
"""simple docstring"""





import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class     _UpperCAmelCase(    lowerCamelCase	):
		lowercase__						=      'Speech2TextFeatureExtractor'
		lowercase__						=      'Speech2TextTokenizer'



		def __init__(					self				,		__a				,		__a)       ->       Optional[Any]:






							'''simple docstring'''
							super().__init__(__a				,		__a)
							_UpperCamelCase							 =  self.feature_extractor
							_UpperCamelCase							 =  False



		def __call__(					self				,		*__a				,		**__a)       ->       str:






							'''simple docstring'''
							# For backward compatibility
							if self._in_target_context_manager:
												return self.current_processor(*__a				,		**__a)

							if "raw_speech" in kwargs:
												warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.''')
												_UpperCamelCase							 =  kwargs.pop('''raw_speech''')
							else:
												_UpperCamelCase							 =  kwargs.pop('''audio'''				,		__a)
							_UpperCamelCase							 =  kwargs.pop('''sampling_rate'''				,		__a)
							_UpperCamelCase							 =  kwargs.pop('''text'''				,		__a)
							if len(__a) > 0:
												_UpperCamelCase							 =  args[0]
												_UpperCamelCase							 =  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:
												_UpperCamelCase							 =  self.feature_extractor(__a				,		*__a				,		sampling_rate=__a				,		**__a)
							if text is not None:
												_UpperCamelCase							 =  self.tokenizer(__a				,		**__a)

							if text is None:
												return inputs
							elif audio is None:
												return encodings
							else:
												_UpperCamelCase							 =  encodings['''input_ids''']
												return inputs



		def 						UpperCAmelCase							(					self				,		*__a				,		**__a)       ->       str:






							'''simple docstring'''
							return self.tokenizer.batch_decode(*__a				,		**__a)



		def 						UpperCAmelCase							(					self				,		*__a				,		**__a)       ->       Optional[Any]:






							'''simple docstring'''
							return self.tokenizer.decode(*__a				,		**__a)




		@contextmanager
		def 						UpperCAmelCase							(					self)       ->       Union[str, Any]:






							'''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.''')
							_UpperCamelCase							 =  True
							_UpperCamelCase							 =  self.tokenizer
							yield
							_UpperCamelCase							 =  self.feature_extractor
							_UpperCamelCase							 =  False








"""simple docstring"""





def       lowerCamelCase__		(			__snake_case = 10_00	)							->		int:


					"""simple docstring"""





					_UpperCamelCase							 =  2**power
					_UpperCamelCase							 =  str(__snake_case	)
					_UpperCamelCase							 =  list(__snake_case	)
					_UpperCamelCase							 =  0

					for i in list_num:
										sum_of_num += int(__snake_case	)

					return sum_of_num


if __name__ == "__main__":
				_a														=		int(input("""Enter the power of 2: """).strip())
				print("""2 ^ """, power, """ = """, 2**power)
				_a														=		solution(power)
				print("""Sum of the digits is: """, result)






