


def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		:     Any					=					current_set.copy()
			for row_index, row in enumerate(A__				):
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					row[0]
						for column_index, column in enumerate(A__				):
									if magnitude == 0:
												SCREAMING_SNAKE_CASE_		:     int					=					column
												continue
									SCREAMING_SNAKE_CASE_		:     List[str]					=					column / magnitude
    # Subtract to cancel term
			SCREAMING_SNAKE_CASE_		:     Any					=					current_set[0]
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					[first_row]
			SCREAMING_SNAKE_CASE_		:     Dict					=					current_set[1::]
			for row in current_set:
						SCREAMING_SNAKE_CASE_		:     Tuple					=					[]
						# If first term is 0, it is already in form we want, so we preserve it
						if row[0] == 0:
									final_set.append(A__				)
									continue
						for column_index in range(len(A__				)				):
									temp_row.append(first_row[column_index] - row[column_index]				)
						final_set.append(A__				)
			# Create next recursion iteration set
			if len(final_set[0]				) != 3:
						SCREAMING_SNAKE_CASE_		:     Tuple					=					final_set[0]
						SCREAMING_SNAKE_CASE_		:     List[str]					=					[]
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					[]
						for row in final_set[1::]:
									current_first_column.append(row[0]				)
									next_iteration.append(row[1::]				)
						SCREAMING_SNAKE_CASE_		:     int					=					simplify(A__				)
						for i in range(len(A__				)				):
									resultant[i].insert(0,      current_first_column[i]				)
						resultant.insert(0,      A__				)
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					resultant
			return final_set






def 						a__			(				A__				):
			if len(A__				) == 0:
						raise IndexError('solve_simultaneous() requires n lists of length n+1'				)
			SCREAMING_SNAKE_CASE_		:     List[str]					=					len(A__				) + 1
			if any(len(A__				) != _length for item in equations				):
						raise IndexError('solve_simultaneous() requires n lists of length n+1'				)
			for row in equations:
						if any(not isinstance(A__,      (int, float)				) for column in row				):
									raise ValueError('solve_simultaneous() requires lists of integers'				)
			if len(A__				) == 1:
						return [equations[0][-1] / equations[0][0]]
			SCREAMING_SNAKE_CASE_		:     Any					=					equations.copy()
			if any(0 in row for row in data_set				):
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					data_set.copy()
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					[]
						for row_index, row in enumerate(A__				):
									if 0 not in row:
												SCREAMING_SNAKE_CASE_		:     Optional[int]					=					data_set.pop(A__				)
												break
						if not full_row:
									raise ValueError('solve_simultaneous() requires at least 1 full equation'				)
						data_set.insert(0,      A__				)
			SCREAMING_SNAKE_CASE_		:     Any					=					data_set.copy()
			SCREAMING_SNAKE_CASE_		:     List[str]					=					simplify(A__				)
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					simplified[::-1]
			SCREAMING_SNAKE_CASE_		:     list					=					[]
			for row in simplified:
						SCREAMING_SNAKE_CASE_		:     str					=					row[-1]
						if not solutions:
									if row[-2] == 0:
												solutions.append(0				)
												continue
									solutions.append(current_solution / row[-2]				)
									continue
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					row.copy()[: len(A__				) - 1 :]
						while temp_row[0] == 0:
									temp_row.pop(0				)
						if len(A__				) == 0:
									solutions.append(0				)
									continue
						SCREAMING_SNAKE_CASE_		:     Any					=					temp_row[1::]
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					temp_row[::-1]
						for column_index, column in enumerate(A__				):
									current_solution -= column * solutions[column_index]
						solutions.append(A__				)
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					[]
			for item in solutions:
						final.append(float(round(A__,      5				)				)				)
			return final[::-1]


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				lowerCAmelCase__			:      int								=[
				    [2, 1, 1, 1, 1, 4],
				    [1, 2, 1, 1, 1, 5],
				    [1, 1, 2, 1, 1, 6],
				    [1, 1, 1, 2, 1, 7],
				    [1, 1, 1, 1, 2, 8],
				]
				print(solve_simultaneous(eq))
				print(solve_simultaneous([[4, 2]]))









from ...processing_utils import ProcessorMixin







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						"""WhisperFeatureExtractor"""
							_UpperCAmelCase      =						"""WhisperTokenizer"""






							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




										super().__init__(lowerCAmelCase__			,						lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.feature_extractor
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					False






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__=None			,						lowerCAmelCase__=None			,						lowerCAmelCase__=True       ):





										"""simple docstring"""




										return self.tokenizer.get_decoder_prompt_ids(task=lowerCAmelCase__			,						language=lowerCAmelCase__			,						no_timestamps=lowerCAmelCase__       )






							def __call__(      self			,						*lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										if self._in_target_context_manager:
													return self.current_processor(*lowerCAmelCase__			,						**lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Tuple					=					kwargs.pop('audio'			,						lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     str					=					kwargs.pop('sampling_rate'			,						lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					kwargs.pop('text'			,						lowerCAmelCase__       )
										if len(lowerCAmelCase__       ) > 0:
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					args[0]
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					args[1:]

										if audio is None and text is None:
													raise ValueError('You need to specify either an `audio` or `text` input to process.'       )

										if audio is not None:
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.feature_extractor(lowerCAmelCase__			,						*lowerCAmelCase__			,						sampling_rate=lowerCAmelCase__			,						**lowerCAmelCase__       )
										if text is not None:
													SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.tokenizer(lowerCAmelCase__			,						**lowerCAmelCase__       )

										if text is None:
													return inputs

										elif audio is None:
													return encodings
										else:
													SCREAMING_SNAKE_CASE_		:     Optional[int]					=					encodings['input_ids']
													return inputs






							def 					UpperCamelCase__     (      self			,						*lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""simple docstring"""




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






							def 					UpperCamelCase__     (      self			,						*lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""simple docstring"""




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






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__="np"       ):





										"""simple docstring"""




										return self.tokenizer.get_prompt_ids(lowerCAmelCase__			,						return_tensors=lowerCAmelCase__       )





