from __future__ import annotations
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from typing import TYPE_CHECKING
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import numpy as np
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if TYPE_CHECKING:
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from numpy._typing import NDArray, ArrayLike, _SupportsArray
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x1: ArrayLike = True
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x2: ArrayLike = 5
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x3: ArrayLike = 1.0
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x4: ArrayLike = 1 + 1j
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x5: ArrayLike = np.int8(1)
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x6: ArrayLike = np.float64(1)
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x7: ArrayLike = np.complex128(1)
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x8: ArrayLike = np.array([1, 2, 3])
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x9: ArrayLike = [1, 2, 3]
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x10: ArrayLike = (1, 2, 3)
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x11: ArrayLike = "foo"
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x12: ArrayLike = memoryview(b'foo')
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class A:
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def __array__(self, dtype: np.dtype | None = None) -> NDArray[np.float64]:
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return np.array([1.0, 2.0, 3.0])
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x13: ArrayLike = A()
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scalar: _SupportsArray[np.dtype[np.int64]] = np.int64(1)
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scalar.__array__()
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array: _SupportsArray[np.dtype[np.int_]] = np.array(1)
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array.__array__()
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a: _SupportsArray[np.dtype[np.float64]] = A()
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a.__array__()
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a.__array__()
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# Escape hatch for when you mean to make something like an object
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# array.
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object_array_scalar: object = (i for i in range(10))
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np.array(object_array_scalar)
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