


"""simple docstring"""







from ...processing_utils import ProcessorMixin




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	snake_case__       :						List[Any]					      =    "SpeechT5FeatureExtractor"
	snake_case__       :						int					      =    "SpeechT5Tokenizer"




	def __init__(				self    :       Union[str, Any]    ,    UpperCAmelCase__    :       List[str]    ,    UpperCAmelCase__    :       Dict     ) ->			Optional[Any]:
						super().__init__(UpperCAmelCase__    ,    UpperCAmelCase__     )




	def __call__(				self    :       Tuple    ,    *UpperCAmelCase__    :       str    ,    **UpperCAmelCase__    :       str     ) ->			Union[str, Any]:
						__SCREAMING_SNAKE_CASE		     =       kwargs.pop("audio"    ,    UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       kwargs.pop("text"    ,    UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       kwargs.pop("text_target"    ,    UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       kwargs.pop("audio_target"    ,    UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       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:
											__SCREAMING_SNAKE_CASE		     =       self.feature_extractor(UpperCAmelCase__    ,    *UpperCAmelCase__    ,    sampling_rate=UpperCAmelCase__    ,    **UpperCAmelCase__     )
						elif text is not None:
											__SCREAMING_SNAKE_CASE		     =       self.tokenizer(UpperCAmelCase__    ,    **UpperCAmelCase__     )
						else:
											__SCREAMING_SNAKE_CASE		     =       None

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

						if inputs is None:
											return targets

						if targets is not None:
											__SCREAMING_SNAKE_CASE		     =       labels

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

						return inputs




	def 						UpperCAmelCase_   (				self    :       List[Any]    ,    *UpperCAmelCase__    :       List[str]    ,    **UpperCAmelCase__    :       str     ) ->			str:
						__SCREAMING_SNAKE_CASE		     =       kwargs.pop("input_values"    ,    UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       kwargs.pop("input_ids"    ,    UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       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:
											__SCREAMING_SNAKE_CASE		     =       self.feature_extractor.pad(UpperCAmelCase__    ,    *UpperCAmelCase__    ,    **UpperCAmelCase__     )
						elif input_ids is not None:
											__SCREAMING_SNAKE_CASE		     =       self.tokenizer.pad(UpperCAmelCase__    ,    **UpperCAmelCase__     )
						else:
											__SCREAMING_SNAKE_CASE		     =       None

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

						if inputs is None:
											return targets

						if targets is not None:
											__SCREAMING_SNAKE_CASE		     =       labels

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

						return inputs




	def 						UpperCAmelCase_   (				self    :       Tuple    ,    *UpperCAmelCase__    :       List[Any]    ,    **UpperCAmelCase__    :       List[Any]     ) ->			Dict:
						return self.tokenizer.batch_decode(*UpperCAmelCase__    ,    **UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Any    ,    *UpperCAmelCase__    :       List[str]    ,    **UpperCAmelCase__    :       Tuple     ) ->			Dict:
						return self.tokenizer.decode(*UpperCAmelCase__    ,    **UpperCAmelCase__     )




"""simple docstring"""







from __future__ import annotations




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					if len(lowerCAmelCase_     ) < 2:
										raise ValueError("Monogons and Digons are not polygons in the Euclidean space"     )
					if any(i <= 0 for i in nums     ):
										raise ValueError("All values must be greater than 0"     )
					__SCREAMING_SNAKE_CASE		     =       nums.copy()
					copy_nums.sort()
					return copy_nums[-1] < sum(copy_nums[:-1]     )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
