snapshot before regression test

2026-07-18 19:25:40 +08:00
commit 796222a300
2295 changed files with 206453 additions and 0 deletions
--- a/Test/SimdTest/temp/c3a6aed1f1fb8b1e.pyi
+++ b/Test/SimdTest/temp/c3a6aed1f1fb8b1e.pyi
@@ -0,0 +1,240 @@
+"""
+Auto-generated Python stub file from numpy.__init__.py
+Module: numpy.__init__
+"""
+
+
+from stdint import *
+import stdlib
+import string
+import vipermath
+import stdio
+import t, c
+import memhub
+
+MAX_NDIM: t.CDefine = 4
+float64: t.CTypedef = t.CDouble
+float32: t.CTypedef = t.CFloat
+int64: t.CTypedef = t.CLong
+int32: t.CTypedef = t.CInt
+uint8: t.CTypedef = t.CUnsignedChar
+pi: t.CDefine = 3.14159265358979323846
+e:  t.CDefine = 2.71828182845904523536
+
+@t.Object
+class ndarray[T]:
+    data: T | t.CPtr
+    shape: t.CArray[t.CSizeT, MAX_NDIM]
+    strides: t.CArray[t.CSizeT, MAX_NDIM]
+    ndim: t.CInt
+    size: t.CSizeT
+    owns_data: t.CInt
+    pool: memhub.MemManager | t.CPtr
+    def __new__(self: ndarray, pool: memhub.MemManager | t.CPtr, n: t.CSizeT) -> t.CInt: pass
+    def __add__(self: ndarray, other: ndarray[T] | t.CPtr) -> ndarray[T] | t.CPtr: pass
+    def __sub__(self: ndarray, other: ndarray[T] | t.CPtr) -> ndarray[T] | t.CPtr: pass
+    def __mul__(self: ndarray, other: ndarray[T] | t.CPtr) -> ndarray[T] | t.CPtr: pass
+    def __truediv__(self: ndarray, other: ndarray[T] | t.CPtr) -> ndarray[T] | t.CPtr: pass
+    def __floordiv__(self: ndarray, other: ndarray[T] | t.CPtr) -> ndarray[T] | t.CPtr: pass
+    def __mod__(self: ndarray, other: ndarray[T] | t.CPtr) -> ndarray[T] | t.CPtr: pass
+    def __neg__(self: ndarray) -> ndarray[T] | t.CPtr: pass
+    def __len__(self: ndarray) -> t.CInt: pass
+    def at2d(self: ndarray, row: t.CSizeT, col: t.CSizeT) -> T: pass
+    def set2d(self: ndarray, row: t.CSizeT, col: t.CSizeT, val: T) -> t.CInt: pass
+    def delete(self: ndarray) -> t.CInt: pass
+    def fill(self: ndarray, val: T) -> t.CInt: pass
+    def copy(self: ndarray) -> ndarray[T] | t.CPtr: pass
+    def reshape(self: ndarray, new_shape: INTPTR, new_ndim: t.CInt) -> ndarray[T] | t.CPtr: pass
+    def sum(self: ndarray) -> T: pass
+    def mean(self: ndarray) -> T: pass
+    def min(self: ndarray) -> T: pass
+    def max(self: ndarray) -> T: pass
+    def argmax(self: ndarray) -> t.CInt: pass
+    def argmin(self: ndarray) -> t.CInt: pass
+    def dot(self: ndarray, other: ndarray[T] | t.CPtr) -> T: pass
+    def T(self: ndarray) -> ndarray[T] | t.CPtr: pass
+    def print_arr(self: ndarray) -> t.CInt: pass
+
+Float64Array: t.CTypedef = ndarray[t.CDouble]
+Float32Array: t.CTypedef = ndarray[t.CFloat]
+Int64Array: t.CTypedef = ndarray[t.CLong]
+Int32Array: t.CTypedef = ndarray[t.CInt]
+Uint8Array: t.CTypedef = ndarray[t.CUnsignedChar]
+
+def _alloc_ndarray(pool: memhub.MemManager | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def _compute_strides(a: ndarray[t.CDouble] | t.CPtr) -> t.CInt: pass
+
+def _empty_like(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def array(pool: memhub.MemManager | t.CPtr, data: t.CDouble | t.CPtr, n: t.CSizeT) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def zeros(pool: memhub.MemManager | t.CPtr, n: t.CSizeT) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def ones(pool: memhub.MemManager | t.CPtr, n: t.CSizeT) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def full(pool: memhub.MemManager | t.CPtr, n: t.CSizeT, val: t.CDouble) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def arange(pool: memhub.MemManager | t.CPtr, start: t.CDouble, stop: t.CDouble, step: t.CDouble) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def linspace(pool: memhub.MemManager | t.CPtr, start: t.CDouble, stop: t.CDouble, num: t.CSizeT) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def empty2d(pool: memhub.MemManager | t.CPtr, rows: t.CSizeT, cols: t.CSizeT) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def zeros2d(pool: memhub.MemManager | t.CPtr, rows: t.CSizeT, cols: t.CSizeT) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def ones2d(pool: memhub.MemManager | t.CPtr, rows: t.CSizeT, cols: t.CSizeT) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def eye(pool: memhub.MemManager | t.CPtr, n: t.CSizeT) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def diag(pool: memhub.MemManager | t.CPtr, vals: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_abs(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_sqrt(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_exp(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_log(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_sin(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_cos(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_tan(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_pow(a: ndarray[t.CDouble] | t.CPtr, p: t.CDouble) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def add_scalar(a: ndarray[t.CDouble] | t.CPtr, s: t.CDouble) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def mul_scalar(a: ndarray[t.CDouble] | t.CPtr, s: t.CDouble) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def sub_scalar(a: ndarray[t.CDouble] | t.CPtr, s: t.CDouble) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def div_scalar(a: ndarray[t.CDouble] | t.CPtr, s: t.CDouble) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def matmul(a: ndarray[t.CDouble] | t.CPtr, b: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def dot_product(a: ndarray[t.CDouble] | t.CPtr, b: ndarray[t.CDouble] | t.CPtr) -> t.CDouble: pass
