import torch
import torch.nn as nn


class SelfExpression(nn.Module):

    def __init__(self, n):
        super(SelfExpression, self).__init__()
        self.Coefficient = nn.Parameter(1e-08 * torch.ones(n, n, dtype=
            torch.float32), requires_grad=True)

    def forward(self, x):
        y = torch.matmul(self.Coefficient, x)
        return y


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


def get_init_inputs():
    return [[], {'n': 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_clone_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


def call(args):
    primals_1, primals_2 = 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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(64, 4)](primals_2, buf0, 64, 4,
            XBLOCK=4, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_2
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf1)
        del primals_1
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_0[grid(64, 4)](buf1, buf2, 64, 4, XBLOCK=4,
            YBLOCK=32, num_warps=4, num_stages=1)
        del buf1
    return buf2, reinterpret_tensor(buf0, (64, 4), (4, 1), 0)


class SelfExpressionNew(nn.Module):

    def __init__(self, n):
        super(SelfExpressionNew, self).__init__()
        self.Coefficient = nn.Parameter(1e-08 * torch.ones(n, n, dtype=
            torch.float32), requires_grad=True)

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