


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






def 					UpperCamelCase   (      __magic_name__	:				int	)  ->    int:
		"""simple docstring"""




		lowercase__	     =						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
A			:						list[bool | None]        =  [None] * 1_0_0_0_0_0_0_0
A			:						List[str]        =  True
A			:						Union[str, Any]        =  False






def 					UpperCamelCase   (      __magic_name__	:				int	)  ->    bool:
		"""simple docstring"""




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

		lowercase__	     =						chain(next_number(__magic_name__	)	)
		lowercase__	     =						number_chain

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

		return number_chain






def 					UpperCamelCase   (      __magic_name__	:				int = 1000_0000	)  ->    int:
		"""simple docstring"""




		for i in range(1       ,       __magic_name__	):
				if CHAINS[i] is None:
						chain(i + 1	)

		return CHAINS[:number].count(__magic_name__	)


if __name__ == "__main__":
	import doctest

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








'''simple docstring'''




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	'''Wav2Vec2FeatureExtractor'''
					SCREAMING_SNAKE_CASE__				=	'''AutoTokenizer'''


					def __init__(						self				:			Optional[int]      ,						lowerCamelCase_				:			str      ,						lowerCamelCase_				:			Dict						):

							'''simple docstring'''



							super().__init__(lowerCamelCase_      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				int		=			self.feature_extractor
							SCREAMING_SNAKE_CASE :				List[Any]		=			False


					@classmethod
					def lowerCamelCase_			(						cls				:			int      ,						lowerCamelCase_				:			List[str]      ,						**lowerCamelCase_				:			Any						):

							'''simple docstring'''



							try:
									return super().from_pretrained(lowerCamelCase_      ,						**lowerCamelCase_						)
							except OSError:
									warnings.warn(
									    f'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
									    """ include a `tokenizer_class` attribute is deprecated and will be """
									    """removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"""
									    """ attribute to either your `config.json` or `tokenizer_config.json` """
									    """file to suppress this warning: """      ,						lowerCamelCase_      ,						)

									SCREAMING_SNAKE_CASE :				Optional[Any]		=			WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase_      ,						**lowerCamelCase_						)
									SCREAMING_SNAKE_CASE :				Dict		=			WavaVecaCTCTokenizer.from_pretrained(lowerCamelCase_      ,						**lowerCamelCase_						)

									return cls(feature_extractor=lowerCamelCase_      ,						tokenizer=lowerCamelCase_						)


					def __call__(						self				:			List[Any]      ,						*lowerCamelCase_				:			str      ,						**lowerCamelCase_				:			int						):

							'''simple docstring'''



							if self._in_target_context_manager:
									return self.current_processor(*lowerCamelCase_      ,						**lowerCamelCase_						)

							if "raw_speech" in kwargs:
									warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""						)
									SCREAMING_SNAKE_CASE :				Dict		=			kwargs.pop("""raw_speech"""						)
							else:
									SCREAMING_SNAKE_CASE :				Any		=			kwargs.pop("""audio"""      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Tuple		=			kwargs.pop("""sampling_rate"""      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				int		=			kwargs.pop("""text"""      ,						lowerCamelCase_						)
							if len(lowerCamelCase_						) > 0:
									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			args[0]
									SCREAMING_SNAKE_CASE :				Dict		=			args[1:]

							if audio is None and text is None:
									raise ValueError("""You need to specify either an `audio` or `text` input to process."""						)

							if audio is not None:
									SCREAMING_SNAKE_CASE :				str		=			self.feature_extractor(lowerCamelCase_      ,						*lowerCamelCase_      ,						sampling_rate=lowerCamelCase_      ,						**lowerCamelCase_						)
							if text is not None:
									SCREAMING_SNAKE_CASE :				List[Any]		=			self.tokenizer(lowerCamelCase_      ,						**lowerCamelCase_						)

							if text is None:
									return inputs
							elif audio is None:
									return encodings
							else:
									SCREAMING_SNAKE_CASE :				List[str]		=			encodings["""input_ids"""]
									return inputs


					def lowerCamelCase_			(						self				:			Optional[Any]      ,						*lowerCamelCase_				:			Tuple      ,						**lowerCamelCase_				:			Dict						):

							'''simple docstring'''



							if self._in_target_context_manager:
									return self.current_processor.pad(*lowerCamelCase_      ,						**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				str		=			kwargs.pop("""input_features"""      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Dict		=			kwargs.pop("""labels"""      ,						lowerCamelCase_						)
							if len(lowerCamelCase_						) > 0:
									SCREAMING_SNAKE_CASE :				Optional[int]		=			args[0]
									SCREAMING_SNAKE_CASE :				Optional[Any]		=			args[1:]

							if input_features is not None:
									SCREAMING_SNAKE_CASE :				Any		=			self.feature_extractor.pad(lowerCamelCase_      ,						*lowerCamelCase_      ,						**lowerCamelCase_						)
							if labels is not None:
									SCREAMING_SNAKE_CASE :				int		=			self.tokenizer.pad(lowerCamelCase_      ,						**lowerCamelCase_						)

							if labels is None:
									return input_features
							elif input_features is None:
									return labels
							else:
									SCREAMING_SNAKE_CASE :				Dict		=			labels["""input_ids"""]
									return input_features


					def lowerCamelCase_			(						self				:			Optional[int]      ,						*lowerCamelCase_				:			List[str]      ,						**lowerCamelCase_				:			Optional[Any]						):

							'''simple docstring'''



							return self.tokenizer.batch_decode(*lowerCamelCase_      ,						**lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			List[str]      ,						*lowerCamelCase_				:			Optional[int]      ,						**lowerCamelCase_				:			Optional[int]						):

							'''simple docstring'''



							return self.tokenizer.decode(*lowerCamelCase_      ,						**lowerCamelCase_						)



					@contextmanager
					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							warnings.warn(
							    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
							    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
							    """your audio inputs, or in a separate call."""						)
							SCREAMING_SNAKE_CASE :				int		=			True
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.tokenizer
							yield
							SCREAMING_SNAKE_CASE :				int		=			self.feature_extractor
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			False



