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
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_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    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)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14) = 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,), (1,))
    assert_size_stride(primals_7, (1, 4), (4, 1))
    assert_size_stride(primals_8, (1,), (1,))
    assert_size_stride(primals_9, (4, 8), (8, 1))
    assert_size_stride(primals_10, (4,), (1,))
    assert_size_stride(primals_11, (4, 4), (4, 1))
    assert_size_stride(primals_12, (4,), (1,))
    assert_size_stride(primals_13, (1, 4), (4, 1))
    assert_size_stride(primals_14, (1,), (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.mm(buf0, reinterpret_tensor(primals_3, (8, 4), (1, 8
            ), 0), out=buf1)
        del primals_3
        buf2 = buf1
        del buf1
        triton_poi_fused_relu_1[grid(16)](buf2, primals_4, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_4
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf2, reinterpret_tensor(primals_5, (4, 4), (1, 4
            ), 0), out=buf3)
        buf4 = buf3
        del buf3
        triton_poi_fused_relu_1[grid(16)](buf4, primals_6, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_6
        buf6 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_8, buf4, reinterpret_tensor(primals_7,
            (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf6)
        del primals_8
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf0, reinterpret_tensor(primals_9, (8, 4), (1, 8
            ), 0), out=buf7)
        del primals_9
        buf8 = buf7
        del buf7
        triton_poi_fused_relu_1[grid(16)](buf8, primals_10, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_10
        buf9 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf8, reinterpret_tensor(primals_11, (4, 4), (1, 
            4), 0), out=buf9)
        buf10 = buf9
        del buf9
        triton_poi_fused_relu_1[grid(16)](buf10, primals_12, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_12
        buf12 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_14, buf10, reinterpret_tensor(
            primals_13, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf12)
        del primals_14
    return (buf6, buf12, buf0, buf2, buf4, buf8, buf10, primals_13,
        primals_11, primals_7, primals_5)


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


class QNetworkNew(nn.Module):

    def __init__(self, num_inputs, num_actions, hidden_dim):
        super(QNetworkNew, self).__init__()
        self.linear1 = nn.Linear(num_inputs + num_actions, hidden_dim)
        self.linear2 = nn.Linear(hidden_dim, hidden_dim)
        self.linear3 = nn.Linear(hidden_dim, 1)
        self.linear4 = nn.Linear(num_inputs + num_actions, hidden_dim)
        self.linear5 = nn.Linear(hidden_dim, hidden_dim)
        self.linear6 = nn.Linear(hidden_dim, 1)
        self.apply(weights_init_)

    def forward(self, input_0, input_1):
        primals_3 = self.linear1.weight
        primals_4 = self.linear1.bias
        primals_1 = self.linear2.weight
        primals_6 = self.linear2.bias
        primals_7 = self.linear3.weight
        primals_8 = self.linear3.bias
        primals_9 = self.linear4.weight
        primals_10 = self.linear4.bias
        primals_2 = self.linear5.weight
        primals_12 = self.linear5.bias
        primals_13 = self.linear6.weight
        primals_14 = self.linear6.bias
        primals_5 = input_0
        primals_11 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14])
        return output[0], output[1]


Measured latency_ms: 0.015614