"""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    __snake_case       (     __lowerCAmelCase				):
      a__        =		"""AutoTokenizer"""
      a__        =		["""tokenizer"""]

      a__        =		{
          """semantic_prompt""": 1,
          """coarse_prompt""": 2,
          """fine_prompt""": 2,
      }



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





        '''simple docstring'''





        super().__init__(lowercase)

        a__:     Optional[Any]										=	speaker_embeddings



      @classmethod
      def   lowerCamelCase_   (						cls    ,			lowercase    ,			lowercase="speaker_embeddings_path.json"    ,			**lowercase)	->			Union[str, Any]:





        '''simple docstring'''





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

        a__:     int										=	AutoTokenizer.from_pretrained(lowercase    ,			**lowercase)

        return cls(tokenizer=lowercase    ,			speaker_embeddings=lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase="speaker_embeddings_path.json"    ,			lowercase="speaker_embeddings"    ,			lowercase = False    ,			**lowercase    ,			)	->			List[str]:





        '''simple docstring'''





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

          a__:     Optional[int]										=	{}

          a__:     Any										=	save_directory

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

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

              a__:     Optional[Any]										=	tmp_dict

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

        super().save_pretrained(lowercase    ,			lowercase    ,			**lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase = None    ,			**lowercase)	->			Optional[int]:





        '''simple docstring'''





        a__:     Optional[Any]										=	self.speaker_embeddings[voice_preset]

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

          a__:     Dict										=	np.load(lowercase)

        return voice_preset_dict



      def   lowerCamelCase_   (						self    ,			lowercase = None)	->			Dict:





        '''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    ,			lowercase=None    ,			lowercase=None    ,			lowercase="pt"    ,			lowercase=2_56    ,			lowercase=False    ,			lowercase=True    ,			lowercase=False    ,			**lowercase    ,			)	->			Optional[int]:





        '''simple docstring'''





        if voice_preset is not None and not isinstance(lowercase    ,			lowercase):
          if (
              isinstance(lowercase    ,			lowercase)
              and self.speaker_embeddings is not None
              and voice_preset in self.speaker_embeddings
          ):
            a__:     List[str]										=	self._load_voice_preset(lowercase)

          else:
            if isinstance(lowercase    ,			lowercase) and not voice_preset.endswith('.npz'):
              a__:     Optional[Any]										=	voice_preset + '.npz'

            a__:     int										=	np.load(lowercase)

        if voice_preset is not None:
          self._validate_voice_preset_dict(lowercase    ,			**lowercase)
          a__:     int										=	BatchFeature(data=lowercase    ,			tensor_type=lowercase)

        a__:     int										=	self.tokenizer(
            lowercase    ,			return_tensors=lowercase    ,			padding='max_length'    ,			max_length=lowercase    ,			return_attention_mask=lowercase    ,			return_token_type_ids=lowercase    ,			add_special_tokens=lowercase    ,			**lowercase    ,			)

        if voice_preset is not None:
          a__:     Dict										=	voice_preset

        return encoded_text



"""simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union


lowercase__					       =	TypeVar('T')

lowercase__					       =	Union[List[T], Tuple[T, ...]]
lowercase__					       =	Union[T, List[T], Dict[str, T]]
lowercase__					       =	Union[str, bytes, os.PathLike]


