


"""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


__snake_case    					=					logging.get_logger(__name__)

class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase  ):
   """simple docstring"""



   _a   :       Union[str, Any]									=    '''AutoTokenizer'''
   _a   :       Optional[int]									=    ['''tokenizer''']

   _a   :       Union[str, Any]									=    {
       '''semantic_prompt''': 1,
       '''coarse_prompt''': 2,
       '''fine_prompt''': 2,
   }


   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__=None						)      ->							int:
    super().__init__(lowerCamelCase__						)

    lowercase__       :       Any				=						speaker_embeddings


   @classmethod
   def 					UpperCAmelCase__(						cls ,     lowerCamelCase__ ,     lowerCamelCase__="speaker_embeddings_path.json" ,     **lowerCamelCase__						)      ->							str:

    if speaker_embeddings_dict_path is not None:
     lowercase__       :       Any				=						get_file_from_repo(
         lowerCamelCase__ ,     lowerCamelCase__ ,     subfolder=kwargs.pop("""subfolder""" ,     lowerCamelCase__						) ,     cache_dir=kwargs.pop("""cache_dir""" ,     lowerCamelCase__						) ,     force_download=kwargs.pop("""force_download""" ,     lowerCamelCase__						) ,     proxies=kwargs.pop("""proxies""" ,     lowerCamelCase__						) ,     resume_download=kwargs.pop("""resume_download""" ,     lowerCamelCase__						) ,     local_files_only=kwargs.pop("""local_files_only""" ,     lowerCamelCase__						) ,     use_auth_token=kwargs.pop("""use_auth_token""" ,     lowerCamelCase__						) ,     revision=kwargs.pop("""revision""" ,     lowerCamelCase__						) ,     )
     if speaker_embeddings_path is None:
      logger.warning(
          F'''`{os.path.join(lowerCamelCase__ ,     lowerCamelCase__						)}` 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`.'''						)
      lowercase__       :       Any				=						None
     else:
      with open(lowerCamelCase__						) as speaker_embeddings_json:
       lowercase__       :       List[str]				=						json.load(lowerCamelCase__						)
    else:
     lowercase__       :       Optional[int]				=						None

    lowercase__       :       List[Any]				=						AutoTokenizer.from_pretrained(lowerCamelCase__ ,     **lowerCamelCase__						)

    return cls(tokenizer=lowerCamelCase__ ,     speaker_embeddings=lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__="speaker_embeddings_path.json" ,     lowerCamelCase__="speaker_embeddings" ,     lowerCamelCase__ = False ,     **lowerCamelCase__ ,     )      ->							str:
    if self.speaker_embeddings is not None:
     os.makedirs(os.path.join(lowerCamelCase__ ,     lowerCamelCase__ ,     """v2"""						) ,     exist_ok=lowerCamelCase__						)

     lowercase__       :       str				=						{}

     lowercase__       :       Tuple				=						save_directory

     for prompt_key in self.speaker_embeddings:
      if prompt_key != "repo_or_path":
       lowercase__       :       int				=						self._load_voice_preset(lowerCamelCase__						)

       lowercase__       :       Optional[Any]				=						{}
       for key in self.speaker_embeddings[prompt_key]:
        np.save(
            os.path.join(
                embeddings_dict["""repo_or_path"""] ,     lowerCamelCase__ ,     F'''{prompt_key}_{key}'''						) ,     voice_preset[key] ,     allow_pickle=lowerCamelCase__ ,     )
        lowercase__       :       Dict				=						os.path.join(lowerCamelCase__ ,     F'''{prompt_key}_{key}.npy'''						)

       lowercase__       :       Tuple				=						tmp_dict

     with open(os.path.join(lowerCamelCase__ ,     lowerCamelCase__						) ,     """w"""						) as fp:
      json.dump(lowerCamelCase__ ,     lowerCamelCase__						)

    super().save_pretrained(lowerCamelCase__ ,     lowerCamelCase__ ,     **lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ = None ,     **lowerCamelCase__						)      ->							List[Any]:
    lowercase__       :       Optional[int]				=						self.speaker_embeddings[voice_preset]

    lowercase__       :       Any				=						{}
    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}].'''						)

     lowercase__       :       Tuple				=						get_file_from_repo(
         self.speaker_embeddings.get("""repo_or_path""" ,     """/"""						) ,     voice_preset_paths[key] ,     subfolder=kwargs.pop("""subfolder""" ,     lowerCamelCase__						) ,     cache_dir=kwargs.pop("""cache_dir""" ,     lowerCamelCase__						) ,     force_download=kwargs.pop("""force_download""" ,     lowerCamelCase__						) ,     proxies=kwargs.pop("""proxies""" ,     lowerCamelCase__						) ,     resume_download=kwargs.pop("""resume_download""" ,     lowerCamelCase__						) ,     local_files_only=kwargs.pop("""local_files_only""" ,     lowerCamelCase__						) ,     use_auth_token=kwargs.pop("""use_auth_token""" ,     lowerCamelCase__						) ,     revision=kwargs.pop("""revision""" ,     lowerCamelCase__						) ,     )
     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.'''						)

     lowercase__       :       Tuple				=						np.load(lowerCamelCase__						)

    return voice_preset_dict


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ = None						)      ->							List[str]:
    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 ,     lowerCamelCase__=None ,     lowerCamelCase__=None ,     lowerCamelCase__="pt" ,     lowerCamelCase__=256 ,     lowerCamelCase__=False ,     lowerCamelCase__=True ,     lowerCamelCase__=False ,     **lowerCamelCase__ ,     )      ->							Union[str, Any]:
    if voice_preset is not None and not isinstance(lowerCamelCase__ ,     lowerCamelCase__						):
     if (
         isinstance(lowerCamelCase__ ,     lowerCamelCase__						)
         and self.speaker_embeddings is not None
         and voice_preset in self.speaker_embeddings
     ):
      lowercase__       :       Union[str, Any]				=						self._load_voice_preset(lowerCamelCase__						)

     else:
      if isinstance(lowerCamelCase__ ,     lowerCamelCase__						) and not voice_preset.endswith(""".npz"""						):
       lowercase__       :       str				=						voice_preset + """.npz"""

      lowercase__       :       Any				=						np.load(lowerCamelCase__						)

    if voice_preset is not None:
     self._validate_voice_preset_dict(lowerCamelCase__ ,     **lowerCamelCase__						)
     lowercase__       :       List[str]				=						BatchFeature(data=lowerCamelCase__ ,     tensor_type=lowerCamelCase__						)

    lowercase__       :       List[str]				=						self.tokenizer(
        lowerCamelCase__ ,     return_tensors=lowerCamelCase__ ,     padding="""max_length""" ,     max_length=lowerCamelCase__ ,     return_attention_mask=lowerCamelCase__ ,     return_token_type_ids=lowerCamelCase__ ,     add_special_tokens=lowerCamelCase__ ,     **lowerCamelCase__ ,     )

    if voice_preset is not None:
     lowercase__       :       Union[str, Any]				=						voice_preset

    return encoded_text



"""simple docstring"""


def    _lowerCamelCase   (     lowerCamelCase__	:							str  ):
 assert column_title.isupper()
 lowercase__       :       int				=						0
 lowercase__       :       Optional[int]				=						len(lowerCamelCase__  ) - 1
 lowercase__       :       int				=						0

 while index >= 0:
  lowercase__       :       List[str]				=						(ord(column_title[index]  ) - 64) * pow(26       ,		lowerCamelCase__  )
  answer += value
  power += 1
  index -= 1

 return answer


if __name__ == "__main__":
   from doctest import testmod

   testmod()