





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


_lowerCamelCase :    Optional[int]  							= logging.get_logger(__name__)


class 						lowerCamelCase       (__lowerCamelCase      ):







       """simple docstring"""




       UpperCAmelCase_      						=   "AutoTokenizer"
       UpperCAmelCase_      						=   ["tokenizer"]

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






       def __init__( self     :					Union[str, Any],			_UpperCAmelCase     :					Optional[int],			_UpperCAmelCase     :					Union[str, Any]=None						)				->			Union[str, Any]:





              """simple docstring"""
              super().__init__(_UpperCAmelCase						)

              SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						speaker_embeddings






       @classmethod
       def  A_     ( cls     :					Any,			_UpperCAmelCase     :					List[str],			_UpperCAmelCase     :					Dict="speaker_embeddings_path.json",			**_UpperCAmelCase     :					Optional[Any]						)				->			List[Any]:





              """simple docstring"""

              if speaker_embeddings_dict_path is not None:
                     SCREAMING_SNAKE_CASE__       :      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`.'''						)
                            SCREAMING_SNAKE_CASE__       :      Dict     =						None
                     else:
                            with open(_UpperCAmelCase						) as speaker_embeddings_json:
                                   SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						json.load(_UpperCAmelCase						)
              else:
                     SCREAMING_SNAKE_CASE__       :      List[Any]     =						None

              SCREAMING_SNAKE_CASE__       :      List[str]     =						AutoTokenizer.from_pretrained(_UpperCAmelCase,			**_UpperCAmelCase						)

              return cls(tokenizer=_UpperCAmelCase,			speaker_embeddings=_UpperCAmelCase						)






       def  A_     ( self     :					str,			_UpperCAmelCase     :					Optional[int],			_UpperCAmelCase     :					List[str]="speaker_embeddings_path.json",			_UpperCAmelCase     :					Optional[Any]="speaker_embeddings",			_UpperCAmelCase     :					bool = False,			**_UpperCAmelCase     :					List[str],			)				->			Union[str, Any]:





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

                     SCREAMING_SNAKE_CASE__       :      Optional[int]     =						{}

                     SCREAMING_SNAKE_CASE__       :      Any     =						save_directory

                     for prompt_key in self.speaker_embeddings:
                            if prompt_key != "repo_or_path":
                                   SCREAMING_SNAKE_CASE__       :      List[Any]     =						self._load_voice_preset(_UpperCAmelCase						)

                                   SCREAMING_SNAKE_CASE__       :      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,			)
                                          SCREAMING_SNAKE_CASE__       :      List[str]     =						os.path.join(_UpperCAmelCase,			F'''{prompt_key}_{key}.npy'''						)

                                   SCREAMING_SNAKE_CASE__       :      Optional[int]     =						tmp_dict

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

              super().save_pretrained(_UpperCAmelCase,			_UpperCAmelCase,			**_UpperCAmelCase						)






       def  A_     ( self     :					List[Any],			_UpperCAmelCase     :					str = None,			**_UpperCAmelCase     :					List[Any]						)				->			List[str]:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      str     =						self.speaker_embeddings[voice_preset]

              SCREAMING_SNAKE_CASE__       :      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}].'''						)

                     SCREAMING_SNAKE_CASE__       :      List[Any]     =						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.'''						)

                     SCREAMING_SNAKE_CASE__       :      int     =						np.load(_UpperCAmelCase						)

              return voice_preset_dict






       def  A_     ( self     :					int,			_UpperCAmelCase     :					Optional[dict] = None						)				->			Optional[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     :					List[Any],			_UpperCAmelCase     :					Optional[Any]=None,			_UpperCAmelCase     :					Union[str, Any]=None,			_UpperCAmelCase     :					Optional[int]="pt",			_UpperCAmelCase     :					List[str]=2_5_6,			_UpperCAmelCase     :					int=False,			_UpperCAmelCase     :					Optional[int]=True,			_UpperCAmelCase     :					Any=False,			**_UpperCAmelCase     :					List[str],			)				->			List[Any]:





              """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
                     ):
                            SCREAMING_SNAKE_CASE__       :      List[str]     =						self._load_voice_preset(_UpperCAmelCase						)

                     else:
                            if isinstance(_UpperCAmelCase,			_UpperCAmelCase						) and not voice_preset.endswith(".npz"						):
                                   SCREAMING_SNAKE_CASE__       :      Optional[int]     =						voice_preset + ".npz"

                            SCREAMING_SNAKE_CASE__       :      List[str]     =						np.load(_UpperCAmelCase						)

              if voice_preset is not None:
                     self._validate_voice_preset_dict(_UpperCAmelCase,			**_UpperCAmelCase						)
                     SCREAMING_SNAKE_CASE__       :      Any     =						BatchFeature(data=_UpperCAmelCase,			tensor_type=_UpperCAmelCase						)

              SCREAMING_SNAKE_CASE__       :      Dict     =						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:
                     SCREAMING_SNAKE_CASE__       :      str     =						voice_preset

              return encoded_text













def _a (  SCREAMING_SNAKE_CASE__      :  str       )	->    str:

							'''simple docstring'''






							if not all(char in "01" for char in bin_string       ):
														raise ValueError("Non-binary value was passed to the function"       )
							if not bin_string:
														raise ValueError("Empty string was passed to the function"       )
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						""
							while len(SCREAMING_SNAKE_CASE__       ) % 3 != 0:
														SCREAMING_SNAKE_CASE__       :      str     =						"0" + bin_string
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						[
							    bin_string[index : index + 3]
							    for index in range(len(SCREAMING_SNAKE_CASE__       )       )
							    if index % 3 == 0
							]
							for bin_group in bin_string_in_3_list:
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						0
														for index, val in enumerate(SCREAMING_SNAKE_CASE__       ):
																					oct_val += int(2 ** (2 - index) * int(SCREAMING_SNAKE_CASE__       )       )
														oct_string += str(SCREAMING_SNAKE_CASE__       )
							return oct_string


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

		testmod()






