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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_per_fused_mean_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    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, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp5 = 16.0
    tmp6 = tmp4 / tmp5
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp6, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4
    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.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.full([1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tl.store(in_out_ptr0 + x0, tmp5, xmask)


@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


@triton.jit
def triton_poi_fused_add_mul_sigmoid_3(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    x4 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last')
    tmp2 = tl.sigmoid(tmp1)
    tmp3 = tmp0 * tmp2
    tmp5 = tl.sigmoid(tmp4)
    tmp6 = tmp0 * tmp5
    tmp7 = tmp3 + tmp6
    tl.store(out_ptr0 + x3, tmp7, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_4, (1,), (1,))
    assert_size_stride(primals_5, (4, 1, 1, 1), (1, 1, 1, 1))
    assert_size_stride(primals_6, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(1, 
            1), padding=(0, 0), 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 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf2 = reinterpret_tensor(buf1, (4, 4, 1, 1), (4, 1, 1, 1), 0)
        del buf1
        get_raw_stream(0)
        triton_per_fused_mean_0[grid(16)](buf2, primals_2, 16, 16, XBLOCK=8,
            num_warps=2, num_stages=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, 1, 1), (1, 1, 1, 1))
        buf4 = buf3
        del buf3
        triton_poi_fused_convolution_relu_1[grid(4)](buf4, primals_4, 4,
            XBLOCK=4, num_warps=1, num_stages=1)
        del primals_4
        buf5 = extern_kernels.convolution(buf4, primals_5, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 4, 1, 1), (4, 1, 1, 1))
        buf6 = buf5
        del buf5
        triton_poi_fused_convolution_2[grid(16)](buf6, primals_6, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_6
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_sigmoid_3[grid(256)](primals_2, buf0, buf6,
            buf7, 256, XBLOCK=256, num_warps=4, num_stages=1)
    return (buf7, primals_1, primals_2, primals_3, primals_5, buf0, buf2,
        buf4, buf6)


class GAB(nn.Module):

    def __init__(self, input_dim, reduction=4):
        super(GAB, self).__init__()
        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
        self.conv1 = nn.Conv2d(input_dim, input_dim // reduction,
            kernel_size=1, stride=1)
        self.conv2 = nn.Conv2d(input_dim // reduction, input_dim,
            kernel_size=1, stride=1)
        self.relu = nn.ReLU(inplace=True)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        z = self.global_avgpool(x)
        z = self.relu(self.conv1(z))
        z = self.sigmoid(self.conv2(z))
        return x * z


class SpatialAttention2d(nn.Module):

    def __init__(self, channel):
        super(SpatialAttention2d, self).__init__()
        self.squeeze = nn.Conv2d(channel, 1, kernel_size=1, bias=False)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        z = self.squeeze(x)
        z = self.sigmoid(z)
        return x * z


class SCseNew(nn.Module):

    def __init__(self, dim):
        super(SCseNew, self).__init__()
        self.satt = SpatialAttention2d(dim)
        self.catt = GAB(dim)

    def forward(self, input_0):
        primals_1 = self.satt.squeeze.weight
        primals_3 = self.catt.conv1.weight
        primals_4 = self.catt.conv1.bias
        primals_5 = self.catt.conv2.weight
        primals_6 = self.catt.conv2.bias
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]


Measured latency_ms: 0.015