import torch
import torch.nn as nn


class GraphLinear(nn.Module):
    """
    Generalization of 1x1 convolutions on Graphs
    """

    def __init__(self, in_channels, out_channels):
        super(GraphLinear, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.W = nn.Parameter(torch.FloatTensor(out_channels, in_channels))
        self.b = nn.Parameter(torch.FloatTensor(out_channels))
        self.reset_parameters()

    def reset_parameters(self):
        w_stdv = 1 / (self.in_channels * self.out_channels)
        self.W.data.uniform_(-w_stdv, w_stdv)
        self.b.data.uniform_(-w_stdv, w_stdv)

    def forward(self, x):
        return torch.matmul(self.W[None, :], x) + self.b[None, :, None]


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 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
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_0(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
    x3 = xindex
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(primals_1, (16, 4, 4), (0, 4,
            1), 0), reinterpret_tensor(primals_2, (16, 4, 4), (16, 4, 1), 0
            ), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_add_0[grid(256)](buf1, primals_3, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_3
    return buf1, reinterpret_tensor(primals_2, (16, 4, 4), (16, 1, 4), 0)


class GraphLinearNew(nn.Module):
    """
    Generalization of 1x1 convolutions on Graphs
    """

    def __init__(self, in_channels, out_channels):
        super(GraphLinearNew, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.W = nn.Parameter(torch.FloatTensor(out_channels, in_channels))
        self.b = nn.Parameter(torch.FloatTensor(out_channels))
        self.reset_parameters()

    def reset_parameters(self):
        w_stdv = 1 / (self.in_channels * self.out_channels)
        self.W.data.uniform_(-w_stdv, w_stdv)
        self.b.data.uniform_(-w_stdv, w_stdv)

    def forward(self, input_0):
        primals_1 = self.W
        primals_3 = self.b
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
