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
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_per_fused_renorm_0(in_ptr0, out_ptr1, xnumel, rnumel, XBLOCK: tl
    .constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(xmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tmp6 = libdevice.sqrt(tmp5)
    tmp7 = 1.0
    tmp8 = tmp6 > tmp7
    tmp9 = 1e-07
    tmp10 = tmp6 + tmp9
    tmp11 = tl.full([1, 1], 1, tl.int32)
    tmp12 = tmp11 / tmp10
    tmp13 = tmp12 * tmp7
    tmp14 = tl.where(tmp8, tmp13, tmp7)
    tmp15 = tmp0 * tmp14
    tl.store(out_ptr1 + (r1 + 64 * x0), tmp15, xmask)


@triton.jit
def triton_poi_fused_convolution_1(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + x2, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_renorm_0[grid(4)](primals_1, buf1, 4, 64, XBLOCK=1,
            num_warps=2, num_stages=1)
        buf2 = extern_kernels.convolution(primals_3, buf1, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 1, 1), (4, 1, 1, 1))
        buf3 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        triton_poi_fused_convolution_1[grid(16)](buf2, primals_2, buf3, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_2
        buf4 = torch.ops.aten.set_.source_Tensor(primals_1, buf1)
        assert_size_stride(buf4, (4, 4, 4, 4), (64, 16, 4, 1))
        del buf2
        del primals_1
    return buf3, primals_3, buf1


class Conv2dWithConstraintNew(nn.Conv2d):

    def __init__(self, *args, max_norm=1, **kwargs):
        self.max_norm = max_norm
        super(Conv2dWithConstraintNew, self).__init__(*args, **kwargs)

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


Measured latency_ms: 0.038824