



"""simple docstring"""







from ...processing_utils import ProcessorMixin






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					Tuple							  =     "SpeechT5FeatureExtractor"
			A__	:					List[Any]							  =     "SpeechT5Tokenizer"


			def __init__(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Optional[Any]:
				super().__init__(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)


			def __call__(      self						,	*SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)      ->	str:
				A__											=					kwargs.pop("audio"						,	SCREAMING_SNAKE_CASE__		)
				A__											=					kwargs.pop("text"						,	SCREAMING_SNAKE_CASE__		)
				A__											=					kwargs.pop("text_target"						,	SCREAMING_SNAKE_CASE__		)
				A__											=					kwargs.pop("audio_target"						,	SCREAMING_SNAKE_CASE__		)
				A__											=					kwargs.pop("sampling_rate"						,	SCREAMING_SNAKE_CASE__		)

				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:
					A__											=					self.feature_extractor(SCREAMING_SNAKE_CASE__						,	*SCREAMING_SNAKE_CASE__						,	sampling_rate=SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)
				elif text is not None:
					A__											=					self.tokenizer(SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)
				else:
					A__											=					None

				if audio_target is not None:
					A__											=					self.feature_extractor(audio_target=SCREAMING_SNAKE_CASE__						,	*SCREAMING_SNAKE_CASE__						,	sampling_rate=SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)
					A__											=					targets["input_values"]
				elif text_target is not None:
					A__											=					self.tokenizer(SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)
					A__											=					targets["input_ids"]
				else:
					A__											=					None

				if inputs is None:
					return targets

				if targets is not None:
					A__											=					labels

					A__											=					targets.get("attention_mask"		)
					if decoder_attention_mask is not None:
						A__											=					decoder_attention_mask

				return inputs


			def   snake_case__	(      self						,	*SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)      ->	int:
				A__											=					kwargs.pop("input_values"						,	SCREAMING_SNAKE_CASE__		)
				A__											=					kwargs.pop("input_ids"						,	SCREAMING_SNAKE_CASE__		)
				A__											=					kwargs.pop("labels"						,	SCREAMING_SNAKE_CASE__		)

				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:
					A__											=					self.feature_extractor.pad(SCREAMING_SNAKE_CASE__						,	*SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)
				elif input_ids is not None:
					A__											=					self.tokenizer.pad(SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)
				else:
					A__											=					None

				if labels is not None:
					if "input_ids" in labels or (isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		) and "input_ids" in labels[0]):
						A__											=					self.tokenizer.pad(SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)
						A__											=					targets["input_ids"]
					else:
						A__											=					self.feature_extractor.feature_size
						A__											=					self.feature_extractor.num_mel_bins
						A__											=					self.feature_extractor.pad(SCREAMING_SNAKE_CASE__						,	*SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)
						A__											=					feature_size_hack
						A__											=					targets["input_values"]
				else:
					A__											=					None

				if inputs is None:
					return targets

				if targets is not None:
					A__											=					labels

					A__											=					targets.get("attention_mask"		)
					if decoder_attention_mask is not None:
						A__											=					decoder_attention_mask

				return inputs


			def   snake_case__	(      self						,	*SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)      ->	Dict:
				return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)







			def   snake_case__	(      self						,	*SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)      ->	int:
				return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)











def 						A_							(	_lowerCAmelCase	)		->					bool:
 return str(_lowerCAmelCase	) == str(_lowerCAmelCase	)[::-1]



def 						A_							(	_lowerCAmelCase	)		->					int:
 return int(_lowerCAmelCase	) + int(str(_lowerCAmelCase	)[::-1]	)



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


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