




'''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   :						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			:   List[Any]       ,						lowerCamelCase_			:   List[Any]       ,						lowerCamelCase_			:   Dict=None  )						->   Optional[Any]:
								super().__init__(_SCREAMING_SNAKE_CASE  )

								__magic_name__							:	List[Any]    							=							speaker_embeddings

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

								if speaker_embeddings_dict_path is not None:
													__magic_name__							:	List[str]    							=							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\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`.'''  )
																		__magic_name__							:	str    							=							None
													else:
																		with open(_SCREAMING_SNAKE_CASE  ) as speaker_embeddings_json:
																							__magic_name__							:	Optional[int]    							=							json.load(_SCREAMING_SNAKE_CASE  )
								else:
													__magic_name__							:	int    							=							None

								__magic_name__							:	List[str]    							=							AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE       ,						**_SCREAMING_SNAKE_CASE  )

								return cls(tokenizer=_SCREAMING_SNAKE_CASE       ,						speaker_embeddings=_SCREAMING_SNAKE_CASE  )

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]       ,						lowerCamelCase_			:   Union[str, Any]       ,						lowerCamelCase_			:   Dict="speaker_embeddings_path.json"       ,						lowerCamelCase_			:   Optional[int]="speaker_embeddings"       ,						lowerCamelCase_			:   Optional[int] = False       ,						**lowerCamelCase_			:   int       ,						)						->   int:
								if self.speaker_embeddings is not None:
													os.makedirs(os.path.join(_SCREAMING_SNAKE_CASE       ,						_SCREAMING_SNAKE_CASE       ,						'''v2'''  )       ,						exist_ok=_SCREAMING_SNAKE_CASE  )

													__magic_name__							:	List[Any]    							=							{}

													__magic_name__							:	List[str]    							=							save_directory

													for prompt_key in self.speaker_embeddings:
																		if prompt_key != "repo_or_path":
																							__magic_name__							:	Optional[Any]    							=							self._load_voice_preset(_SCREAMING_SNAKE_CASE  )

																							__magic_name__							:	Any    							=							{}
																							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       ,						)
																												__magic_name__							:	Optional[Any]    							=							os.path.join(_SCREAMING_SNAKE_CASE       ,						F'''{prompt_key}_{key}.npy'''  )

																							__magic_name__							:	Dict    							=							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 							UpperCAmelCase__       ( self			:   Union[str, Any]       ,						lowerCamelCase_			:   int = None       ,						**lowerCamelCase_			:   Union[str, Any]  )						->   List[Any]:
								__magic_name__							:	Union[str, Any]    							=							self.speaker_embeddings[voice_preset]

								__magic_name__							:	Optional[Any]    							=							{}
								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}].'''  )

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

													__magic_name__							:	List[str]    							=							np.load(_SCREAMING_SNAKE_CASE  )

								return voice_preset_dict

			def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   Any = None  )						->   List[Any]:
								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_			:   Optional[int]=None       ,						lowerCamelCase_			:   int=None       ,						lowerCamelCase_			:   List[Any]="pt"       ,						lowerCamelCase_			:   int=256       ,						lowerCamelCase_			:   Tuple=False       ,						lowerCamelCase_			:   Optional[int]=True       ,						lowerCamelCase_			:   Optional[int]=False       ,						**lowerCamelCase_			:   Any       ,						)						->   Tuple:
								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
													):
																		__magic_name__							:	str    							=							self._load_voice_preset(_SCREAMING_SNAKE_CASE  )

													else:
																		if isinstance(_SCREAMING_SNAKE_CASE       ,						_SCREAMING_SNAKE_CASE  ) and not voice_preset.endswith('''.npz'''  ):
																							__magic_name__							:	int    							=							voice_preset + '''.npz'''

																		__magic_name__							:	int    							=							np.load(_SCREAMING_SNAKE_CASE  )

								if voice_preset is not None:
													self._validate_voice_preset_dict(_SCREAMING_SNAKE_CASE       ,						**_SCREAMING_SNAKE_CASE  )
													__magic_name__							:	Optional[Any]    							=							BatchFeature(data=_SCREAMING_SNAKE_CASE       ,						tensor_type=_SCREAMING_SNAKE_CASE  )

