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_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 2
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_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.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tl.store(in_out_ptr0 + x0, tmp3, xmask)


@triton.jit
def triton_poi_fused_maximum_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 // 16
    x4 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr1 + x4, xmask)
    tmp4 = tl.load(in_ptr2 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp5 = tl.sigmoid(tmp4)
    tmp6 = tmp5 * tmp2
    tmp7 = triton_helpers.maximum(tmp3, tmp6)
    tl.store(out_ptr0 + x4, tmp7, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (2, 4), (4, 1))
    assert_size_stride(primals_3, (2,), (1,))
    assert_size_stride(primals_4, (4, 2), (2, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_7, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_mean_0[grid(16)](buf1, primals_1, 16, 16, XBLOCK=1,
            num_warps=2, num_stages=1)
        buf2 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (4, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 2), (1, 4), 0), out=buf2)
        del primals_2
        buf3 = buf2
        del buf2
        triton_poi_fused_relu_1[grid(8)](buf3, primals_3, 8, XBLOCK=8,
            num_warps=1, num_stages=1)
        del primals_3
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf3, reinterpret_tensor(primals_4,
            (2, 4), (1, 2), 0), alpha=1, beta=1, out=buf4)
        del primals_5
        buf5 = extern_kernels.convolution(primals_1, primals_6, stride=(1, 
            1), padding=(0, 0), 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))
        buf6 = buf5
        del buf5
        triton_poi_fused_convolution_2[grid(64)](buf6, primals_7, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_7
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_maximum_mul_sigmoid_3[grid(256)](buf4, primals_1,
            buf6, buf7, 256, XBLOCK=256, num_warps=4, num_stages=1)
    return buf7, primals_1, primals_6, reinterpret_tensor(buf1, (4, 4), (4,
        1), 0), buf3, buf4, buf6, primals_4


class cSEmodule(nn.Module):
    """ SpatialSequeezeExcitationModule
        input: [B, C, H, W] torch tensor
        output: [B, C, H, W] torch tensor
    """

    def __init__(self, in_channel):
        super().__init__()
        self.global_avg = nn.AdaptiveAvgPool2d(1)
        self.flatten = nn.Flatten()
        self.down_linear = nn.Linear(in_channel, in_channel // 2)
        self.up_linear = nn.Linear(in_channel // 2, in_channel)
        self.relu = nn.ReLU()
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        b, c, _h, _w = x.shape
        skip_connection = x
        x = self.global_avg(x)
        x = self.flatten(x)
        x = self.down_linear(x)
        x = self.relu(x)
        x = self.up_linear(x)
        x = self.sigmoid(x)
        x = x.reshape(b, c, 1, 1)
        x = x * skip_connection
        return x


class sSEmodule(nn.Module):
    """ ChannelSequeezeExcitationModule
        input: [B, C, H, W] torch tensor
        output: [B, C, H, W] torch tensor
    """

    def __init__(self, in_channel):
        super().__init__()
        self.conv2d = nn.Conv2d(in_channel, 1, 1)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        skip_connection = x
        x = self.conv2d(x)
        x = self.sigmoid(x)
        None
        x = x * skip_connection
        return x


class scSEmoduleNew(nn.Module):
    """ ConcurrentSpatialChannelSequeezeExcitationModule
        input: [B, C, H, W] torch tensor
        output: [B, C, H, W] torch tensor
    """

    def __init__(self, in_channel):
        super().__init__()
        self.cSEmodule = cSEmodule(in_channel=in_channel)
        self.sSEmodule = sSEmodule(in_channel=in_channel)

    def forward(self, input_0):
        primals_2 = self.cSEmodule.down_linear.weight
        primals_3 = self.cSEmodule.down_linear.bias
        primals_4 = self.cSEmodule.up_linear.weight
        primals_5 = self.cSEmodule.up_linear.bias
        primals_6 = self.sSEmodule.conv2d.weight
        primals_7 = self.sSEmodule.conv2d.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.035808