Fix ty invalid assignment (#15222)

* Fix ty invalid assignment diagnostics

* updating DIRECTORY.md

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

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

* Fix unused typing import

* Fix gradient accumulation type handling

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

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

* Fix automatic differentiation gradient dtype handling

---------

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

Author: kadubhumika

cellular_automata/conways_game_of_life.py

@@ -78,6 +78,7 @@ def generate_images(cells: list[list[int]], frames: int) -> list[Image.Image]:
         # Create output image
         img = Image.new("RGB", (len(cells[0]), len(cells)))
         pixels = img.load()
+        assert pixels is not None
 
         # Save cells to image
         for x in range(len(cells)):

cellular_automata/one_dimensional.py

@@ -55,6 +55,7 @@ def generate_image(cells: list[list[int]]) -> Image.Image:
     # Create the output image
     img = Image.new("RGB", (len(cells[0]), len(cells)))
     pixels = img.load()
+    assert pixels is not None
     # Generates image
     for w in range(img.width):
         for h in range(img.height):

data_structures/binary_tree/non_recursive_segment_tree.py

@@ -39,7 +39,7 @@
 from __future__ import annotations
 
 from collections.abc import Callable
-from typing import Any, TypeVar
+from typing import TypeVar, cast
 
 T = TypeVar("T")
 
@@ -57,10 +57,9 @@ def __init__(self, arr: list[T], fnc: Callable[[T, T], T]) -> None:
         ...             lambda a, b: (a[0] + b[0], a[1] + b[1])).query(0, 2)
         (6, 9)
         """
-        any_type: Any | T = None
 
         self.N: int = len(arr)
-        self.st: list[T] = [any_type for _ in range(self.N)] + arr
+        self.st: list[T] = [cast(T, None) for _ in range(self.N)] + arr
         self.fn = fnc
         self.build()
 

data_structures/heap/binomial_heap.py

@@ -222,6 +222,7 @@ def insert(self, val):
             if val < self.min_node.val:
                 self.min_node = new_node
             # Put new_node as a bottom_root in heap
+            assert self.bottom_root is not None
             self.bottom_root.left = new_node
             new_node.parent = self.bottom_root
             self.bottom_root = new_node
@@ -283,6 +284,7 @@ def delete_min(self):
 
             # Update bottom root
             self.bottom_root = self.bottom_root.parent
+            assert self.bottom_root is not None
             self.bottom_root.left = None
 
             # Update min_node

data_structures/linked_list/doubly_linked_list.py

@@ -2,21 +2,25 @@
 https://en.wikipedia.org/wiki/Doubly_linked_list
 """
 
+from __future__ import annotations
+
+from typing import Any
+
 
 class Node:
-    def __init__(self, data):
+    def __init__(self, data: Any):
         self.data = data
-        self.previous = None
-        self.next = None
+        self.previous: Node | None = None
+        self.next: Node | None = None
 
     def __str__(self):
         return f"{self.data}"
 
 
 class DoublyLinkedList:
     def __init__(self):
-        self.head = None
-        self.tail = None
+        self.head: Node | None = None
+        self.tail: Node | None = None
 
     def __iter__(self):
         """
@@ -93,13 +97,18 @@ def insert_at_nth(self, index: int, data):
             new_node.next = self.head
             self.head = new_node
         elif index == length:
+            assert self.tail is not None
             self.tail.next = new_node
+            assert self.tail is not None
             new_node.previous = self.tail
             self.tail = new_node
         else:
             temp = self.head
+            assert temp is not None
             for _ in range(index):
                 temp = temp.next
+                assert temp is not None
+            assert temp.previous is not None
             temp.previous.next = new_node
             new_node.previous = temp.previous
             new_node.next = temp
@@ -141,23 +150,32 @@ def delete_at_nth(self, index: int):
         if length == 1:
             self.head = self.tail = None
         elif index == 0:
+            assert self.head is not None
             self.head = self.head.next
+            assert self.head is not None
             self.head.previous = None
         elif index == length - 1:
+            assert self.tail is not None
             delete_node = self.tail
             self.tail = self.tail.previous
+            assert self.tail is not None
             self.tail.next = None
         else:
             temp = self.head
+            assert temp is not None
             for _ in range(index):
                 temp = temp.next
+                assert temp is not None
             delete_node = temp
+            assert temp.next is not None
+            assert temp.previous is not None
             temp.next.previous = temp.previous
             temp.previous.next = temp.next
         return delete_node.data
 
