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
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_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_native_layer_norm_relu_1(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp1, tmp3)
    tmp5 = tmp2 + tmp4
    tmp7 = triton_helpers.maximum(tmp1, tmp6)
    tmp8 = tmp5 + tmp7
    tmp10 = triton_helpers.maximum(tmp1, tmp9)
    tmp11 = tmp8 + tmp10
    tmp12 = 4.0
    tmp13 = tmp11 / tmp12
    tmp14 = tmp2 - tmp13
    tmp15 = tmp14 * tmp14
    tmp16 = tmp4 - tmp13
    tmp17 = tmp16 * tmp16
    tmp18 = tmp15 + tmp17
    tmp19 = tmp7 - tmp13
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = tmp10 - tmp13
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp25 = tmp24 / tmp12
    tmp26 = 1e-05
    tmp27 = tmp25 + tmp26
    tmp28 = libdevice.rsqrt(tmp27)
    tl.store(out_ptr0 + x0, tmp13, xmask)
    tl.store(out_ptr1 + x0, tmp28, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_relu_2(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp3 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = tmp2 - tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 * tmp7
    tmp10 = tmp8 + tmp9
    tl.store(out_ptr0 + x2, tmp10, 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, primals_15, primals_16, primals_17,
        primals_18, primals_19, primals_20, primals_21, primals_22) = 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,), (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, (4,), (1,))
    assert_size_stride(primals_10, (4,), (1,))
    assert_size_stride(primals_11, (1, 4), (4, 1))
    assert_size_stride(primals_12, (1,), (1,))
    assert_size_stride(primals_13, (4, 8), (8, 1))
    assert_size_stride(primals_14, (4,), (1,))
    assert_size_stride(primals_15, (4,), (1,))
    assert_size_stride(primals_16, (4,), (1,))
    assert_size_stride(primals_17, (4, 4), (4, 1))
    assert_size_stride(primals_18, (4,), (1,))
    assert_size_stride(primals_19, (4,), (1,))
    assert_size_stride(primals_20, (4,), (1,))
    assert_size_stride(primals_21, (1, 4), (4, 1))
    assert_size_stride(primals_22, (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, 1), (1, 4), torch.float32)
        buf3 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        triton_poi_fused_native_layer_norm_relu_1[grid(4)](buf1, buf2, buf3,
            4, XBLOCK=4, num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_relu_2[grid(16)](buf1, buf2,
            buf3, primals_5, primals_6, buf4, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        del primals_6
        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 = buf3
        del buf3
        buf7 = buf2
        del buf2
        triton_poi_fused_native_layer_norm_relu_1[grid(4)](buf5, buf6, buf7,
            4, XBLOCK=4, num_warps=1, num_stages=1)
        buf8 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_relu_2[grid(16)](buf5, buf6,
            buf7, primals_9, primals_10, buf8, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        del primals_10
        buf10 = reinterpret_tensor(buf7, (4, 1), (1, 1), 0)
        del buf7
        extern_kernels.addmm(primals_12, buf8, reinterpret_tensor(
            primals_11, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf10)
        del primals_12
        buf11 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_14, buf0, reinterpret_tensor(
            primals_13, (8, 4), (1, 8), 0), alpha=1, beta=1, out=buf11)
        del primals_13
        del primals_14
        buf12 = buf6
        del buf6
        buf13 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        triton_poi_fused_native_layer_norm_relu_1[grid(4)](buf11, buf12,
            buf13, 4, XBLOCK=4, num_warps=1, num_stages=1)
        buf14 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_relu_2[grid(16)](buf11, buf12,
            buf13, primals_15, primals_16, buf14, 16, XBLOCK=16, num_warps=
            1, num_stages=1)
        del primals_16
        buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_18, buf14, reinterpret_tensor(
            primals_17, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf15)
        del primals_18
        buf16 = buf13
        del buf13
        buf17 = buf12
        del buf12
        triton_poi_fused_native_layer_norm_relu_1[grid(4)](buf15, buf16,
            buf17, 4, XBLOCK=4, num_warps=1, num_stages=1)
        buf18 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_relu_2[grid(16)](buf15, buf16,
            buf17, primals_19, primals_20, buf18, 16, XBLOCK=16, num_warps=
            1, num_stages=1)
        del buf16
        del primals_20
        buf20 = reinterpret_tensor(buf17, (4, 1), (1, 1), 0)
        del buf17
        extern_kernels.addmm(primals_22, buf18, reinterpret_tensor(
            primals_21, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf20)
        del primals_22
    return (buf10, buf20, primals_5, primals_9, primals_15, primals_19,
        buf0, buf1, buf4, buf5, buf8, buf11, buf14, buf15, buf18,
        primals_21, primals_17, primals_11, primals_7)


class LN_TD3CriticNew(nn.Module):

    def __init__(self, state_dim, action_dim, hidden_size1, hidden_size2):
        super(LN_TD3CriticNew, self).__init__()
        self.l1 = nn.Linear(state_dim + action_dim, hidden_size1)
        self.ln1 = nn.LayerNorm(hidden_size1)
        self.l2 = nn.Linear(hidden_size1, hidden_size2)
        self.ln2 = nn.LayerNorm(hidden_size2)
        self.l3 = nn.Linear(hidden_size2, 1)
        self.l4 = nn.Linear(state_dim + action_dim, hidden_size1)
        self.ln4 = nn.LayerNorm(hidden_size1)
        self.l5 = nn.Linear(hidden_size1, hidden_size2)
        self.ln5 = nn.LayerNorm(hidden_size2)
        self.l6 = nn.Linear(hidden_size2, 1)

    def Q1(self, inputs, actions):
        xu = torch.cat([inputs, actions], 1)
        x1 = F.relu(self.l1(xu))
        x1 = self.ln1(x1)
        x1 = F.relu(self.l2(x1))
        x1 = self.ln2(x1)
        x1 = self.l3(x1)
        return x1

    def forward(self, input_0, input_1):
        primals_3 = self.l1.weight
        primals_4 = self.l1.bias
        primals_5 = self.ln1.weight
        primals_6 = self.ln1.bias
        primals_1 = self.l2.weight
        primals_8 = self.l2.bias
        primals_9 = self.ln2.weight
        primals_10 = self.ln2.bias
        primals_11 = self.l3.weight
        primals_12 = self.l3.bias
        primals_13 = self.l4.weight
        primals_14 = self.l4.bias
        primals_15 = self.ln4.weight
        primals_16 = self.ln4.bias
        primals_2 = self.l5.weight
        primals_18 = self.l5.bias
        primals_19 = self.ln5.weight
        primals_20 = self.ln5.bias
        primals_21 = self.l6.weight
        primals_22 = self.l6.bias
        primals_7 = input_0
        primals_17 = 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,
            primals_15, primals_16, primals_17, primals_18, primals_19,
            primals_20, primals_21, primals_22])
        return output[0], output[1]


Measured latency_ms: 0.196876