


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


A				:       List[Any]      =  logging.get_logger(__name__)






class      UpperCamelCase(	_a ):
					snake_case_		:					List[str]    					=						"""AutoTokenizer"""
					snake_case_		:					Dict    					=						["""tokenizer"""]

					snake_case_		:					int    					=						{
					    """semantic_prompt""": 1,
					    """coarse_prompt""": 2,
					    """fine_prompt""": 2,
					}


					def __init__(    self						:		List[Any]  ,     SCREAMING_SNAKE_CASE						:		int  ,     SCREAMING_SNAKE_CASE						:		Dict=None    )      ->						Dict:




											'''simple docstring'''

											super().__init__(SCREAMING_SNAKE_CASE    )

											__snake_case				=		speaker_embeddings


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




											'''simple docstring'''

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

											__snake_case				=		AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE  ,     **SCREAMING_SNAKE_CASE    )

											return cls(tokenizer=SCREAMING_SNAKE_CASE  ,     speaker_embeddings=SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple  ,     SCREAMING_SNAKE_CASE						:		Dict  ,     SCREAMING_SNAKE_CASE						:		Dict="speaker_embeddings_path.json"  ,     SCREAMING_SNAKE_CASE						:		Tuple="speaker_embeddings"  ,     SCREAMING_SNAKE_CASE						:		bool = False  ,     **SCREAMING_SNAKE_CASE						:		Tuple  ,     )      ->						str:




											'''simple docstring'''

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

																	__snake_case				=		{}

																	__snake_case				=		save_directory

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

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

																													__snake_case				=		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 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]  ,     SCREAMING_SNAKE_CASE						:		str = None  ,     **SCREAMING_SNAKE_CASE						:		Optional[Any]    )      ->						int:




											'''simple docstring'''

											__snake_case				=		self.speaker_embeddings[voice_preset]

											__snake_case				=		{}
											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}].'''    )

																	__snake_case				=		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.'''    )

																	__snake_case				=		np.load(SCREAMING_SNAKE_CASE    )

											return voice_preset_dict


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]  ,     SCREAMING_SNAKE_CASE						:		Optional[dict] = None    )      ->						Any:




											'''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						:		int  ,     SCREAMING_SNAKE_CASE						:		Any=None  ,     SCREAMING_SNAKE_CASE						:		Any=None  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]="pt"  ,     SCREAMING_SNAKE_CASE						:		Optional[int]=2_5_6  ,     SCREAMING_SNAKE_CASE						:		int=False  ,     SCREAMING_SNAKE_CASE						:		List[Any]=True  ,     SCREAMING_SNAKE_CASE						:		Optional[int]=False  ,     **SCREAMING_SNAKE_CASE						:		Any  ,     )      ->						Any:




											'''simple docstring'''

											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
																	):
																							__snake_case				=		self._load_voice_preset(SCREAMING_SNAKE_CASE    )

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

																							__snake_case				=		np.load(SCREAMING_SNAKE_CASE    )

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

											__snake_case				=		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:
																	__snake_case				=		voice_preset

											return encoded_text









# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


A				:       Union[str, Any]      =  logging.get_logger(__name__)

A				:       Dict[Optional[str], Type[Formatter]]      =  {}
A				:       Dict[Optional[str], str]      =  {}
A				:       Dict[Optional[str], Exception]      =  {}





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase = None   ,						)    ->						str:


						'''simple docstring'''






						__snake_case				=		aliases if aliases is not None else []
						if format_type in _FORMAT_TYPES:
												logger.warning(
												    F'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'''			)
						__snake_case				=		formatter_cls
						for alias in set(aliases + [format_type]			):
												if alias in _FORMAT_TYPES_ALIASES:
																		logger.warning(
																		    F'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'''			)
												__snake_case				=		format_type





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase = None			)    ->						List[Any]:


						'''simple docstring'''






						__snake_case				=		aliases if aliases is not None else []
						for alias in set(aliases + [format_type]			):
												__snake_case				=		unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['python'])
_register_formatter(ArrowFormatter, 'arrow', aliases=['pa', 'pyarrow'])
_register_formatter(NumpyFormatter, 'numpy', aliases=['np'])
_register_formatter(PandasFormatter, 'pandas', aliases=['pd'])
_register_formatter(CustomFormatter, 'custom')

if config.TORCH_AVAILABLE:
	from .torch_formatter import TorchFormatter

	_register_formatter(TorchFormatter, 'torch', aliases=['pt', 'pytorch'])
else:
	A				:       Any      =  ValueError('PyTorch needs to be installed to be able to return PyTorch tensors.')
	_register_unavailable_formatter(_torch_error, 'torch', aliases=['pt', 'pytorch'])

if config.TF_AVAILABLE:
	from .tf_formatter import TFFormatter

	_register_formatter(TFFormatter, 'tensorflow', aliases=['tf'])
else:
	A				:       Tuple      =  ValueError('Tensorflow needs to be installed to be able to return Tensorflow tensors.')
	_register_unavailable_formatter(_tf_error, 'tensorflow', aliases=['tf'])

if config.JAX_AVAILABLE:
	from .jax_formatter import JaxFormatter

	_register_formatter(JaxFormatter, 'jax', aliases=[])
else:
	A				:       Optional[int]      =  ValueError('JAX needs to be installed to be able to return JAX arrays.')
	_register_unavailable_formatter(_jax_error, 'jax', aliases=[])





def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						Optional[str]:


						'''simple docstring'''






						if format_type in _FORMAT_TYPES_ALIASES:
												return _FORMAT_TYPES_ALIASES[format_type]
						else:
												return format_type





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						**_lowerCAmelCase			)    ->						Formatter:


						'''simple docstring'''






						__snake_case				=		get_format_type_from_alias(_lowerCAmelCase			)
						if format_type in _FORMAT_TYPES:
												return _FORMAT_TYPES[format_type](**_lowerCAmelCase			)
						if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
												raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
						else:
												raise ValueError(
												    F'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None			)}, but got \'{format_type}\''''			)





