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


class LR(nn.Module):

    def __init__(self, feature_nums, output_dim=1):
        super(LR, self).__init__()
        self.linear = nn.Linear(feature_nums, output_dim)
        self.bias = nn.Parameter(torch.zeros((output_dim,)))

    def forward(self, x):
        """
            :param x: Int tensor of size (batch_size, feature_nums, latent_nums)
            :return: pctrs
        """
        out = self.bias + torch.sum(self.linear(x), dim=1)
        return out.unsqueeze(1)


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


def get_init_inputs():
    return [[], {'feature_nums': 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
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_add_sum_0(in_ptr0, in_ptr1, 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 % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + 0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tmp2 = tl.load(in_ptr1 + (x0 + 16 * x1), xmask)
    tmp3 = tl.load(in_ptr1 + (4 + x0 + 16 * x1), xmask)
    tmp5 = tl.load(in_ptr1 + (8 + x0 + 16 * x1), xmask)
    tmp7 = tl.load(in_ptr1 + (12 + x0 + 16 * x1), xmask)
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = tmp1 + tmp8
    tl.store(out_ptr0 + x2, tmp9, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (1,), (1,))
    assert_size_stride(primals_2, (1, 4), (4, 1))
    assert_size_stride(primals_3, (1,), (1,))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_4, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 1), (1, 4), 0
            ), alpha=1, beta=1, out=buf1)
        del primals_2
        del primals_3
        buf2 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_sum_0[grid(16)](primals_1, buf1, buf2, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del buf1
        del primals_1
    return reinterpret_tensor(buf2, (4, 1, 4, 1), (4, 4, 1, 1), 0
        ), reinterpret_tensor(primals_4, (64, 4), (4, 1), 0)


class LRNew(nn.Module):

    def __init__(self, feature_nums, output_dim=1):
        super(LRNew, self).__init__()
        self.linear = nn.Linear(feature_nums, output_dim)
        self.bias = nn.Parameter(torch.zeros((output_dim,)))

    def forward(self, input_0):
        primals_1 = self.bias
        primals_2 = self.linear.weight
        primals_3 = self.linear.bias
        primals_4 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
