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 as nn
import torch.nn.functional as F
from torch.distributions import Normal
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_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, None)
    tl.store(out_ptr0 + x2, tmp6, None)


@triton.jit
def triton_poi_fused_add_clamp_mul_softplus_tanh_1(in_ptr0, in_ptr1,
    in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp4 = tl.load(in_ptr1 + x0, xmask)
    tmp5 = tl.load(in_ptr2 + x0, xmask)
    tmp1 = libdevice.tanh(tmp0)
    tmp2 = 4.0
    tmp3 = tmp1 * tmp2
    tmp6 = 20.0
    tmp7 = tmp5 > tmp6
    tmp8 = tl_math.exp(tmp5)
    tmp9 = libdevice.log1p(tmp8)
    tmp10 = tl.where(tmp7, tmp5, tmp9)
    tmp11 = 0.001
    tmp12 = tmp10 + tmp11
    tmp13 = tmp4 * tmp12
    tmp14 = tmp3 + tmp13
    tmp15 = -4.0
    tmp16 = triton_helpers.maximum(tmp14, tmp15)
    tmp17 = triton_helpers.minimum(tmp16, tmp2)
    tl.store(out_ptr0 + x0, tmp17, 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, (256, 4), (4, 1))
    assert_size_stride(primals_2, (256,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (256, 256), (256, 1))
    assert_size_stride(primals_5, (256,), (1,))
    assert_size_stride(primals_6, (4, 256), (256, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 256), (256, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 256), (256, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 256), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 256), (4096, 1024, 256, 1), 0
            )
        del buf0
        buf11 = empty_strided_cuda((4, 4, 4, 256), (4096, 1024, 256, 1),
            torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(16384)](buf1,
            primals_2, buf11, 16384, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 256), (256, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 256), (256, 1), 0),
            reinterpret_tensor(primals_4, (256, 256), (1, 256), 0), out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 256), (4096, 1024, 256, 1), 0
            )
        del buf2
        buf10 = empty_strided_cuda((4, 4, 4, 256), (4096, 1024, 256, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_0[grid(16384)](buf3,
            primals_5, buf10, 16384, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 256),
            (256, 1), 0), reinterpret_tensor(primals_6, (256, 4), (1, 256),
            0), alpha=1, beta=1, out=buf4)
        del primals_7
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf3, (64, 256),
            (256, 1), 0), reinterpret_tensor(primals_8, (256, 4), (1, 256),
            0), alpha=1, beta=1, out=buf5)
        del primals_9
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf7 = torch.ops.aten.normal_functional.default(buf6)
        buf8 = buf7
        del buf7
        buf9 = buf6
        del buf6
        triton_poi_fused_add_clamp_mul_softplus_tanh_1[grid(256)](buf4,
            buf8, buf5, buf9, 256, XBLOCK=256, num_warps=4, num_stages=1)
    return buf9, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf1, (64, 256), (256, 1), 0
        ), reinterpret_tensor(buf3, (64, 256), (256, 1), 0
        ), buf4, buf5, buf8, primals_8, primals_6, buf10, primals_4, buf11


class ActorNew(nn.Module):

    def __init__(self, state_dim, action_dim, max_action):
        super(ActorNew, self).__init__()
        self.l1 = nn.Linear(state_dim, 256)
        self.l2 = nn.Linear(256, 256)
        self.l3_sigma = nn.Linear(256, action_dim)
        self.l3_mean = nn.Linear(256, action_dim)
        self.max_action = max_action
        self.action_dim = action_dim
        self.state_dim = state_dim

    def forward_all(self, state, *args):
        a = F.relu(self.l1(state))
        a = F.relu(self.l2(a))
        mean = self.max_action * torch.tanh(self.l3_mean(a))
        sigma = F.softplus(self.l3_sigma(a)) + 0.001
        normal = Normal(mean, sigma)
        action = normal.rsample()
        log_prob = normal.log_prob(action).sum(1, keepdim=True)
        action = action.clamp(-self.max_action, self.max_action)
        return action, log_prob, mean, sigma

    def forward_all_sample(self, state, *args):
        a = F.relu(self.l1(state))
        a = F.relu(self.l2(a))
        mean = self.max_action * torch.tanh(self.l3_mean(a))
        sigma = F.softplus(self.l3_sigma(a)) + 0.001
        normal = Normal(mean, sigma)
        action1 = normal.rsample().clamp(-self.max_action, self.max_action)
        action2 = normal.rsample().clamp(-self.max_action, self.max_action)
        prob1 = normal.log_prob(action1).sum(1, keepdim=True)
        prob2 = normal.log_prob(action2).sum(1, keepdim=True)
        probm = normal.log_prob(mean).sum(1, keepdim=True)
        prob1 = torch.exp(prob1)
        prob2 = torch.exp(prob2)
        probm = torch.exp(probm)
        return action1, action2, mean, sigma, prob1, prob2, probm

    def forward(self, input_0):
        primals_1 = self.l1.weight
        primals_2 = self.l1.bias
        primals_4 = self.l2.weight
        primals_5 = self.l2.bias
        primals_6 = self.l3_sigma.weight
        primals_7 = self.l3_sigma.bias
        primals_8 = self.l3_mean.weight
        primals_9 = self.l3_mean.bias
        primals_3 = 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]


Measured latency_ms: 0.076578