






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


_SCREAMING_SNAKE_CASE   =  logging.get_logger(__name__)






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		List[Any]					='''AutoTokenizer'''
       __lowerCAmelCase    :		List[str]					=['''tokenizer''']

       __lowerCAmelCase    :		str					={
           '''semantic_prompt''': 1,
           '''coarse_prompt''': 2,
           '''fine_prompt''': 2,
       }





       def __init__(				self	:Dict,      snake_case	:List[str],      snake_case	:Any=None):



          """simple docstring"""

          super().__init__(snake_case)

          _lowercase              =speaker_embeddings





       @classmethod
       def 				UpperCamelCase__						(				cls	:Dict,      snake_case	:List[str],      snake_case	:int="speaker_embeddings_path.json",      **snake_case	:int):



          """simple docstring"""

          if speaker_embeddings_dict_path is not None:
             _lowercase              =get_file_from_repo(
                 snake_case,      snake_case,      subfolder=kwargs.pop('subfolder',      snake_case),      cache_dir=kwargs.pop('cache_dir',      snake_case),      force_download=kwargs.pop('force_download',      snake_case),      proxies=kwargs.pop('proxies',      snake_case),      resume_download=kwargs.pop('resume_download',      snake_case),      local_files_only=kwargs.pop('local_files_only',      snake_case),      use_auth_token=kwargs.pop('use_auth_token',      snake_case),      revision=kwargs.pop('revision',      snake_case),      )
             if speaker_embeddings_path is None:
                logger.warning(
                    f'''`{os.path.join(snake_case,      snake_case)}` 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              =None
             else:
                with open(snake_case) as speaker_embeddings_json:
                   _lowercase              =json.load(snake_case)
          else:
             _lowercase              =None

          _lowercase              =AutoTokenizer.from_pretrained(snake_case,      **snake_case)

          return cls(tokenizer=snake_case,      speaker_embeddings=snake_case)





       def 				UpperCamelCase__						(				self	:Tuple,      snake_case	:List[Any],      snake_case	:Optional[int]="speaker_embeddings_path.json",      snake_case	:Tuple="speaker_embeddings",      snake_case	:bool = False,      **snake_case	:Optional[Any],      ):



          """simple docstring"""

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

             _lowercase              ={}

             _lowercase              =save_directory

             for prompt_key in self.speaker_embeddings:
                if prompt_key != "repo_or_path":
                   _lowercase              =self._load_voice_preset(snake_case)

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

                   _lowercase              =tmp_dict

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

          super().save_pretrained(snake_case,      snake_case,      **snake_case)





       def 				UpperCamelCase__						(				self	:Dict,      snake_case	:str = None,      **snake_case	:List[Any]):



          """simple docstring"""

          _lowercase              =self.speaker_embeddings[voice_preset]

          _lowercase              ={}
          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              =get_file_from_repo(
                 self.speaker_embeddings.get('repo_or_path',      '/'),      voice_preset_paths[key],      subfolder=kwargs.pop('subfolder',      snake_case),      cache_dir=kwargs.pop('cache_dir',      snake_case),      force_download=kwargs.pop('force_download',      snake_case),      proxies=kwargs.pop('proxies',      snake_case),      resume_download=kwargs.pop('resume_download',      snake_case),      local_files_only=kwargs.pop('local_files_only',      snake_case),      use_auth_token=kwargs.pop('use_auth_token',      snake_case),      revision=kwargs.pop('revision',      snake_case),      )
             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              =np.load(snake_case)

          return voice_preset_dict





       def 				UpperCamelCase__						(				self	:Tuple,      snake_case	:Optional[dict] = None):



          """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	:Optional[int],      snake_case	:Optional[int]=None,      snake_case	:Optional[int]=None,      snake_case	:Any="pt",      snake_case	:Union[str, Any]=256,      snake_case	:Optional[int]=False,      snake_case	:List[str]=True,      snake_case	:int=False,      **snake_case	:Tuple,      ):



          """simple docstring"""

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

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

                _lowercase              =np.load(snake_case)

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

          _lowercase              =self.tokenizer(
              snake_case,      return_tensors=snake_case,      padding='max_length',      max_length=snake_case,      return_attention_mask=snake_case,      return_token_type_ids=snake_case,      add_special_tokens=snake_case,      **snake_case,      )

          if voice_preset is not None:
             _lowercase              =voice_preset

          return encoded_text










def        _snake_case   (_snake_case				:       list      ,    _snake_case				:       int      ,    _snake_case				:       int = 0      ,    _snake_case				:       int = 0)					->	int:
   _lowercase              =right or len(_snake_case) - 1
   if left > right:
      return -1
   elif list_data[left] == key:
      return left
   elif list_data[right] == key:
      return right
   else:
      return search(_snake_case      ,    _snake_case      ,    left + 1      ,    right - 1)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


