





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


UpperCamelCase__   : List[str]					  = logging.get_logger(__name__)







class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					lowerCamelCase     :     Dict			    =       'AutoTokenizer'
					lowerCamelCase     :     Tuple			    =       ['tokenizer']

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





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






						'''simple docstring'''







						super().__init__(__lowercase		)

						UpperCAmelCase_								=     speaker_embeddings





					@classmethod
					def        SCREAMING_SNAKE_CASE							(						cls    :  Any ,					__lowercase    :  List[Any] ,					__lowercase    :  str="speaker_embeddings_path.json" ,					**__lowercase    :  Union[str, Any]		):






						'''simple docstring'''







						if speaker_embeddings_dict_path is not None:
							UpperCAmelCase_								=     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
                    , 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`."""		)
								UpperCAmelCase_								=     None
							else:
								with open(__lowercase		) as speaker_embeddings_json:
									UpperCAmelCase_								=     json.load(__lowercase		)
						else:
							UpperCAmelCase_								=     None

						UpperCAmelCase_								=     AutoTokenizer.from_pretrained(__lowercase ,					**__lowercase		)

						return cls(tokenizer=__lowercase ,					speaker_embeddings=__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any] ,					__lowercase    :  int ,					__lowercase    :  Tuple="speaker_embeddings_path.json" ,					__lowercase    :  List[str]="speaker_embeddings" ,					__lowercase    :  bool = False ,					**__lowercase    :  str ,					):






						'''simple docstring'''







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

							UpperCAmelCase_								=     {}

							UpperCAmelCase_								=     save_directory

							for prompt_key in self.speaker_embeddings:
								if prompt_key != "repo_or_path":
									UpperCAmelCase_								=     self._load_voice_preset(__lowercase		)

									UpperCAmelCase_								=     {}
									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 ,					)
										UpperCAmelCase_								=     os.path.join(__lowercase ,					F"""{prompt_key}_{key}.npy"""		)

									UpperCAmelCase_								=     tmp_dict

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

						super().save_pretrained(__lowercase ,					__lowercase ,					**__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[int] ,					__lowercase    :  str = None ,					**__lowercase    :  Optional[int]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.speaker_embeddings[voice_preset]

						UpperCAmelCase_								=     {}
						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}]."""		)

							UpperCAmelCase_								=     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
                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
                    embeddings."""		)

							UpperCAmelCase_								=     np.load(__lowercase		)

						return voice_preset_dict





					def        SCREAMING_SNAKE_CASE							(						self    :  int ,					__lowercase    :  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] ,					__lowercase    :  Tuple=None ,					__lowercase    :  Tuple=None ,					__lowercase    :  Any="pt" ,					__lowercase    :  Optional[int]=2_56 ,					__lowercase    :  Tuple=False ,					__lowercase    :  Dict=True ,					__lowercase    :  Tuple=False ,					**__lowercase    :  List[str] ,					):






						'''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
							):
								UpperCAmelCase_								=     self._load_voice_preset(__lowercase		)

							else:
								if isinstance(__lowercase ,					__lowercase		) and not voice_preset.endswith(""".npz"""		):
									UpperCAmelCase_								=     voice_preset + """.npz"""

								UpperCAmelCase_								=     np.load(__lowercase		)

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

						UpperCAmelCase_								=     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:
							UpperCAmelCase_								=     voice_preset

						return encoded_text









from __future__ import annotations




def    A_(			A   ):
	if not nums:
		raise ValueError("""List is empty"""   )
	return sum(A   ) / len(A   )


if __name__ == "__main__":
				import doctest

				doctest.testmod()


