pre-commit autoupdate 2025-09-11 (#12963)

* pre-commit autoupdate 2025-09-11

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

.pre-commit-config.yaml

@@ -16,7 +16,7 @@ repos:
       - id: auto-walrus
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.12.12
+    rev: v0.13.0
     hooks:
       - id: ruff-check
       - id: ruff-format

data_structures/arrays/sudoku_solver.py

@@ -149,7 +149,7 @@ def search(values):
     if all(len(values[s]) == 1 for s in squares):
         return values  ## Solved!
     ## Chose the unfilled square s with the fewest possibilities
-    n, s = min((len(values[s]), s) for s in squares if len(values[s]) > 1)
+    _n, s = min((len(values[s]), s) for s in squares if len(values[s]) > 1)
     return some(search(assign(values.copy(), s, d)) for d in values[s])
 
 

data_structures/trie/radix_tree.py

@@ -115,7 +115,7 @@ class RadixNode:
         if not incoming_node:
             return False
         else:
-            matching_string, remaining_prefix, remaining_word = incoming_node.match(
+            _matching_string, remaining_prefix, remaining_word = incoming_node.match(
                 word
             )
             # If there is remaining prefix, the word can't be on the tree
@@ -144,7 +144,7 @@ class RadixNode:
         if not incoming_node:
             return False
         else:
-            matching_string, remaining_prefix, remaining_word = incoming_node.match(
+            _matching_string, remaining_prefix, remaining_word = incoming_node.match(
                 word
             )
             # If there is remaining prefix, the word can't be on the tree

graphs/graph_adjacency_list.py

@@ -448,7 +448,7 @@ class TestGraphAdjacencyList(unittest.TestCase):
         (
             undirected_graph,
             directed_graph,
-            random_vertices,
+            _random_vertices,
             random_edges,
         ) = self.__generate_graphs(20, 0, 100, 4)
 
@@ -502,7 +502,7 @@ class TestGraphAdjacencyList(unittest.TestCase):
             undirected_graph,
             directed_graph,
             random_vertices,
-            random_edges,
+            _random_edges,
         ) = self.__generate_graphs(20, 0, 100, 4)
 
         for vertex in random_vertices:
@@ -516,7 +516,7 @@ class TestGraphAdjacencyList(unittest.TestCase):
             undirected_graph,
             directed_graph,
             random_vertices,
-            random_edges,
+            _random_edges,
         ) = self.__generate_graphs(20, 0, 100, 4)
 
         for i in range(101):
@@ -530,7 +530,7 @@ class TestGraphAdjacencyList(unittest.TestCase):
         (
             undirected_graph,
             directed_graph,
-            random_vertices,
+            _random_vertices,
             random_edges,
         ) = self.__generate_graphs(20, 0, 100, 4)
 
@@ -569,7 +569,7 @@ class TestGraphAdjacencyList(unittest.TestCase):
             undirected_graph,
             directed_graph,
             random_vertices,
-            random_edges,
+            _random_edges,
         ) = self.__generate_graphs(20, 0, 100, 4)
 
         for vertex in random_vertices:

graphs/graph_adjacency_matrix.py

@@ -469,7 +469,7 @@ class TestGraphMatrix(unittest.TestCase):
         (
             undirected_graph,
             directed_graph,
-            random_vertices,
+            _random_vertices,
             random_edges,
         ) = self.__generate_graphs(20, 0, 100, 4)
 
@@ -523,7 +523,7 @@ class TestGraphMatrix(unittest.TestCase):
             undirected_graph,
             directed_graph,
             random_vertices,
-            random_edges,
+            _random_edges,
         ) = self.__generate_graphs(20, 0, 100, 4)
 
         for vertex in random_vertices:
@@ -537,7 +537,7 @@ class TestGraphMatrix(unittest.TestCase):
             undirected_graph,
             directed_graph,
             random_vertices,
-            random_edges,
+            _random_edges,
         ) = self.__generate_graphs(20, 0, 100, 4)
 
         for i in range(101):
@@ -551,7 +551,7 @@ class TestGraphMatrix(unittest.TestCase):
         (
             undirected_graph,
             directed_graph,
-            random_vertices,
+            _random_vertices,
             random_edges,
         ) = self.__generate_graphs(20, 0, 100, 4)
 
