
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


SCREAMING_SNAKE_CASE							:int							 =				logging.get_logger(__name__)





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     "AutoTokenizer"
	snake_case_					    =     ["tokenizer"]

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






	def __init__(       self					: Optional[Any]							,A					: Any							,A					: Union[str, Any]=None ):
					super().__init__(A )

					__A   			= speaker_embeddings






	@classmethod
	def 				UpperCamelCase_  (       cls					: Any							,A					: str							,A					: List[str]="speaker_embeddings_path.json"							,**A					: Optional[int] ):

					if speaker_embeddings_dict_path is not None:
									__A   			= 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`.''' )
													__A   			= None
									else:
													with open(A ) as speaker_embeddings_json:
																	__A   			= json.load(A )
					else:
									__A   			= None

					__A   			= AutoTokenizer.from_pretrained(A							,**A )

					return cls(tokenizer=A							,speaker_embeddings=A )






	def 				UpperCamelCase_  (       self					: Optional[Any]							,A					: str							,A					: int="speaker_embeddings_path.json"							,A					: List[Any]="speaker_embeddings"							,A					: bool = False							,**A					: List[str]							,):
					if self.speaker_embeddings is not None:
									os.makedirs(os.path.join(A							,A							,"v2" )							,exist_ok=A )

									__A   			= {}

									__A   			= save_directory

									for prompt_key in self.speaker_embeddings:
													if prompt_key != "repo_or_path":
																	__A   			= self._load_voice_preset(A )

																	__A   			= {}
																	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							,)
																					__A   			= os.path.join(A							,f'''{prompt_key}_{key}.npy''' )

																	__A   			= tmp_dict

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

					super().save_pretrained(A							,A							,**A )






	def 				UpperCamelCase_  (       self					: Dict							,A					: str = None							,**A					: Optional[int] ):
					__A   			= self.speaker_embeddings[voice_preset]

					__A   			= {}
					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}].''' )

									__A   			= 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.''' )

									__A   			= np.load(A )

					return voice_preset_dict






	def 				UpperCamelCase_  (       self					: List[str]							,A					: Optional[dict] = None ):
					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]							,A					: str=None							,A					: Dict=None							,A					: Dict="pt"							,A					: List[Any]=2_56							,A					: Optional[int]=False							,A					: Optional[Any]=True							,A					: Any=False							,**A					: Union[str, Any]							,):
					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
									):
													__A   			= self._load_voice_preset(A )

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

													__A   			= np.load(A )

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

					__A   			= 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:
									__A   			= voice_preset

					return encoded_text




def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			List[Any]:







 """simple docstring"""


 if b == 0:
  return 1
 if (b % 2) == 0:
  return actual_power(__snake_case     ,						int(b / 2						)						) * actual_power(__snake_case     ,						int(b / 2						)						)
 else:
  return a * actual_power(__snake_case     ,						int(b / 2						)						) * actual_power(__snake_case     ,						int(b / 2						)						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			float:







 """simple docstring"""


 if b < 0:
  return 1 / actual_power(__snake_case     ,						__snake_case						)
 return actual_power(__snake_case     ,						__snake_case						)


if __name__ == "__main__":
  print(power(-2, -3))

