






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   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		_snake_case    :    Optional[int]       						=  '''AutoTokenizer'''
		_snake_case    :    List[str]       						=  ['''tokenizer''']

		_snake_case    :    List[str]       						=  {
		    '''semantic_prompt''': 1,
		    '''coarse_prompt''': 2,
		    '''fine_prompt''': 2,
		}



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase=None   )	->							int:
							super().__init__(_UpperCamelCase   )

							UpperCAmelCase_    :      List[str]            =						speaker_embeddings



		@classmethod
		def 					__UpperCAmelCase			(							cls				,		_UpperCamelCase				,		_UpperCamelCase="speaker_embeddings_path.json"				,		**_UpperCamelCase   )	->							Any:

							if speaker_embeddings_dict_path is not None:
												UpperCAmelCase_    :      List[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`."   )
																	UpperCAmelCase_    :      Tuple            =						None
												else:
																	with open(_UpperCamelCase   ) as speaker_embeddings_json:
																						UpperCAmelCase_    :      int            =						json.load(_UpperCamelCase   )
							else:
												UpperCAmelCase_    :      Optional[Any]            =						None

							UpperCAmelCase_    :      List[str]            =						AutoTokenizer.from_pretrained(_UpperCamelCase				,		**_UpperCamelCase   )

							return cls(tokenizer=_UpperCamelCase				,		speaker_embeddings=_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase="speaker_embeddings_path.json"				,		_UpperCamelCase="speaker_embeddings"				,		_UpperCamelCase = False				,		**_UpperCamelCase				,		)	->							Optional[int]:
							if self.speaker_embeddings is not None:
												os.makedirs(os.path.join(_UpperCamelCase				,		_UpperCamelCase				,		'v2'   )				,		exist_ok=_UpperCamelCase   )

												UpperCAmelCase_    :      Dict            =						{}

												UpperCAmelCase_    :      Tuple            =						save_directory

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

																						UpperCAmelCase_    :      List[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				,		)
																											UpperCAmelCase_    :      Optional[Any]            =						os.path.join(_UpperCamelCase				,		f"{prompt_key}_{key}.npy"   )

																						UpperCAmelCase_    :      str            =						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				,		_UpperCamelCase = None				,		**_UpperCamelCase   )	->							int:
							UpperCAmelCase_    :      str            =						self.speaker_embeddings[voice_preset]

							UpperCAmelCase_    :      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}]."   )

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

												UpperCAmelCase_    :      List[str]            =						np.load(_UpperCamelCase   )

							return voice_preset_dict



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase = None   )	->							Optional[int]:
							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				,		_UpperCamelCase=None				,		_UpperCamelCase=None				,		_UpperCamelCase="pt"				,		_UpperCamelCase=2_5_6				,		_UpperCamelCase=False				,		_UpperCamelCase=True				,		_UpperCamelCase=False				,		**_UpperCamelCase				,		)	->							List[str]:
							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
												):
																	UpperCAmelCase_    :      List[Any]            =						self._load_voice_preset(_UpperCamelCase   )

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

																	UpperCAmelCase_    :      Optional[int]            =						np.load(_UpperCamelCase   )

							if voice_preset is not None:
												self._validate_voice_preset_dict(_UpperCamelCase				,		**_UpperCamelCase   )
												UpperCAmelCase_    :      Optional[int]            =						BatchFeature(data=_UpperCamelCase				,		tensor_type=_UpperCamelCase   )

