






'''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


UpperCamelCase    :      Dict  			=		logging.get_logger(__name__)




class 		UpperCamelCase__    (a			):




		'''simple docstring'''





		_UpperCamelCase				  =					'AutoTokenizer'
		_UpperCamelCase				  =					['tokenizer']

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






		def __init__(      self   ,_lowerCAmelCase   ,_lowerCAmelCase=None       ):
								super().__init__(__A       )

								lowerCamelCase__         =  speaker_embeddings






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

								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\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__         =  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   ,_lowerCAmelCase   ,_lowerCAmelCase="speaker_embeddings_path.json"   ,_lowerCAmelCase="speaker_embeddings"   ,_lowerCAmelCase = False   ,**_lowerCAmelCase   ,):
								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   ,_lowerCAmelCase = None   ,**_lowerCAmelCase       ):
								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\n                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n                    embeddings.'''       )

														lowerCamelCase__         =  np.load(__A       )

								return voice_preset_dict






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase = 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   ,_lowerCAmelCase=None   ,_lowerCAmelCase=None   ,_lowerCAmelCase="pt"   ,_lowerCAmelCase=2_56   ,_lowerCAmelCase=False   ,_lowerCAmelCase=True   ,_lowerCAmelCase=False   ,**_lowerCAmelCase   ,):
								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









'''simple docstring'''







UpperCamelCase    :      Tuple  			=		[sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_00_00)]
def 						A__			(				__lowerCAmelCase		:			int						):
						lowerCamelCase__         =  0
						while number:
												# Increased Speed Slightly by checking every 5 digits together.
												sum_of_digits_squared += DIGITS_SQUARED[number % 10_0000]
												number //= 10_0000

						return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
UpperCamelCase    :      list[bool | None]  			=		[None] * 10_00_00_00
UpperCamelCase    :      Tuple  			=		True
UpperCamelCase    :      Optional[int]  			=		False
def 						A__			(				__lowerCAmelCase		:			int						):

						if CHAINS[number - 1] is not None:
												return CHAINS[number - 1]  # type: ignore

						lowerCamelCase__         =  chain(next_number(__lowerCAmelCase						)						)
						lowerCamelCase__         =  number_chain

						while number < 1000_0000:
												lowerCamelCase__         =  number_chain
												number *= 10

						return number_chain
def 						A__			(				__lowerCAmelCase		:			int = 1000_0000						):
						for i in range(1   ,    __lowerCAmelCase						):
												if CHAINS[i] is None:
																		chain(i + 1						)

						return CHAINS[:number].count(__lowerCAmelCase						)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							print(F'{solution() = }')

