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.utils.data
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 = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x1 = xindex // 2
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + x1, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 2, tl.int64)
    tmp9 = tl.load(in_ptr1 + x1, 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_leaky_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 32
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + x2, tmp7, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_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
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + x2, tmp7, 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, 1), (1, 1))
    assert_size_stride(primals_2, (4, 1), (1, 1))
    assert_size_stride(primals_3, (32, 2), (2, 1))
    assert_size_stride(primals_4, (32,), (1,))
    assert_size_stride(primals_5, (32, 32), (32, 1))
    assert_size_stride(primals_6, (32,), (1,))
    assert_size_stride(primals_7, (16, 32), (32, 1))
    assert_size_stride(primals_8, (16,), (1,))
    assert_size_stride(primals_9, (1, 16), (16, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(8)](primals_1, primals_2, buf0, 8,
            XBLOCK=8, num_warps=1, num_stages=1)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        extern_kernels.mm(buf0, reinterpret_tensor(primals_3, (2, 32), (1, 
            2), 0), out=buf1)
        del primals_3
        buf2 = buf1
        del buf1
        triton_poi_fused_leaky_relu_1[grid(128)](buf2, primals_4, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_4
        buf3 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        extern_kernels.mm(buf2, reinterpret_tensor(primals_5, (32, 32), (1,
            32), 0), out=buf3)
        buf4 = buf3
        del buf3
        triton_poi_fused_leaky_relu_1[grid(128)](buf4, primals_6, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_6
        buf5 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        extern_kernels.mm(buf4, reinterpret_tensor(primals_7, (32, 16), (1,
            32), 0), out=buf5)
        buf6 = buf5
        del buf5
        triton_poi_fused_leaky_relu_2[grid(64)](buf6, primals_8, 64, XBLOCK
            =64, num_warps=1, num_stages=1)
        del primals_8
        buf7 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf6, reinterpret_tensor(primals_9, (16, 1), (1, 
            16), 0), out=buf7)
    return buf7, buf0, buf2, buf4, buf6, primals_9, primals_7, primals_5


def add_layer(seq, ix, n_inputs, n_outputs, nonlin, normalization):
    seq.add_module('L' + str(ix), nn.Linear(n_inputs, n_outputs))
    if ix > 0 and normalization:
        if normalization == 'LN':
            seq.main.add_module('A' + str(ix), nn.LayerNorm(n_outputs))
        else:
            raise ValueError('Unknown normalization: {}'.format(normalization))
    if nonlin == 'LeakyReLU':
        seq.add_module('N' + str(ix), nn.LeakyReLU(0.2, inplace=True))
    elif nonlin == 'ReLU':
        seq.add_module('N' + str(ix), nn.ReLU(inplace=True))
    elif nonlin == 'Sigmoid':
        seq.add_module('N' + str(ix), nn.Sigmoid())


class D_concatNew(nn.Module):

    def __init__(self, insizes=[1, 1], layerSizes=[32, 32, 16], nonlin=
        'LeakyReLU', normalization=None):
        super(D_concatNew, self).__init__()
        insize = sum(insizes)
        self.main = nn.Sequential()
        for ix, n_inputs, n_outputs in zip(range(len(layerSizes)), [insize] +
            layerSizes[:-1], layerSizes):
            add_layer(self.main, ix, n_inputs, n_outputs, nonlin, normalization
                )
            self.PhiD = n_outputs
        self.V = nn.Linear(self.PhiD, 1, bias=False)
        self.V.weight.data *= 100

    def forward(self, input_0, input_1):
        primals_3 = self.main.L0.weight
        primals_4 = self.main.L0.bias
        primals_5 = self.main.L1.weight
        primals_6 = self.main.L1.bias
        primals_7 = self.main.L2.weight
        primals_8 = self.main.L2.bias
        primals_9 = self.V.weight
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]


Measured latency_ms: 0.077252