var = MyType() raise a var-annotated error

[pythoniste]

Bug Report

Given that :

MyType = dict[str, int]

This line raise an var-annotated error :

d = MyType()

I have to write :

d:MyType = MyType()

This is redundant. I think this is a bug because the following code works perfectly fine :

d = dict[str, int]()

And it is basically the same thing.

Side note : I know that the following code exists :

from typing import NewType
MyType2 = NewType("MyType2", dict[str, int])
d = MyType2({})

But I do not want to create a new type, I just want to create a specific annotation (a shortcut) and deal with multiple dictionaries that resemble to each other.

In my original code, my type is dict[tuple[int, Decimal], dict[SomeDataclass, dict[str, SomeOtherDataclass]]]. You might understand why I do which to use a shortcut.

To Reproduce

This is a complete example :

d1: dict[str, int] = {}
d2 = dict[str, int]()

MyType = dict[str, int]  # This is the shortcut

d3: MyType = {}
d4 = MyType()

from typing import NewType
MyType2 = NewType("MyType2", dict[str, int])

d5: MyType2
d6 = MyType2({})

Expected Behavior

mypy my_example.py should not return any errors.

Actual Behavior

mypy my_example.py returns :

my_example.py:7:1: error: Need type annotation for "d4" (hint: "d4: Dict[<type>, <type>] = ...") [var-annotated]

Your Environment

• Mypy version used: 1.5.1
• Mypy command-line flags: None
• Mypy configuration options from mypy.ini (and other config files): None
• Python version used: 3.10.12

[samtygier-stfc]

I see the same thing using numpy.ndarray.

import numpy as np
a = np.ndarray((2,), buffer=np.array([1,2,3]), offset=np.int_().itemsize, dtype=int)
b = np.ndarray((2,2), buffer=bytes(range(4)), dtype=np.int8)

gives

nda_test.py:2: error: Need type annotation for "a"  [var-annotated]
nda_test.py:3: error: Need type annotation for "b"  [var-annotated