



"""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__		)








'''simple docstring'''





def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int,_SCREAMING_SNAKE_CASE					: int ):






			"""simple docstring"""




			return int((input_a, input_a).count(1 ) != 0 )






def 						UpperCAmelCase__(				):






			"""simple docstring"""




			assert or_gate(0,0 ) == 0
			assert or_gate(0,1 ) == 1
			assert or_gate(1,0 ) == 1
			assert or_gate(1,1 ) == 1


if __name__ == "__main__":
					print(or_gate(0, 1))
					print(or_gate(1, 0))
					print(or_gate(0, 0))
					print(or_gate(1, 1))



