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, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1), xmask)
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16 % 4
    x3 = xindex // 64
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), xmask)
    tl.store(out_ptr0 + x4, tmp0, xmask)


@triton.jit
def triton_poi_fused_clone_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 16
    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 % 4
    x3 = xindex // 4
    y0 = yindex % 4
    y1 = yindex // 4
    x5 = xindex
    y4 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x3 + 16 * x2 + 64 * y1), xmask & ymask)
    tl.store(out_ptr0 + (x5 + 16 * y4), tmp0, xmask & ymask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 1, 1), (16, 4, 1, 1, 1), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(64)](primals_1, buf0, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((1, 16, 16), (256, 16, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(primals_2, (1, 16, 4), (64, 4,
            1), 0), reinterpret_tensor(buf0, (1, 4, 16), (0, 16, 1), 0),
            out=buf1)
        del buf0
        buf2 = empty_strided_cuda((4, 4, 4, 1, 4, 1), (64, 16, 4, 4, 1, 1),
            torch.float32)
        triton_poi_fused_clone_1[grid(256)](buf1, buf2, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf3 = reinterpret_tensor(buf1, (4, 4, 16), (64, 16, 1), 0)
        del buf1
        extern_kernels.bmm(primals_4, reinterpret_tensor(buf2, (4, 4, 16),
            (64, 16, 1), 0), out=buf3)
        buf4 = reinterpret_tensor(buf2, (4, 4, 4, 4, 1, 1), (64, 16, 4, 1, 
            1, 1), 0)
        del buf2
        triton_poi_fused_clone_2[grid(16, 16)](buf3, buf4, 16, 16, XBLOCK=
            16, YBLOCK=16, num_warps=4, num_stages=1)
        buf5 = buf3
        del buf3
        extern_kernels.bmm(primals_3, reinterpret_tensor(buf4, (4, 4, 16),
            (64, 16, 1), 0), out=buf5)
        del buf4
    return reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 4, 1, 16), 0
        ), reinterpret_tensor(primals_3, (4, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(primals_4, (4, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(primals_2, (1, 4, 16), (64, 1, 4), 0)


class TriaffineNew(nn.Module):
    """
    Triaffine layer for second-order scoring.

    This function has a tensor of weights `W` and bias terms if needed.
    The score `s(x, y, z)` of the vector triple `(x, y, z)` is computed as `x^T z^T W y`.
    Usually, `x` and `y` can be concatenated with bias terms.

    References:
        - Yu Zhang, Zhenghua Li and Min Zhang (ACL'20)
          Efficient Second-Order TreeCRF for Neural Dependency Parsing
          https://www.aclweb.org/anthology/2020.acl-main.302/
        - Xinyu Wang, Jingxian Huang, and Kewei Tu (ACL'19)
          Second-Order Semantic Dependency Parsing with End-to-End Neural Networks
          https://www.aclweb.org/anthology/P19-1454/

    Args:
        n_in (int):
            The dimension of the input feature.
        bias_x (bool):
            If True, add a bias term for tensor x. Default: False.
        bias_y (bool):
            If True, add a bias term for tensor y. Default: False.
    """

    def __init__(self, n_in, bias_x=False, bias_y=False):
        super().__init__()
        self.n_in = n_in
        self.bias_x = bias_x
        self.bias_y = bias_y
        self.weight = nn.Parameter(torch.Tensor(n_in + bias_x, n_in, n_in +
            bias_y))
        self.reset_parameters()

    def extra_repr(self):
        s = f'n_in={self.n_in}'
        if self.bias_x:
            s += f', bias_x={self.bias_x}'
        if self.bias_y:
            s += f', bias_y={self.bias_y}'
        return s

    def reset_parameters(self):
        nn.init.zeros_(self.weight)

    def forward(self, input_0, input_1, input_2):
        primals_1 = self.weight
        primals_2 = input_0
        primals_3 = input_1
        primals_4 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.063033