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.dataloader
import torch.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_cat_view_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 5
    x1 = xindex // 5
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 5, tl.int64)
    tmp9 = 1.0
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp6, tmp9, tmp10)
    tmp12 = tl.where(tmp4, tmp5, tmp11)
    tl.store(out_ptr0 + x2, tmp12, xmask)


def call(args):
    primals_1, primals_2, primals_3 = 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, (5, 4, 5), (20, 5, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 5), (5, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_view_0[grid(80)](primals_1, buf0, 80, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((16, 20), (20, 1), torch.float32)
        extern_kernels.mm(buf0, reinterpret_tensor(primals_3, (5, 20), (20,
            1), 0), out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((4, 4, 5), (20, 5, 1), torch.float32)
        triton_poi_fused_cat_view_0[grid(80)](primals_2, buf2, 80, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_2
        buf3 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf1, (4, 16, 5), (80, 5, 1),
            0), reinterpret_tensor(buf2, (4, 5, 4), (20, 1, 5), 0), out=buf3)
        del buf1
    return reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 1, 4), 0
        ), buf2, reinterpret_tensor(buf0, (5, 16), (1, 5), 0)


class biaffine_mappingNew(nn.Module):

    def __init__(self, input_size_x, input_size_y, output_size, bias_x,
        bias_y, initializer=None):
        super(biaffine_mappingNew, self).__init__()
        self.bias_x = bias_x
        self.bias_y = bias_y
        self.output_size = output_size
        self.initilizer = None
        if self.bias_x:
            input_size1 = input_size_x + 1
            input_size2 = input_size_y + 1
        self.biaffine_map = nn.Parameter(torch.Tensor(input_size1,
            output_size, input_size2))
        self.initialize()

    def initialize(self):
        if self.initilizer is None:
            torch.nn.init.orthogonal_(self.biaffine_map)
        else:
            self.initilizer(self.biaffine_map)

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


Measured latency_ms: 0.021416