






def 				_a     (   UpperCamelCase_    : List[Any]      )       ->      Union[str, Any]:
						"""simple docstring"""







						lowerCAmelCase__       					=  current_set.copy()
						for row_index, row in enumerate(A__      ):
												lowerCAmelCase__       					=  row[0]
												for column_index, column in enumerate(A__      ):
																		if magnitude == 0:
																								lowerCAmelCase__       					=  column
																								continue
																		lowerCAmelCase__       					=  column / magnitude
																		 # Subtract to cancel term
						lowerCAmelCase__       					=  current_set[0]
						lowerCAmelCase__       					=  [first_row]
						lowerCAmelCase__       					=  current_set[1::]
						for row in current_set:
												lowerCAmelCase__       					=  []
												# 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:
												lowerCAmelCase__       					=  final_set[0]
												lowerCAmelCase__       					=  []
												lowerCAmelCase__       					=  []
												for row in final_set[1::]:
																		current_first_column.append(row[0]      )
																		next_iteration.append(row[1::]      )
												lowerCAmelCase__       					=  simplify(A__      )
												for i in range(len(A__      )      ):
																		resultant[i].insert(0			,      current_first_column[i]      )
												resultant.insert(0			,      A__      )
												lowerCAmelCase__       					=  resultant
						return final_set


def 				_a     (   UpperCamelCase_    : int      )       ->      Union[str, Any]:
						"""simple docstring"""







						if len(A__      ) == 0:
												raise IndexError("solve_simultaneous() requires n lists of length n+1"      )
						lowerCAmelCase__       					=  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]]
						lowerCAmelCase__       					=  equations.copy()
						if any(0 in row for row in data_set      ):
												lowerCAmelCase__       					=  data_set.copy()
												lowerCAmelCase__       					=  []
												for row_index, row in enumerate(A__      ):
																		if 0 not in row:
																								lowerCAmelCase__       					=  data_set.pop(A__      )
																								break
												if not full_row:
																		raise ValueError("solve_simultaneous() requires at least 1 full equation"      )
												data_set.insert(0			,      A__      )
						lowerCAmelCase__       					=  data_set.copy()
						lowerCAmelCase__       					=  simplify(A__      )
						lowerCAmelCase__       					=  simplified[::-1]
						lowerCAmelCase__       					=  []
						for row in simplified:
												lowerCAmelCase__       					=  row[-1]
												if not solutions:
																		if row[-2] == 0:
																								solutions.append(0      )
																								continue
																		solutions.append(current_solution / row[-2]      )
																		continue
												lowerCAmelCase__       					=  row.copy()[: len(A__      ) - 1 :]
												while temp_row[0] == 0:
																		temp_row.pop(0      )
												if len(A__      ) == 0:
																		solutions.append(0      )
																		continue
												lowerCAmelCase__       					=  temp_row[1::]
												lowerCAmelCase__       					=  temp_row[::-1]
												for column_index, column in enumerate(A__      ):
																		current_solution -= column * solutions[column_index]
												solutions.append(A__      )
						lowerCAmelCase__       					=  []
						for item in solutions:
												final.append(float(round(A__			,      5      )      )      )
						return final[::-1]


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	a_							=			[
	    [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]]))









'''simple docstring'''






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Optional[Any]							=			['image_processor', 'tokenizer']
  SCREAMING_SNAKE_CASE    :  Union[str, Any]							=			'CLIPImageProcessor'
  SCREAMING_SNAKE_CASE    :  Union[str, Any]							=			('CLIPTokenizer', 'CLIPTokenizerFast')






  def __init__(      self   :     List[Any]					,lowercase__   :     Dict=None					,lowercase__   :     Union[str, Any]=None					,**lowercase__   :     Tuple			):
   __lowercase							  = None
   if "feature_extractor" in kwargs:
    warnings.warn(
        '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
        ''' instead.'''					,lowercase__					,)
    __lowercase							  = kwargs.pop('''feature_extractor'''			)

   __lowercase							  = image_processor if image_processor is not None else feature_extractor
   if image_processor is None:
    raise ValueError('''You need to specify an `image_processor`.'''			)
   if tokenizer is None:
    raise ValueError('''You need to specify a `tokenizer`.'''			)

   super().__init__(lowercase__					,lowercase__			)






  def __call__(      self   :     List[Any]					,lowercase__   :     str=None					,lowercase__   :     List[Any]=None					,lowercase__   :     Optional[Any]=None					,**lowercase__   :     int			):

   if text is None and images is None:
    raise ValueError('''You have to specify either text or images. Both cannot be none.'''			)

   if text is not None:
    __lowercase							  = self.tokenizer(lowercase__					,return_tensors=lowercase__					,**lowercase__			)

   if images is not None:
    __lowercase							  = self.image_processor(lowercase__					,return_tensors=lowercase__					,**lowercase__			)

   if text is not None and images is not None:
    __lowercase							  = image_features.pixel_values
    return encoding
   elif text is not None:
    return encoding
   else:
    return BatchEncoding(data=dict(**lowercase__			)					,tensor_type=lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]					,*lowercase__   :     List[str]					,**lowercase__   :     int			):
   return self.tokenizer.batch_decode(*lowercase__					,**lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]					,*lowercase__   :     Optional[int]					,**lowercase__   :     Union[str, Any]			):
   return self.tokenizer.decode(*lowercase__					,**lowercase__			)






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = self.tokenizer.model_input_names
   __lowercase							  = self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   warnings.warn(
       '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''					,lowercase__					,)
   return self.image_processor_class






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   warnings.warn(
       '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''					,lowercase__					,)
   return self.image_processor

