@@ -18,9 +18,12 @@ def compute_transform_tables(
1818 insert_cost : int ,
1919) -> tuple [list [list [int ]], list [list [str ]]]:
2020 """
21- Finds the most cost efficient sequence
21+ Finds the most cost- efficient sequence
2222 for converting one string into another.
2323
24+ >>> compute_transform_tables("", "", 1, 2, 3, 3)
25+ ([[0]], [['0']])
26+
2427 >>> costs, operations = compute_transform_tables("cat", "cut", 1, 2, 3, 3)
2528 >>> costs[0][:4]
2629 [0, 3, 6, 9]
@@ -30,10 +33,8 @@ def compute_transform_tables(
3033 ['0', 'Ic', 'Iu', 'It']
3134 >>> operations[3][:4]
3235 ['Dt', 'Dt', 'Rtu', 'Ct']
33-
34- >>> compute_transform_tables("", "", 1, 2, 3, 3)
35- ([[0]], [['0']])
3636 """
37+
3738 source_seq = list (source_string )
3839 destination_seq = list (destination_string )
3940 len_source_seq = len (source_seq )
@@ -86,18 +87,13 @@ def assemble_transformation(ops: list[list[str]], i: int, j: int) -> list[str]:
8687 >>> assemble_transformation(ops, x, y)
8788 ['Cc', 'Rau', 'Ct']
8889
89- >>> ops1 = [['0']]
90- >>> x1 = len(ops1) - 1
91- >>> y1 = len(ops1[0]) - 1
92- >>> assemble_transformation(ops1, x1, y1)
90+ >>> assemble_transformation([['0']], 0, 0)
9391 []
9492
9593 >>> ops2 = [['0', 'I1', 'I2', 'I3'],
9694 ... ['D1', 'C1', 'I2', 'I3'],
9795 ... ['D2', 'D2', 'R23', 'R23']]
98- >>> x2 = len(ops2) - 1
99- >>> y2 = len(ops2[0]) - 1
100- >>> assemble_transformation(ops2, x2, y2)
96+ >>> assemble_transformation(ops2, 2, 3)
10197 ['C1', 'I2', 'R23']
10298 """
10399 if i == 0 and j == 0 :
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