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 = tmp5 + tmp6
    tmp8 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp9 = tmp7 + tmp8
    tmp10 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = 4.0
    tmp13 = tmp11 / tmp12
    tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
    tmp15 = tl.where(tmp4, tmp13, tmp14)
    tmp16 = tmp0 >= tmp3
    tl.full([1], 2, tl.int64)
    tmp19 = tl.load(in_ptr0 + (x0 + 64 * x2), tmp16 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp20 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), tmp16 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp21 = triton_helpers.maximum(tmp19, tmp20)
    tmp22 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), tmp16 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp23 = triton_helpers.maximum(tmp21, tmp22)
    tmp24 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), tmp16 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp25 = triton_helpers.maximum(tmp23, tmp24)
    tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
    tmp27 = tl.where(tmp16, tmp25, tmp26)
    tmp28 = tl.where(tmp4, tmp15, 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, 3, 3), (18, 9, 3, 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=(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 = 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=3, multi_branch=False):
        super(SpatialAttentionNew, self).__init__()
        assert kernel_size in (3, 7), 'kernel size must be 3 or 7'
        self.conv1 = nn.Conv2d(2, 1, 3, padding=1, bias=False)
        self.multi_branch = multi_branch
        if multi_branch:
            self.conv2 = nn.Conv2d(2, 1, 5, padding=2, bias=False)
            self.conv3 = nn.Conv2d(2, 1, 7, padding=3, bias=False)
        self.sigmoid = nn.Sigmoid()

    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: 0.032577