import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import numpy as np
import torch.utils.data
import torch
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_per_fused_abs_sub_sum_0(in_ptr0, out_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4
    rnumel = 9
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r1 = rindex % 3
    r2 = rindex // 3
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (4 + r1 + 4 * r2 + 64 * x0), rmask & xmask,
        other=0.0)
    tmp1 = tl.load(in_ptr0 + (r1 + 4 * r2 + 64 * x0), rmask & xmask, other=0.0)
    tmp3 = tl.load(in_ptr0 + (17 + r1 + 4 * r2 + 64 * x0), rmask & xmask,
        other=0.0)
    tmp4 = tl.load(in_ptr0 + (16 + r1 + 4 * r2 + 64 * x0), rmask & xmask,
        other=0.0)
    tmp2 = tmp0 - tmp1
    tmp5 = tmp3 - tmp4
    tmp6 = tmp2 - tmp5
    tmp7 = tl_math.abs(tmp6)
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK, RBLOCK])
    tmp10 = tl.where(rmask & xmask, tmp8, 0)
    tmp11 = tl.sum(tmp10, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp11, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4,), (1,), torch.float32)
        get_raw_stream(0)
        triton_per_fused_abs_sub_sum_0[grid(4)](arg0_1, buf0, 4, 9, XBLOCK=
            1, num_warps=2, num_stages=1)
        del arg0_1
    return buf0,


class IntegralGrad:
    grad_norm_scale = 20

    def __init__(self):
        return

    @staticmethod
    def grad_merge(grad_x_mtx, grad_y_mtx, dim=-3):
        grad_mtx = torch.cat((grad_x_mtx, grad_y_mtx), dim=dim)
        return grad_mtx

    @staticmethod
    def grad_split(grad_mtx):
        grad_x_mtx = grad_mtx[..., 0:1, :, :]
        grad_y_mtx = grad_mtx[..., 1:2, :, :]
        return grad_x_mtx, grad_y_mtx

    @staticmethod
    def get_gradien_from_img(pic, grad_norm):
        ori_img = np.array(pic)
        if ori_img.ndim == 2:
            ori_img = np.expand_dims(ori_img, 2)
        pad_ori_img = np.pad(ori_img, ((0, 1), (0, 1), (0, 0)), 'edge')
        grad_mtx = IntegralGrad.get_gradien_img(pad_ori_img)
        grad_mtx = IntegralGrad.normalize(grad_mtx, grad_norm)
        grad_mtx = grad_mtx.permute(2, 0, 1)
        return grad_mtx

    @staticmethod
    def normalize(grad_mtx, grad_norm):
        if grad_norm['need_norm']:
            grad_mtx /= 20.0
        return grad_mtx

    @staticmethod
    def get_gradien_img(ori_img):
        ori_mtx = torch.tensor(ori_img).float()
        grad_x_mtx = ori_mtx[:-1, 1:, :] - ori_mtx[:-1, :-1, :]
        grad_y_mtx = ori_mtx[1:, :-1, :] - ori_mtx[:-1, :-1, :]
        return IntegralGrad.grad_merge(grad_x_mtx, grad_y_mtx, dim=-1)

    @staticmethod
    def get_gradien(ori_mtx):
        ori_np = ori_mtx.numpy()
        if len(ori_np.shape) == 3:
            pad_mtd = (0, 0), (0, 1), (0, 1)
        elif len(ori_np.shape) == 4:
            pad_mtd = (0, 0), (0, 0), (0, 1), (0, 1)
        else:
            raise ValueError('Unknown ori_np.shape')
        ori_mtx = torch.tensor(np.pad(ori_np, pad_mtd, 'edge'))
        grad_x_mtx = ori_mtx[..., :, :-1, 1:] - ori_mtx[..., :, :-1, :-1]
        grad_y_mtx = ori_mtx[..., :, 1:, :-1] - ori_mtx[..., :, :-1, :-1]
        return IntegralGrad.grad_merge(grad_x_mtx, grad_y_mtx)

