



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 				snake_case__(_UpperCamelCase      ):

					"""simple docstring"""






					lowercase_								=		"""AutoTokenizer"""
					lowercase_								=		["""tokenizer"""]

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







					def __init__(							self :     Any				,       SCREAMING_SNAKE_CASE :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     int=None					):
									super().__init__(SCREAMING_SNAKE_CASE					)

									lowercase__					:   Optional[Any]													=					speaker_embeddings







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

									if speaker_embeddings_dict_path is not None:
													lowercase__					:   int													=					get_file_from_repo(
													    SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       subfolder=kwargs.pop("subfolder"				,       SCREAMING_SNAKE_CASE					)				,       cache_dir=kwargs.pop("cache_dir"				,       SCREAMING_SNAKE_CASE					)				,       force_download=kwargs.pop("force_download"				,       SCREAMING_SNAKE_CASE					)				,       proxies=kwargs.pop("proxies"				,       SCREAMING_SNAKE_CASE					)				,       resume_download=kwargs.pop("resume_download"				,       SCREAMING_SNAKE_CASE					)				,       local_files_only=kwargs.pop("local_files_only"				,       SCREAMING_SNAKE_CASE					)				,       use_auth_token=kwargs.pop("use_auth_token"				,       SCREAMING_SNAKE_CASE					)				,       revision=kwargs.pop("revision"				,       SCREAMING_SNAKE_CASE					)				,       )
													if speaker_embeddings_path is None:
																	logger.warning(
																	    f"""`{os.path.join(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)}` 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`."""					)
																	lowercase__					:   int													=					None
													else:
																	with open(SCREAMING_SNAKE_CASE					) as speaker_embeddings_json:
																					lowercase__					:   Optional[int]													=					json.load(SCREAMING_SNAKE_CASE					)
									else:
													lowercase__					:   int													=					None

									lowercase__					:   str													=					AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)

									return cls(tokenizer=SCREAMING_SNAKE_CASE				,       speaker_embeddings=SCREAMING_SNAKE_CASE					)







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

													lowercase__					:   Union[str, Any]													=					{}

													lowercase__					:   Optional[int]													=					save_directory

													for prompt_key in self.speaker_embeddings:
																	if prompt_key != "repo_or_path":
																					lowercase__					:   str													=					self._load_voice_preset(SCREAMING_SNAKE_CASE					)

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

																					lowercase__					:   Optional[int]													=					tmp_dict

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

									super().save_pretrained(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     List[str]				,       SCREAMING_SNAKE_CASE :     str = None				,       **SCREAMING_SNAKE_CASE :     Union[str, Any]					):
									lowercase__					:   str													=					self.speaker_embeddings[voice_preset]

									lowercase__					:   str													=					{}
									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__					:   List[Any]													=					get_file_from_repo(
													    self.speaker_embeddings.get("repo_or_path"				,       "/"					)				,       voice_preset_paths[key]				,       subfolder=kwargs.pop("subfolder"				,       SCREAMING_SNAKE_CASE					)				,       cache_dir=kwargs.pop("cache_dir"				,       SCREAMING_SNAKE_CASE					)				,       force_download=kwargs.pop("force_download"				,       SCREAMING_SNAKE_CASE					)				,       proxies=kwargs.pop("proxies"				,       SCREAMING_SNAKE_CASE					)				,       resume_download=kwargs.pop("resume_download"				,       SCREAMING_SNAKE_CASE					)				,       local_files_only=kwargs.pop("local_files_only"				,       SCREAMING_SNAKE_CASE					)				,       use_auth_token=kwargs.pop("use_auth_token"				,       SCREAMING_SNAKE_CASE					)				,       revision=kwargs.pop("revision"				,       SCREAMING_SNAKE_CASE					)				,       )
													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."""					)

													lowercase__					:   str													=					np.load(SCREAMING_SNAKE_CASE					)

									return voice_preset_dict







					def 			snake_case					(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     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 :     Any				,       SCREAMING_SNAKE_CASE :     List[str]=None				,       SCREAMING_SNAKE_CASE :     Optional[int]=None				,       SCREAMING_SNAKE_CASE :     Any="pt"				,       SCREAMING_SNAKE_CASE :     List[str]=256				,       SCREAMING_SNAKE_CASE :     Tuple=False				,       SCREAMING_SNAKE_CASE :     int=True				,       SCREAMING_SNAKE_CASE :     Any=False				,       **SCREAMING_SNAKE_CASE :     Optional[Any]				,       ):
									if voice_preset is not None and not isinstance(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					):
													if (
													    isinstance(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)
													    and self.speaker_embeddings is not None
													    and voice_preset in self.speaker_embeddings
													):
																	lowercase__					:   Union[str, Any]													=					self._load_voice_preset(SCREAMING_SNAKE_CASE					)

													else:
																	if isinstance(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					) and not voice_preset.endswith(".npz"					):
																					lowercase__					:   int													=					voice_preset + ".npz"

																	lowercase__					:   Any													=					np.load(SCREAMING_SNAKE_CASE					)

									if voice_preset is not None:
													self._validate_voice_preset_dict(SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)
													lowercase__					:   Union[str, Any]													=					BatchFeature(data=SCREAMING_SNAKE_CASE				,       tensor_type=SCREAMING_SNAKE_CASE					)

									lowercase__					:   Optional[Any]													=					self.tokenizer(
									    SCREAMING_SNAKE_CASE				,       return_tensors=SCREAMING_SNAKE_CASE				,       padding="max_length"				,       max_length=SCREAMING_SNAKE_CASE				,       return_attention_mask=SCREAMING_SNAKE_CASE				,       return_token_type_ids=SCREAMING_SNAKE_CASE				,       add_special_tokens=SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE				,       )

									if voice_preset is not None:
													lowercase__					:   Tuple													=					voice_preset

									return encoded_text









from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

lowerCAmelCase__					  =  TypeVar('''KEY''')
lowerCAmelCase__					  =  TypeVar('''VAL''')

