



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


lowercase_	       =			logging.get_logger(__name__)






class 						__a (     __snake_case				):
	SCREAMING_SNAKE_CASE       =       'AutoTokenizer'
	SCREAMING_SNAKE_CASE       =       ['tokenizer']

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



	def __init__(						self							:    List[Any]				,	snake_case_							:    Dict				,	snake_case_							:    Optional[int]=None)->							Dict:
						super().__init__(A_)

						__lowerCAmelCase	   =speaker_embeddings



	@classmethod
	def 		UpperCamelCase							(						cls							:    int				,	snake_case_							:    List[Any]				,	snake_case_							:    List[Any]="speaker_embeddings_path.json"				,	**snake_case_							:    str)->							Any:




						if speaker_embeddings_dict_path is not None:
											__lowerCAmelCase	   =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
                    , 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`.""")
																__lowerCAmelCase	   =None
											else:
																with open(A_) as speaker_embeddings_json:
																					__lowerCAmelCase	   =json.load(A_)
						else:
											__lowerCAmelCase	   =None

						__lowerCAmelCase	   =AutoTokenizer.from_pretrained(A_				,	**A_)

						return cls(tokenizer=A_				,	speaker_embeddings=A_)



	def 		UpperCamelCase							(						self							:    Union[str, Any]				,	snake_case_							:    Dict				,	snake_case_							:    List[str]="speaker_embeddings_path.json"				,	snake_case_							:    Dict="speaker_embeddings"				,	snake_case_							:    Optional[Any] = False				,	**snake_case_							:    Optional[int]				,	)->							Optional[Any]:




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

											__lowerCAmelCase	   ={}

											__lowerCAmelCase	   =save_directory

											for prompt_key in self.speaker_embeddings:
																if prompt_key != "repo_or_path":
																					__lowerCAmelCase	   =self._load_voice_preset(A_)

																					__lowerCAmelCase	   ={}
																					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_				,	)
																										__lowerCAmelCase	   =os.path.join(A_				,	F"""{prompt_key}_{key}.npy""")

																					__lowerCAmelCase	   =tmp_dict

											with open(os.path.join(A_				,	A_)				,	"""w""") as fp:
																json.dump(A_				,	A_)

						super().save_pretrained(A_				,	A_				,	**A_)



	def 		UpperCamelCase							(						self							:    Any				,	snake_case_							:    Optional[int] = None				,	**snake_case_							:    List[str])->							List[Any]:
						__lowerCAmelCase	   =self.speaker_embeddings[voice_preset]

						__lowerCAmelCase	   ={}
						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	   =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
                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
                    embeddings.""")

											__lowerCAmelCase	   =np.load(A_)

						return voice_preset_dict



	def 		UpperCamelCase							(						self							:    List[Any]				,	snake_case_							:    Optional[int] = None)->							Tuple:




						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							:    Optional[Any]				,	snake_case_							:    Optional[int]=None				,	snake_case_							:    List[Any]=None				,	snake_case_							:    int="pt"				,	snake_case_							:    Any=2_56				,	snake_case_							:    int=False				,	snake_case_							:    List[str]=True				,	snake_case_							:    Dict=False				,	**snake_case_							:    List[Any]				,	)->							Optional[int]:




						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
											):
																__lowerCAmelCase	   =self._load_voice_preset(A_)

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

																__lowerCAmelCase	   =np.load(A_)

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

						__lowerCAmelCase	   =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:
											__lowerCAmelCase	   =voice_preset

						return encoded_text









def 					__lowerCAmelCase		(  )  ->						Tuple:
					__lowerCAmelCase	   =[]
					__lowerCAmelCase	   =1

					while len(__lowerCamelCase	) < 1E6:
										constant.append(str(__lowerCamelCase	)	)
										i += 1

					__lowerCAmelCase	   ="""""".join(__lowerCamelCase	)

					return (
					    int(constant[0]	)
					    * int(constant[9]	)
					    * int(constant[99]	)
					    * int(constant[999]	)
					    * int(constant[9999]	)
					    * int(constant[99999]	)
					    * int(constant[999999]	)
					)


if __name__ == "__main__":
				print(solution())




