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.cuda
from torch import nn
import torch.distributed
import torch.utils.data
import torch.optim
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_eq_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    tmp0 = tl.load(in_ptr0 + 0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tmp2 = -1.0
    tmp3 = tmp1 == tmp2
    tl.store(out_ptr0 + tl.full([XBLOCK], 0, tl.int32), tmp3, None)


@triton.jit
def triton_poi_fused_mul_scalar_tensor_where_1(in_ptr0, in_ptr1, 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 = tl.load(in_ptr0 + 0).to(tl.int1)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tmp2 = tl.load(in_ptr1 + 0)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK])
    tmp4 = float('nan')
    tmp5 = tl.where(tmp1, tmp4, tmp3)
    tmp6 = 1.0
    tmp7 = tmp5 * tmp6
    tmp8 = 4.0
    tmp9 = tmp7 * tmp8
    tl.store(out_ptr0 + x0, tmp9, xmask)


@triton.jit
def triton_poi_fused_convolution_2(in_ptr0, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 4
    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
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x1), xmask & ymask)
    tl.store(out_ptr0 + (x1 + 4 * y0), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_mul_3(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tl.store(in_out_ptr0 + x0, tmp2, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (1, 4))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = torch.ops.aten._linalg_slogdet.default(primals_2)
        buf1 = buf0[0]
        buf2 = buf0[1]
        buf3 = buf0[2]
        buf4 = buf0[3]
        del buf0
        buf5 = empty_strided_cuda((), (), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_eq_0[grid(1)](buf1, buf5, 1, XBLOCK=1, num_warps=1,
            num_stages=1)
        del buf1
        buf6 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused_mul_scalar_tensor_where_1[grid(4)](buf5, buf2,
            buf6, 4, XBLOCK=4, num_warps=1, num_stages=1)
        del buf2
        buf7 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        triton_poi_fused_convolution_2[grid(4, 4)](primals_2, buf7, 4, 4,
            XBLOCK=4, YBLOCK=4, num_warps=1, num_stages=1)
        buf8 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4,
            4, 1, 4), (16, 4, 4, 1), 0), buf7, stride=(1, 1), padding=(0, 0
            ), dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=1, bias=None)
        assert_size_stride(buf8, (4, 4, 1, 4), (16, 4, 4, 1))
        del buf7
        buf9 = reinterpret_tensor(buf8, (4, 4, 4), (16, 4, 1), 0)
        del buf8
        triton_poi_fused_mul_3[grid(64)](buf9, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
    return buf9, buf6, reinterpret_tensor(primals_1, (4, 4, 1, 4), (16, 4, 
        8, 1), 0), buf3, buf4, buf5, reinterpret_tensor(primals_2, (4, 4, 1,
        1), (1, 4, 4, 4), 0)


class InvConvNearNew(nn.Module):

    def __init__(self, channels, n_split=4, no_jacobian=False, **kwargs):
        super().__init__()
        assert n_split % 2 == 0
        self.channels = channels
        self.n_split = n_split
        self.no_jacobian = no_jacobian
        w_init = torch.qr(torch.FloatTensor(self.n_split, self.n_split).
            normal_())[0]
        if torch.det(w_init) < 0:
            w_init[:, 0] = -1 * w_init[:, 0]
        self.weight = nn.Parameter(w_init)

    def store_inverse(self):
        self.weight_inv = torch.inverse(self.weight.float())

    def forward(self, input_0):
        primals_2 = self.weight
        primals_1 = input_0
        output = call([primals_1, primals_2])
        return output[0], output[1]


Measured latency_ms: 0.030614