     def delete(self, data) -> str:
         current = self.head
+        assert current is not None
 
         while current.data != data:  # Find the position to delete
             if current.next:
@@ -172,6 +190,8 @@ def delete(self, data) -> str:
             self.delete_tail()
 
         else:  # Before: 1 <--> 2(current) <--> 3
+            assert current.previous is not None
+            assert current.next is not None
             current.previous.next = current.next  # 1 --> 3
             current.next.previous = current.previous  # 1 <--> 3
         return data

data_structures/linked_list/singly_linked_list.py

@@ -45,7 +45,7 @@ def __init__(self):
         >>> linked_list.head is None
         True
         """
-        self.head = None
+        self.head: Node | None = None
 
     def __iter__(self) -> Iterator[Any]:
         """
@@ -153,8 +153,10 @@ def __setitem__(self, index: int, data: Any) -> None:
         if not 0 <= index < len(self):
             raise ValueError("list index out of range.")
         current = self.head
+        assert current is not None
         for _ in range(index):
             current = current.next_node
+            assert current is not None
         current.data = data
 
     def insert_tail(self, data: Any) -> None:
@@ -215,8 +217,10 @@ def insert_nth(self, index: int, data: Any) -> None:
             self.head = new_node
         else:
             temp = self.head
+            assert temp is not None
             for _ in range(index - 1):
                 temp = temp.next_node
+                assert temp is not None
             new_node.next_node = temp.next_node
             temp.next_node = new_node
 
@@ -316,10 +320,13 @@ def delete_nth(self, index: int = 0) -> Any:
             self.head = self.head.next_node
         else:
             temp = self.head
+            assert temp is not None
             for _ in range(index - 1):
                 temp = temp.next_node
+                assert temp is not None
             delete_node = temp.next_node
-            temp.next_node = temp.next_node.next_node
+            assert delete_node is not None
+            temp.next_node = delete_node.next_node
         return delete_node.data
 
     def is_empty(self) -> bool:

fractals/mandelbrot.py

@@ -109,6 +109,7 @@ def get_image(
     """
     img = Image.new("RGB", (image_width, image_height))
     pixels = img.load()
+    assert pixels is not None
 
     # loop through the image-coordinates
     for image_x in range(image_width):

machine_learning/automatic_differentiation.py

@@ -9,7 +9,6 @@
 
 from __future__ import annotations
 
-from collections import defaultdict
 from enum import Enum
 from types import TracebackType
 from typing import Any, Self
@@ -258,7 +257,7 @@ def gradient(self, target: Variable, source: Variable) -> np.ndarray | None:
         """
 
         # partial derivatives with respect to target
-        partial_deriv = defaultdict(lambda: 0)
+        partial_deriv: dict[Variable, np.ndarray] = {}
         partial_deriv[target] = np.ones_like(target.to_ndarray())
 
         # iterating through each operations in the computation graph
@@ -270,7 +269,10 @@ def gradient(self, target: Variable, source: Variable) -> np.ndarray | None:
                 # of variables with respect to the target
                 dparam_doutput = self.derivative(param, operation)
                 dparam_dtarget = dparam_doutput * partial_deriv[operation.output]
-                partial_deriv[param] += dparam_dtarget
+                partial_deriv[param] = (
+                    partial_deriv.get(param, np.zeros_like(dparam_dtarget))
+                    + dparam_dtarget
+                )
 
                 if param.result_of and param.result_of != OpType.NOOP:
                     operation_queue.append(param.result_of)

networking_flow/minimum_cut.py

@@ -71,7 +71,7 @@ def mincut(graph: list[list[int]], source: int, sink: int) -> list[tuple[int, in
     parent = [-1] * (len(residual))
     res = []
     while bfs(residual, source, sink, parent):
-        path_flow = float("inf")
+        path_flow = max(max(row) for row in residual)
         s = sink
 
         while s != source:

neural_network/input_data.py

@@ -21,6 +21,7 @@
 import os
 import typing
 import urllib
+import urllib.request
 
 import numpy as np
 from tensorflow.python.framework import dtypes, random_seed