@@ -590,7 +590,7 @@ class TestGraphMatrix(unittest.TestCase):
             undirected_graph,
             directed_graph,
             random_vertices,
-            random_edges,
+            _random_edges,
         ) = self.__generate_graphs(20, 0, 100, 4)
 
         for vertex in random_vertices:

knapsack/tests/test_greedy_knapsack.py

@@ -28,7 +28,7 @@ class TestClass(unittest.TestCase):
         # profit = [10, 20, 30, 40, 50, 60]
         # weight = [2, 4, 6, 8, 10, 12]
         # max_weight = -15
-        pytest.raises(ValueError, match="max_weight must greater than zero.")
+        pytest.raises(ValueError, match=r"max_weight must greater than zero.")
 
     def test_negative_profit_value(self):
         """
@@ -38,7 +38,7 @@ class TestClass(unittest.TestCase):
         # profit = [10, -20, 30, 40, 50, 60]
         # weight = [2, 4, 6, 8, 10, 12]
         # max_weight = 15
-        pytest.raises(ValueError, match="Weight can not be negative.")
+        pytest.raises(ValueError, match=r"Weight can not be negative.")
 
     def test_negative_weight_value(self):
         """
@@ -48,7 +48,7 @@ class TestClass(unittest.TestCase):
         # profit = [10, 20, 30, 40, 50, 60]
         # weight = [2, -4, 6, -8, 10, 12]
         # max_weight = 15
-        pytest.raises(ValueError, match="Profit can not be negative.")
+        pytest.raises(ValueError, match=r"Profit can not be negative.")
 
     def test_null_max_weight(self):
         """
@@ -58,7 +58,7 @@ class TestClass(unittest.TestCase):
         # profit = [10, 20, 30, 40, 50, 60]
         # weight = [2, 4, 6, 8, 10, 12]
         # max_weight = null
-        pytest.raises(ValueError, match="max_weight must greater than zero.")
+        pytest.raises(ValueError, match=r"max_weight must greater than zero.")
 
     def test_unequal_list_length(self):
         """
@@ -68,7 +68,9 @@ class TestClass(unittest.TestCase):
         # profit = [10, 20, 30, 40, 50]
         # weight = [2, 4, 6, 8, 10, 12]
         # max_weight = 100
-        pytest.raises(IndexError, match="The length of profit and weight must be same.")
+        pytest.raises(
+            IndexError, match=r"The length of profit and weight must be same."
+        )
 
 
 if __name__ == "__main__":

linear_algebra/gaussian_elimination.py

@@ -33,7 +33,7 @@ def retroactive_resolution(
            [ 0.5]])
     """
 
-    rows, columns = np.shape(coefficients)
+    rows, _columns = np.shape(coefficients)
 
     x: NDArray[float64] = np.zeros((rows, 1), dtype=float)
     for row in reversed(range(rows)):

linear_algebra/jacobi_iteration_method.py

@@ -112,7 +112,7 @@ def jacobi_iteration_method(
         (coefficient_matrix, constant_matrix), axis=1
     )
 
-    rows, cols = table.shape
+    rows, _cols = table.shape
 
     strictly_diagonally_dominant(table)
 
@@ -149,7 +149,7 @@ def jacobi_iteration_method(
 
     # Here we get 'i_col' - these are the column numbers, for each row
     # without diagonal elements, except for the last column.
-    i_row, i_col = np.where(masks)
+    _i_row, i_col = np.where(masks)
     ind = i_col.reshape(-1, rows - 1)
 
