




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 a			(  __SCREAMING_SNAKE_CASE	):
						"""simple docstring"""

						UpperCamelCase_						:							Any													=		"AutoTokenizer"
						UpperCamelCase_						:							Any													=		["tokenizer"]

						UpperCamelCase_						:							Optional[int]													=		{
						    "semantic_prompt": 1,
						    "coarse_prompt": 2,
						    "fine_prompt": 2,
						}






						def __init__(			self			:  int					,				lowerCamelCase__			:  int					,				lowerCamelCase__			:  List[str]=None					)     ->    Union[str, Any]:
													"""simple docstring"""


													super().__init__(UpperCAmelCase_					)

													__lowercase			  =     speaker_embeddings






						@classmethod
						def      UpperCAmelCase_   (			cls			:  Optional[Any]					,				lowerCamelCase__			:  List[Any]					,				lowerCamelCase__			:  int="speaker_embeddings_path.json"					,				**lowerCamelCase__			:  Optional[int]					)     ->    Union[str, Any]:
													"""simple docstring"""


													if speaker_embeddings_dict_path is not None:
																				__lowercase			  =     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`.'					)
																											__lowercase			  =     None
																				else:
																											with open(UpperCAmelCase_					) as speaker_embeddings_json:
																																		__lowercase			  =     json.load(UpperCAmelCase_					)
													else:
																				__lowercase			  =     None

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

													return cls(tokenizer=UpperCAmelCase_					,				speaker_embeddings=UpperCAmelCase_					)






						def      UpperCAmelCase_   (			self			:  List[Any]					,				lowerCamelCase__			:  Optional[Any]					,				lowerCamelCase__			:  int="speaker_embeddings_path.json"					,				lowerCamelCase__			:  int="speaker_embeddings"					,				lowerCamelCase__			:  Optional[int] = False					,				**lowerCamelCase__			:  List[str]					,				)     ->    Any:
													"""simple docstring"""


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

																				__lowercase			  =     {}

																				__lowercase			  =     save_directory

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

																																		__lowercase			  =     {}
																																		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_					,				)
																																									__lowercase			  =     os.path.join(UpperCAmelCase_					,				f'{prompt_key}_{key}.npy'					)

																																		__lowercase			  =     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			:  Tuple					,				lowerCamelCase__			:  int = None					,				**lowerCamelCase__			:  str					)     ->    Optional[Any]:
													"""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'''					,				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.'					)

																				__lowercase			  =     np.load(UpperCAmelCase_					)

													return voice_preset_dict






						def      UpperCAmelCase_   (			self			:  Union[str, Any]					,				lowerCamelCase__			:  Tuple = None					)     ->    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			:  Dict					,				lowerCamelCase__			:  Optional[int]=None					,				lowerCamelCase__			:  Dict=None					,				lowerCamelCase__			:  Tuple="pt"					,				lowerCamelCase__			:  Union[str, Any]=256					,				lowerCamelCase__			:  List[str]=False					,				lowerCamelCase__			:  Optional[Any]=True					,				lowerCamelCase__			:  Optional[Any]=False					,				**lowerCamelCase__			:  Optional[Any]					,				)     ->    Union[str, 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
																				):
																											__lowercase			  =     self._load_voice_preset(UpperCAmelCase_					)

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

																											__lowercase			  =     np.load(UpperCAmelCase_					)

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

													__lowercase			  =     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:
																				__lowercase			  =     voice_preset

													return encoded_text











'''simple docstring'''





from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin


class 					snake_case			(UpperCamelCase		,   UpperCamelCase ):



       @register_to_config
       def __init__(					self  ,UpperCAmelCase_ = 768  ,)	->				List[Any]:
           super().__init__()

           lowercase__									=    nn.Parameter(torch.zeros(1  ,UpperCAmelCase_   )   )
           lowercase__									=    nn.Parameter(torch.ones(1  ,UpperCAmelCase_   )   )



       def 				_a   (					self  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = None  ,)	->				Any:
           lowercase__									=    nn.Parameter(self.mean.to(UpperCAmelCase_   ).to(UpperCAmelCase_   )   )
           lowercase__									=    nn.Parameter(self.std.to(UpperCAmelCase_   ).to(UpperCAmelCase_   )   )
           return self



       def 				_a   (					self  ,UpperCAmelCase_   )	->				Tuple:
           lowercase__									=    (embeds - self.mean) * 1.0 / self.std
           return embeds







       def 				_a   (					self  ,UpperCAmelCase_   )	->				List[str]:
           lowercase__									=    (embeds * self.std) + self.mean
           return embeds




