






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


_a      	=  logging.get_logger(__name__)





class __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=      "AutoTokenizer"
	UpperCamelCase__								=      ["tokenizer"]

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

	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase=None    ):
							"""simple docstring"""







							super().__init__(UpperCAmelCase    )

							_UpperCAmelCase									=			speaker_embeddings

	@classmethod
	def        UpperCamelCase    (							cls       ,    UpperCAmelCase       ,    UpperCAmelCase="speaker_embeddings_path.json"       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							if speaker_embeddings_dict_path is not None:
													_UpperCAmelCase									=			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`."""    )
																			_UpperCAmelCase									=			None
													else:
																			with open(UpperCAmelCase    ) as speaker_embeddings_json:
																									_UpperCAmelCase									=			json.load(UpperCAmelCase    )
							else:
													_UpperCAmelCase									=			None

							_UpperCAmelCase									=			AutoTokenizer.from_pretrained(UpperCAmelCase       ,    **UpperCAmelCase    )

							return cls(tokenizer=UpperCAmelCase       ,    speaker_embeddings=UpperCAmelCase    )

	def        UpperCamelCase    (							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    )

													_UpperCAmelCase									=			{}

													_UpperCAmelCase									=			save_directory

													for prompt_key in self.speaker_embeddings:
																			if prompt_key != "repo_or_path":
																									_UpperCAmelCase									=			self._load_voice_preset(UpperCAmelCase    )

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

																									_UpperCAmelCase									=			tmp_dict

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

							super().save_pretrained(UpperCAmelCase       ,    UpperCAmelCase       ,    **UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase = None       ,    **UpperCAmelCase    ):
							"""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'       ,    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."""    )

													_UpperCAmelCase									=			np.load(UpperCAmelCase    )

							return voice_preset_dict

	def        UpperCamelCase    (							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
													):
																			_UpperCAmelCase									=			self._load_voice_preset(UpperCAmelCase    )

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

																			_UpperCAmelCase									=			np.load(UpperCAmelCase    )

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

							_UpperCAmelCase									=			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:
													_UpperCAmelCase									=			voice_preset

							return encoded_text







'''simple docstring'''




from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


__SCREAMING_SNAKE_CASE     :int       =				logging.get_logger(__name__)







class   A_	:
 _lowerCamelCase  : str
 _lowerCamelCase  : str           =							None




 @staticmethod
 def      lowercase	(					):
      raise NotImplementedError




 def      lowercase	(					self       :    Union[str, Any]	,			snake_case_       :    Optional[int]	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    List[Any]    ):
      raise NotImplementedError




 def      lowercase	(					self       :    Any	,			snake_case_       :    int    ):
      raise NotImplementedError




 def      lowercase	(					self       :    List[str]    ):
      if not self.is_available():
           raise RuntimeError(
               f'You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.'    )




 @classmethod
 def      lowercase	(					cls       :    List[Any]    ):
      return f'`pip install {cls.pip_package or cls.name}`'







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : int           =							"""optuna"""




 @staticmethod
 def      lowercase	(					):
      return is_optuna_available()




 def      lowercase	(					self       :    List[str]	,			snake_case_       :    Any	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    Tuple    ):
      return run_hp_search_optuna(snake_case_	,			snake_case_	,			snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    int	,			snake_case_       :    Optional[int]    ):
      return default_hp_space_optuna(snake_case_    )







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : Any           =							"""ray"""
 _lowerCamelCase  : Tuple           =							"""'ray[tune]'"""




 @staticmethod
 def      lowercase	(					):
      return is_ray_available()




 def      lowercase	(					self       :    Optional[Any]	,			snake_case_       :    Any	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    List[str]    ):
      return run_hp_search_ray(snake_case_	,			snake_case_	,			snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    Any	,			snake_case_       :    str    ):
      return default_hp_space_ray(snake_case_    )







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : int           =							"""sigopt"""




 @staticmethod
 def      lowercase	(					):
      return is_sigopt_available()




 def      lowercase	(					self       :    Any	,			snake_case_       :    int	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    Dict    ):
      return run_hp_search_sigopt(snake_case_	,			snake_case_	,			snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    Dict	,			snake_case_       :    Optional[Any]    ):
      return default_hp_space_sigopt(snake_case_    )







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : Optional[int]           =							"""wandb"""




 @staticmethod
 def      lowercase	(					):
      return is_wandb_available()




 def      lowercase	(					self       :    Optional[Any]	,			snake_case_       :    Optional[Any]	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    Optional[Any]    ):
      return run_hp_search_wandb(snake_case_	,			snake_case_	,			snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    Any	,			snake_case_       :    Union[str, Any]    ):
      return default_hp_space_wandb(snake_case_    )





__SCREAMING_SNAKE_CASE     :Dict       =				{
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def 	UpperCAmelCase_							(					)		->       str:


     '''simple docstring'''



     _UpperCAmelCase     							=   [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
     if len(__lowercase       ) > 0:
          _UpperCAmelCase     							=   available_backends[0].name
          if len(__lowercase       ) > 1:
               logger.info(
                   f'{len(__lowercase       )} hyperparameter search backends available. Using {name} as the default.'       )
          return name
     raise RuntimeError(
         "No hyperparameter search backend available.\n"
         + "\n".join(
             f' - To install {backend.name} run {backend.pip_install()}'
             for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()       )       )




