






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


UpperCAmelCase__      			=						logging.get_logger(__name__)




class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						snake_case__        =      """AutoTokenizer"""
						snake_case__        =      ["""tokenizer"""]

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



						def __init__(self:					List[str] ,					__UpperCAmelCase:					str ,					__UpperCAmelCase:					List[Any]=None   )    ->     Tuple:




											'''simple docstring'''



											super().__init__(__UpperCAmelCase   )

											__a					:						Optional[int]      =					speaker_embeddings



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




											'''simple docstring'''



											if speaker_embeddings_dict_path is not None:
																__a					:						Optional[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\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`.'   )
																					__a					:						Union[str, Any]      =					None
																else:
																					with open(__UpperCAmelCase   ) as speaker_embeddings_json:
																										__a					:						Optional[Any]      =					json.load(__UpperCAmelCase   )
											else:
																__a					:						Any      =					None

											__a					:						List[str]      =					AutoTokenizer.from_pretrained(__UpperCAmelCase ,					**__UpperCAmelCase   )

											return cls(tokenizer=__UpperCAmelCase ,					speaker_embeddings=__UpperCAmelCase   )



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




											'''simple docstring'''



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

																__a					:						Dict      =					{}

																__a					:						Dict      =					save_directory

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

																										__a					:						Union[str, Any]      =					{}
																										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 ,					)
																															__a					:						Dict      =					os.path.join(__UpperCAmelCase ,					f'{prompt_key}_{key}.npy'   )

																										__a					:						int      =					tmp_dict

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

											super().save_pretrained(__UpperCAmelCase ,					__UpperCAmelCase ,					**__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					List[Any] ,					__UpperCAmelCase:					str = None ,					**__UpperCAmelCase:					List[Any]   )    ->     Optional[Any]:




											'''simple docstring'''



											__a					:						Union[str, Any]      =					self.speaker_embeddings[voice_preset]

											__a					:						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}].'   )

																__a					:						int      =					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\n                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n                    embeddings.'   )

																__a					:						int      =					np.load(__UpperCAmelCase   )

											return voice_preset_dict



						def        UpperCAmelCase__					(self:					int ,					__UpperCAmelCase:					Optional[dict] = None   )    ->     List[str]:




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




											'''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
																):
																					__a					:						Optional[int]      =					self._load_voice_preset(__UpperCAmelCase   )

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

																					__a					:						Optional[int]      =					np.load(__UpperCAmelCase   )

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

											__a					:						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:
																__a					:						Optional[int]      =					voice_preset

											return encoded_text













def     a_   (__A      )				->						Dict:



					"""simple docstring"""
					if not head:
										return True
					# split the list to two parts
					__a      ,			__a					:						Any      =					head.next, head
					while fast and fast.next:
										__a					:						Optional[int]      =					fast.next.next
										__a					:						Optional[int]      =					slow.next
					__a					:						Optional[int]      =					slow.next
					__a					:						Tuple      =					None  # Don't forget here! But forget still works!
					# reverse the second part
					__a					:						Any      =					None
					while second:
										__a					:						int      =					second.next
										__a					:						int      =					node
										__a					:						Union[str, Any]      =					second
										__a					:						Union[str, Any]      =					nxt
					# compare two parts
					# second part has the same or one less node
					while node:
										if node.val != head.val:
															return False
										__a					:						int      =					node.next
										__a					:						Dict      =					head.next
					return True







def     a_   (__A      )				->						Union[str, Any]:



					"""simple docstring"""
					if not head or not head.next:
										return True

					# 1. Get the midpoint (slow)
					__a					:						Union[str, Any]      =					head
					while fast and fast.next:
										__a      ,			__a					:						List[str]      =					fast.next.next, slow.next

					# 2. Push the second half into the stack
					__a					:						Dict      =					[slow.val]
					while slow.next:
										__a					:						Optional[Any]      =					slow.next
										stack.append(slow.val      )

					# 3. Comparison
					while stack:
										if stack.pop() != cur.val:
															return False
										__a					:						List[str]      =					cur.next

					return True







def     a_   (__A      )				->						List[str]:



					"""simple docstring"""
					if not head or not head.next:
										return True
					__a					:						Optional[int]      =					{}
					__a					:						Optional[int]      =					0
					while head:
										if head.val in d:
															d[head.val].append(__A      )
										else:
															__a					:						Dict      =					[pos]
										__a					:						Dict      =					head.next
										pos += 1
					__a					:						Dict      =					pos - 1
					__a					:						Optional[int]      =					0
					for v in d.values():
										if len(__A      ) % 2 != 0:
															middle += 1
										else:
															__a					:						Dict      =					0
															for i in range(0				,       len(__A      )      ):
																				if v[i] + v[len(__A      ) - 1 - step] != checksum:
																									return False
																				step += 1
										if middle > 1:
															return False
					return True





