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
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_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_add_1(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
    x3 = xindex
    x0 = xindex % 4
    x2 = xindex // 16
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 8, 4), (32, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(128)](primals_1, primals_2, buf0, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(buf0, primals_3, out=buf1)
        del primals_3
        buf2 = buf1
        del buf1
        triton_poi_fused_add_1[grid(64)](buf2, primals_4, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_4
    return buf2, reinterpret_tensor(buf0, (4, 8, 4), (32, 1, 8), 0)


def weights_init_(m):
    if isinstance(m, nn.Linear) or isinstance(m, nn.Conv2d) or isinstance(m,
        nn.ConvTranspose2d):
        torch.nn.init.xavier_uniform_(m.weight, gain=1)
        torch.nn.init.constant_(m.bias, 0)


class Swish(nn.Module):

    def __init__(self):
        super(Swish, self).__init__()

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


class EnsembleFC(nn.Module):

    def __init__(self, in_features: 'int', out_features: 'int',
        ensemble_size: 'int', weight_decay: 'float'=0.0, bias: 'bool'=True
        ) ->None:
        super(EnsembleFC, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.ensemble_size = ensemble_size
        self.weight = nn.Parameter(torch.Tensor(ensemble_size, in_features,
            out_features))
        self.weight_decay = weight_decay
        if bias:
            self.bias = nn.Parameter(torch.Tensor(ensemble_size, out_features))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self) ->None:
        pass

    def forward(self, input: 'torch.Tensor') ->torch.Tensor:
        w_times_x = torch.bmm(input, self.weight)
        return torch.add(w_times_x, self.bias[:, None, :])

    def extra_repr(self) ->str:
        return 'in_features={}, out_features={}, bias={}'.format(self.
            in_features, self.out_features, self.bias is not None)


class EnsembleModelNew(nn.Module):

    def __init__(self, feature_size, ensemble_size, use_decay=False):
        super(EnsembleModelNew, self).__init__()
        self.nn1 = EnsembleFC(feature_size + feature_size, feature_size,
            ensemble_size, weight_decay=2.5e-05)
        self.use_decay = use_decay
        self.apply(weights_init_)
        self.swish = Swish()

    def get_decay_loss(self):
        decay_loss = 0.0
        for m in self.children():
            if isinstance(m, EnsembleFC):
                decay_loss += m.weight_decay * torch.sum(torch.square(m.weight)
                    ) / 2.0
        return decay_loss

    def loss(self, mean, labels):
        """
        mean, logvar: Ensemble_size x N x dim
        labels: N x dim
        """
        assert len(mean.shape) == len(labels.shape) == 3
        mse_loss = torch.mean(torch.pow(mean - labels, 2), dim=(1, 2))
        total_loss = torch.sum(mse_loss)
        return total_loss, mse_loss

    def forward(self, input_0, input_1):
        primals_3 = self.nn1.weight
        primals_4 = self.nn1.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.019998