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

							return encoded_text














__UpperCAmelCase								=						{
    'Pillow': 'Pillow<10.0.0',
    'accelerate': 'accelerate>=0.20.3',
    'av': 'av==9.2.0',
    'beautifulsoup4': 'beautifulsoup4',
    'black': 'black~=23.1',
    'codecarbon': 'codecarbon==1.2.0',
    'cookiecutter': 'cookiecutter==1.7.3',
    'dataclasses': 'dataclasses',
    'datasets': 'datasets!=2.5.0',
    'decord': 'decord==0.6.0',
    'deepspeed': 'deepspeed>=0.9.3',
    'diffusers': 'diffusers',
    'dill': 'dill<0.3.5',
    'evaluate': 'evaluate>=0.2.0',
    'fairscale': 'fairscale>0.3',
    'faiss-cpu': 'faiss-cpu',
    'fastapi': 'fastapi',
    'filelock': 'filelock',
    'flax': 'flax>=0.4.1,<=0.7.0',
    'ftfy': 'ftfy',
    'fugashi': 'fugashi>=1.0',
    'GitPython': 'GitPython<3.1.19',
    'hf-doc-builder': 'hf-doc-builder>=0.3.0',
    'huggingface-hub': 'huggingface-hub>=0.14.1,<1.0',
    'importlib_metadata': 'importlib_metadata',
    'ipadic': 'ipadic>=1.0.0,<2.0',
    'isort': 'isort>=5.5.4',
    'jax': 'jax>=0.2.8,!=0.3.2,<=0.4.13',
    'jaxlib': 'jaxlib>=0.1.65,<=0.4.13',
    'jieba': 'jieba',
    'kenlm': 'kenlm',
    'keras-nlp': 'keras-nlp>=0.3.1',
    'librosa': 'librosa',
    'nltk': 'nltk',
    'natten': 'natten>=0.14.6',
    'numpy': 'numpy>=1.17',
    'onnxconverter-common': 'onnxconverter-common',
    'onnxruntime-tools': 'onnxruntime-tools>=1.4.2',
    'onnxruntime': 'onnxruntime>=1.4.0',
    'opencv-python': 'opencv-python',
    'optuna': 'optuna',
    'optax': 'optax>=0.0.8,<=0.1.4',
    'packaging': 'packaging>=20.0',
    'parameterized': 'parameterized',
    'phonemizer': 'phonemizer',
    'protobuf': 'protobuf',
    'psutil': 'psutil',
    'pyyaml': 'pyyaml>=5.1',
    'pydantic': 'pydantic<2',
    'pytest': 'pytest>=7.2.0',
    'pytest-timeout': 'pytest-timeout',
    'pytest-xdist': 'pytest-xdist',
    'python': 'python>=3.8.0',
    'ray[tune]': 'ray[tune]',
    'regex': 'regex!=2019.12.17',
    'requests': 'requests',
    'rhoknp': 'rhoknp>=1.1.0,<1.3.1',
    'rjieba': 'rjieba',
    'rouge-score': 'rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1',
    'ruff': 'ruff>=0.0.241,<=0.0.259',
    'sacrebleu': 'sacrebleu>=1.4.12,<2.0.0',
    'sacremoses': 'sacremoses',
    'safetensors': 'safetensors>=0.3.1',
    'sagemaker': 'sagemaker>=2.31.0',
    'scikit-learn': 'scikit-learn',
    'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
    'sigopt': 'sigopt',
    'starlette': 'starlette',
    'sudachipy': 'sudachipy>=0.6.6',
    'sudachidict_core': 'sudachidict_core>=20220729',
    'tensorflow-cpu': 'tensorflow-cpu>=2.6,<2.14',
    'tensorflow': 'tensorflow>=2.6,<2.14',
    'tensorflow-text': 'tensorflow-text<2.14',
    'tf2onnx': 'tf2onnx',
    'timeout-decorator': 'timeout-decorator',
    'timm': 'timm',
    'tokenizers': 'tokenizers>=0.11.1,!=0.11.3,<0.14',
    'torch': 'torch>=1.9,!=1.12.0',
    'torchaudio': 'torchaudio',
    'torchvision': 'torchvision',
    'pyctcdecode': 'pyctcdecode>=0.4.0',
    'tqdm': 'tqdm>=4.27',
    'unidic': 'unidic>=1.0.2',
    'unidic_lite': 'unidic_lite>=1.0.7',
    'urllib3': 'urllib3<2.0.0',
    'uvicorn': 'uvicorn',
}






