import torch
import torch.nn as nn


class EncoderNO2(nn.Module):

    def __init__(self, D, H, M):
        super().__init__()
        self.D = D
        self.M = M
        self.H = H
        self.enc1 = nn.Linear(in_features=self.D, out_features=self.H)
        self.enc2 = nn.Linear(in_features=self.H, out_features=self.M * 2)
        self.loc_param = nn.Linear(in_features=self.M, out_features=self.M)
        self.scale_param = nn.Linear(in_features=self.M, out_features=self.M)

    def forward(self, x):
        x = self.enc1(x)
        x = nn.functional.relu(x)
        x = self.enc2(x)
        x = x.view(-1, 2, self.M)
        mu = self.loc_param(x[:, 0, :])
        log_var = self.scale_param(x[:, 1, :])
        std = torch.exp(log_var / 2)
        return mu, std


def get_inputs():
    return [torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {'D': 4, 'H': 4, 'M': 4}]

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_threshold_backward_0(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_div_exp_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    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 = 0.5
    tmp4 = tmp2 * tmp3
    tmp5 = tl_math.exp(tmp4)
    tl.store(in_out_ptr0 + x2, tmp5, 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, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (8, 4), (4, 1))
    assert_size_stride(primals_5, (8,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(256)](buf1,
            primals_2, buf6, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 8), (8, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_4, (4, 8), (1, 4), 0),
            alpha=1, beta=1, out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf2, (64, 4), (
            8, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf3)
        del primals_7
        buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (64, 4), (8, 1), 4),
            reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf4)
        buf5 = buf4
        del buf4
        triton_poi_fused_div_exp_1[grid(256)](buf5, primals_9, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_9
    return buf3, buf5, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(
        buf2, (64, 4), (8, 1), 0), reinterpret_tensor(buf2, (64, 4), (8, 1), 4
        ), buf5, primals_8, primals_6, primals_4, buf6


class EncoderNO2New(nn.Module):

    def __init__(self, D, H, M):
        super().__init__()
        self.D = D
        self.M = M
        self.H = H
        self.enc1 = nn.Linear(in_features=self.D, out_features=self.H)
        self.enc2 = nn.Linear(in_features=self.H, out_features=self.M * 2)
        self.loc_param = nn.Linear(in_features=self.M, out_features=self.M)
        self.scale_param = nn.Linear(in_features=self.M, out_features=self.M)

    def forward(self, input_0):
        primals_1 = self.enc1.weight
        primals_2 = self.enc1.bias
        primals_4 = self.enc2.weight
        primals_5 = self.enc2.bias
        primals_6 = self.loc_param.weight
        primals_7 = self.loc_param.bias
        primals_8 = self.scale_param.weight
        primals_9 = self.scale_param.bias
        primals_3 = 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]
