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.triton_helpers import libdevice, math as tl_math
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_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 32
    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_mul_tanh_1(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp2 = tl.load(in_ptr1 + x0, xmask)
    tmp1 = libdevice.tanh(tmp0)
    tmp3 = tmp1 + tmp2
    tmp4 = 0.5
    tmp5 = tmp3 * tmp4
    tl.store(out_ptr0 + x0, tmp5, xmask)


@triton.jit
def triton_poi_fused_softplus_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp3 = 20.0
    tmp4 = tmp2 > tmp3
    tmp5 = tl_math.exp(tmp2)
    tmp6 = libdevice.log1p(tmp5)
    tmp7 = tmp6 * tmp1
    tmp8 = tl.where(tmp4, tmp0, tmp7)
    tl.store(out_ptr0 + x0, tmp8, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 8), (8, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(32)](primals_1, primals_2, buf0, 32,
            XBLOCK=32, num_warps=1, num_stages=1)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_4, buf0, reinterpret_tensor(primals_3,
            (8, 4), (1, 8), 0), alpha=1, beta=1, out=buf1)
        del primals_3
        del primals_4
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_add_mul_tanh_1[grid(16)](buf1, primals_5, buf2, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_5
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, buf2, reinterpret_tensor(primals_6,
            (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf3)
        del primals_7
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_9, buf2, reinterpret_tensor(primals_8,
            (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf4)
        del primals_9
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_softplus_2[grid(16)](buf4, buf5, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
    return buf3, buf5, buf0, buf1, buf2, buf4, primals_8, primals_6


class CombinerNew(nn.Module):
    """
    Parameterizes `q(z_t | z_{t-1}, x_{t:T}, m{t:T}, s)`, which is the basic building block
    of the guide (i.e. the variational distribution). The dependence on `x_{t:T} and m_{t:T}` is
    through the hidden state of the RNN (see the PyTorch module `rnn` below)
    """

    def __init__(self, z_dim, static_dim, rnn_dim):
        super().__init__()
        self.concat_dim = z_dim + static_dim
        self.lin_z_to_hidden = nn.Linear(self.concat_dim, rnn_dim)
        self.lin_hidden_to_loc = nn.Linear(rnn_dim, z_dim)
        self.lin_hidden_to_scale = nn.Linear(rnn_dim, z_dim)
        self.tanh = nn.Tanh()
        self.softplus = nn.Softplus()

    def forward(self, input_0, input_1, input_2):
        primals_3 = self.lin_z_to_hidden.weight
        primals_4 = self.lin_z_to_hidden.bias
        primals_1 = self.lin_hidden_to_loc.weight
        primals_7 = self.lin_hidden_to_loc.bias
        primals_2 = self.lin_hidden_to_scale.weight
        primals_9 = self.lin_hidden_to_scale.bias
        primals_5 = input_0
        primals_6 = input_1
        primals_8 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0], output[1]


Measured latency_ms: 0.11285