import torch
from torch import nn


class NormedConv2d(nn.Conv2d):
    """Normalized Conv2d Layer.

    Args:
        tempeature (float, optional): Tempeature term. Default to 20.
        power (int, optional): Power term. Default to 1.0.
        eps (float, optional): The minimal value of divisor to
             keep numerical stability. Default to 1e-6.
        norm_over_kernel (bool, optional): Normalize over kernel.
             Default to False.
    """

    def __init__(self, *args, tempearture=20, power=1.0, eps=1e-06,
        norm_over_kernel=False, **kwargs):
        super(NormedConv2d, self).__init__(*args, **kwargs)
        self.tempearture = tempearture
        self.power = power
        self.norm_over_kernel = norm_over_kernel
        self.eps = eps

    def forward(self, x):
        if not self.norm_over_kernel:
            weight_ = self.weight / (self.weight.norm(dim=1, keepdim=True).
                pow(self.power) + self.eps)
        else:
            weight_ = self.weight / (self.weight.view(self.weight.size(0), 
                -1).norm(dim=1, keepdim=True).pow(self.power)[..., None,
                None] + self.eps)
        x_ = x / (x.norm(dim=1, keepdim=True).pow(self.power) + self.eps)
        x_ = x_ * self.tempearture
        if hasattr(self, 'conv2d_forward'):
            x_ = self.conv2d_forward(x_, weight_)
        elif torch.__version__ >= '1.8':
            x_ = self._conv_forward(x_, weight_, self.bias)
        else:
            x_ = self._conv_forward(x_, weight_)
        return x_


def get_inputs():
    return [torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 4}]

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_poi_fused_add_div_linalg_vector_norm_pow_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 % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr0 + (48 + x0 + 64 * 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 = 1e-06
    tmp14 = tmp12 + tmp13
    tmp15 = tmp0 / tmp14
    tl.store(out_ptr0 + x3, tmp15, xmask)


@triton.jit
def triton_poi_fused_add_div_linalg_vector_norm_mul_pow_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
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr0 + (48 + x0 + 64 * 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 = 1e-06
    tmp14 = tmp12 + tmp13
    tmp15 = tmp0 / tmp14
    tmp16 = 20.0
    tmp17 = tmp15 * tmp16
    tl.store(out_ptr0 + x3, tmp17, xmask)


@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_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_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_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, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (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_add_div_linalg_vector_norm_pow_0[grid(256)](primals_1,
            buf0, 256, XBLOCK=256, num_warps=4, num_stages=1)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_div_linalg_vector_norm_mul_pow_1[grid(256)](
            primals_2, buf1, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = extern_kernels.convolution(buf1, buf0, 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 = buf2
        del buf2
        triton_poi_fused_convolution_2[grid(16)](buf3, primals_3, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_3
    return buf3, primals_1, buf0, buf1


class NormedConv2dNew(nn.Conv2d):
    """Normalized Conv2d Layer.

    Args:
        tempeature (float, optional): Tempeature term. Default to 20.
        power (int, optional): Power term. Default to 1.0.
        eps (float, optional): The minimal value of divisor to
             keep numerical stability. Default to 1e-6.
        norm_over_kernel (bool, optional): Normalize over kernel.
             Default to False.
    """

    def __init__(self, *args, tempearture=20, power=1.0, eps=1e-06,
        norm_over_kernel=False, **kwargs):
        super(NormedConv2dNew, self).__init__(*args, **kwargs)
        self.tempearture = tempearture
        self.power = power
        self.norm_over_kernel = norm_over_kernel
        self.eps = eps

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