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
from torch import nn
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_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 1600
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 400
    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_1(in_ptr0, in_ptr1, in_ptr2, 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 = tl.load(in_ptr1 + x0, xmask)
    tmp2 = tl.load(in_ptr2 + x0, xmask)
    tmp3 = 0.5
    tmp4 = tmp2 * tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tmp1 * tmp5
    tmp7 = tmp0 + tmp6
    tl.store(out_ptr0 + x0, tmp7, 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) = args
    args.clear()
    assert_size_stride(primals_1, (4, 1024), (1024, 1))
    assert_size_stride(primals_2, (400, 1024), (1024, 1))
    assert_size_stride(primals_3, (400,), (1,))
    assert_size_stride(primals_4, (4, 400), (400, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 400), (400, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (400, 4), (4, 1))
    assert_size_stride(primals_9, (400,), (1,))
    assert_size_stride(primals_10, (1024, 400), (400, 1))
    assert_size_stride(primals_11, (1024,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 400), (400, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (1024, 
            400), (1, 1024), 0), out=buf0)
        del primals_2
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(1600)](buf1, primals_3, 1600, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf1, reinterpret_tensor(primals_4,
            (400, 4), (1, 400), 0), alpha=1, beta=1, out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, buf1, reinterpret_tensor(primals_6,
            (400, 4), (1, 400), 0), alpha=1, beta=1, out=buf3)
        del primals_7
        buf4 = torch.ops.aten.randn.default([4, 4], dtype=torch.float32,
            device=device(type='cuda', index=0), pin_memory=False)
        buf5 = buf4
        del buf4
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_add_exp_mul_1[grid(16)](buf2, buf5, buf3, buf6, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf7 = empty_strided_cuda((4, 400), (400, 1), torch.float32)
        extern_kernels.mm(buf6, reinterpret_tensor(primals_8, (4, 400), (1,
            4), 0), out=buf7)
        buf8 = buf7
        del buf7
        triton_poi_fused_relu_0[grid(1600)](buf8, primals_9, 1600, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_9
        buf9 = empty_strided_cuda((4, 1024), (1024, 1), torch.float32)
        extern_kernels.addmm(primals_11, buf8, reinterpret_tensor(
            primals_10, (400, 1024), (1, 400), 0), alpha=1, beta=1, out=buf9)
        del primals_11
    return (buf9, buf2, buf3, primals_1, buf1, buf3, buf5, buf6, buf8,
        primals_10, primals_8, primals_6, primals_4)


class MLP_VAENew(nn.Module):

    def __init__(self, ZDIMS):
        super().__init__()
        self.z_dims = ZDIMS
        self.fc1 = nn.Linear(1024, 400)
        self.relu = nn.ReLU()
        self.fc21 = nn.Linear(400, ZDIMS)
        self.fc22 = nn.Linear(400, ZDIMS)
        self.fc3 = nn.Linear(ZDIMS, 400)
        self.fc4 = nn.Linear(400, 1024)

    def encoder(self, x):
        """
        Input vector x --> fully connected 1 --> RELU --> fully connected 21, fully connected 22
        
        Parameters
        ----------
        x: [batch size, 784], batch size number of digits of 28x28 pixels each
        
        Returns 
        -------
        (mu, logvar): ZDIMS mean units one for each latent dimension, ZDIMS variance units one for each 
        latent dimension
        """
        batch_size = x.shape[0]
        x = x.view(batch_size, -1)
        h1 = self.relu(self.fc1(x))
        mu = self.fc21(h1)
        logvar = self.fc22(h1)
        return mu, logvar

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

    def decoder(self, z):
        h3 = self.relu(self.fc3(z))
        return self.fc4(h3)

    def sample(self, n):
        with torch.no_grad():
            z = torch.randn(n, self.z_dims)
            z = z
            samples = self.decoder(z)
            samples = torch.clamp(samples, 0, 1)
        return samples.cpu().numpy()

    def forward(self, input_0):
        primals_2 = self.fc1.weight
        primals_3 = self.fc1.bias
        primals_4 = self.fc21.weight
        primals_5 = self.fc21.bias
        primals_6 = self.fc22.weight
        primals_7 = self.fc22.bias
        primals_8 = self.fc3.weight
        primals_9 = self.fc3.bias
        primals_10 = self.fc4.weight
        primals_11 = self.fc4.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])
        return output[0], output[1], output[2]


Measured latency_ms: 0.091694