

'''simple docstring'''



def   a__			(    a__		):




     """simple docstring"""


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







def   a__			(    a__		):




     """simple docstring"""


     if len(a__		) == 0:
          raise IndexError("""solve_simultaneous() requires n lists of length n+1"""		)
     __SCREAMING_SNAKE_CASE      = 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      = equations.copy()
     if any(0 in row for row in data_set		):
          __SCREAMING_SNAKE_CASE      = data_set.copy()
          __SCREAMING_SNAKE_CASE      = []
          for row_index, row in enumerate(a__		):
               if 0 not in row:
                    __SCREAMING_SNAKE_CASE      = 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      = data_set.copy()
     __SCREAMING_SNAKE_CASE      = simplify(a__		)
     __SCREAMING_SNAKE_CASE      = simplified[::-1]
     __SCREAMING_SNAKE_CASE      = []
     for row in simplified:
          __SCREAMING_SNAKE_CASE      = row[-1]
          if not solutions:
               if row[-2] == 0:
                    solutions.append(0		)
                    continue
               solutions.append(current_solution / row[-2]		)
               continue
          __SCREAMING_SNAKE_CASE      = 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      = temp_row[1::]
          __SCREAMING_SNAKE_CASE      = temp_row[::-1]
          for column_index, column in enumerate(a__		):
               current_solution -= column * solutions[column_index]
          solutions.append(a__		)
     __SCREAMING_SNAKE_CASE      = []
     for item in solutions:
          final.append(float(round(a__ ,       5		)		)		)
     return final[::-1]


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  UpperCAmelCase					:			Any       =	[
      [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'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase					:			Tuple       =	logging.get_logger(__name__)

UpperCAmelCase					:			Any       =	{'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = "ctrl"
 lowerCAmelCase__       = ["past_key_values"]
 lowerCAmelCase__       = {
     "max_position_embeddings": "n_positions",
     "hidden_size": "n_embd",
     "num_attention_heads": "n_head",
     "num_hidden_layers": "n_layer",
 }
 def __init__(						self   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Tuple=246_534   ,				__SCREAMING_SNAKE_CASE   :			str=256   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=1_280   ,				__SCREAMING_SNAKE_CASE   :			Dict=8_192   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=48   ,				__SCREAMING_SNAKE_CASE   :			Dict=16   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=0.1   ,				__SCREAMING_SNAKE_CASE   :			str=0.1   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=1E-6   ,				__SCREAMING_SNAKE_CASE   :			Tuple=0.02   ,				__SCREAMING_SNAKE_CASE   :			Tuple=True   ,				**__SCREAMING_SNAKE_CASE   :			Union[str, Any]   ,				)    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = vocab_size
      __SCREAMING_SNAKE_CASE      = n_positions
      __SCREAMING_SNAKE_CASE      = n_embd
      __SCREAMING_SNAKE_CASE      = n_layer
      __SCREAMING_SNAKE_CASE      = n_head
      __SCREAMING_SNAKE_CASE      = dff
      __SCREAMING_SNAKE_CASE      = resid_pdrop
      __SCREAMING_SNAKE_CASE      = embd_pdrop
      __SCREAMING_SNAKE_CASE      = layer_norm_epsilon
      __SCREAMING_SNAKE_CASE      = initializer_range

      __SCREAMING_SNAKE_CASE      = use_cache

      super().__init__(**__SCREAMING_SNAKE_CASE     )