								__magic_name__							:	int    							=							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:
													__magic_name__							:	Union[str, Any]    							=							voice_preset

								return encoded_text









from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput


@dataclass
class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=None
			UpperCamelCase__								=None
			UpperCamelCase__								=None
			UpperCamelCase__								=None






class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			def __init__( self			:   List[Any]       ,						lowerCamelCase_			:   Optional[Any]=1       ,						lowerCamelCase_			:   Any=0       ,						lowerCamelCase_			:   Dict=2       ,						lowerCamelCase_			:   Dict=512       ,						lowerCamelCase_			:   int="cls"       ,						lowerCamelCase_			:   Dict=False       ,						lowerCamelCase_			:   List[Any]=True       ,						**lowerCamelCase_			:   Optional[Any]       ,						)						->   Optional[int]:
								super().__init__(pad_token_id=lowerCamelCase_       ,						bos_token_id=lowerCamelCase_       ,						eos_token_id=lowerCamelCase_       ,						**lowerCamelCase_  )
								__magic_name__							:	Dict    							=							project_dim
								__magic_name__							:	int    							=							pooler_fn
								__magic_name__							:	str    							=							learn_encoder
								__magic_name__							:	Optional[int]    							=							use_attention_mask






class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=[R'''pooler''', R'''logit_scale''']
			UpperCamelCase__								=[R'''position_ids''', R'''predictions.decoder.bias''']
			UpperCamelCase__								='''roberta'''
			UpperCamelCase__								=RobertaSeriesConfig

			def __init__( self			:   str       ,						lowerCamelCase_			:   Optional[Any]  )						->   Optional[Any]:
								super().__init__(lowerCamelCase_  )
								__magic_name__							:	Tuple    							=							XLMRobertaModel(lowerCamelCase_  )
								__magic_name__							:	Any    							=							nn.Linear(config.hidden_size       ,						config.project_dim  )
								__magic_name__							:	Optional[int]    							=							getattr(lowerCamelCase_       ,						'''has_pre_transformation'''       ,						lowerCamelCase_  )
								if self.has_pre_transformation:
													__magic_name__							:	List[Any]    							=							nn.Linear(config.hidden_size       ,						config.project_dim  )
													__magic_name__							:	Union[str, Any]    							=							nn.LayerNorm(config.hidden_size       ,						eps=config.layer_norm_eps  )
								self.post_init()


			def 							UpperCAmelCase__       ( self			:   Optional[Any]       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[bool] = None       ,						lowerCamelCase_			:   Optional[bool] = None       ,						lowerCamelCase_			:   Optional[bool] = None       ,						)						->   List[Any]:
								__magic_name__							:	Optional[int]    							=							return_dict if return_dict is not None else self.config.use_return_dict

								__magic_name__							:	Any    							=							self.base_model(
								    input_ids=lowerCamelCase_       ,						attention_mask=lowerCamelCase_       ,						token_type_ids=lowerCamelCase_       ,						position_ids=lowerCamelCase_       ,						head_mask=lowerCamelCase_       ,						inputs_embeds=lowerCamelCase_       ,						encoder_hidden_states=lowerCamelCase_       ,						encoder_attention_mask=lowerCamelCase_       ,						output_attentions=lowerCamelCase_       ,						output_hidden_states=True if self.has_pre_transformation else output_hidden_states       ,						return_dict=lowerCamelCase_       ,						)

								if self.has_pre_transformation:
													__magic_name__							:	Any    							=							outputs['''hidden_states'''][-2]
													__magic_name__							:	Dict    							=							self.pre_LN(lowerCamelCase_  )
													__magic_name__							:	int    							=							self.transformation_pre(lowerCamelCase_  )

													return TransformationModelOutput(
													    projection_state=lowerCamelCase_       ,						last_hidden_state=outputs.last_hidden_state       ,						hidden_states=outputs.hidden_states       ,						attentions=outputs.attentions       ,						)
								else:
													__magic_name__							:	Union[str, Any]    							=							self.transformation(outputs.last_hidden_state  )
													return TransformationModelOutput(
													    projection_state=lowerCamelCase_       ,						last_hidden_state=outputs.last_hidden_state       ,						hidden_states=outputs.hidden_states       ,						attentions=outputs.attentions       ,						)



