





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        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      "AutoTokenizer"
 lowercase_		=      ["tokenizer"]

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

 def __init__(self    :       Dict	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       Optional[int]=None)	->Dict:



   '''simple docstring'''



   super().__init__(UpperCAmelCase_)

   lowerCamelCase__:				Any        =speaker_embeddings

 @classmethod
 def 							SCREAMING_SNAKE_CASE_ (cls    :       str	,			UpperCAmelCase_    :       Optional[int]	,			UpperCAmelCase_    :       Union[str, Any]="speaker_embeddings_path.json"	,			**UpperCAmelCase_    :       Optional[Any])	->str:



   '''simple docstring'''



   if speaker_embeddings_dict_path is not None:
     lowerCamelCase__:				Optional[int]        =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`.""")
       lowerCamelCase__:				Union[str, Any]        =None
     else:
       with open(UpperCAmelCase_) as speaker_embeddings_json:
         lowerCamelCase__:				Any        =json.load(UpperCAmelCase_)
   else:
     lowerCamelCase__:				Optional[Any]        =None

   lowerCamelCase__:				Dict        =AutoTokenizer.from_pretrained(UpperCAmelCase_	,			**UpperCAmelCase_)

   return cls(tokenizer=UpperCAmelCase_	,			speaker_embeddings=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any]	,			UpperCAmelCase_    :       Dict	,			UpperCAmelCase_    :       str="speaker_embeddings_path.json"	,			UpperCAmelCase_    :       Dict="speaker_embeddings"	,			UpperCAmelCase_    :       bool = False	,			**UpperCAmelCase_    :       int	,			)	->List[Any]:



   '''simple docstring'''



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

     lowerCamelCase__:				Any        ={}

     lowerCamelCase__:				Optional[int]        =save_directory

     for prompt_key in self.speaker_embeddings:
       if prompt_key != "repo_or_path":
         lowerCamelCase__:				List[str]        =self._load_voice_preset(UpperCAmelCase_)

         lowerCamelCase__:				Dict        ={}
         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_	,			)
           lowerCamelCase__:				List[Any]        =os.path.join(UpperCAmelCase_	,			F"""{prompt_key}_{key}.npy""")

         lowerCamelCase__:				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 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			UpperCAmelCase_    :       str = None	,			**UpperCAmelCase_    :       Optional[Any])	->str:



   '''simple docstring'''



   lowerCamelCase__:				int        =self.speaker_embeddings[voice_preset]

   lowerCamelCase__:				List[str]        ={}
   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__:				Dict        =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.""")

     lowerCamelCase__:				Dict        =np.load(UpperCAmelCase_)

   return voice_preset_dict

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str]	,			UpperCAmelCase_    :       Optional[dict] = None)	->List[Any]:



   '''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    :       str	,			UpperCAmelCase_    :       Tuple=None	,			UpperCAmelCase_    :       Any=None	,			UpperCAmelCase_    :       Optional[Any]="pt"	,			UpperCAmelCase_    :       Dict=256	,			UpperCAmelCase_    :       List[Any]=False	,			UpperCAmelCase_    :       Dict=True	,			UpperCAmelCase_    :       Optional[int]=False	,			**UpperCAmelCase_    :       Tuple	,			)	->Dict:



   '''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
     ):
       lowerCamelCase__:				List[Any]        =self._load_voice_preset(UpperCAmelCase_)

     else:
       if isinstance(UpperCAmelCase_	,			UpperCAmelCase_) and not voice_preset.endswith(".npz"):
         lowerCamelCase__:				Union[str, Any]        =voice_preset + ".npz"

       lowerCamelCase__:				List[Any]        =np.load(UpperCAmelCase_)

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

   lowerCamelCase__:				List[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:
     lowerCamelCase__:				Dict        =voice_preset

   return encoded_text








def 					lowerCAmelCase_			(	__a		)					->		str:







  """simple docstring"""





  if isinstance(__a     ,   __a		):
    raise TypeError("'float' object cannot be interpreted as an integer"		)
  if isinstance(__a     ,   __a		):
    raise TypeError("'str' object cannot be interpreted as an integer"		)

  if num == 0:
    return "0b0"

  lowerCamelCase__:				Optional[int]        =False

  if num < 0:
    lowerCamelCase__:				Optional[Any]        =True
    lowerCamelCase__:				List[Any]        =-num

  lowerCamelCase__:				list[int]        =[]
  while num > 0:
    binary.insert(0     ,   num % 2		)
    num >>= 1

  if negative:
    return "-0b" + "".join(str(__a		) for e in binary		)

  return "0b" + "".join(str(__a		) for e in binary		)


if __name__ == "__main__":
     import doctest

     doctest.testmod()

