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 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 = 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_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_add_exp_mul_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 8
    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)
    tmp6 = tl.load(in_ptr2 + x0, xmask)
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = tl_math.exp(tmp2)
    tmp5 = tmp3 * tmp4
    tmp7 = tmp5 + tmp6
    tl.store(out_ptr0 + x0, tmp7, xmask)


@triton.jit
def triton_poi_fused_cat_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 24
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x1 = xindex // 6
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 2, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (2 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 6, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x1 + (-2 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, 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) = 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, 4), (4, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (2, 4), (4, 1))
    assert_size_stride(primals_8, (2,), (1,))
    assert_size_stride(primals_9, (2, 4), (4, 1))
    assert_size_stride(primals_10, (2,), (1,))
    assert_size_stride(primals_11, (4, 6), (6, 1))
    assert_size_stride(primals_12, (4,), (1,))
    assert_size_stride(primals_13, (4, 4), (4, 1))
    assert_size_stride(primals_14, (4,), (1,))
    assert_size_stride(primals_15, (4, 4), (4, 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_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf0, reinterpret_tensor(primals_3, (8, 4), (1, 8
            ), 0), out=buf1)
        del primals_3
        buf2 = buf1
        del buf1
        triton_poi_fused_relu_1[grid(16)](buf2, primals_4, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_4
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf2, reinterpret_tensor(primals_5, (4, 4), (1, 4
            ), 0), out=buf3)
        buf4 = buf3
        del buf3
        triton_poi_fused_relu_1[grid(16)](buf4, primals_6, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_6
        buf5 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        extern_kernels.addmm(primals_8, buf4, reinterpret_tensor(primals_7,
            (4, 2), (1, 4), 0), alpha=1, beta=1, out=buf5)
        del primals_8
        buf6 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        extern_kernels.addmm(primals_10, buf4, reinterpret_tensor(primals_9,
            (4, 2), (1, 4), 0), alpha=1, beta=1, out=buf6)
        del primals_10
        buf7 = torch.ops.aten.randn.default([4, 2], dtype=torch.float32,
            device=device(type='cuda', index=0), pin_memory=False)
        buf8 = buf7
        del buf7
        buf9 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        triton_poi_fused_add_exp_mul_2[grid(8)](buf6, buf8, buf5, buf9, 8,
            XBLOCK=8, num_warps=1, num_stages=1)
        buf10 = empty_strided_cuda((4, 6), (6, 1), torch.float32)
        triton_poi_fused_cat_3[grid(24)](buf9, primals_2, buf10, 24, XBLOCK
            =32, num_warps=1, num_stages=1)
        del primals_2
        buf11 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf10, reinterpret_tensor(primals_11, (6, 4), (1,
            6), 0), out=buf11)
        buf12 = buf11
        del buf11
        triton_poi_fused_relu_1[grid(16)](buf12, primals_12, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_12
        buf13 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf12, reinterpret_tensor(primals_13, (4, 4), (1,
            4), 0), out=buf13)
        buf14 = buf13
        del buf13
        triton_poi_fused_relu_1[grid(16)](buf14, primals_14, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_14
        buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_16, buf14, reinterpret_tensor(
            primals_15, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf15)
        del primals_16
    return (buf15, buf6, buf5, buf9, buf0, buf2, buf4, buf6, buf8, buf10,
        buf12, buf14, primals_15, primals_13, primals_11, primals_9,
        primals_7, primals_5)


class Encoder(nn.Module):

    def __init__(self, sample_size, condition_size, hidden_size):
        super().__init__()
        self.fc1 = nn.Linear(sample_size + condition_size, hidden_size)
        self.fc2 = nn.Dropout(p=0.5)
        self.fc3 = nn.Linear(hidden_size, hidden_size)

    def forward(self, x, c):
        x = torch.cat([x, c], 1)
        p_x = F.relu(self.fc1(x))
        p_x = self.fc2(p_x)
        p_x = F.relu(self.fc3(p_x))
        return p_x


class LatentZ(nn.Module):

    def __init__(self, hidden_size, latent_size):
        super().__init__()
        self.mu = nn.Linear(hidden_size, latent_size)
        self.logvar = nn.Linear(hidden_size, latent_size)

    def forward(self, p_x):
        mu = self.mu(p_x)
        logvar = self.logvar(p_x)
        std = torch.exp(0.5 * logvar)
        eps = torch.randn_like(std)
        return std * eps + mu, logvar, mu


class Decoder(nn.Module):

    def __init__(self, latent_size, condition_size, hidden_size_1,
        hidden_size_2, sample_size):
        super().__init__()
        self.fc1 = nn.Linear(latent_size + condition_size, hidden_size_1)
        self.fc2 = nn.Dropout(p=0.5)
        self.fc3 = nn.Linear(hidden_size_1, hidden_size_2)
        self.fc4 = nn.Linear(hidden_size_2, sample_size)

    def forward(self, z_x, c):
        z = torch.cat([z_x, c], 1)
        q_x = F.relu(self.fc1(z))
        q_x = self.fc2(q_x)
        q_x = F.relu(self.fc3(q_x))
        q_x = self.fc4(q_x)
        return q_x


class CVAENew(nn.Module):

    def __init__(self, sample_size, condition_size, hidden_encoder_size,
        hidden_decoder_1_size, hidden_decoder_2_size, latent_size=2):
        super().__init__()
        self.sample_size = sample_size
        self.condition_size = condition_size
        self.hidden_encoder_size = hidden_encoder_size
        self.hidden_decoder_1__size = hidden_decoder_1_size
        self.hidden_decoder_2__size = hidden_decoder_2_size
        self.latent_size = latent_size
        self.encoder = Encoder(sample_size, condition_size, hidden_encoder_size
            )
        self.latent_z = LatentZ(hidden_encoder_size, latent_size)
        self.decoder = Decoder(latent_size, condition_size,
            hidden_decoder_1_size, hidden_decoder_2_size, sample_size)

    def forward(self, input_0, input_1):
        primals_3 = self.encoder.fc1.weight
        primals_4 = self.encoder.fc1.bias
        primals_1 = self.encoder.fc3.weight
        primals_6 = self.encoder.fc3.bias
        primals_7 = self.latent_z.mu.weight
        primals_8 = self.latent_z.mu.bias
        primals_9 = self.latent_z.logvar.weight
        primals_10 = self.latent_z.logvar.bias
        primals_11 = self.decoder.fc1.weight
        primals_12 = self.decoder.fc1.bias
        primals_2 = self.decoder.fc3.weight
        primals_14 = self.decoder.fc3.bias
        primals_5 = self.decoder.fc4.weight
        primals_16 = self.decoder.fc4.bias
        primals_13 = input_0
        primals_15 = 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], output[3]


Measured latency_ms: 0.053123