





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





class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   int		      =     '''AutoTokenizer'''
				SCREAMING_SNAKE_CASE__     :   Dict		      =     ['''tokenizer''']

				SCREAMING_SNAKE_CASE__     :   Dict		      =     {
				    '''semantic_prompt''': 1,
				    '''coarse_prompt''': 2,
				    '''fine_prompt''': 2,
				}






				def __init__(  self			, _lowerCAmelCase			, _lowerCAmelCase=None					):

										"""simple docstring"""




										super().__init__(_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   speaker_embeddings






				@classmethod
				def 			snake_case_    (  cls			, _lowerCAmelCase			, _lowerCAmelCase="speaker_embeddings_path.json"			, **_lowerCAmelCase					):

										"""simple docstring"""




										if speaker_embeddings_dict_path is not None:
																__SCREAMING_SNAKE_CASE:				Optional[Any]           =   get_file_from_repo(
																    _lowerCAmelCase			, _lowerCAmelCase			, subfolder=kwargs.pop('''subfolder'''			, _lowerCAmelCase					)			, cache_dir=kwargs.pop('''cache_dir'''			, _lowerCAmelCase					)			, force_download=kwargs.pop('''force_download'''			, _lowerCAmelCase					)			, proxies=kwargs.pop('''proxies'''			, _lowerCAmelCase					)			, resume_download=kwargs.pop('''resume_download'''			, _lowerCAmelCase					)			, local_files_only=kwargs.pop('''local_files_only'''			, _lowerCAmelCase					)			, use_auth_token=kwargs.pop('''use_auth_token'''			, _lowerCAmelCase					)			, revision=kwargs.pop('''revision'''			, _lowerCAmelCase					)			, )
																if speaker_embeddings_path is None:
																						logger.warning(
																						    f"""`{os.path.join(_lowerCAmelCase			, _lowerCAmelCase					)}` 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`."""					)
																						__SCREAMING_SNAKE_CASE:				List[Any]           =   None
																else:
																						with open(_lowerCAmelCase					) as speaker_embeddings_json:
																												__SCREAMING_SNAKE_CASE:				str           =   json.load(_lowerCAmelCase					)
										else:
																__SCREAMING_SNAKE_CASE:				int           =   None

										__SCREAMING_SNAKE_CASE:				Tuple           =   AutoTokenizer.from_pretrained(_lowerCAmelCase			, **_lowerCAmelCase					)

										return cls(tokenizer=_lowerCAmelCase			, speaker_embeddings=_lowerCAmelCase					)






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase="speaker_embeddings_path.json"			, _lowerCAmelCase="speaker_embeddings"			, _lowerCAmelCase = False			, **_lowerCAmelCase			, ):

										"""simple docstring"""




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

																__SCREAMING_SNAKE_CASE:				str           =   {}

																__SCREAMING_SNAKE_CASE:				List[str]           =   save_directory

																for prompt_key in self.speaker_embeddings:
																						if prompt_key != "repo_or_path":
																												__SCREAMING_SNAKE_CASE:				List[str]           =   self._load_voice_preset(_lowerCAmelCase					)

																												__SCREAMING_SNAKE_CASE:				Tuple           =   {}
																												for key in self.speaker_embeddings[prompt_key]:
																																		np.save(
																																		    os.path.join(
																																		        embeddings_dict['''repo_or_path''']			, _lowerCAmelCase			, f"""{prompt_key}_{key}"""					)			, voice_preset[key]			, allow_pickle=_lowerCAmelCase			, )
																																		__SCREAMING_SNAKE_CASE:				List[str]           =   os.path.join(_lowerCAmelCase			, f"""{prompt_key}_{key}.npy"""					)

																												__SCREAMING_SNAKE_CASE:				List[Any]           =   tmp_dict

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

										super().save_pretrained(_lowerCAmelCase			, _lowerCAmelCase			, **_lowerCAmelCase					)






				def 			snake_case_    (  self			, _lowerCAmelCase = None			, **_lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   self.speaker_embeddings[voice_preset]

										__SCREAMING_SNAKE_CASE:				Tuple           =   {}
										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}]."""					)

																__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   get_file_from_repo(
																    self.speaker_embeddings.get('''repo_or_path'''			, '''/'''					)			, voice_preset_paths[key]			, subfolder=kwargs.pop('''subfolder'''			, _lowerCAmelCase					)			, cache_dir=kwargs.pop('''cache_dir'''			, _lowerCAmelCase					)			, force_download=kwargs.pop('''force_download'''			, _lowerCAmelCase					)			, proxies=kwargs.pop('''proxies'''			, _lowerCAmelCase					)			, resume_download=kwargs.pop('''resume_download'''			, _lowerCAmelCase					)			, local_files_only=kwargs.pop('''local_files_only'''			, _lowerCAmelCase					)			, use_auth_token=kwargs.pop('''use_auth_token'''			, _lowerCAmelCase					)			, revision=kwargs.pop('''revision'''			, _lowerCAmelCase					)			, )
																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."""					)

																__SCREAMING_SNAKE_CASE:				Optional[int]           =   np.load(_lowerCAmelCase					)

										return voice_preset_dict






				def 			snake_case_    (  self			, _lowerCAmelCase = None					):

										"""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			, _lowerCAmelCase=None			, _lowerCAmelCase=None			, _lowerCAmelCase="pt"			, _lowerCAmelCase=256			, _lowerCAmelCase=False			, _lowerCAmelCase=True			, _lowerCAmelCase=False			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										if voice_preset is not None and not isinstance(_lowerCAmelCase			, _lowerCAmelCase					):
																if (
																    isinstance(_lowerCAmelCase			, _lowerCAmelCase					)
																    and self.speaker_embeddings is not None
																    and voice_preset in self.speaker_embeddings
																):
																						__SCREAMING_SNAKE_CASE:				Optional[int]           =   self._load_voice_preset(_lowerCAmelCase					)

																else:
																						if isinstance(_lowerCAmelCase			, _lowerCAmelCase					) and not voice_preset.endswith('''.npz'''					):
																												__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   voice_preset + '''.npz'''

																						__SCREAMING_SNAKE_CASE:				str           =   np.load(_lowerCAmelCase					)

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

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.tokenizer(
										    _lowerCAmelCase			, return_tensors=_lowerCAmelCase			, padding='''max_length'''			, max_length=_lowerCAmelCase			, return_attention_mask=_lowerCAmelCase			, return_token_type_ids=_lowerCAmelCase			, add_special_tokens=_lowerCAmelCase			, **_lowerCAmelCase			, )

										if voice_preset is not None:
																__SCREAMING_SNAKE_CASE:				List[Any]           =   voice_preset

										return encoded_text










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase							:       str					 =	{
    """configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase							:       Optional[Any]					 =	[
							    """PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """PegasusXForConditionalGeneration""",
							    """PegasusXModel""",
							    """PegasusXPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_pegasus_x import (
														    PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
														    PegasusXForConditionalGeneration,
														    PegasusXModel,
														    PegasusXPreTrainedModel,
														)


else:
							import sys

							lowerCAmelCase							:       str					 =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



