



'''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


lowerCamelCase		       =				logging.get_logger(__name__)





class _UpperCamelCase						(   UpperCAmelCase_						):





				'''simple docstring'''





				lowerCAmelCase__    			=			'''AutoTokenizer'''
				lowerCAmelCase__    			=			['''tokenizer''']

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

				def __init__(							self     :     str     ,				_lowerCAmelCase     :     str     ,				_lowerCAmelCase     :     Dict=None):







											'''simple docstring'''

											super().__init__(lowerCAmelCase__)

											__lowercase						=speaker_embeddings

				@classmethod
				def  __lowerCamelCase   (							cls     :     Tuple     ,				_lowerCAmelCase     :     Any     ,				_lowerCAmelCase     :     Tuple="speaker_embeddings_path.json"     ,				**_lowerCAmelCase     :     int):







											'''simple docstring'''

											if speaker_embeddings_dict_path is not None:
																		__lowercase						=get_file_from_repo(
																		    lowerCAmelCase__     ,				lowerCAmelCase__     ,				subfolder=kwargs.pop('subfolder'     ,				lowerCAmelCase__)     ,				cache_dir=kwargs.pop('cache_dir'     ,				lowerCAmelCase__)     ,				force_download=kwargs.pop('force_download'     ,				lowerCAmelCase__)     ,				proxies=kwargs.pop('proxies'     ,				lowerCAmelCase__)     ,				resume_download=kwargs.pop('resume_download'     ,				lowerCAmelCase__)     ,				local_files_only=kwargs.pop('local_files_only'     ,				lowerCAmelCase__)     ,				use_auth_token=kwargs.pop('use_auth_token'     ,				lowerCAmelCase__)     ,				revision=kwargs.pop('revision'     ,				lowerCAmelCase__)     ,				)
																		if speaker_embeddings_path is None:
																									logger.warning(
																									    f"""`{os.path.join(lowerCAmelCase__     ,				lowerCAmelCase__)}` does not exists\n                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json\n                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.""")
																									__lowercase						=None
																		else:
																									with open(lowerCAmelCase__) as speaker_embeddings_json:
																																__lowercase						=json.load(lowerCAmelCase__)
											else:
																		__lowercase						=None

											__lowercase						=AutoTokenizer.from_pretrained(lowerCAmelCase__     ,				**lowerCAmelCase__)

											return cls(tokenizer=lowerCAmelCase__     ,				speaker_embeddings=lowerCAmelCase__)

				def  __lowerCamelCase   (							self     :     Tuple     ,				_lowerCAmelCase     :     Any     ,				_lowerCAmelCase     :     int="speaker_embeddings_path.json"     ,				_lowerCAmelCase     :     Optional[int]="speaker_embeddings"     ,				_lowerCAmelCase     :     bool = False     ,				**_lowerCAmelCase     :     Any     ,				):







											'''simple docstring'''

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

																		__lowercase						={}

																		__lowercase						=save_directory

																		for prompt_key in self.speaker_embeddings:
																									if prompt_key != "repo_or_path":
																																__lowercase						=self._load_voice_preset(lowerCAmelCase__)

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

																																__lowercase						=tmp_dict

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

											super().save_pretrained(lowerCAmelCase__     ,				lowerCAmelCase__     ,				**lowerCAmelCase__)

				def  __lowerCamelCase   (							self     :     List[Any]     ,				_lowerCAmelCase     :     str = None     ,				**_lowerCAmelCase     :     Tuple):







											'''simple docstring'''

											__lowercase						=self.speaker_embeddings[voice_preset]

											__lowercase						={}
											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}].""")

																		__lowercase						=get_file_from_repo(
																		    self.speaker_embeddings.get('repo_or_path'     ,				'/')     ,				voice_preset_paths[key]     ,				subfolder=kwargs.pop('subfolder'     ,				lowerCAmelCase__)     ,				cache_dir=kwargs.pop('cache_dir'     ,				lowerCAmelCase__)     ,				force_download=kwargs.pop('force_download'     ,				lowerCAmelCase__)     ,				proxies=kwargs.pop('proxies'     ,				lowerCAmelCase__)     ,				resume_download=kwargs.pop('resume_download'     ,				lowerCAmelCase__)     ,				local_files_only=kwargs.pop('local_files_only'     ,				lowerCAmelCase__)     ,				use_auth_token=kwargs.pop('use_auth_token'     ,				lowerCAmelCase__)     ,				revision=kwargs.pop('revision'     ,				lowerCAmelCase__)     ,				)
																		if path is None:
																									raise ValueError(
																									    f"""`{os.path.join(self.speaker_embeddings.get('repo_or_path'     ,				'/')     ,				voice_preset_paths[key])}` does not exists\n                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n                    embeddings.""")

