import torch
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
from warnings import warn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 16
    x0 = xindex % 16
    x2 = xindex // 256
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr0 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp9 & xmask,
        other=0.0)
    tmp11 = tmp10 * tmp10
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp9, tmp11, tmp12)
    tmp14 = tmp0 >= tmp7
    tmp15 = tl.full([1], 12, tl.int64)
    tmp16 = tmp0 < tmp15
    tmp17 = tmp14 & tmp16
    tmp18 = tl.load(in_ptr0 + (x0 + 16 * (-8 + x1) + 64 * x2), tmp17 &
        xmask, other=0.0)
    tmp19 = tmp18 * tmp18
    tmp20 = tmp19 * tmp18
    tmp21 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
    tmp22 = tl.where(tmp17, tmp20, tmp21)
    tmp23 = tmp0 >= tmp15
    tl.full([1], 16, tl.int64)
    tmp26 = tl.load(in_ptr0 + (x0 + 16 * (-12 + x1) + 64 * x2), tmp23 &
        xmask, other=0.0)
    tmp27 = tmp26 * tmp26
    tmp28 = tmp27 * tmp27
    tmp29 = tl.full(tmp28.shape, 0.0, tmp28.dtype)
    tmp30 = tl.where(tmp23, tmp28, tmp29)
    tmp31 = tl.where(tmp17, tmp22, tmp30)
    tmp32 = tl.where(tmp9, tmp13, tmp31)
    tmp33 = tl.where(tmp4, tmp5, tmp32)
    tl.store(out_ptr0 + x3, tmp33, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 16, 4, 4), (256, 16, 4, 1), torch.float32
            )
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(1024)](arg0_1, buf0, 1024, XBLOCK=256,
            num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class MonomialNNNew(nn.Module):
    """A network that expands its input to a given list of monomials.

    Its output shape will be (n_samples, n_input_units * n_degrees)

    :param degrees: max degree to be included, or a list of degrees that will be used
    :type degrees: int or list[int] or tuple[int]
    """

    def __init__(self, degrees):
        super(MonomialNNNew, self).__init__()
        if isinstance(degrees, int):
            degrees = [d for d in range(1, degrees + 1)]
        self.degrees = tuple(degrees)
        if len(self.degrees) == 0:
            raise ValueError('No degrees used, check `degrees` argument again')
        if 0 in degrees:
            warn(
                'One of the degrees is 0 which might introduce redundant features'
                )
        if len(set(self.degrees)) < len(self.degrees):
            warn(f'Duplicate degrees found: {self.degrees}')

    def __repr__(self):
        return f'{self.__class__.__name__}(degrees={self.degrees})'

    def __str__(self):
        return self.__repr__()

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0]


Measured latency_ms: 0.014045