import torch
import torch.nn as nn
import torch.utils.data


def sample_K_laplace_MN(MAP, upper_Qinv, lower_HHinv):
    Z = MAP.data.new(MAP.size()).normal_(mean=0, std=1)
    all_mtx_sample = MAP + torch.matmul(torch.matmul(lower_HHinv, Z),
        upper_Qinv)
    weight_mtx_sample = all_mtx_sample[:, :-1]
    bias_mtx_sample = all_mtx_sample[:, -1]
    return weight_mtx_sample, bias_mtx_sample


class Linear_2L_KFRA(nn.Module):

    def __init__(self, input_dim, output_dim, n_hid):
        super(Linear_2L_KFRA, self).__init__()
        self.n_hid = n_hid
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.fc1 = nn.Linear(input_dim, self.n_hid)
        self.fc2 = nn.Linear(self.n_hid, self.n_hid)
        self.fc3 = nn.Linear(self.n_hid, output_dim)
        self.act = nn.ReLU(inplace=True)
        self.one = None
        self.a2 = None
        self.h2 = None
        self.a1 = None
        self.h1 = None
        self.a0 = None

    def forward(self, x):
        self.one = x.new(x.shape[0], 1).fill_(1)
        a0 = x.view(-1, self.input_dim)
        self.a0 = torch.cat((a0.data, self.one), dim=1)
        h1 = self.fc1(a0)
        self.h1 = h1.data
        a1 = self.act(h1)
        self.a1 = torch.cat((a1.data, self.one), dim=1)
        h2 = self.fc2(a1)
        self.h2 = h2.data
        a2 = self.act(h2)
        self.a2 = torch.cat((a2.data, self.one), dim=1)
        h3 = self.fc3(a2)
        return h3

    def sample_predict(self, x, Nsamples, Qinv1, HHinv1, MAP1, Qinv2,
        HHinv2, MAP2, Qinv3, HHinv3, MAP3):
        predictions = x.data.new(Nsamples, x.shape[0], self.output_dim)
        x = x.view(-1, self.input_dim)
        for i in range(Nsamples):
            w1, b1 = sample_K_laplace_MN(MAP1, Qinv1, HHinv1)
            a = torch.matmul(x, torch.t(w1)) + b1.unsqueeze(0)
            a = self.act(a)
            w2, b2 = sample_K_laplace_MN(MAP2, Qinv2, HHinv2)
            a = torch.matmul(a, torch.t(w2)) + b2.unsqueeze(0)
            a = self.act(a)
            w3, b3 = sample_K_laplace_MN(MAP3, Qinv3, HHinv3)
            y = torch.matmul(a, torch.t(w3)) + b3.unsqueeze(0)
            predictions[i] = y
        return predictions


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


def get_init_inputs():
    return [[], {'input_dim': 4, 'output_dim': 4, 'n_hid': 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
import torch.nn as nn
import torch.utils.data
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_fill_0(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = 1.0
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 20
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 5
    x1 = xindex // 5
    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], 5, tl.int64)
    tmp9 = 1.0
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp6, tmp9, tmp10)
    tmp12 = tl.where(tmp4, tmp5, tmp11)
    tl.store(out_ptr0 + x2, tmp12, xmask)


@triton.jit
def triton_poi_fused_relu_2(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)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = 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,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_fill_0[grid(4)](buf0, 4, XBLOCK=4, num_warps=1,
            num_stages=1)
        buf1 = empty_strided_cuda((4, 5), (5, 1), torch.float32)
        triton_poi_fused_cat_1[grid(20)](primals_1, buf1, 20, XBLOCK=32,
            num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 4),
            (1, 4), 0), out=buf2)
        del primals_2
        buf3 = buf2
        del buf2
        triton_poi_fused_relu_2[grid(16)](buf3, primals_3, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_3
        buf4 = empty_strided_cuda((4, 5), (5, 1), torch.float32)
        triton_poi_fused_cat_1[grid(20)](buf3, buf4, 20, XBLOCK=32,
            num_warps=1, num_stages=1)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf3, reinterpret_tensor(primals_4, (4, 4), (1, 4
            ), 0), out=buf5)
        buf6 = buf5
        del buf5
        triton_poi_fused_relu_2[grid(16)](buf6, primals_5, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_5
        buf7 = empty_strided_cuda((4, 5), (5, 1), torch.float32)
        triton_poi_fused_cat_1[grid(20)](buf6, buf7, 20, XBLOCK=32,
            num_warps=1, num_stages=1)
        buf8 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, buf6, reinterpret_tensor(primals_6,
            (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf8)
        del primals_7
    return (buf8, buf7, buf6, buf4, buf3, buf1, buf0, primals_1, buf3, buf6,
        primals_6, primals_4)


def sample_K_laplace_MN(MAP, upper_Qinv, lower_HHinv):
    Z = MAP.data.new(MAP.size()).normal_(mean=0, std=1)
    all_mtx_sample = MAP + torch.matmul(torch.matmul(lower_HHinv, Z),
        upper_Qinv)
    weight_mtx_sample = all_mtx_sample[:, :-1]
    bias_mtx_sample = all_mtx_sample[:, -1]
    return weight_mtx_sample, bias_mtx_sample


class Linear_2L_KFRANew(nn.Module):

    def __init__(self, input_dim, output_dim, n_hid):
        super(Linear_2L_KFRANew, self).__init__()
        self.n_hid = n_hid
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.fc1 = nn.Linear(input_dim, self.n_hid)
        self.fc2 = nn.Linear(self.n_hid, self.n_hid)
        self.fc3 = nn.Linear(self.n_hid, output_dim)
        self.act = nn.ReLU(inplace=True)
        self.one = None
        self.a2 = None
        self.h2 = None
        self.a1 = None
        self.h1 = None
        self.a0 = None

    def sample_predict(self, x, Nsamples, Qinv1, HHinv1, MAP1, Qinv2,
        HHinv2, MAP2, Qinv3, HHinv3, MAP3):
        predictions = x.data.new(Nsamples, x.shape[0], self.output_dim)
        x = x.view(-1, self.input_dim)
        for i in range(Nsamples):
            w1, b1 = sample_K_laplace_MN(MAP1, Qinv1, HHinv1)
            a = torch.matmul(x, torch.t(w1)) + b1.unsqueeze(0)
            a = self.act(a)
            w2, b2 = sample_K_laplace_MN(MAP2, Qinv2, HHinv2)
            a = torch.matmul(a, torch.t(w2)) + b2.unsqueeze(0)
            a = self.act(a)
            w3, b3 = sample_K_laplace_MN(MAP3, Qinv3, HHinv3)
            y = torch.matmul(a, torch.t(w3)) + b3.unsqueeze(0)
            predictions[i] = y
        return predictions

    def forward(self, input_0):
        primals_1 = self.fc1.weight
        primals_3 = self.fc1.bias
        primals_2 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_4 = self.fc3.weight
        primals_7 = self.fc3.bias
        primals_6 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]
