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 import triton_helpers
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_poi_fused_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 2
    x0 = xindex % 16
    x2 = xindex // 32
    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 + 64 * x2), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp9 = triton_helpers.maximum(tmp7, tmp8)
    tmp10 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp11 = triton_helpers.maximum(tmp9, tmp10)
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp4, tmp11, tmp12)
    tmp14 = tmp0 >= tmp3
    tl.full([1], 2, tl.int64)
    tmp17 = tl.load(in_ptr0 + (x0 + 64 * x2), tmp14 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp18 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), tmp14 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp19 = tmp17 + tmp18
    tmp20 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), tmp14 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp21 = tmp19 + tmp20
    tmp22 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), tmp14 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp23 = tmp21 + tmp22
    tmp24 = 4.0
    tmp25 = tmp23 / tmp24
    tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
    tmp27 = tl.where(tmp14, tmp25, tmp26)
    tmp28 = tl.where(tmp4, tmp13, tmp27)
    tl.store(out_ptr0 + x3, tmp28, xmask)


@triton.jit
def triton_poi_fused_sigmoid_1(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.sigmoid(tmp0)
    tl.store(in_out_ptr0 + x0, tmp1, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 2, 7, 7), (98, 49, 7, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(128)](primals_1, buf0, 128, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
            padding=(3, 3), 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 = buf1
        del buf1
        triton_poi_fused_sigmoid_1[grid(64)](buf2, 64, XBLOCK=64, num_warps
            =1, num_stages=1)
    return buf2, primals_2, buf0, buf2


class SpatialAttentionNew(nn.Module):

    def __init__(self, kernel_size=7):
        super(SpatialAttentionNew, self).__init__()
        assert kernel_size in (3, 7), 'kernel size must be 3 or 7'
        padding = 3 if kernel_size == 7 else 1
        self.conv1 = nn.Conv2d(in_channels=2, out_channels=1, kernel_size=
            kernel_size, padding=padding, bias=False)

    def forward(self, input_0):
        primals_2 = self.conv1.weight
        primals_1 = input_0
        output = call([primals_1, primals_2])
        return output[0]


Measured latency_ms: 0.033324