import torch
import torch.nn as nn


class LinearLR(nn.Module):
    """[u * v + res] version of torch.nn.Linear"""

    def __init__(self, in_features, out_features, rank_ratio=0.25, bias=
        True, device=None, dtype=None):
        super().__init__()
        sliced_rank = int(min(in_features, out_features) * rank_ratio)
        self.u = nn.Linear(in_features, sliced_rank, bias=False, device=
            device, dtype=dtype)
        self.v = nn.Linear(sliced_rank, out_features, bias=bias, device=
            device, dtype=dtype)
        self.res = nn.Linear(in_features, out_features, bias=False, device=
            device, dtype=dtype)

    def freeze(self):
        for param in self.res.parameters():
            param.requires_grad = False

    def unfreeze(self):
        for param in self.res.parameters():
            param.requires_grad = True

    def forward(self, input):
        return self.v(self.u(input)) + self.res(input)


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


def get_init_inputs():
    return [[], {'in_features': 4, 'out_features': 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, in_ptr1, 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')
    tmp3 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (1, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 1), (1, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 1), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf0, reinterpret_tensor(primals_3, (1, 4), (1, 1
            ), 0), out=buf1)
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf2)
        del primals_5
        buf3 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf1
        get_raw_stream(0)
        triton_poi_fused_add_0[grid(256)](buf3, primals_4, buf2, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del buf2
        del primals_4
    return buf3, reinterpret_tensor(primals_2, (64, 4), (4, 1), 0
        ), buf0, primals_3


class LinearLRNew(nn.Module):
    """[u * v + res] version of torch.nn.Linear"""

    def __init__(self, in_features, out_features, rank_ratio=0.25, bias=
        True, device=None, dtype=None):
        super().__init__()
        sliced_rank = int(min(in_features, out_features) * rank_ratio)
        self.u = nn.Linear(in_features, sliced_rank, bias=False, device=
            device, dtype=dtype)
        self.v = nn.Linear(sliced_rank, out_features, bias=bias, device=
            device, dtype=dtype)
        self.res = nn.Linear(in_features, out_features, bias=False, device=
            device, dtype=dtype)

    def freeze(self):
        for param in self.res.parameters():
            param.requires_grad = False

    def unfreeze(self):
        for param in self.res.parameters():
            param.requires_grad = True

    def forward(self, input_0):
        primals_1 = self.u.weight
        primals_3 = self.v.weight
        primals_4 = self.v.bias
        primals_5 = self.res.weight
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
