



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_    :  Optional[Any]						  =		logging.get_logger(__name__)





class  lowerCamelCase__	(    UpperCAmelCase_):
     """simple docstring"""



     _A      =      'AutoTokenizer'
     _A      =      ['tokenizer']

     _A      =      {
         'semantic_prompt': 1,
         'coarse_prompt': 2,
         'fine_prompt': 2,
     }







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






           '''simple docstring'''



           super().__init__(__a     )

           lowerCamelCase             =		speaker_embeddings







     @classmethod
     def     _a   (cls					,      __a					,      __a="speaker_embeddings_path.json"					,      **__a     ):






           '''simple docstring'''



           if speaker_embeddings_dict_path is not None:
                 lowerCamelCase             =		get_file_from_repo(
                     __a					,      __a					,      subfolder=kwargs.pop("subfolder"					,      __a     )					,      cache_dir=kwargs.pop("cache_dir"					,      __a     )					,      force_download=kwargs.pop("force_download"					,      __a     )					,      proxies=kwargs.pop("proxies"					,      __a     )					,      resume_download=kwargs.pop("resume_download"					,      __a     )					,      local_files_only=kwargs.pop("local_files_only"					,      __a     )					,      use_auth_token=kwargs.pop("use_auth_token"					,      __a     )					,      revision=kwargs.pop("revision"					,      __a     )					,      )
                 if speaker_embeddings_path is None:
                       logger.warning(
                           F"""`{os.path.join(__a					,      __a     )}` 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`."""     )
                       lowerCamelCase             =		None
                 else:
                       with open(__a     ) as speaker_embeddings_json:
                             lowerCamelCase             =		json.load(__a     )
           else:
                 lowerCamelCase             =		None

           lowerCamelCase             =		AutoTokenizer.from_pretrained(__a					,      **__a     )

           return cls(tokenizer=__a					,      speaker_embeddings=__a     )







     def     _a   (self					,      __a					,      __a="speaker_embeddings_path.json"					,      __a="speaker_embeddings"					,      __a = False					,      **__a					,      ):






           '''simple docstring'''



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

                 lowerCamelCase             =		{}

                 lowerCamelCase             =		save_directory

                 for prompt_key in self.speaker_embeddings:
                       if prompt_key != "repo_or_path":
                             lowerCamelCase             =		self._load_voice_preset(__a     )

                             lowerCamelCase             =		{}
                             for key in self.speaker_embeddings[prompt_key]:
                                   np.save(
                                       os.path.join(
                                           embeddings_dict["repo_or_path"]					,      __a					,      F"""{prompt_key}_{key}"""     )					,      voice_preset[key]					,      allow_pickle=__a					,      )
                                   lowerCamelCase             =		os.path.join(__a					,      F"""{prompt_key}_{key}.npy"""     )

                             lowerCamelCase             =		tmp_dict

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

           super().save_pretrained(__a					,      __a					,      **__a     )







     def     _a   (self					,      __a = None					,      **__a     ):






           '''simple docstring'''



           lowerCamelCase             =		self.speaker_embeddings[voice_preset]

           lowerCamelCase             =		{}
           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}]."""     )

                 lowerCamelCase             =		get_file_from_repo(
                     self.speaker_embeddings.get("repo_or_path"					,      "/"     )					,      voice_preset_paths[key]					,      subfolder=kwargs.pop("subfolder"					,      __a     )					,      cache_dir=kwargs.pop("cache_dir"					,      __a     )					,      force_download=kwargs.pop("force_download"					,      __a     )					,      proxies=kwargs.pop("proxies"					,      __a     )					,      resume_download=kwargs.pop("resume_download"					,      __a     )					,      local_files_only=kwargs.pop("local_files_only"					,      __a     )					,      use_auth_token=kwargs.pop("use_auth_token"					,      __a     )					,      revision=kwargs.pop("revision"					,      __a     )					,      )
                 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."""     )

                 lowerCamelCase             =		np.load(__a     )

           return voice_preset_dict







     def     _a   (self					,      __a = 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					,      __a=None					,      __a=None					,      __a="pt"					,      __a=2_56					,      __a=False					,      __a=True					,      __a=False					,      **__a					,      ):






           '''simple docstring'''



           if voice_preset is not None and not isinstance(__a					,      __a     ):
                 if (
                     isinstance(__a					,      __a     )
                     and self.speaker_embeddings is not None
                     and voice_preset in self.speaker_embeddings
                 ):
                       lowerCamelCase             =		self._load_voice_preset(__a     )

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

                       lowerCamelCase             =		np.load(__a     )

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

           lowerCamelCase             =		self.tokenizer(
               __a					,      return_tensors=__a					,      padding="max_length"					,      max_length=__a					,      return_attention_mask=__a					,      return_token_type_ids=__a					,      add_special_tokens=__a					,      **__a					,      )

           if voice_preset is not None:
                 lowerCamelCase             =		voice_preset

           return encoded_text




a_    :  Dict						  =		8.314462  # Unit - J mol-1 K-1

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      if moles < 0 or kelvin < 0 or volume < 0:
            raise ValueError("Invalid inputs. Enter positive value."			)
      return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      if moles < 0 or kelvin < 0 or pressure < 0:
            raise ValueError("Invalid inputs. Enter positive value."			)
      return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


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

     testmod()