@dataclass(frozen=_UpperCamelCase    ,      slots=_UpperCamelCase      )
class 				snake_case__(Generic[KEY, VAL]      ):

					"""simple docstring"""






					lowercase_								=		42
					lowercase_								=		42






class 				snake_case__(_Item      ):

					"""simple docstring"""






					def __init__(							self :     List[str]					):
									super().__init__(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)







					def __bool__(							self :     Tuple					):
									return False






lowerCAmelCase__					  =  _DeletedItem()

class 				snake_case__(MutableMapping[KEY, VAL]      ):

					"""simple docstring"""






					def __init__(							self :     Any				,       SCREAMING_SNAKE_CASE :     int = 8				,       SCREAMING_SNAKE_CASE :     float = 0.75					):
									lowercase__					:   Any													=					initial_block_size
									lowercase__					:   list[_Item | None]													=					[None] * initial_block_size
									assert 0.0 < capacity_factor < 1.0
									lowercase__					:   Dict													=					capacity_factor
									lowercase__					:   Optional[int]													=					0







					def 			snake_case					(							self :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     KEY					):
									return hash(SCREAMING_SNAKE_CASE					) % len(self._buckets					)







					def 			snake_case					(							self :     Optional[int]				,       SCREAMING_SNAKE_CASE :     int					):
									return (ind + 1) % len(self._buckets					)







					def 			snake_case					(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     int				,       SCREAMING_SNAKE_CASE :     KEY				,       SCREAMING_SNAKE_CASE :     VAL					):
									lowercase__					:   Tuple													=					self._buckets[ind]
									if not stored:
													lowercase__					:   int													=					_Item(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)
													self._len += 1
													return True
									elif stored.key == key:
													lowercase__					:   str													=					_Item(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)
													return True
									else:
													return False







					def 			snake_case					(							self :     str					):
									lowercase__					:   str													=					len(self._buckets					) * self._capacity_factor
									return len(self					) >= int(SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     List[str]					):
									if len(self._buckets					) <= self._initial_block_size:
													return False
									lowercase__					:   Optional[Any]													=					len(self._buckets					) * self._capacity_factor / 2
									return len(self					) < limit







					def 			snake_case					(							self :     List[Any]				,       SCREAMING_SNAKE_CASE :     int					):
									lowercase__					:   Tuple													=					self._buckets
									lowercase__					:   Optional[int]													=					[None] * new_size
									lowercase__					:   int													=					0
									for item in old_buckets:
													if item:
																	self._add_item(item.key				,       item.val					)







					def 			snake_case					(							self :     int					):
									self._resize(len(self._buckets					) * 2					)







					def 			snake_case					(							self :     Optional[Any]					):
									self._resize(len(self._buckets					) // 2					)







					def 			snake_case					(							self :     List[Any]				,       SCREAMING_SNAKE_CASE :     KEY					):
									lowercase__					:   Tuple													=					self._get_bucket_index(SCREAMING_SNAKE_CASE					)
									for _ in range(len(self._buckets					)					):
													yield ind
													lowercase__					:   Union[str, Any]													=					self._get_next_ind(SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     str				,       SCREAMING_SNAKE_CASE :     KEY				,       SCREAMING_SNAKE_CASE :     VAL					):
									for ind in self._iterate_buckets(SCREAMING_SNAKE_CASE					):
													if self._try_set(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					):
																	break







					def __setitem__(							self :     List[str]				,       SCREAMING_SNAKE_CASE :     KEY				,       SCREAMING_SNAKE_CASE :     VAL					):
									if self._is_full():
													self._size_up()

									self._add_item(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)







					def __delitem__(							self :     int				,       SCREAMING_SNAKE_CASE :     KEY					):
									for ind in self._iterate_buckets(SCREAMING_SNAKE_CASE					):
													lowercase__					:   Union[str, Any]													=					self._buckets[ind]
													if item is None:
																	raise KeyError(SCREAMING_SNAKE_CASE					)
													if item is _deleted:
																	continue
													if item.key == key:
																	lowercase__					:   Optional[int]													=					_deleted
																	self._len -= 1
																	break
									if self._is_sparse():
													self._size_down()







					def __getitem__(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     KEY					):
									for ind in self._iterate_buckets(SCREAMING_SNAKE_CASE					):
													lowercase__					:   Any													=					self._buckets[ind]
													if item is None:
																	break
													if item is _deleted:
																	continue
													if item.key == key:
																	return item.val
									raise KeyError(SCREAMING_SNAKE_CASE					)







					def __len__(							self :     Optional[Any]					):
									return self._len







					def __iter__(							self :     List[str]					):
									yield from (item.key for item in self._buckets if item)







					def __repr__(							self :     str					):
									lowercase__					:   int													=					" ,".join(
									    f"""{item.key}: {item.val}""" for item in self._buckets if item					)
									return f"""HashMap({val_string})"""




