
from ...processing_utils import ProcessorMixin

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Dict					  =  "SpeechT5FeatureExtractor"
							__UpperCAmelCase   :							List[str]					  =  "SpeechT5Tokenizer"

							def __init__(							self	:  List[Any]   ,     lowerCamelCase	:  int   ,     lowerCamelCase	:  Optional[Any]    )     ->   Dict:
														super().__init__(lowerCamelCase   ,     lowerCamelCase    )

							def __call__(							self	:  List[Any]   ,     *lowerCamelCase	:  Dict   ,     **lowerCamelCase	:  Union[str, Any]    )     ->   Tuple:
														__snake_case		:      Optional[Any]										=       kwargs.pop("audio"   ,     lowerCamelCase    )
														__snake_case		:      Optional[Any]										=       kwargs.pop("text"   ,     lowerCamelCase    )
														__snake_case		:      Tuple										=       kwargs.pop("text_target"   ,     lowerCamelCase    )
														__snake_case		:      List[Any]										=       kwargs.pop("audio_target"   ,     lowerCamelCase    )
														__snake_case		:      List[Any]										=       kwargs.pop("sampling_rate"   ,     lowerCamelCase    )

														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:
																					__snake_case		:      str										=       self.feature_extractor(lowerCamelCase   ,     *lowerCamelCase   ,     sampling_rate=lowerCamelCase   ,     **lowerCamelCase    )
														elif text is not None:
																					__snake_case		:      List[Any]										=       self.tokenizer(lowerCamelCase   ,     **lowerCamelCase    )
														else:
																					__snake_case		:      List[Any]										=       None

														if audio_target is not None:
																					__snake_case		:      Tuple										=       self.feature_extractor(audio_target=lowerCamelCase   ,     *lowerCamelCase   ,     sampling_rate=lowerCamelCase   ,     **lowerCamelCase    )
																					__snake_case		:      List[str]										=       targets["input_values"]
														elif text_target is not None:
																					__snake_case		:      Union[str, Any]										=       self.tokenizer(lowerCamelCase   ,     **lowerCamelCase    )
																					__snake_case		:      Dict										=       targets["input_ids"]
														else:
																					__snake_case		:      int										=       None

														if inputs is None:
																					return targets

														if targets is not None:
																					__snake_case		:      List[str]										=       labels

																					__snake_case		:      int										=       targets.get("attention_mask"    )
																					if decoder_attention_mask is not None:
																												__snake_case		:      Dict										=       decoder_attention_mask

														return inputs

							def    __snake_case (							self	:  Tuple   ,     *lowerCamelCase	:  Tuple   ,     **lowerCamelCase	:  str    )     ->   Tuple:
														__snake_case		:      str										=       kwargs.pop("input_values"   ,     lowerCamelCase    )
														__snake_case		:      Union[str, Any]										=       kwargs.pop("input_ids"   ,     lowerCamelCase    )
														__snake_case		:      Dict										=       kwargs.pop("labels"   ,     lowerCamelCase    )

														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:
																					__snake_case		:      str										=       self.feature_extractor.pad(lowerCamelCase   ,     *lowerCamelCase   ,     **lowerCamelCase    )
														elif input_ids is not None:
																					__snake_case		:      List[str]										=       self.tokenizer.pad(lowerCamelCase   ,     **lowerCamelCase    )
														else:
																					__snake_case		:      Tuple										=       None

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

														if inputs is None:
																					return targets

														if targets is not None:
																					__snake_case		:      Union[str, Any]										=       labels

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

														return inputs

							def    __snake_case (							self	:  List[str]   ,     *lowerCamelCase	:  Any   ,     **lowerCamelCase	:  Union[str, Any]    )     ->   Optional[int]:
														return self.tokenizer.batch_decode(*lowerCamelCase   ,     **lowerCamelCase    )






							def    __snake_case (							self	:  Any   ,     *lowerCamelCase	:  List[str]   ,     **lowerCamelCase	:  Tuple    )     ->   Tuple:
														return self.tokenizer.decode(*lowerCamelCase   ,     **lowerCamelCase    )





def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							return x if y == 0 else greatest_common_divisor(__lowerCamelCase							,     x % y     )







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							return (x * y) // greatest_common_divisor(__lowerCamelCase							,     __lowerCamelCase     )







def     lowerCAmelCase_					( __lowerCamelCase = 2_0     ):
							__snake_case		:      Optional[Any]										=       1
							for i in range(1							,     n + 1     ):
														__snake_case		:      Any										=       lcm(__lowerCamelCase							,     __lowerCamelCase     )
							return g


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