     #'i_col' is converted to a two-dimensional list 'ind', which will be

machine_learning/polynomial_regression.py

@@ -93,7 +93,7 @@ class PolynomialRegression:
         ...
         ValueError: Data must have dimensions N x 1
         """
-        rows, *remaining = data.shape
+        _rows, *remaining = data.shape
         if remaining:
             raise ValueError("Data must have dimensions N x 1")
 

machine_learning/principle_component_analysis.py

@@ -65,7 +65,7 @@ def main() -> None:
     """
     Driver function to execute PCA and display results.
     """
-    data_x, data_y = collect_dataset()
+    data_x, _data_y = collect_dataset()
 
     # Number of principal components to retain
     n_components = 2

maths/chinese_remainder_theorem.py

@@ -65,7 +65,7 @@ def invert_modulo(a: int, n: int) -> int:
     1
 
     """
-    (b, x) = extended_euclid(a, n)
+    (b, _x) = extended_euclid(a, n)
     if b < 0:
         b = (b % n + n) % n
     return b

maths/modular_division.py

@@ -31,7 +31,7 @@ def modular_division(a: int, b: int, n: int) -> int:
     assert n > 1
     assert a > 0
     assert greatest_common_divisor(a, n) == 1
-    (d, t, s) = extended_gcd(n, a)  # Implemented below
+    (_d, _t, s) = extended_gcd(n, a)  # Implemented below
     x = (b * s) % n
     return x
 
@@ -47,7 +47,7 @@ def invert_modulo(a: int, n: int) -> int:
     1
 
     """
-    (b, x) = extended_euclid(a, n)  # Implemented below
+    (b, _x) = extended_euclid(a, n)  # Implemented below
     if b < 0:
         b = (b % n + n) % n
     return b

neural_network/convolution_neural_network.py

@@ -317,7 +317,7 @@ class CNN:
         print((" - - Shape: Test_Data  ", np.shape(datas_test)))
         for p in range(len(datas_test)):
             data_test = np.asmatrix(datas_test[p])
-            data_focus1, data_conved1 = self.convolute(
+            _data_focus1, data_conved1 = self.convolute(
                 data_test,
                 self.conv1,
                 self.w_conv1,
@@ -339,7 +339,7 @@ class CNN:
     def convolution(self, data):
         # return the data of image after convoluting process so we can check it out
         data_test = np.asmatrix(data)
-        data_focus1, data_conved1 = self.convolute(
+        _data_focus1, data_conved1 = self.convolute(
             data_test,
             self.conv1,
             self.w_conv1,

project_euler/problem_551/sol1.py

@@ -185,7 +185,7 @@ def solution(n: int = 10**15) -> int:
     i = 1
     dn = 0
     while True:
-        diff, terms_jumped = next_term(digits, 20, i + dn, n)
+        _diff, terms_jumped = next_term(digits, 20, i + dn, n)
         dn += terms_jumped
         if dn == n - i:
             break

pyproject.toml

@@ -124,7 +124,6 @@ lint.ignore = [
   "S311",    # Standard pseudo-random generators are not suitable for cryptographic purposes -- FIX ME
   "SIM905",  # Consider using a list literal instead of `str.split` -- DO NOT FIX
   "SLF001",  # Private member accessed: `_Iterator` -- FIX ME
-  "UP038",   # Use `X | Y` in `{}` call instead of `(X, Y)` -- DO NOT FIX
 ]
 
 lint.per-file-ignores."data_structures/hashing/tests/test_hash_map.py" = [

scheduling/multi_level_feedback_queue.py

@@ -255,7 +255,7 @@ class MLFQ:
 
         #  all queues except last one have round_robin algorithm
         for i in range(self.number_of_queues - 1):
-            finished, self.ready_queue = self.round_robin(
+            _finished, self.ready_queue = self.round_robin(
                 self.ready_queue, self.time_slices[i]
             )
         #  the last queue has first_come_first_served algorithm