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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
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


@triton.jit
def triton_poi_fused_clamp_div_mul_pow_sqrt_sum_0(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
    x3 = xindex
    x0 = xindex % 4
    x2 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (4 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (8 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr0 + (12 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = 0.0
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = 1.0
    tmp16 = triton_helpers.minimum(tmp14, tmp15)
    tmp17 = tmp16 / tmp12
    tmp18 = tmp0 * tmp17
    tl.store(out_ptr0 + x3, tmp18, 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)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clamp_div_mul_pow_sqrt_sum_0[grid(256)](primals_1,
            buf0, 256, XBLOCK=256, num_warps=4, num_stages=1)
        buf1 = extern_kernels.convolution(primals_3, buf0, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 1, 1), (4, 1, 1, 1))
        buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        triton_poi_fused_convolution_1[grid(16)](buf1, primals_2, buf2, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_2
        buf3 = torch.ops.aten.set_.source_Tensor(primals_1, buf0)
        assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
        del buf1
        del primals_1
    return buf2, primals_3, buf0


class MaxNormConstraintConv2dNew(nn.Conv2d):

    def __init__(self, *args, max_norm_value=1, norm_axis=2, **kwargs):
        self.max_norm_value = max_norm_value
        self.norm_axis = norm_axis
        super().__init__(*args, **kwargs)

    def _max_norm(self, w):
        with torch.no_grad():
            norms = torch.sqrt(torch.sum(torch.square(w), dim=self.
                norm_axis, keepdim=True))
            desired = torch.clamp(norms, 0, self.max_norm_value)
            w *= desired / norms
        return w

    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: 0.026489