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
import torch.nn as nn
from collections import OrderedDict
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_tanh_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 2000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 500
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = 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, (500, 8), (8, 1))
    assert_size_stride(primals_4, (500,), (1,))
    assert_size_stride(primals_5, (784, 500), (500, 1))
    assert_size_stride(primals_6, (784,), (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, 500), (500, 1), torch.float32)
        extern_kernels.mm(buf0, reinterpret_tensor(primals_3, (8, 500), (1,
            8), 0), out=buf1)
        del primals_3
        buf2 = buf1
        del buf1
        triton_poi_fused_tanh_1[grid(2000)](buf2, primals_4, 2000, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_4
        buf3 = empty_strided_cuda((4, 784), (784, 1), torch.float32)
        extern_kernels.addmm(primals_6, buf2, reinterpret_tensor(primals_5,
            (500, 784), (1, 500), 0), alpha=1, beta=1, out=buf3)
        del primals_6
    return buf3, buf0, buf2, primals_5


class DecoderNew(nn.Module):

    def __init__(self, style_dim, class_dim):
        super(DecoderNew, self).__init__()
        self.linear_model = nn.Sequential(OrderedDict([('linear_1', nn.
            Linear(in_features=style_dim + class_dim, out_features=500,
            bias=True)), ('tan_h_1', nn.Tanh()), ('linear_2', nn.Linear(
            in_features=500, out_features=784, bias=True))]))

    def forward(self, input_0, input_1):
        primals_3 = self.linear_model.linear_1.weight
        primals_4 = self.linear_model.linear_1.bias
        primals_5 = self.linear_model.linear_2.weight
        primals_6 = self.linear_model.linear_2.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]


Measured latency_ms: 0.042628