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
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 = 256
    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 = libdevice.tanh(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, xmask)


@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, 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) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (4, 4), (4, 1))
    assert_size_stride(primals_11, (4,), (1,))
    assert_size_stride(primals_12, (1, 4), (4, 1))
    assert_size_stride(primals_13, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_tanh_0[grid(256)](buf1, primals_3, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf2
        triton_poi_fused_tanh_0[grid(256)](buf3, primals_5, 256, 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, 4), (
            4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf4)
        del primals_7
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(256)](buf4, buf5, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf6 = reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf4
        triton_poi_fused__softmax_2[grid(256)](buf5, buf6, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf7 = reinterpret_tensor(buf5, (64, 4), (4, 1), 0)
        del buf5
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf7)
        del primals_8
        buf8 = reinterpret_tensor(buf7, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf7
        triton_poi_fused_tanh_0[grid(256)](buf8, primals_9, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_9
        buf9 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf8, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_10, (4, 4), (1, 4), 0), out=buf9)
        buf10 = reinterpret_tensor(buf9, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf9
        triton_poi_fused_tanh_0[grid(256)](buf10, primals_11, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_11
        buf12 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_13, reinterpret_tensor(buf10, (64, 4),
            (4, 1), 0), reinterpret_tensor(primals_12, (4, 1), (1, 4), 0),
            alpha=1, beta=1, out=buf12)
        del primals_13
    return (buf6, reinterpret_tensor(buf12, (4, 4, 4, 1), (16, 4, 1, 1), 0),
        reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), buf1, buf3, buf6,
        buf8, buf10, primals_12, primals_10, primals_6, primals_4)


class LinActorCriticNew(torch.nn.Module):

    def __init__(self, actor_lr, epsilon, in_dim, h_dim, out_dim):
        super(LinActorCriticNew, self).__init__()
        self.in_dim = in_dim
        self.out_dim = out_dim
        self.h_dim = h_dim
        self.epsilon = epsilon
        self.define_network()
        self.device = torch.device('cuda:0' if torch.cuda.is_available() else
            'cpu:0')
        self

    def normalize(self, tensor):
        return (tensor - tensor.mean()) / (torch.std(tensor) + 1e-05)

    def define_network(self):
        self.relu = torch.nn.LeakyReLU()
        self.leaky_relu = torch.nn.LeakyReLU()
        self.sigmoid = torch.nn.Sigmoid()
        self.tanh = torch.nn.Tanh()
        self.softmax = torch.nn.Softmax(dim=-1)
        size = self.h_dim
        self.p1 = torch.nn.Linear(self.in_dim, size)
        self.p2 = torch.nn.Linear(size, size)
        self.v1 = torch.nn.Linear(self.in_dim, size)
        self.v2 = torch.nn.Linear(size, size)
        self.pi = torch.nn.Linear(size, self.out_dim)
        self.value = torch.nn.Linear(size, 1)
        self.critic_loss = torch.nn.MSELoss()

    def forward(self, input_0):
        primals_2 = self.p1.weight
        primals_3 = self.p1.bias
        primals_4 = self.p2.weight
        primals_5 = self.p2.bias
        primals_6 = self.v1.weight
        primals_7 = self.v1.bias
        primals_8 = self.v2.weight
        primals_9 = self.v2.bias
        primals_10 = self.pi.weight
        primals_11 = self.pi.bias
        primals_12 = self.value.weight
        primals_13 = self.value.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,
            primals_10, primals_11, primals_12, primals_13])
        return output[0], output[1]


Measured latency_ms: 0.058711