



'''simple docstring'''





import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


lowercase_									=    logging.get_logger(__name__)






class __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			int		=			'AutoTokenizer'
 __lowerCamelCase       :			Tuple		=			['tokenizer']

 __lowerCamelCase       :			List[str]		=			{
     'semantic_prompt': 1,
     'coarse_prompt': 2,
     'fine_prompt': 2,
 }



 def __init__(self					,  A					,  A=None			)       ->	str:


      """simple docstring"""



      super().__init__(A			)

      _a           =    speaker_embeddings



 @classmethod
 def    a__    (cls					,  A					,  A="speaker_embeddings_path.json"					,  **A			)       ->	Optional[int]:


      """simple docstring"""



      if speaker_embeddings_dict_path is not None:
           _a           =    get_file_from_repo(
               A					,  A					,  subfolder=kwargs.pop('''subfolder'''					,  A			)					,  cache_dir=kwargs.pop('''cache_dir'''					,  A			)					,  force_download=kwargs.pop('''force_download'''					,  A			)					,  proxies=kwargs.pop('''proxies'''					,  A			)					,  resume_download=kwargs.pop('''resume_download'''					,  A			)					,  local_files_only=kwargs.pop('''local_files_only'''					,  A			)					,  use_auth_token=kwargs.pop('''use_auth_token'''					,  A			)					,  revision=kwargs.pop('''revision'''					,  A			)					,  )
           if speaker_embeddings_path is None:
                logger.warning(
                    f'''`{os.path.join(A					,  A			)}` does not exists
                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.'''			)
                _a           =    None
           else:
                with open(A			) as speaker_embeddings_json:
                     _a           =    json.load(A			)
      else:
           _a           =    None

      _a           =    AutoTokenizer.from_pretrained(A					,  **A			)

      return cls(tokenizer=A					,  speaker_embeddings=A			)



 def    a__    (self					,  A					,  A="speaker_embeddings_path.json"					,  A="speaker_embeddings"					,  A = False					,  **A					,  )       ->	int:


      """simple docstring"""



      if self.speaker_embeddings is not None:
           os.makedirs(os.path.join(A					,  A					,  '''v2'''			)					,  exist_ok=A			)

           _a           =    {}

           _a           =    save_directory

           for prompt_key in self.speaker_embeddings:
                if prompt_key != "repo_or_path":
                     _a           =    self._load_voice_preset(A			)

                     _a           =    {}
                     for key in self.speaker_embeddings[prompt_key]:
                          np.save(
                              os.path.join(
                                  embeddings_dict['''repo_or_path''']					,  A					,  f'''{prompt_key}_{key}'''			)					,  voice_preset[key]					,  allow_pickle=A					,  )
                          _a           =    os.path.join(A					,  f'''{prompt_key}_{key}.npy'''			)

                     _a           =    tmp_dict

           with open(os.path.join(A					,  A			)					,  '''w'''			) as fp:
                json.dump(A					,  A			)

      super().save_pretrained(A					,  A					,  **A			)



 def    a__    (self					,  A = None					,  **A			)       ->	Dict:


      """simple docstring"""



      _a           =    self.speaker_embeddings[voice_preset]

      _a           =    {}
      for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
           if key not in voice_preset_paths:
                raise ValueError(
                    f'''Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].'''			)

           _a           =    get_file_from_repo(
               self.speaker_embeddings.get('''repo_or_path'''					,  '''/'''			)					,  voice_preset_paths[key]					,  subfolder=kwargs.pop('''subfolder'''					,  A			)					,  cache_dir=kwargs.pop('''cache_dir'''					,  A			)					,  force_download=kwargs.pop('''force_download'''					,  A			)					,  proxies=kwargs.pop('''proxies'''					,  A			)					,  resume_download=kwargs.pop('''resume_download'''					,  A			)					,  local_files_only=kwargs.pop('''local_files_only'''					,  A			)					,  use_auth_token=kwargs.pop('''use_auth_token'''					,  A			)					,  revision=kwargs.pop('''revision'''					,  A			)					,  )
           if path is None:
                raise ValueError(
                    f'''`{os.path.join(self.speaker_embeddings.get('repo_or_path'					,  '/'			)					,  voice_preset_paths[key]			)}` does not exists
                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
                    embeddings.'''			)

           _a           =    np.load(A			)

      return voice_preset_dict



 def    a__    (self					,  A = None			)       ->	int:


      """simple docstring"""



      for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
           if key not in voice_preset:
                raise ValueError(f'''Voice preset unrecognized, missing {key} as a key.'''			)

           if not isinstance(voice_preset[key]					,  np.ndarray			):
                raise ValueError(f'''{key} voice preset must be a {str(self.preset_shape[key]			)}D ndarray.'''			)

           if len(voice_preset[key].shape			) != self.preset_shape[key]:
                raise ValueError(f'''{key} voice preset must be a {str(self.preset_shape[key]			)}D ndarray.'''			)



 def __call__(self					,  A=None					,  A=None					,  A="pt"					,  A=256					,  A=False					,  A=True					,  A=False					,  **A					,  )       ->	Optional[int]:


      """simple docstring"""



      if voice_preset is not None and not isinstance(A					,  A			):
           if (
               isinstance(A					,  A			)
               and self.speaker_embeddings is not None
               and voice_preset in self.speaker_embeddings
           ):
                _a           =    self._load_voice_preset(A			)

           else:
                if isinstance(A					,  A			) and not voice_preset.endswith('''.npz'''			):
                     _a           =    voice_preset + '''.npz'''

                _a           =    np.load(A			)

      if voice_preset is not None:
           self._validate_voice_preset_dict(A					,  **A			)
           _a           =    BatchFeature(data=A					,  tensor_type=A			)

      _a           =    self.tokenizer(
          A					,  return_tensors=A					,  padding='''max_length'''					,  max_length=A					,  return_attention_mask=A					,  return_token_type_ids=A					,  add_special_tokens=A					,  **A					,  )

      if voice_preset is not None:
           _a           =    voice_preset

      return encoded_text








'''simple docstring'''





def lowerCAmelCase    (__A):



     """simple docstring"""



     if not isinstance(__A    ,      __A):
          raise ValueError('''multiplicative_persistence() only accepts integral values''')
     if num < 0:
          raise ValueError('''multiplicative_persistence() does not accept negative values''')

     _a           =    0
     _a           =    str(__A)

     while len(__A) != 1:
          _a           =    [int(__A) for i in num_string]

          _a           =    1
          for i in range(0    ,      len(__A)):
               total *= numbers[i]

          _a           =    str(__A)

          steps += 1
     return steps




def lowerCAmelCase    (__A):



     """simple docstring"""



     if not isinstance(__A    ,      __A):
          raise ValueError('''additive_persistence() only accepts integral values''')
     if num < 0:
          raise ValueError('''additive_persistence() does not accept negative values''')

     _a           =    0
     _a           =    str(__A)

     while len(__A) != 1:
          _a           =    [int(__A) for i in num_string]

          _a           =    0
          for i in range(0    ,      len(__A)):
               total += numbers[i]

          _a           =    str(__A)

          steps += 1
     return steps


if __name__ == "__main__":
  import doctest

  doctest.testmod()



