


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



class  lowerCAmelCase				(				a							):
	_lowerCamelCase   :					int			      = """AutoTokenizer"""
	_lowerCamelCase   :					Tuple			      = ["""tokenizer"""]

	_lowerCamelCase   :					str			      = {
	    """semantic_prompt""": 1,
	    """coarse_prompt""": 2,
	    """fine_prompt""": 2,
	}



	def __init__(					self ,					snake_case__ ,					snake_case__=None    ):
		super().__init__(snake_case__    )

		lowerCAmelCase					:    List[str]      							= speaker_embeddings



	@classmethod
	def 				lowercase     (					cls ,					snake_case__ ,					snake_case__="speaker_embeddings_path.json" ,					**snake_case__    ):

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

		lowerCAmelCase					:    List[str]      							= AutoTokenizer.from_pretrained(snake_case__ ,					**snake_case__    )

		return cls(tokenizer=snake_case__ ,					speaker_embeddings=snake_case__    )



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__="speaker_embeddings_path.json" ,					snake_case__="speaker_embeddings" ,					snake_case__ = False ,					**snake_case__ ,					):
		if self.speaker_embeddings is not None:
			os.makedirs(os.path.join(snake_case__ ,					snake_case__ ,					'v2'    ) ,					exist_ok=snake_case__    )

			lowerCAmelCase					:    str      							= {}

			lowerCAmelCase					:    Optional[Any]      							= save_directory

			for prompt_key in self.speaker_embeddings:
				if prompt_key != "repo_or_path":
					lowerCAmelCase					:    Union[str, Any]      							= self._load_voice_preset(snake_case__    )

					lowerCAmelCase					:    Dict      							= {}
					for key in self.speaker_embeddings[prompt_key]:
						np.save(
						    os.path.join(
						        embeddings_dict['repo_or_path'] ,					snake_case__ ,					f"{prompt_key}_{key}"    ) ,					voice_preset[key] ,					allow_pickle=snake_case__ ,					)
						lowerCAmelCase					:    Optional[Any]      							= os.path.join(snake_case__ ,					f"{prompt_key}_{key}.npy"    )

					lowerCAmelCase					:    Optional[int]      							= tmp_dict

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

		super().save_pretrained(snake_case__ ,					snake_case__ ,					**snake_case__    )



	def 				lowercase     (					self ,					snake_case__ = None ,					**snake_case__    ):
		lowerCAmelCase					:    Optional[Any]      							= self.speaker_embeddings[voice_preset]

		lowerCAmelCase					:    Union[str, 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}]."    )

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

			lowerCAmelCase					:    Any      							= np.load(snake_case__    )

		return voice_preset_dict



	def 				lowercase     (					self ,					snake_case__ = None    ):
		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 ,					snake_case__=None ,					snake_case__=None ,					snake_case__="pt" ,					snake_case__=256 ,					snake_case__=False ,					snake_case__=True ,					snake_case__=False ,					**snake_case__ ,					):
		if voice_preset is not None and not isinstance(snake_case__ ,					snake_case__    ):
			if (
			    isinstance(snake_case__ ,					snake_case__    )
			    and self.speaker_embeddings is not None
			    and voice_preset in self.speaker_embeddings
			):
				lowerCAmelCase					:    Any      							= self._load_voice_preset(snake_case__    )

			else:
				if isinstance(snake_case__ ,					snake_case__    ) and not voice_preset.endswith('.npz'    ):
					lowerCAmelCase					:    List[str]      							= voice_preset + '.npz'

				lowerCAmelCase					:    Tuple      							= np.load(snake_case__    )

		if voice_preset is not None:
			self._validate_voice_preset_dict(snake_case__ ,					**snake_case__    )
			lowerCAmelCase					:    Union[str, Any]      							= BatchFeature(data=snake_case__ ,					tensor_type=snake_case__    )

		lowerCAmelCase					:    List[Any]      							= self.tokenizer(
		    snake_case__ ,					return_tensors=snake_case__ ,					padding='max_length' ,					max_length=snake_case__ ,					return_attention_mask=snake_case__ ,					return_token_type_ids=snake_case__ ,					add_special_tokens=snake_case__ ,					**snake_case__ ,					)

		if voice_preset is not None:
			lowerCAmelCase					:    Tuple      							= voice_preset

		return encoded_text




'''simple docstring'''




def 			__UpperCamelCase    (    _A    :				int				)    ->      bool:




	"""simple docstring"""





	return number & 1 == 0


if __name__ == "__main__":
				import doctest

				doctest.testmod()
