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
import torch.nn.functional as F
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_tanh_0(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 = 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,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (16, 4), (4, 1))
    assert_size_stride(primals_3, (16,), (1,))
    assert_size_stride(primals_4, (4, 16), (16, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (16, 4), (4, 1))
    assert_size_stride(primals_7, (16,), (1,))
    assert_size_stride(primals_8, (1, 16), (16, 1))
    assert_size_stride(primals_9, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 16),
            (1, 4), 0), out=buf0)
        del primals_2
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_tanh_0[grid(64)](buf1, primals_3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf1, reinterpret_tensor(primals_4,
            (16, 4), (1, 16), 0), alpha=1, beta=1, out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_6, (4, 16),
            (1, 4), 0), out=buf3)
        del primals_6
        buf4 = buf3
        del buf3
        triton_poi_fused_tanh_0[grid(64)](buf4, primals_7, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_7
        buf6 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_9, buf4, reinterpret_tensor(primals_8,
            (16, 1), (1, 16), 0), alpha=1, beta=1, out=buf6)
        del primals_9
    return buf2, buf6, primals_1, buf1, buf4, primals_8, primals_4


def set_init(layers):
    for layer in layers:
        nn.init.normal_(layer.weight, mean=0.0, std=0.1)
        nn.init.constant_(layer.bias, 0.0)


class NetNew(nn.Module):

    def __init__(self, s_dim, a_dim, hidden=16):
        super(NetNew, self).__init__()
        self.s_dim = s_dim
        self.a_dim = a_dim
        self.pi1 = nn.Linear(s_dim, hidden)
        self.pi2 = nn.Linear(hidden, a_dim)
        self.v1 = nn.Linear(s_dim, hidden)
        self.v2 = nn.Linear(hidden, 1)
        set_init([self.pi1, self.pi2, self.v1, self.v2])
        self.distribution = torch.distributions.Categorical
        self.initialized = True

    def choose_action(self, s):
        self.eval()
        logits, _ = self.forward(s)
        prob = F.softmax(logits, dim=1).data
        m = self.distribution(prob)
        return m.sample().numpy()[0]

    def loss_func(self, s, a, v_t):
        self.train()
        logits, values = self.forward(s)
        td = v_t - values
        c_loss = td.pow(2)
        probs = F.softmax(logits, dim=1)
        m = self.distribution(probs)
        exp_v = m.log_prob(a) * td.detach().squeeze()
        a_loss = -exp_v
        total_loss = (c_loss + a_loss).mean()
        return total_loss

    def forward(self, input_0):
        primals_2 = self.pi1.weight
        primals_3 = self.pi1.bias
        primals_4 = self.pi2.weight
        primals_5 = self.pi2.bias
        primals_6 = self.v1.weight
        primals_7 = self.v1.bias
        primals_8 = self.v2.weight
        primals_9 = self.v2.bias
        primals_1 = input_0
        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: 0.056963