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
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__to_copy_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tmp0.to(tl.float64)
    tl.store(out_ptr0 + x0, tmp1, xmask)


@triton.jit
def triton_poi_fused__to_copy_convolution_view_1(in_ptr0, out_ptr1,
    out_ptr2, 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)
    tmp1 = tmp0.to(tl.float32)
    tl.store(out_ptr1 + (y0 + 4 * x1), tmp1, xmask & ymask)
    tl.store(out_ptr2 + (x1 + 4 * y0), tmp1, xmask & ymask)


@triton.jit
def triton_poi_fused_mul_neg_2(in_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_out_ptr0 + 0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tmp2 = -tmp1
    tmp3 = 4.0
    tmp4 = tmp2 * tmp3
    tmp5 = tmp4 * tmp3
    tl.store(in_out_ptr0 + tl.full([XBLOCK], 0, tl.int32), tmp5, None)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (1, 4))
    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), (1, 4), torch.float64)
        get_raw_stream(0)
        triton_poi_fused__to_copy_0[grid(16)](primals_1, buf0, 16, XBLOCK=
            16, num_warps=1, num_stages=1)
        buf1 = torch.ops.aten.linalg_inv_ex.default(buf0)
        del buf0
        buf2 = buf1[0]
        del buf1
        buf5 = empty_strided_cuda((4, 4, 1, 1), (1, 4, 4, 4), torch.float32)
        buf11 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        triton_poi_fused__to_copy_convolution_view_1[grid(4, 4)](buf2, buf5,
            buf11, 4, 4, XBLOCK=4, YBLOCK=4, num_warps=1, num_stages=1)
        buf6 = torch.ops.aten._linalg_slogdet.default(primals_1)
        del primals_1
        buf8 = buf6[1]
        buf9 = buf6[2]
        buf10 = buf6[3]
        del buf6
        buf12 = extern_kernels.convolution(primals_2, buf11, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 4, 4, 4), (64, 16, 4, 1))
        del buf11
        buf13 = buf8
        del buf8
        triton_poi_fused_mul_neg_2[grid(1)](buf13, 1, XBLOCK=1, num_warps=1,
            num_stages=1)
    return buf12, buf13, primals_2, buf5, buf9, buf10, reinterpret_tensor(buf2,
        (4, 4), (4, 1), 0)


class Flow(nn.Module):
    """
    Generic class for flow functions
    """

    def __init__(self):
        super().__init__()

    def forward(self, z):
        """
        :param z: input variable, first dimension is batch dim
        :return: transformed z and log of absolute determinant
        """
        raise NotImplementedError('Forward pass has not been implemented.')

    def inverse(self, z):
        raise NotImplementedError('This flow has no algebraic inverse.')


class Invertible1x1ConvNew(Flow):
    """
    Invertible 1x1 convolution introduced in the Glow paper
    Assumes 4d input/output tensors of the form NCHW
    """

    def __init__(self, num_channels, use_lu=False):
        """
        Constructor
        :param num_channels: Number of channels of the data
        :param use_lu: Flag whether to parametrize weights through the LU decomposition
        """
        super().__init__()
        self.num_channels = num_channels
        self.use_lu = use_lu
        Q = torch.qr(torch.randn(self.num_channels, self.num_channels))[0]
        if use_lu:
            P, L, U = torch.lu_unpack(*Q.lu())
            self.register_buffer('P', P)
            self.L = nn.Parameter(L)
            S = U.diag()
            self.register_buffer('sign_S', torch.sign(S))
            self.log_S = nn.Parameter(torch.log(torch.abs(S)))
            self.U = nn.Parameter(torch.triu(U, diagonal=1))
            self.register_buffer('eye', torch.diag(torch.ones(self.
                num_channels)))
        else:
            self.W = nn.Parameter(Q)

    def _assemble_W(self, inverse=False):
        L = torch.tril(self.L, diagonal=-1) + self.eye
        U = torch.triu(self.U, diagonal=1) + torch.diag(self.sign_S * torch
            .exp(self.log_S))
        if inverse:
            if self.log_S.dtype == torch.float64:
                L_inv = torch.inverse(L)
                U_inv = torch.inverse(U)
            else:
                L_inv = torch.inverse(L.double()).type(self.log_S.dtype)
                U_inv = torch.inverse(U.double()).type(self.log_S.dtype)
            W = U_inv @ L_inv @ self.P.t()
        else:
            W = self.P @ L @ U
        return W

    def inverse(self, z):
        if self.use_lu:
            W = self._assemble_W()
            log_det = torch.sum(self.log_S)
        else:
            W = self.W
            log_det = torch.slogdet(self.W)[1]
        W = W.view(self.num_channels, self.num_channels, 1, 1)
        z_ = torch.nn.functional.conv2d(z, W)
        log_det = log_det * z.size(2) * z.size(3)
        return z_, log_det

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


Measured latency_ms: 0.013774