import torch
from torch import device
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
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_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 3000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 750
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_clamp_exp_ge_le_logical_and_2(in_ptr0, in_ptr1,
    out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = -4.0
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = 15.0
    tmp6 = triton_helpers.minimum(tmp4, tmp5)
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp2 >= tmp3
    tmp9 = tmp2 <= tmp5
    tmp10 = tmp8 & tmp9
    tl.store(out_ptr0 + x2, tmp7, xmask)
    tl.store(out_ptr1 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_cat_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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.load(in_ptr2 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp11 = tl.load(in_ptr3 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp12 = tmp10 * tmp11
    tmp13 = tmp9 + tmp12
    tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
    tmp15 = tl.where(tmp6, tmp13, tmp14)
    tmp16 = tl.where(tmp4, tmp5, tmp15)
    tl.store(out_ptr0 + x2, tmp16, xmask)


@triton.jit
def triton_poi_fused_mul_tanh_4(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 = libdevice.tanh(tmp0)
    tmp2 = 4.0
    tmp3 = tmp1 * tmp2
    tl.store(out_ptr0 + x0, tmp3, 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) = 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, (750, 8), (8, 1))
    assert_size_stride(primals_4, (750,), (1,))
    assert_size_stride(primals_5, (750, 750), (750, 1))
    assert_size_stride(primals_6, (750,), (1,))
    assert_size_stride(primals_7, (4, 750), (750, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4, 750), (750, 1))
    assert_size_stride(primals_10, (4,), (1,))
    assert_size_stride(primals_11, (750, 8), (8, 1))
    assert_size_stride(primals_12, (750,), (1,))
    assert_size_stride(primals_13, (750, 750), (750, 1))
    assert_size_stride(primals_14, (750,), (1,))
    assert_size_stride(primals_15, (4, 750), (750, 1))
    assert_size_stride(primals_16, (4,), (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_2
        buf1 = empty_strided_cuda((4, 750), (750, 1), torch.float32)
        extern_kernels.mm(buf0, reinterpret_tensor(primals_3, (8, 750), (1,
            8), 0), out=buf1)
        del primals_3
        buf2 = buf1
        del buf1
        triton_poi_fused_relu_1[grid(3000)](buf2, primals_4, 3000, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_4
        buf3 = empty_strided_cuda((4, 750), (750, 1), torch.float32)
        extern_kernels.mm(buf2, reinterpret_tensor(primals_5, (750, 750), (
            1, 750), 0), out=buf3)
        buf4 = buf3
        del buf3
        triton_poi_fused_relu_1[grid(3000)](buf4, primals_6, 3000, XBLOCK=
            128, num_warps=4, 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,
            (750, 4), (1, 750), 0), alpha=1, beta=1, out=buf5)
        del primals_8
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf4, reinterpret_tensor(primals_9, (750, 4), (1,
            750), 0), out=buf6)
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf17 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        triton_poi_fused_clamp_exp_ge_le_logical_and_2[grid(16)](buf6,
            primals_10, buf7, buf17, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_10
        buf8 = torch.ops.aten.randn.default([4, 4], dtype=torch.float32,
            device=device(type='cuda', index=0), pin_memory=False)
        buf9 = buf8
        del buf8
        buf10 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        triton_poi_fused_cat_3[grid(32)](primals_1, buf5, buf7, buf9, buf10,
            32, XBLOCK=32, num_warps=1, num_stages=1)
        del primals_1
        buf11 = empty_strided_cuda((4, 750), (750, 1), torch.float32)
        extern_kernels.mm(buf10, reinterpret_tensor(primals_11, (8, 750), (
            1, 8), 0), out=buf11)
        buf12 = buf11
        del buf11
        triton_poi_fused_relu_1[grid(3000)](buf12, primals_12, 3000, XBLOCK
            =128, num_warps=4, num_stages=1)
        del primals_12
        buf13 = empty_strided_cuda((4, 750), (750, 1), torch.float32)
        extern_kernels.mm(buf12, reinterpret_tensor(primals_13, (750, 750),
            (1, 750), 0), out=buf13)
        buf14 = buf13
        del buf13
        triton_poi_fused_relu_1[grid(3000)](buf14, primals_14, 3000, XBLOCK
            =128, num_warps=4, num_stages=1)
        del primals_14
        buf15 = buf6
        del buf6
        extern_kernels.addmm(primals_16, buf14, reinterpret_tensor(
            primals_15, (750, 4), (1, 750), 0), alpha=1, beta=1, out=buf15)
        del primals_16
        buf16 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_mul_tanh_4[grid(16)](buf15, buf16, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
    return (buf16, buf5, buf7, buf0, buf2, buf4, buf7, buf9, buf10, buf12,
        buf14, buf15, primals_15, primals_13, primals_11, buf17, primals_9,
        primals_7, primals_5)


class VAENew(nn.Module):

    def __init__(self, state_dim, action_dim, latent_dim, max_action):
        super(VAENew, self).__init__()
        self.e1 = nn.Linear(state_dim + action_dim, 750)
        self.e2 = nn.Linear(750, 750)
        self.mean = nn.Linear(750, latent_dim)
        self.log_std = nn.Linear(750, latent_dim)
        self.d1 = nn.Linear(state_dim + latent_dim, 750)
        self.d2 = nn.Linear(750, 750)
        self.d3 = nn.Linear(750, action_dim)
        self.max_action = max_action
        self.latent_dim = latent_dim

    def decode(self, state, z=None):
        if z is None:
            z = torch.randn((state.shape[0], self.latent_dim)).clamp(-0.5, 0.5)
        a = F.relu(self.d1(torch.cat([state, z], 1)))
        a = F.relu(self.d2(a))
        return self.max_action * torch.tanh(self.d3(a))

    def forward(self, input_0, input_1):
        primals_3 = self.e1.weight
        primals_4 = self.e1.bias
        primals_5 = self.e2.weight
        primals_6 = self.e2.bias
        primals_7 = self.mean.weight
        primals_8 = self.mean.bias
        primals_9 = self.log_std.weight
        primals_10 = self.log_std.bias
        primals_11 = self.d1.weight
        primals_12 = self.d1.bias
        primals_13 = self.d2.weight
        primals_14 = self.d2.bias
        primals_15 = self.d3.weight
        primals_16 = self.d3.bias
        primals_1 = input_0
        primals_2 = 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])
        return output[0], output[1], output[2]


Measured latency_ms: 0.077951