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 libdevice
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 3
    x0 = xindex % 16
    x2 = xindex // 48
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x2), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp5 * tmp5
    tmp7 = tl.load(in_ptr1 + (x0 + 16 * x2), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp8 = tmp7 * tmp7
    tmp9 = tmp6 + tmp8
    tmp10 = 1e-06
    tmp11 = tmp9 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
    tmp14 = tl.where(tmp4, tmp12, tmp13)
    tmp15 = tmp0 >= tmp3
    tmp16 = tl.full([1], 2, tl.int64)
    tmp17 = tmp0 < tmp16
    tmp18 = tmp15 & tmp17
    tmp19 = tl.load(in_ptr2 + (x0 + 16 * x2), tmp18 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp20 = tmp19 * tmp19
    tmp21 = tl.load(in_ptr3 + (x0 + 16 * x2), tmp18 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp22 = tmp21 * tmp21
    tmp23 = tmp20 + tmp22
    tmp24 = tmp23 + tmp10
    tmp25 = libdevice.sqrt(tmp24)
    tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
    tmp27 = tl.where(tmp18, tmp25, tmp26)
    tmp28 = tmp0 >= tmp16
    tl.full([1], 3, tl.int64)
    tmp31 = tl.load(in_ptr4 + (x0 + 16 * x2), tmp28 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp32 = tmp31 * tmp31
    tmp33 = tl.load(in_ptr5 + (x0 + 16 * x2), tmp28 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp34 = tmp33 * tmp33
    tmp35 = tmp32 + tmp34
    tmp36 = tmp35 + tmp10
    tmp37 = libdevice.sqrt(tmp36)
    tmp38 = tl.full(tmp37.shape, 0.0, tmp37.dtype)
    tmp39 = tl.where(tmp28, tmp37, tmp38)
    tmp40 = tl.where(tmp18, tmp27, tmp39)
    tmp41 = tl.where(tmp4, tmp14, tmp40)
    tl.store(out_ptr0 + x3, tmp41, xmask)


def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (1, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(arg2_1, (1, 1, 3, 3), (9, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(reinterpret_tensor(arg0_1, (4, 1,
            4, 4), (64, 0, 4, 1), 0), arg1_1, stride=(1, 1), padding=(1, 1),
            dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=1, bias=None)
        assert_size_stride(buf0, (4, 1, 4, 4), (16, 16, 4, 1))
        buf1 = extern_kernels.convolution(reinterpret_tensor(arg0_1, (4, 1,
            4, 4), (64, 0, 4, 1), 0), arg2_1, stride=(1, 1), padding=(1, 1),
            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 = extern_kernels.convolution(reinterpret_tensor(arg0_1, (4, 1,
            4, 4), (64, 0, 4, 1), 16), arg1_1, stride=(1, 1), padding=(1, 1
            ), dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=1, bias=None)
        assert_size_stride(buf2, (4, 1, 4, 4), (16, 16, 4, 1))
        buf3 = extern_kernels.convolution(reinterpret_tensor(arg0_1, (4, 1,
            4, 4), (64, 0, 4, 1), 16), arg2_1, stride=(1, 1), padding=(1, 1
            ), 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(reinterpret_tensor(arg0_1, (4, 1,
            4, 4), (64, 0, 4, 1), 32), arg1_1, stride=(1, 1), padding=(1, 1
            ), 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))
        del arg1_1
        buf5 = extern_kernels.convolution(reinterpret_tensor(arg0_1, (4, 1,
            4, 4), (64, 0, 4, 1), 32), arg2_1, stride=(1, 1), padding=(1, 1
            ), dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=1, bias=None)
        assert_size_stride(buf5, (4, 1, 4, 4), (16, 16, 4, 1))
        del arg0_1
        del arg2_1
        buf6 = empty_strided_cuda((4, 3, 4, 4), (48, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(192)](buf0, buf1, buf2, buf3, buf4,
            buf5, buf6, 192, XBLOCK=128, num_warps=4, num_stages=1)
        del buf0
        del buf1
        del buf2
        del buf3
        del buf4
        del buf5
    return buf6,


class GetGradientNew(nn.Module):
    """ generate the gradient map
    """

    def __init__(self):
        super(GetGradientNew, self).__init__()
        kernel_v = [[0, -1, 0], [0, 0, 0], [0, 1, 0]]
        kernel_h = [[0, 0, 0], [-1, 0, 1], [0, 0, 0]]
        kernel_h = torch.FloatTensor(kernel_h).unsqueeze(0).unsqueeze(0)
        kernel_v = torch.FloatTensor(kernel_v).unsqueeze(0).unsqueeze(0)
        self.weight_h = nn.Parameter(data=kernel_h, requires_grad=False)
        self.weight_v = nn.Parameter(data=kernel_v, requires_grad=False)

    def forward(self, input_0):
        arg1_1 = self.weight_h
        arg2_1 = self.weight_v
        arg0_1 = input_0
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0]


Measured latency_ms: 0.093539