    @staticmethod
    def to_grad_norm(grad_mtx):
        grad_x_mtx, grad_y_mtx = IntegralGrad.grad_split(grad_mtx)
        grad_img_mtx = torch.sqrt(torch.pow(grad_x_mtx, 2) + torch.pow(
            grad_y_mtx, 2))
        grad_img_mtx = IntegralGrad.grad_norm_scale * grad_img_mtx
        return grad_img_mtx

    @staticmethod
    def to_grad_img(grad_mtx):
        grad_x_mtx, grad_y_mtx = IntegralGrad.grad_split(grad_mtx)
        grad_img_mtx = torch.sqrt(torch.pow(grad_x_mtx, 2) + torch.pow(
            grad_y_mtx, 2))
        grad_img = IntegralGrad.grad_norm_scale * grad_img_mtx.type(torch.uint8
            ).numpy()
        return grad_img

    @staticmethod
    def integral_grad_path_x2y_auto_C(ori_mtx, grad_mtx, bottom=0):
        integrated_mtx = 0 * ori_mtx
        grad_x_mtx, grad_y_mtx = IntegralGrad.grad_split(grad_mtx)
        width = integrated_mtx.shape[-1]
        height = integrated_mtx.shape[-2]
        for x in range(0, width):
            for y in range(0, height):
                if x == 0 and y == 0:
                    integrated_mtx[..., 0, y, x] = bottom
                elif y == 0:
                    integrated_mtx[..., 0, y, x] = integrated_mtx[..., 0, y,
                        x - 1] + grad_x_mtx[..., 0, y, x - 1]
                else:
                    integrated_mtx[..., 0, y, x] = integrated_mtx[..., 0, y -
                        1, x] + grad_y_mtx[..., 0, y - 1, x]
        min_val = torch.min(integrated_mtx)
        if min_val < bottom:
            integrated_mtx += bottom - min_val
        return integrated_mtx

    @staticmethod
    def integral_grad_path_y2x_auto_C(ori_mtx, grad_mtx, bottom=0):
        integrated_mtx = 0 * ori_mtx
        grad_x_mtx, grad_y_mtx = IntegralGrad.grad_split(grad_mtx)
        width = integrated_mtx.shape[-1]
        height = integrated_mtx.shape[-2]
        for y in range(0, height):
            for x in range(0, width):
                if x == 0 and y == 0:
                    integrated_mtx[..., 0, y, x] = bottom
                elif x == 0:
                    integrated_mtx[..., 0, y, x] = integrated_mtx[..., 0, y -
                        1, x] + grad_y_mtx[..., 0, y - 1, x]
                else:
                    integrated_mtx[..., 0, y, x] = integrated_mtx[..., 0, y,
                        x - 1] + grad_x_mtx[..., 0, y, x - 1]
        min_val = torch.min(integrated_mtx)
        if min_val < bottom:
            integrated_mtx += bottom - min_val
        return integrated_mtx

    @staticmethod
    def integral_grad_mtx(ori_mtx, grad_mtx):
        integrated_mtx = 0 * ori_mtx
        grad_x_mtx, grad_y_mtx = IntegralGrad.grad_split(grad_mtx)
        width = integrated_mtx.shape[-1]
        height = integrated_mtx.shape[-2]
        for x in range(0, width):
            for y in range(0, height):
                if x == 0 and y == 0:
                    integrated_mtx[..., 0, y, x] = 0
                elif y == 0:
                    integrated_mtx[..., 0, y, x] = integrated_mtx[..., 0, y,
                        x - 1] + grad_x_mtx[..., 0, y, x - 1]
                else:
                    integrated_mtx[..., 0, y, x] = integrated_mtx[..., 0, y -
                        1, x] + grad_y_mtx[..., 0, y - 1, x]
        return integrated_mtx

    @staticmethod
    def to_integrated_img(integrated_mtx):
        min_val = torch.min(integrated_mtx)
        if min_val < 0:
            integrated_mtx -= min_val
        return integrated_mtx.type(torch.uint8).numpy()


class IntegIndepenPathLossNew(nn.Module):

    def __init__(self) ->None:
        super(IntegIndepenPathLossNew, self).__init__()

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0]


Measured latency_ms: 0.014805