import torch
import torch.nn.functional as F
from torch.autograd import Variable
import torch.utils.data


class Invertible1x1Conv(torch.nn.Module):
    """
    The layer outputs both the convolution, and the log determinant
    of its weight matrix.  If reverse=True it does convolution with
    inverse
    """

    def __init__(self, c):
        super(Invertible1x1Conv, self).__init__()
        self.conv = torch.nn.Conv1d(c, c, kernel_size=1, stride=1, padding=
            0, bias=False)
        W = torch.qr(torch.FloatTensor(c, c).normal_())[0]
        if torch.det(W) < 0:
            W[:, 0] = -1 * W[:, 0]
        W = W.view(c, c, 1)
        W = W.contiguous()
        self.conv.weight.data = W

    def forward(self, z):
        batch_size, _group_size, n_of_groups = z.size()
        W = self.conv.weight.squeeze()
        log_det_W = batch_size * n_of_groups * torch.logdet(W.unsqueeze(0).
            float()).squeeze()
        z = self.conv(z)
        return z, log_det_W

    def infer(self, z):
        _batch_size, _group_size, _n_of_groups = z.size()
        W = self.conv.weight.squeeze()
        if not hasattr(self, 'W_inverse'):
            W_inverse = W.float().inverse()
            W_inverse = Variable(W_inverse[..., None])
            if z.type() == 'torch.cuda.HalfTensor' or z.type(
                ) == 'torch.HalfTensor':
                W_inverse = W_inverse.half()
            self.W_inverse = W_inverse
        z = F.conv1d(z, self.W_inverse, bias=None, stride=1, padding=0)
        return z


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


def get_init_inputs():
    return [[], {'c': 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.functional as F
from torch.autograd import Variable
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_eq_mul_0(in_out_ptr0, 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])
    tmp4 = tl.load(in_out_ptr0 + 0)
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK])
    tmp2 = -1.0
    tmp3 = tmp1 == tmp2
    tmp6 = float('nan')
    tmp7 = tl.where(tmp3, tmp6, tmp5)
    tmp8 = 16.0
    tmp9 = tmp7 * tmp8
    tl.store(out_ptr0 + tl.full([XBLOCK], 0, tl.int32), tmp3, None)
    tl.store(in_out_ptr0 + tl.full([XBLOCK], 0, tl.int32), tmp9, None)


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, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = torch.ops.aten._linalg_slogdet.default(reinterpret_tensor(
            primals_2, (1, 4, 4), (16, 4, 1), 0))
        buf1 = buf0[0]
        buf2 = buf0[1]
        buf3 = buf0[2]
        buf4 = buf0[3]
        del buf0
        buf5 = empty_strided_cuda((1,), (1,), torch.bool)
        buf7 = reinterpret_tensor(buf2, (), (), 0)
        del buf2
        get_raw_stream(0)
        triton_poi_fused_eq_mul_0[grid(1)](buf7, buf1, buf5, 1, XBLOCK=1,
            num_warps=1, num_stages=1)
        del buf1
        buf6 = extern_kernels.convolution(primals_1, primals_2, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 4), (16, 4, 1))
    return buf6, buf7, primals_1, primals_2, buf3, buf4, buf5


class Invertible1x1ConvNew(torch.nn.Module):
    """
    The layer outputs both the convolution, and the log determinant
    of its weight matrix.  If reverse=True it does convolution with
    inverse
    """

    def __init__(self, c):
        super(Invertible1x1ConvNew, self).__init__()
        self.conv = torch.nn.Conv1d(c, c, kernel_size=1, stride=1, padding=
            0, bias=False)
        W = torch.qr(torch.FloatTensor(c, c).normal_())[0]
        if torch.det(W) < 0:
            W[:, 0] = -1 * W[:, 0]
        W = W.view(c, c, 1)
        W = W.contiguous()
        self.conv.weight.data = W

    def infer(self, z):
        _batch_size, _group_size, _n_of_groups = z.size()
        W = self.conv.weight.squeeze()
        if not hasattr(self, 'W_inverse'):
            W_inverse = W.float().inverse()
            W_inverse = Variable(W_inverse[..., None])
            if z.type() == 'torch.cuda.HalfTensor' or z.type(
                ) == 'torch.HalfTensor':
                W_inverse = W_inverse.half()
            self.W_inverse = W_inverse
        z = F.conv1d(z, self.W_inverse, bias=None, stride=1, padding=0)
        return z

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