import torch
from torch._inductor.select_algorithm import extern_kernels
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.nn as nn
import torch.nn.init
import torch.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_poi_fused_replication_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 96
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x1 = xindex // 6 % 4
    x2 = xindex // 24
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (4 * (3 * (3 <= x1) + x1 * (x1 < 3)) + 16 * x2 +
        (3 * (3 <= 0 * (0 >= -1 + x0) + (-1 + x0) * (-1 + x0 > 0)) + (0 * (
        0 >= -1 + x0) + (-1 + x0) * (-1 + x0 > 0)) * (0 * (0 >= -1 + x0) + 
        (-1 + x0) * (-1 + x0 > 0) < 3))), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_replication_pad2d_1(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 96
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 6
    x2 = xindex // 24
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (4 * (3 * (3 <= 0 * (0 >= -1 + x1) + (-1 + x1) *
        (-1 + x1 > 0)) + (0 * (0 >= -1 + x1) + (-1 + x1) * (-1 + x1 > 0)) *
        (0 * (0 >= -1 + x1) + (-1 + x1) * (-1 + x1 > 0) < 3)) + 16 * x2 + (
        3 * (3 <= x0) + x0 * (x0 < 3))), xmask)
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_abs_mul_pow_sub_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x2 = xindex // 64
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr2 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp7 = tl.load(in_ptr3 + x3, xmask)
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 - tmp4
    tmp6 = tl_math.abs(tmp5)
    tmp8 = tmp7 * tmp7
    tmp9 = tmp8 * tmp8
    tmp10 = tmp6 * tmp9
    tl.store(out_ptr0 + x3, tmp10, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 4, 4), (16, 16, 4, 1))
    assert_size_stride(primals_2, (1, 1, 1, 3), (3, 3, 3, 1))
    assert_size_stride(primals_3, (1, 1, 3, 1), (3, 3, 1, 1))
    assert_size_stride(primals_4, (1, 1, 1, 3), (3, 3, 3, 1))
    assert_size_stride(primals_5, (1, 1, 3, 1), (3, 3, 1, 1))
    assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 4, 6), (24, 24, 6, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_replication_pad2d_0[grid(96)](primals_1, buf0, 96,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 1, 4, 4), (16, 16, 4, 1))
        buf2 = empty_strided_cuda((4, 1, 6, 4), (24, 24, 4, 1), torch.float32)
        triton_poi_fused_replication_pad2d_1[grid(96)](primals_1, buf2, 96,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf3 = extern_kernels.convolution(buf2, primals_3, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 1, 4, 4), (16, 16, 4, 1))
        buf4 = extern_kernels.convolution(buf0, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 1, 4, 4), (16, 16, 4, 1))
        buf5 = empty_strided_cuda((4, 1, 6, 4), (24, 24, 4, 1), torch.float32)
        triton_poi_fused_replication_pad2d_1[grid(96)](buf4, buf5, 96,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf6 = extern_kernels.convolution(buf5, primals_5, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 1, 4, 4), (16, 16, 4, 1))
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_abs_mul_pow_sub_2[grid(256)](buf1, buf3, buf6,
            primals_6, buf7, 256, XBLOCK=128, num_warps=4, num_stages=1)
    return (buf7, primals_2, primals_3, primals_4, primals_5, primals_6,
        buf0, buf1, buf2, buf3, buf4, buf5, buf6)


class HessianRespNew(nn.Module):

    def __init__(self):
        super(HessianRespNew, self).__init__()
        self.gx = nn.Conv2d(1, 1, kernel_size=(1, 3), bias=False)
        self.gx.weight.data = torch.from_numpy(np.array([[[[0.5, 0, -0.5]]]
            ], dtype=np.float32))
        self.gy = nn.Conv2d(1, 1, kernel_size=(3, 1), bias=False)
        self.gy.weight.data = torch.from_numpy(np.array([[[[0.5], [0], [-
            0.5]]]], dtype=np.float32))
        self.gxx = nn.Conv2d(1, 1, kernel_size=(1, 3), bias=False)
        self.gxx.weight.data = torch.from_numpy(np.array([[[[1.0, -2.0, 1.0
            ]]]], dtype=np.float32))
        self.gyy = nn.Conv2d(1, 1, kernel_size=(3, 1), bias=False)
        self.gyy.weight.data = torch.from_numpy(np.array([[[[1.0], [-2.0],
            [1.0]]]], dtype=np.float32))
        return

    def forward(self, input_0, input_1):
        primals_2 = self.gx.weight
        primals_3 = self.gy.weight
        primals_4 = self.gxx.weight
        primals_5 = self.gyy.weight
        primals_1 = input_0
        primals_6 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]


Measured latency_ms: 0.013997