

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   (      lowercase__						):
			UpperCamelCase        =					'''AutoTokenizer'''
			UpperCamelCase        =					['''tokenizer''']

			UpperCamelCase        =					{
			    '''semantic_prompt''': 1,
			    '''coarse_prompt''': 2,
			    '''fine_prompt''': 2,
			}



			def __init__(						self       :  Dict			,		A       :  Any			,		A       :  Optional[int]=None)       ->   str:



						"""simple docstring"""






						super().__init__(_a)

						_UpperCAmelCase			=       speaker_embeddings



			@classmethod
			def    _lowerCamelCase	(						cls       :  Tuple			,		A       :  Any			,		A       :  Dict="speaker_embeddings_path.json"			,		**A       :  Any)       ->   Optional[Any]:



						"""simple docstring"""






						if speaker_embeddings_dict_path is not None:
									_UpperCAmelCase			=       get_file_from_repo(
									    _a			,		_a			,		subfolder=kwargs.pop('subfolder'			,		_a)			,		cache_dir=kwargs.pop('cache_dir'			,		_a)			,		force_download=kwargs.pop('force_download'			,		_a)			,		proxies=kwargs.pop('proxies'			,		_a)			,		resume_download=kwargs.pop('resume_download'			,		_a)			,		local_files_only=kwargs.pop('local_files_only'			,		_a)			,		use_auth_token=kwargs.pop('use_auth_token'			,		_a)			,		revision=kwargs.pop('revision'			,		_a)			,		)
									if speaker_embeddings_path is None:
												logger.warning(
												    F"`{os.path.join(_a			,		_a)}` 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			=       None
									else:
												with open(_a) as speaker_embeddings_json:
															_UpperCAmelCase			=       json.load(_a)
						else:
									_UpperCAmelCase			=       None

						_UpperCAmelCase			=       AutoTokenizer.from_pretrained(_a			,		**_a)

						return cls(tokenizer=_a			,		speaker_embeddings=_a)



			def    _lowerCamelCase	(						self       :  str			,		A       :  Tuple			,		A       :  List[str]="speaker_embeddings_path.json"			,		A       :  Tuple="speaker_embeddings"			,		A       :  bool = False			,		**A       :  List[Any]			,		)       ->   Optional[Any]:



						"""simple docstring"""






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

									_UpperCAmelCase			=       {}

									_UpperCAmelCase			=       save_directory

									for prompt_key in self.speaker_embeddings:
												if prompt_key != "repo_or_path":
															_UpperCAmelCase			=       self._load_voice_preset(_a)

															_UpperCAmelCase			=       {}
															for key in self.speaker_embeddings[prompt_key]:
																		np.save(
																		    os.path.join(
																		        embeddings_dict['repo_or_path']			,		_a			,		F"{prompt_key}_{key}")			,		voice_preset[key]			,		allow_pickle=_a			,		)
																		_UpperCAmelCase			=       os.path.join(_a			,		F"{prompt_key}_{key}.npy")

															_UpperCAmelCase			=       tmp_dict

									with open(os.path.join(_a			,		_a)			,		'w') as fp:
												json.dump(_a			,		_a)

						super().save_pretrained(_a			,		_a			,		**_a)



			def    _lowerCamelCase	(						self       :  Optional[Any]			,		A       :  str = None			,		**A       :  Dict)       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       self.speaker_embeddings[voice_preset]

						_UpperCAmelCase			=       {}
						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			=       get_file_from_repo(
									    self.speaker_embeddings.get('repo_or_path'			,		'/')			,		voice_preset_paths[key]			,		subfolder=kwargs.pop('subfolder'			,		_a)			,		cache_dir=kwargs.pop('cache_dir'			,		_a)			,		force_download=kwargs.pop('force_download'			,		_a)			,		proxies=kwargs.pop('proxies'			,		_a)			,		resume_download=kwargs.pop('resume_download'			,		_a)			,		local_files_only=kwargs.pop('local_files_only'			,		_a)			,		use_auth_token=kwargs.pop('use_auth_token'			,		_a)			,		revision=kwargs.pop('revision'			,		_a)			,		)
									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			=       np.load(_a)

						return voice_preset_dict



			def    _lowerCamelCase	(						self       :  Dict			,		A       :  Optional[dict] = None)       ->   Optional[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			,		A       :  str=None			,		A       :  Optional[Any]=None			,		A       :  Optional[Any]="pt"			,		A       :  List[Any]=2_56			,		A       :  List[Any]=False			,		A       :  str=True			,		A       :  Optional[Any]=False			,		**A       :  str			,		)       ->   str:



						"""simple docstring"""






						if voice_preset is not None and not isinstance(_a			,		_a):
									if (
									    isinstance(_a			,		_a)
									    and self.speaker_embeddings is not None
									    and voice_preset in self.speaker_embeddings
									):
												_UpperCAmelCase			=       self._load_voice_preset(_a)

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

												_UpperCAmelCase			=       np.load(_a)

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

						_UpperCAmelCase			=       self.tokenizer(
						    _a			,		return_tensors=_a			,		padding='max_length'			,		max_length=_a			,		return_attention_mask=_a			,		return_token_type_ids=_a			,		add_special_tokens=_a			,		**_a			,		)

						if voice_preset is not None:
									_UpperCAmelCase			=       voice_preset

						return encoded_text





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase__       =	{
    "configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"],
    "tokenization_deberta": ["DebertaTokenizer"],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	["DebertaTokenizerFast"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "DebertaForMaskedLM",
							    "DebertaForQuestionAnswering",
							    "DebertaForSequenceClassification",
							    "DebertaForTokenClassification",
							    "DebertaModel",
							    "DebertaPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFDebertaForMaskedLM",
							    "TFDebertaForQuestionAnswering",
							    "TFDebertaForSequenceClassification",
							    "TFDebertaForTokenClassification",
							    "TFDebertaModel",
							    "TFDebertaPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
							from .tokenization_deberta import DebertaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_deberta_fast import DebertaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_deberta import (
														    DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DebertaForMaskedLM,
														    DebertaForQuestionAnswering,
														    DebertaForSequenceClassification,
														    DebertaForTokenClassification,
														    DebertaModel,
														    DebertaPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_deberta import (
														    TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFDebertaForMaskedLM,
														    TFDebertaForQuestionAnswering,
														    TFDebertaForSequenceClassification,
														    TFDebertaForTokenClassification,
														    TFDebertaModel,
														    TFDebertaPreTrainedModel,
														)


else:
							import sys

							UpperCAmelCase__       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
