


from queue import PriorityQueue
from typing import Any

import numpy as np




def _a (  SCREAMING_SNAKE_CASE__      :  dict			,				SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  set			,				SCREAMING_SNAKE_CASE__      :  set			,				SCREAMING_SNAKE_CASE__      :  dict			,				SCREAMING_SNAKE_CASE__      :  dict			,				SCREAMING_SNAKE_CASE__      :  PriorityQueue			,				SCREAMING_SNAKE_CASE__      :  dict			,				SCREAMING_SNAKE_CASE__      :  float | int			,				)	->    float | int:

							'''simple docstring'''






							for nxt, d in graph[v]:
														if nxt in visited_forward:
																					continue
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						cst_fwd.get(SCREAMING_SNAKE_CASE__			,				np.inf       )
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						cst_fwd[v] + d
														if new_cost_f < old_cost_f:
																					queue.put((new_cost_f, nxt)       )
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						new_cost_f
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						v
														if nxt in visited_backward:
																					if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
																												SCREAMING_SNAKE_CASE__       :      Optional[int]     =						cst_fwd[v] + d + cst_bwd[nxt]
							return shortest_distance




def _a (  SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  dict			,				SCREAMING_SNAKE_CASE__      :  dict       )	->    int:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						-1

							SCREAMING_SNAKE_CASE__       :      List[str]     =						set()
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						set()
							SCREAMING_SNAKE_CASE__       :      int     =						{source: 0}
							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						{destination: 0}
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						{source: None}
							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						{destination: None}
							SCREAMING_SNAKE_CASE__       :      PriorityQueue[Any]     =						PriorityQueue()
							SCREAMING_SNAKE_CASE__       :      PriorityQueue[Any]     =						PriorityQueue()

							SCREAMING_SNAKE_CASE__       :      Dict     =						np.inf

							queue_forward.put((0, source)       )
							queue_backward.put((0, destination)       )

							if source == destination:
														return 0

							while not queue_forward.empty() and not queue_backward.empty():
														SCREAMING_SNAKE_CASE__       :      List[str]     =						queue_forward.get()
														visited_forward.add(SCREAMING_SNAKE_CASE__       )

														SCREAMING_SNAKE_CASE__       :      List[Any]     =						queue_backward.get()
														visited_backward.add(SCREAMING_SNAKE_CASE__       )

														SCREAMING_SNAKE_CASE__       :      Any     =						pass_and_relaxation(
														    SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				)

														SCREAMING_SNAKE_CASE__       :      List[Any]     =						pass_and_relaxation(
														    SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				)

														if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
																					break

							if shortest_distance != np.inf:
														SCREAMING_SNAKE_CASE__       :      int     =						shortest_distance
							return shortest_path_distance


_lowerCamelCase :    Optional[Any]  							= {
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''G''', 2]],
    '''F''': [],
    '''G''': [['''F''', 1]],
}
_lowerCamelCase :    Tuple  							= {
    '''B''': [['''E''', 1]],
    '''C''': [['''B''', 1]],
    '''D''': [['''C''', 1]],
    '''F''': [['''D''', 1], ['''G''', 1]],
    '''E''': [[None, np.inf]],
    '''G''': [['''E''', 2]],
}

if __name__ == "__main__":
		import doctest

		doctest.testmod()













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 :    Optional[int]  							= logging.get_logger(__name__)


class 						lowerCamelCase       (__lowerCamelCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   "AutoTokenizer"
							UpperCAmelCase_      						=   ["tokenizer"]

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






							def __init__( self     :					Union[str, Any],			_UpperCAmelCase     :					Optional[int],			_UpperCAmelCase     :					Union[str, Any]=None						)				->			Union[str, Any]:





														"""simple docstring"""
														super().__init__(_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						speaker_embeddings






							@classmethod
							def  A_     ( cls     :					Any,			_UpperCAmelCase     :					List[str],			_UpperCAmelCase     :					Dict="speaker_embeddings_path.json",			**_UpperCAmelCase     :					Optional[Any]						)				->			List[Any]:





														"""simple docstring"""

														if speaker_embeddings_dict_path is not None:
																					SCREAMING_SNAKE_CASE__       :      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`.'''						)
																												SCREAMING_SNAKE_CASE__       :      Dict     =						None
																					else:
																												with open(_UpperCAmelCase						) as speaker_embeddings_json:
																																			SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						json.load(_UpperCAmelCase						)
														else:
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						None

														SCREAMING_SNAKE_CASE__       :      List[str]     =						AutoTokenizer.from_pretrained(_UpperCAmelCase,			**_UpperCAmelCase						)

														return cls(tokenizer=_UpperCAmelCase,			speaker_embeddings=_UpperCAmelCase						)






							def  A_     ( self     :					str,			_UpperCAmelCase     :					Optional[int],			_UpperCAmelCase     :					List[str]="speaker_embeddings_path.json",			_UpperCAmelCase     :					Optional[Any]="speaker_embeddings",			_UpperCAmelCase     :					bool = False,			**_UpperCAmelCase     :					List[str],			)				->			Union[str, Any]:





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

																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						{}

																					SCREAMING_SNAKE_CASE__       :      Any     =						save_directory

																					for prompt_key in self.speaker_embeddings:
																												if prompt_key != "repo_or_path":
																																			SCREAMING_SNAKE_CASE__       :      List[Any]     =						self._load_voice_preset(_UpperCAmelCase						)

																																			SCREAMING_SNAKE_CASE__       :      int     =						{}
																																			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,			)
																																										SCREAMING_SNAKE_CASE__       :      List[str]     =						os.path.join(_UpperCAmelCase,			F'''{prompt_key}_{key}.npy'''						)

																																			SCREAMING_SNAKE_CASE__       :      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  A_     ( self     :					List[Any],			_UpperCAmelCase     :					str = None,			**_UpperCAmelCase     :					List[Any]						)				->			List[str]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						self.speaker_embeddings[voice_preset]

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						{}
														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}].'''						)

																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						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.'''						)

																					SCREAMING_SNAKE_CASE__       :      int     =						np.load(_UpperCAmelCase						)

														return voice_preset_dict






							def  A_     ( self     :					int,			_UpperCAmelCase     :					Optional[dict] = None						)				->			Optional[int]:





														"""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     :					List[Any],			_UpperCAmelCase     :					Optional[Any]=None,			_UpperCAmelCase     :					Union[str, Any]=None,			_UpperCAmelCase     :					Optional[int]="pt",			_UpperCAmelCase     :					List[str]=2_5_6,			_UpperCAmelCase     :					int=False,			_UpperCAmelCase     :					Optional[int]=True,			_UpperCAmelCase     :					Any=False,			**_UpperCAmelCase     :					List[str],			)				->			List[Any]:





														"""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
																					):
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						self._load_voice_preset(_UpperCAmelCase						)

																					else:
																												if isinstance(_UpperCAmelCase,			_UpperCAmelCase						) and not voice_preset.endswith(".npz"						):
																																			SCREAMING_SNAKE_CASE__       :      Optional[int]     =						voice_preset + ".npz"

																												SCREAMING_SNAKE_CASE__       :      List[str]     =						np.load(_UpperCAmelCase						)

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

														SCREAMING_SNAKE_CASE__       :      Dict     =						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:
																					SCREAMING_SNAKE_CASE__       :      str     =						voice_preset

														return encoded_text






