



'''simple docstring'''

from ...processing_utils import ProcessorMixin



class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						"""SpeechT5FeatureExtractor"""
					A_						    =						"""SpeechT5Tokenizer"""




					def __init__(  self				:	Optional[int]     ,  UpperCamelCase_				:	Any     ,  UpperCamelCase_				:	List[str]					)  ->       List[Any]:



						'''simple docstring'''





						super().__init__(UpperCamelCase_     ,  UpperCamelCase_					)




					def __call__(  self				:	Union[str, Any]     ,  *UpperCamelCase_				:	Tuple     ,  **UpperCamelCase_				:	Any					)  ->       str:



						'''simple docstring'''





						_lowercase					:       str       =		kwargs.pop('audio'     ,  UpperCamelCase_					)
						_lowercase					:       Any       =		kwargs.pop('text'     ,  UpperCamelCase_					)
						_lowercase					:       List[str]       =		kwargs.pop('text_target'     ,  UpperCamelCase_					)
						_lowercase					:       int       =		kwargs.pop('audio_target'     ,  UpperCamelCase_					)
						_lowercase					:       Any       =		kwargs.pop('sampling_rate'     ,  UpperCamelCase_					)

						if audio is not None and text is not None:
							raise ValueError(
							    'Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?'					)
						if audio_target is not None and text_target is not None:
							raise ValueError(
							    'Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?'					)
						if audio is None and audio_target is None and text is None and text_target is None:
							raise ValueError(
							    'You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.'					)

						if audio is not None:
							_lowercase					:       Union[str, Any]       =		self.feature_extractor(UpperCamelCase_     ,  *UpperCamelCase_     ,  sampling_rate=UpperCamelCase_     ,  **UpperCamelCase_					)
						elif text is not None:
							_lowercase					:       int       =		self.tokenizer(UpperCamelCase_     ,  **UpperCamelCase_					)
						else:
							_lowercase					:       Optional[int]       =		None

						if audio_target is not None:
							_lowercase					:       Tuple       =		self.feature_extractor(audio_target=UpperCamelCase_     ,  *UpperCamelCase_     ,  sampling_rate=UpperCamelCase_     ,  **UpperCamelCase_					)
							_lowercase					:       str       =		targets['input_values']
						elif text_target is not None:
							_lowercase					:       List[str]       =		self.tokenizer(UpperCamelCase_     ,  **UpperCamelCase_					)
							_lowercase					:       Tuple       =		targets['input_ids']
						else:
							_lowercase					:       Optional[Any]       =		None

						if inputs is None:
							return targets

						if targets is not None:
							_lowercase					:       Optional[Any]       =		labels

							_lowercase					:       List[Any]       =		targets.get('attention_mask'					)
							if decoder_attention_mask is not None:
								_lowercase					:       Union[str, Any]       =		decoder_attention_mask

						return inputs




					def 						__UpperCAmelCase     (  self				:	List[str]     ,  *UpperCamelCase_				:	str     ,  **UpperCamelCase_				:	Any					)  ->       List[Any]:



						'''simple docstring'''





						_lowercase					:       Optional[Any]       =		kwargs.pop('input_values'     ,  UpperCamelCase_					)
						_lowercase					:       int       =		kwargs.pop('input_ids'     ,  UpperCamelCase_					)
						_lowercase					:       Dict       =		kwargs.pop('labels'     ,  UpperCamelCase_					)

						if input_values is not None and input_ids is not None:
							raise ValueError('Cannot process both `input_values` and `input_ids` inputs.'					)
						if input_values is None and input_ids is None and labels is None:
							raise ValueError(
							    'You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.'					)

						if input_values is not None:
							_lowercase					:       int       =		self.feature_extractor.pad(UpperCamelCase_     ,  *UpperCamelCase_     ,  **UpperCamelCase_					)
						elif input_ids is not None:
							_lowercase					:       int       =		self.tokenizer.pad(UpperCamelCase_     ,  **UpperCamelCase_					)
						else:
							_lowercase					:       Union[str, Any]       =		None

						if labels is not None:
							if "input_ids" in labels or (isinstance(UpperCamelCase_     ,  UpperCamelCase_					) and "input_ids" in labels[0]):
								_lowercase					:       Any       =		self.tokenizer.pad(UpperCamelCase_     ,  **UpperCamelCase_					)
								_lowercase					:       Dict       =		targets['input_ids']
							else:
								_lowercase					:       Dict       =		self.feature_extractor.feature_size
								_lowercase					:       Union[str, Any]       =		self.feature_extractor.num_mel_bins
								_lowercase					:       int       =		self.feature_extractor.pad(UpperCamelCase_     ,  *UpperCamelCase_     ,  **UpperCamelCase_					)
								_lowercase					:       Any       =		feature_size_hack
								_lowercase					:       List[str]       =		targets['input_values']
						else:
							_lowercase					:       Dict       =		None

						if inputs is None:
							return targets

						if targets is not None:
							_lowercase					:       Optional[Any]       =		labels

							_lowercase					:       Union[str, Any]       =		targets.get('attention_mask'					)
							if decoder_attention_mask is not None:
								_lowercase					:       Tuple       =		decoder_attention_mask

						return inputs




					def 						__UpperCAmelCase     (  self				:	List[str]     ,  *UpperCamelCase_				:	List[str]     ,  **UpperCamelCase_				:	Union[str, Any]					)  ->       Optional[Any]:



						'''simple docstring'''





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




					def 						__UpperCAmelCase     (  self				:	int     ,  *UpperCamelCase_				:	Tuple     ,  **UpperCamelCase_				:	int					)  ->       str:



						'''simple docstring'''





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





'''simple docstring'''

def 						__UpperCamelCase				(							_lowercase					) ->			bool:
	return str(_lowercase					) == str(_lowercase					)[::-1]







def 						__UpperCamelCase				(							_lowercase					) ->			int:
	return int(_lowercase					) + int(str(_lowercase					)[::-1]					)







def 						__UpperCamelCase				(							_lowercase = 1_0000					) ->			int:
	_lowercase					:       List[str]       =		[]
	for num in range(1,				_lowercase					):
		_lowercase					:       Tuple       =		0
		_lowercase					:       Tuple       =		num
		while iterations < 50:
			_lowercase					:       Union[str, Any]       =		sum_reverse(_lowercase					)
			iterations += 1
			if is_palindrome(_lowercase					):
				break
		else:
			lychrel_nums.append(_lowercase					)
	return len(_lowercase					)


if __name__ == "__main__":
		print(F'''{solution() = }''')
