# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_6/inductor_cache/ss/css2vgc4fn2whgjtxqbz26ufoeubsrx5ky4ggjari7vgaadues7h.py
# Topologically Sorted Source Nodes: [dPdy, dQdx, sub_2, abs_1, res], Original ATen: [aten.sub, aten.abs, aten.sum]
# Source node to ATen node mapping:
#   abs_1 => abs_1
#   dPdy => sub
#   dQdx => sub_1
#   res => sum_1
#   sub_2 => sub_2
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%slice_9, %slice_12), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%slice_15, %slice_18), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub, %sub_1), kwargs = {})
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%sub_2,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%abs_1, [-3, -2, -1]), kwargs = {})
triton_per_fused_abs_sub_sum_0 = async_compile.triton('triton_per_fused_abs_sub_sum_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.persistent_reduction(
    size_hints=[4, 16],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_abs_sub_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@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, :]
    roffset = 0
    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)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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)
        # Topologically Sorted Source Nodes: [dPdy, dQdx, sub_2, abs_1, res], Original ATen: [aten.sub, aten.abs, aten.sum]
        stream0 = get_raw_stream(0)
        triton_per_fused_abs_sub_sum_0.run(arg0_1, buf0, 4, 9, grid=grid(4), stream=stream0)
        del arg0_1
    return (buf0, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import numpy as np
import torch.utils.data
import torch
import torch.nn as nn


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 IntegIndepenPathLoss(nn.Module):

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

    def forward(self, input):
        grad_x_mtx, grad_y_mtx = IntegralGrad.grad_split(input)
        dPdy = grad_x_mtx[..., :, 1:, :-1] - grad_x_mtx[..., :, :-1, :-1]
        dQdx = grad_y_mtx[..., :, :-1, 1:] - grad_y_mtx[..., :, :-1, :-1]
        reduce_axes = -3, -2, -1
        res = (dPdy - dQdx).abs().sum(dim=reduce_axes)
        return res


def get_inputs():
    return [torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {}]

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]
