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 math as tl_math
import torch.nn as 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 = 24000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 1500
    x1 = xindex // 1500
    tmp0 = tl.load(in_out_ptr0 + (x0 + 1504 * x1), 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 + (x0 + 1504 * x1), tmp4, xmask)


@triton.jit
def triton_poi_fused_exp_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 480
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 30
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl_math.exp(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1500, 16), (16, 1))
    assert_size_stride(primals_3, (1500,), (1,))
    assert_size_stride(primals_4, (1500, 1500), (1500, 1))
    assert_size_stride(primals_5, (1500,), (1,))
    assert_size_stride(primals_6, (30, 1500), (1500, 1))
    assert_size_stride(primals_7, (30,), (1,))
    assert_size_stride(primals_8, (30, 1500), (1500, 1))
    assert_size_stride(primals_9, (30,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 1500), (1504, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 16), (16, 1), 
            0), reinterpret_tensor(primals_2, (16, 1500), (1, 16), 0), out=buf0
            )
        del primals_2
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(24000)](buf1, primals_3, 24000, XBLOCK
            =256, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((16, 1500), (1504, 1), torch.float32)
        extern_kernels.mm(buf1, reinterpret_tensor(primals_4, (1500, 1500),
            (1, 1500), 0), out=buf2)
        buf3 = buf2
        del buf2
        triton_poi_fused_relu_0[grid(24000)](buf3, primals_5, 24000, XBLOCK
            =256, num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((16, 30), (30, 1), torch.float32)
        extern_kernels.addmm(primals_7, buf3, reinterpret_tensor(primals_6,
            (1500, 30), (1, 1500), 0), alpha=1, beta=1, out=buf4)
        del primals_7
        buf5 = empty_strided_cuda((16, 30), (30, 1), torch.float32)
        extern_kernels.mm(buf3, reinterpret_tensor(primals_8, (1500, 30), (
            1, 1500), 0), out=buf5)
        buf6 = buf5
        del buf5
        triton_poi_fused_exp_1[grid(480)](buf6, primals_9, 480, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_9
    return buf4, buf6, reinterpret_tensor(primals_1, (16, 16), (16, 1), 0
        ), buf1, buf3, buf6, primals_8, primals_6, primals_4


class EncoderNew(nn.Module):
    """
    Takes in data, returns mu and sigma for variational approximation of latent variable.
    """

    def __init__(self, alph_size, seq_len, z_dim=30, hidden_architecture=[
        1500, 1500]):
        super(EncoderNew, self).__init__()
        self.hidden1 = nn.Linear(alph_size * seq_len, hidden_architecture[0])
        self.hidden2 = nn.Linear(hidden_architecture[0], hidden_architecture[1]
            )
        self.final1 = nn.Linear(hidden_architecture[1], z_dim)
        self.final2 = nn.Linear(hidden_architecture[1], z_dim)
        self.relu = nn.ReLU()
        self.alph_size = alph_size
        self.seq_len = seq_len

    def forward(self, input_0):
        primals_2 = self.hidden1.weight
        primals_3 = self.hidden1.bias
        primals_4 = self.hidden2.weight
        primals_5 = self.hidden2.bias
        primals_6 = self.final1.weight
        primals_7 = self.final1.bias
        primals_8 = self.final2.weight
        primals_9 = self.final2.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])
        return output[0], output[1]


Measured latency_ms: 0.068337