

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        a__  (							lowerCamelCase__  ):
   _SCREAMING_SNAKE_CASE   :					Any     	=							'AutoTokenizer'
   _SCREAMING_SNAKE_CASE   :					Union[str, Any]     	=							['tokenizer']

   _SCREAMING_SNAKE_CASE   :					List[Any]     	=							{
       'semantic_prompt': 1,
       'coarse_prompt': 2,
       'fine_prompt': 2,
   }


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase=None  ):





      """simple docstring"""





      super().__init__(_UpperCamelCase  )

      _lowercase      :				Optional[int]      					=       speaker_embeddings


   @classmethod
   def 						_lowerCamelCase       (			cls      ,    _UpperCamelCase      ,    _UpperCamelCase="speaker_embeddings_path.json"      ,    **_UpperCamelCase  ):





      """simple docstring"""





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

      _lowercase      :				int      					=       AutoTokenizer.from_pretrained(_UpperCamelCase      ,    **_UpperCamelCase  )

      return cls(tokenizer=_UpperCamelCase      ,    speaker_embeddings=_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase="speaker_embeddings_path.json"      ,    _UpperCamelCase="speaker_embeddings"      ,    _UpperCamelCase = False      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





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

         _lowercase      :				Union[str, Any]      					=       {}

         _lowercase      :				Optional[Any]      					=       save_directory

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

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

               _lowercase      :				List[Any]      					=       tmp_dict

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

      super().save_pretrained(_UpperCamelCase      ,    _UpperCamelCase      ,    **_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase = None      ,    **_UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Any      					=       self.speaker_embeddings[voice_preset]

      _lowercase      :				Optional[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}].'''  )

         _lowercase      :				Tuple      					=       get_file_from_repo(
             self.speaker_embeddings.get("repo_or_path"      ,    "/"  )      ,    voice_preset_paths[key]      ,    subfolder=kwargs.pop("subfolder"      ,    _UpperCamelCase  )      ,    cache_dir=kwargs.pop("cache_dir"      ,    _UpperCamelCase  )      ,    force_download=kwargs.pop("force_download"      ,    _UpperCamelCase  )      ,    proxies=kwargs.pop("proxies"      ,    _UpperCamelCase  )      ,    resume_download=kwargs.pop("resume_download"      ,    _UpperCamelCase  )      ,    local_files_only=kwargs.pop("local_files_only"      ,    _UpperCamelCase  )      ,    use_auth_token=kwargs.pop("use_auth_token"      ,    _UpperCamelCase  )      ,    revision=kwargs.pop("revision"      ,    _UpperCamelCase  )      ,    )
         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      :				List[Any]      					=       np.load(_UpperCamelCase  )

      return voice_preset_dict


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase = 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      ,    _UpperCamelCase=None      ,    _UpperCamelCase=None      ,    _UpperCamelCase="pt"      ,    _UpperCamelCase=256      ,    _UpperCamelCase=False      ,    _UpperCamelCase=True      ,    _UpperCamelCase=False      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      if voice_preset is not None and not isinstance(_UpperCamelCase      ,    _UpperCamelCase  ):
         if (
             isinstance(_UpperCamelCase      ,    _UpperCamelCase  )
             and self.speaker_embeddings is not None
             and voice_preset in self.speaker_embeddings
         ):
            _lowercase      :				Optional[int]      					=       self._load_voice_preset(_UpperCamelCase  )

         else:
            if isinstance(_UpperCamelCase      ,    _UpperCamelCase  ) and not voice_preset.endswith(".npz"  ):
               _lowercase      :				Tuple      					=       voice_preset + ".npz"

            _lowercase      :				Union[str, Any]      					=       np.load(_UpperCamelCase  )

      if voice_preset is not None:
         self._validate_voice_preset_dict(_UpperCamelCase      ,    **_UpperCamelCase  )
         _lowercase      :				Optional[Any]      					=       BatchFeature(data=_UpperCamelCase      ,    tensor_type=_UpperCamelCase  )

      _lowercase      :				Union[str, Any]      					=       self.tokenizer(
          _UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    padding="max_length"      ,    max_length=_UpperCamelCase      ,    return_attention_mask=_UpperCamelCase      ,    return_token_type_ids=_UpperCamelCase      ,    add_special_tokens=_UpperCamelCase      ,    **_UpperCamelCase      ,    )

      if voice_preset is not None:
         _lowercase      :				List[Any]      					=       voice_preset

      return encoded_text









'''simple docstring'''
import warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


_snake_case         =  logging.get_logger(__name__)


class        a__  (							lowerCamelCase_  ):


   def __init__(			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      warnings.warn(
          "The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
          " Please use ChineseCLIPImageProcessor instead."      ,    _UpperCamelCase      ,    )
      super().__init__(*_UpperCamelCase      ,    **_UpperCamelCase  )






