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
import torch.nn.functional as F
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 = 64
    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 + 64 * 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 = 64.0
    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 = 256
    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 = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x4 = xindex // 64
    x0 = xindex % 64
    x2 = xindex // 256
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.sigmoid(tmp1)
    tmp3 = tmp0 * tmp2
    tmp5 = tl.sigmoid(tmp4)
    tmp6 = tmp0 * tmp5
    tmp7 = triton_helpers.maximum(tmp3, tmp6)
    tl.store(out_ptr0 + x3, 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, 4), (256, 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, 1), (4, 1, 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, 1), (4, 1, 16, 16, 16),
            torch.float32)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_mean_0[grid(16)](buf1, primals_1, 16, 64, XBLOCK=8,
            num_warps=4, 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, 1), padding=(0, 0, 0), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 1, 4, 4, 4), (64, 64, 16, 4, 1))
        buf6 = buf5
        del buf5
        triton_poi_fused_convolution_2[grid(256)](buf6, primals_7, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_7
        buf7 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        triton_poi_fused_maximum_mul_sigmoid_3[grid(1024)](primals_1, buf4,
            buf6, buf7, 1024, XBLOCK=128, 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 ChannelSELayer3D(nn.Module):
    """
    3D extension of Squeeze-and-Excitation (SE) block described in:
        *Hu et al., Squeeze-and-Excitation Networks, arXiv:1709.01507*
        *Zhu et al., AnatomyNet, arXiv:arXiv:1808.05238*
    """

    def __init__(self, num_channels, reduction_ratio=2, act_type=nn.ReLU):
        """
        :param num_channels: No of input channels
        :param reduction_ratio: By how much should the num_channels should be reduced
        """
        super(ChannelSELayer3D, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool3d(1)
        num_channels_reduced = num_channels // reduction_ratio
        self.reduction_ratio = reduction_ratio
        self.fc1 = nn.Linear(num_channels, num_channels_reduced, bias=True)
        self.fc2 = nn.Linear(num_channels_reduced, num_channels, bias=True)
        self.act = act_type()
        self.sigmoid = nn.Sigmoid()

    def forward(self, input_tensor):
        """
        :param input_tensor: X, shape = (batch_size, num_channels, D, H, W)
        :return: output tensor
        """
        batch_size, num_channels, _D, _H, _W = input_tensor.size()
        squeeze_tensor = self.avg_pool(input_tensor)
        fc_out_1 = self.act(self.fc1(squeeze_tensor.view(batch_size,
            num_channels)))
        fc_out_2 = self.sigmoid(self.fc2(fc_out_1))
        output_tensor = torch.mul(input_tensor, fc_out_2.view(batch_size,
            num_channels, 1, 1, 1))
        return output_tensor


class SpatialSELayer3D(nn.Module):
    """
    3D extension of SE block -- squeezing spatially and exciting channel-wise described in:
        *Roy et al., Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks, MICCAI 2018*
    """

    def __init__(self, num_channels):
        """
        :param num_channels: No of input channels
        """
        super(SpatialSELayer3D, self).__init__()
        self.conv = nn.Conv3d(num_channels, 1, 1)
        self.sigmoid = nn.Sigmoid()

    def forward(self, input_tensor, weights=None):
        """
        :param weights: weights for few shot learning
        :param input_tensor: X, shape = (batch_size, num_channels, D, H, W)
        :return: output_tensor
        """
        batch_size, channel, D, H, W = input_tensor.size()
        if weights:
            weights = weights.view(1, channel, 1, 1)
            out = F.conv2d(input_tensor, weights)
        else:
            out = self.conv(input_tensor)
        squeeze_tensor = self.sigmoid(out)
        output_tensor = torch.mul(input_tensor, squeeze_tensor.view(
            batch_size, 1, D, H, W))
        return output_tensor


class SpatialChannelSELayer3DNew(nn.Module):
    """
       3D extension of concurrent spatial and channel squeeze & excitation:
           *Roy et al., Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks,
           arXiv:1803.02579*
       """

    def __init__(self, num_channels, reduction_ratio=2, act_type=nn.ReLU):
        """
        :param num_channels: No of input channels
        :param reduction_ratio: By how much should the num_channels should be reduced
        """
        super(SpatialChannelSELayer3DNew, self).__init__()
        self.cSE = ChannelSELayer3D(num_channels, reduction_ratio, act_type
            =act_type)
        self.sSE = SpatialSELayer3D(num_channels)

    def forward(self, input_0):
        primals_2 = self.cSE.fc1.weight
        primals_3 = self.cSE.fc1.bias
        primals_4 = self.cSE.fc2.weight
        primals_5 = self.cSE.fc2.bias
        primals_6 = self.sSE.conv.weight
        primals_7 = self.sSE.conv.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: 0.045189