																		__lowercase						=np.load(lowerCAmelCase__)

											return voice_preset_dict

				def  __lowerCamelCase   (							self     :     Any     ,				_lowerCAmelCase     :     Optional[dict] = 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     :     Tuple     ,				_lowerCAmelCase     :     Dict=None     ,				_lowerCAmelCase     :     Tuple=None     ,				_lowerCAmelCase     :     List[str]="pt"     ,				_lowerCAmelCase     :     Union[str, Any]=2_5_6     ,				_lowerCAmelCase     :     Tuple=False     ,				_lowerCAmelCase     :     str=True     ,				_lowerCAmelCase     :     List[str]=False     ,				**_lowerCAmelCase     :     str     ,				):







											'''simple docstring'''

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

																		else:
																									if isinstance(lowerCAmelCase__     ,				lowerCAmelCase__) and not voice_preset.endswith('.npz'):
																																__lowercase						=voice_preset + ".npz"

																									__lowercase						=np.load(lowerCAmelCase__)

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

											__lowercase						=self.tokenizer(
											    lowerCAmelCase__     ,				return_tensors=lowerCAmelCase__     ,				padding='max_length'     ,				max_length=lowerCAmelCase__     ,				return_attention_mask=lowerCAmelCase__     ,				return_token_type_ids=lowerCAmelCase__     ,				add_special_tokens=lowerCAmelCase__     ,				**lowerCAmelCase__     ,				)

											if voice_preset is not None:
																		__lowercase						=voice_preset

											return encoded_text






from collections.abc import Callable



class   __lowercase				:




			"""simple docstring"""







			def __init__( self  :     Tuple   ,						lowerCAmelCase__  :     Callable | None = None):
									# Stores actual heap items.
									SCREAMING_SNAKE_CASE_: list  		=       []
									# Stores indexes of each item for supporting updates and deletion.
									SCREAMING_SNAKE_CASE_: dict  		=       {}
									# Stores current size of heap.
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       0
									# Stores function used to evaluate the score of an item on which basis ordering
									# will be done.
									SCREAMING_SNAKE_CASE_: Any  		=       key or (lambda lowerCAmelCase__: x)



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     int):
									return int((i - 1) / 2) if i > 0 else None



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     int):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       int(2 * i + 1)
									return left if 0 < left < self.size else None



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     int):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       int(2 * i + 2)
									return right if 0 < right < self.size else None



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     int):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: str  		=       (
									    self.pos_map[self.arr[j][0]],
									    self.pos_map[self.arr[i][0]],
									)
									# Then swap the items in the list.
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[str]  		=       self.arr[j], self.arr[i]



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     int):
									return self.arr[i][1] < self.arr[j][1]



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     int):
									SCREAMING_SNAKE_CASE_: Any  		=       self._left(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[Any]  		=       self._right(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[Any]  		=       i

									if left is not None and not self._cmp(lowerCAmelCase__   ,						lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       left
									if right is not None and not self._cmp(lowerCAmelCase__   ,						lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_: Tuple  		=       right

									return valid_parent



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     int):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       self._parent(lowerCAmelCase__)
									while parent is not None and not self._cmp(lowerCAmelCase__   ,						lowerCAmelCase__):
															self._swap(lowerCAmelCase__   ,						lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[str]  		=       parent, self._parent(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     int):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self._get_valid_parent(lowerCAmelCase__)
									while valid_parent != index:
															self._swap(lowerCAmelCase__   ,						lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Dict  		=       valid_parent, self._get_valid_parent(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     int):
									if item not in self.pos_map:
															return
									SCREAMING_SNAKE_CASE_: Any  		=       self.pos_map[item]
									SCREAMING_SNAKE_CASE_: int  		=       [item, self.key(lowerCAmelCase__)]
									# Make sure heap is right in both up and down direction.
									# Ideally only one of them will make any change.
									self._heapify_up(lowerCAmelCase__)
									self._heapify_down(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]   ,						lowerCAmelCase__  :     int):
									if item not in self.pos_map:
															return
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.pos_map[item]
									del self.pos_map[item]
									SCREAMING_SNAKE_CASE_: List[str]  		=       self.arr[self.size - 1]
									SCREAMING_SNAKE_CASE_: Tuple  		=       index
									self.size -= 1
									# Make sure heap is right in both up and down direction. Ideally only one
									# of them will make any change- so no performance loss in calling both.
									if self.size > index:
															self._heapify_up(lowerCAmelCase__)
															self._heapify_down(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     int):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       len(self.arr)
									if arr_len == self.size:
															self.arr.append([item, self.key(lowerCAmelCase__)])
									else:
															SCREAMING_SNAKE_CASE_: str  		=       [item, self.key(lowerCAmelCase__)]
									SCREAMING_SNAKE_CASE_: List[Any]  		=       self.size
									self.size += 1
									self._heapify_up(self.size - 1)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									return self.arr[0] if self.size else None




			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_: Dict  		=       self.get_top()
									if top_item_tuple:
															self.delete_item(top_item_tuple[0])
									return top_item_tuple







def 			A_      (    ):
						pass


if __name__ == "__main__":
						import doctest

						doctest.testmod()