+
+def np_sum(a: ndarray[t.CDouble] | t.CPtr) -> t.CDouble: pass
+
+def np_mean(a: ndarray[t.CDouble] | t.CPtr) -> t.CDouble: pass
+
+def np_min(a: ndarray[t.CDouble] | t.CPtr) -> t.CDouble: pass
+
+def np_max(a: ndarray[t.CDouble] | t.CPtr) -> t.CDouble: pass
+
+def np_argmax(a: ndarray[t.CDouble] | t.CPtr) -> t.CInt: pass
+
+def np_argmin(a: ndarray[t.CDouble] | t.CPtr) -> t.CInt: pass
+
+def var(a: ndarray[t.CDouble] | t.CPtr) -> t.CDouble: pass
+
+def std(a: ndarray[t.CDouble] | t.CPtr) -> t.CDouble: pass
+
+def norm(a: ndarray[t.CDouble] | t.CPtr) -> t.CDouble: pass
+
+def clip(a: ndarray[t.CDouble] | t.CPtr, lo: t.CDouble, hi: t.CDouble) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def concatenate(a: ndarray[t.CDouble] | t.CPtr, b: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def sort_arr(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def reverse(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_log10(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_log2(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_floor(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_ceil(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_round(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_sign(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_tanh(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_sinh(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_cosh(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_arcsin(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_arccos(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_arctan(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_arctan2(y: ndarray[t.CDouble] | t.CPtr, x: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_degrees(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_radians(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_isnan(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_isinf(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_maximum(a: ndarray[t.CDouble] | t.CPtr, b: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_minimum(a: ndarray[t.CDouble] | t.CPtr, b: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def cumsum(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def diff(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def flatten(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def trace(a: ndarray[t.CDouble] | t.CPtr) -> t.CDouble: pass
+
+def outer(a: ndarray[t.CDouble] | t.CPtr, b: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_where(condition: ndarray[t.CDouble] | t.CPtr, x: ndarray[t.CDouble] | t.CPtr, y: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_count_nonzero(a: ndarray[t.CDouble] | t.CPtr) -> t.CInt: pass
+
+def np_all(a: ndarray[t.CDouble] | t.CPtr) -> t.CInt: pass
+
+def np_any(a: ndarray[t.CDouble] | t.CPtr) -> t.CInt: pass
+
+def np_equal(a: ndarray[t.CDouble] | t.CPtr, b: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_not_equal(a: ndarray[t.CDouble] | t.CPtr, b: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_less(a: ndarray[t.CDouble] | t.CPtr, b: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_greater(a: ndarray[t.CDouble] | t.CPtr, b: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_less_equal(a: ndarray[t.CDouble] | t.CPtr, b: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_greater_equal(a: ndarray[t.CDouble] | t.CPtr, b: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def empty(pool: memhub.MemManager | t.CPtr, n: t.CSizeT) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def full2d(pool: memhub.MemManager | t.CPtr, rows: t.CSizeT, cols: t.CSizeT, val: t.CDouble) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def zeros_like(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def ones_like(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def arange1(pool: memhub.MemManager | t.CPtr, stop: t.CDouble) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def linspace2(pool: memhub.MemManager | t.CPtr, start: t.CDouble, stop: t.CDouble) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def meshgrid(x: ndarray[t.CDouble] | t.CPtr, y: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def det2x2(a: ndarray[t.CDouble] | t.CPtr) -> t.CDouble: pass
+
+def inv2x2(a: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def cross3(a: ndarray[t.CDouble] | t.CPtr, b: ndarray[t.CDouble] | t.CPtr) -> ndarray[t.CDouble] | t.CPtr: pass
+
+def np_interp(x: t.CDouble, xp: ndarray[t.CDouble] | t.CPtr, fp: ndarray[t.CDouble] | t.CPtr) -> t.CDouble: pass
+
+def prod(a: ndarray[t.CDouble] | t.CPtr) -> t.CDouble: pass
+
+def median(a: ndarray[t.CDouble] | t.CPtr) -> t.CDouble: pass
+
+def percentile(a: ndarray[t.CDouble] | t.CPtr, q: t.CDouble) -> t.CDouble: pass