice_preset=lowerCAmelCase__       )
      _a      =  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
      _a      =  processor(text=self.input_string ,   voice_preset=self.voice_preset       )



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


      """simple docstring"""



      _a      =  self.get_tokenizer()

      _a      =  BarkProcessor(tokenizer=lowerCAmelCase__       )

      _a      =  processor(text=self.input_string       )

      _a      =  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()       )







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase	: Any  				={'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCAmelCase	: int  				=[
						    'VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'ViTMSNModel',
						    'ViTMSNForImageClassification',
						    'ViTMSNPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_vit_msn import (
												    VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ViTMSNForImageClassification,
												    ViTMSNModel,
												    ViTMSNPreTrainedModel,
												)

else:
						import sys

						__lowerCAmelCase	: Union[str, Any]  				=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
