


'''simple docstring'''






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	:	str     =     logging.get_logger(__name__)
class 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		List[str]   		=  'AutoTokenizer'
							lowercase			:		Optional[Any]   		=  ['tokenizer']

							lowercase			:		Dict   		=  {
							    'semantic_prompt': 1,
							    'coarse_prompt': 2,
							    'fine_prompt': 2,
							}





							def __init__(  self		:str  ,_UpperCamelCase		:List[Any]  ,_UpperCamelCase		:List[str]=None			):
									super().__init__(_UpperCamelCase			)

									snake_case_		:							int			       =						speaker_embeddings





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

									if speaker_embeddings_dict_path is not None:
											snake_case_		:							List[Any]			       =						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`.'''			)
													snake_case_		:							Tuple			       =						None
											else:
													with open(_UpperCamelCase			) as speaker_embeddings_json:
															snake_case_		:							Optional[int]			       =						json.load(_UpperCamelCase			)
									else:
											snake_case_		:							Dict			       =						None

									snake_case_		:							List[Any]			       =						AutoTokenizer.from_pretrained(_UpperCamelCase  ,**_UpperCamelCase			)

									return cls(tokenizer=_UpperCamelCase  ,speaker_embeddings=_UpperCamelCase			)





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

											snake_case_		:							List[str]			       =						{}

											snake_case_		:							Dict			       =						save_directory

											for prompt_key in self.speaker_embeddings:
													if prompt_key != "repo_or_path":
															snake_case_		:							Optional[int]			       =						self._load_voice_preset(_UpperCamelCase			)

															snake_case_		:							List[str]			       =						{}
															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  ,)
																	snake_case_		:							Any			       =						os.path.join(_UpperCamelCase  ,F'''{prompt_key}_{key}.npy'''			)

															snake_case_		:							Optional[Any]			       =						tmp_dict

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

									super().save_pretrained(_UpperCamelCase  ,_UpperCamelCase  ,**_UpperCamelCase			)





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:str = None  ,**_UpperCamelCase		:int			):
									snake_case_		:							Dict			       =						self.speaker_embeddings[voice_preset]

									snake_case_		:							Any			       =						{}
									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}].'''			)

											snake_case_		:							Tuple			       =						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.'''			)

											snake_case_		:							Optional[Any]			       =						np.load(_UpperCamelCase			)

									return voice_preset_dict





							def 		a__   (  self		:List[str]  ,_UpperCamelCase		: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		:Union[str, Any]  ,_UpperCamelCase		:List[Any]=None  ,_UpperCamelCase		:List[str]=None  ,_UpperCamelCase		:List[Any]="pt"  ,_UpperCamelCase		:Tuple=2_5_6  ,_UpperCamelCase		:List[str]=False  ,_UpperCamelCase		:Optional[int]=True  ,_UpperCamelCase		:Optional[int]=False  ,**_UpperCamelCase		:Optional[Any]  ,):
									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
											):
													snake_case_		:							Union[str, Any]			       =						self._load_voice_preset(_UpperCamelCase			)

											else:
													if isinstance(_UpperCamelCase  ,_UpperCamelCase			) and not voice_preset.endswith(""".npz"""			):
															snake_case_		:							List[Any]			       =						voice_preset + """.npz"""

													snake_case_		:							List[str]			       =						np.load(_UpperCamelCase			)

									if voice_preset is not None:
											self._validate_voice_preset_dict(_UpperCamelCase  ,**_UpperCamelCase			)
											snake_case_		:							int			       =						BatchFeature(data=_UpperCamelCase  ,tensor_type=_UpperCamelCase			)

									snake_case_		:							List[str]			       =						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:
											snake_case_		:							Optional[Any]			       =						voice_preset

									return encoded_text



'''simple docstring'''






def      UpperCAmelCase      (		lowerCamelCase_  :str ):







		'''simple docstring'''
		return " ".join(
		    """""".join(word[::-1] ) if len(lowerCamelCase_ ) > 4 else word for word in sentence.split() )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							print(reverse_long_words('Hey wollef sroirraw'))