



'''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


a     :    str      						=					logging.get_logger(__name__)

class     UpperCamelCase_				(							__magic_name__    ):
	lowercase    =				'AutoTokenizer'
	lowercase    =				['tokenizer']

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





	def __init__(						self	,	A	,	A=None					)    ->    Tuple:
								super().__init__(A					)

								UpperCAmelCase  :	int							  =	speaker_embeddings





	@classmethod
	def 				_lowercase(						cls	,	A	,	A="speaker_embeddings_path.json"	,	**A					)    ->    int:

								if speaker_embeddings_dict_path is not None:
															UpperCAmelCase  :	List[str]							  =	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`.'''					)
																						UpperCAmelCase  :	int							  =	None
															else:
																						with open(A					) as speaker_embeddings_json:
																													UpperCAmelCase  :	Dict							  =	json.load(A					)
								else:
															UpperCAmelCase  :	Optional[Any]							  =	None

								UpperCAmelCase  :	Optional[int]							  =	AutoTokenizer.from_pretrained(A	,	**A					)

								return cls(tokenizer=A	,	speaker_embeddings=A					)





	def 				_lowercase(						self	,	A	,	A="speaker_embeddings_path.json"	,	A="speaker_embeddings"	,	A = False	,	**A	,	)    ->    Union[str, Any]:
								if self.speaker_embeddings is not None:
															os.makedirs(os.path.join(A	,	A	,	"""v2"""					)	,	exist_ok=A					)

															UpperCAmelCase  :	Optional[Any]							  =	{}

															UpperCAmelCase  :	List[str]							  =	save_directory

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

																													UpperCAmelCase  :	Any							  =	{}
																													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  :	Tuple							  =	os.path.join(A	,	f'''{prompt_key}_{key}.npy'''					)

																													UpperCAmelCase  :	List[str]							  =	tmp_dict

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

								super().save_pretrained(A	,	A	,	**A					)





	def 				_lowercase(						self	,	A = None	,	**A					)    ->    Optional[int]:
								UpperCAmelCase  :	List[str]							  =	self.speaker_embeddings[voice_preset]

								UpperCAmelCase  :	str							  =	{}
								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  :	Optional[Any]							  =	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.'''					)

															UpperCAmelCase  :	str							  =	np.load(A					)

								return voice_preset_dict





	def 				_lowercase(						self	,	A = None					)    ->    Optional[Any]:
								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	,	A=None	,	A=None	,	A="pt"	,	A=256	,	A=False	,	A=True	,	A=False	,	**A	,	)    ->    str:
								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  :	Union[str, Any]							  =	self._load_voice_preset(A					)

															else:
																						if isinstance(A	,	A					) and not voice_preset.endswith(""".npz"""					):
																													UpperCAmelCase  :	Optional[Any]							  =	voice_preset + """.npz"""

																						UpperCAmelCase  :	Union[str, Any]							  =	np.load(A					)

								if voice_preset is not None:
															self._validate_voice_preset_dict(A	,	**A					)
															UpperCAmelCase  :	List[Any]							  =	BatchFeature(data=A	,	tensor_type=A					)

								UpperCAmelCase  :	Any							  =	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  :	Optional[Any]							  =	voice_preset

								return encoded_text









'''simple docstring'''




a     :    Tuple      						=					[sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]



def    __lowerCamelCase						(      _lowercase	)   ->    int:
							UpperCAmelCase  :	str							  =	0
							while number:
														# Increased Speed Slightly by checking every 5 digits together.
														sum_of_digits_squared += DIGITS_SQUARED[number % 1_0_0_0_0_0]
														number //= 1_0_0_0_0_0

							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
a     :    list[bool | None]      						=					[None] * 1_0_0_0_0_0_0_0
a     :    Optional[Any]      						=					True
a     :    List[Any]      						=					False



def    __lowerCamelCase						(      _lowercase	)   ->    bool:

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

							UpperCAmelCase  :	List[str]							  =	chain(next_number(_lowercase	)	)
							UpperCAmelCase  :	Tuple							  =	number_chain

							while number < 1_0_0_0_0_0_0_0:
														UpperCAmelCase  :	List[str]							  =	number_chain
														number *= 1_0

							return number_chain



def    __lowerCamelCase						(      _lowercase = 1_0_0_0_0_0_0_0	)   ->    int:
							for i in range(1			,       _lowercase	):
														if CHAINS[i] is None:
																					chain(i + 1	)

							return CHAINS[:number].count(_lowercase	)


if __name__ == "__main__":
							import doctest

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

