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
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn.functional as F
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_clamp_mul_softplus_tanh_1(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 = 20.0
    tmp2 = tmp0 > tmp1
    tmp3 = tl_math.exp(tmp0)
    tmp4 = libdevice.log1p(tmp3)
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tmp6 = libdevice.tanh(tmp5)
    tmp7 = tmp0 * tmp6
    tmp8 = 6.0
    tmp9 = triton_helpers.minimum(tmp7, tmp8)
    tl.store(out_ptr0 + x0, tmp9, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10) = 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, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (1, 4), (4, 1))
    assert_size_stride(primals_10, (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.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_clamp_mul_softplus_tanh_1[grid(16)](buf1, buf2, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_6, buf2, reinterpret_tensor(primals_5,
            (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf3)
        del primals_6
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_clamp_mul_softplus_tanh_1[grid(16)](buf3, buf4, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_8, buf4, reinterpret_tensor(primals_7,
            (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf5)
        del primals_8
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_clamp_mul_softplus_tanh_1[grid(16)](buf5, buf6, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf8 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_10, buf6, reinterpret_tensor(primals_9,
            (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf8)
        del primals_10
    return (buf8, buf0, buf1, buf2, buf3, buf4, buf5, buf6, primals_9,
        primals_7, primals_5)


def mish(x):
    """
        Mish: A Self Regularized Non-Monotonic Neural Activation Function
        https://arxiv.org/abs/1908.08681v1
        implemented for PyTorch / FastAI by lessw2020
        https://github.com/lessw2020/mish
        param:
            x: output of a layer of a neural network
        return: mish activation function
    """
    return torch.clamp(x * torch.tanh(F.softplus(x)), max=6)


class SoftQNetworkNew(nn.Module):

    def __init__(self, num_inputs, num_actions, hidden_size, init_w=0.003):
        super(SoftQNetworkNew, self).__init__()
        self.linear1 = nn.Linear(num_inputs + num_actions, hidden_size)
        self.linear2 = nn.Linear(hidden_size, hidden_size)
        self.linear2_3 = nn.Linear(hidden_size, hidden_size)
        self.linear3 = nn.Linear(hidden_size, 1)
        self.linear1.weight.data.uniform_(-init_w, init_w)
        self.linear1.bias.data.uniform_(-init_w, init_w)
        self.linear2.weight.data.uniform_(-init_w, init_w)
        self.linear2.bias.data.uniform_(-init_w, init_w)
        self.linear2_3.weight.data.uniform_(-init_w, init_w)
        self.linear2_3.bias.data.uniform_(-init_w, init_w)
        self.linear3.weight.data.uniform_(-init_w, init_w)
        self.linear3.bias.data.uniform_(-init_w, init_w)

    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_2 = self.linear2_3.weight
        primals_8 = self.linear2_3.bias
        primals_9 = self.linear3.weight
        primals_10 = self.linear3.bias
        primals_5 = input_0
        primals_7 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9, primals_10])
        return output[0]


Measured latency_ms: 0.088989