import torch
import torch.nn as nn


class GroupNorm(nn.Module):

    def __init__(self, c_num, group_num=16, eps=1e-10):
        """
        The groupnorm layer from https://arxiv.org/abs/1803.08494
        Args:
            c_num (int): Number of input channels
            group_num (int): Number of group by which to divide the channels
            eps (float): Epsilon
        """
        super(GroupNorm, self).__init__()
        self.group_num = group_num
        self.gamma = nn.Parameter(torch.ones(c_num, 1, 1))
        self.beta = nn.Parameter(torch.zeros(c_num, 1, 1))
        self.eps = eps

    def forward(self, x):
        batch_size, channels, height, width = x.size()
        x = x.view(batch_size, self.group_num, -1)
        mean = x.mean(dim=2, keepdim=True)
        std = x.std(dim=2, keepdim=True)
        x = (x - mean) / (std + self.eps)
        x = x.view(batch_size, channels, height, width)
        return x * self.gamma + self.beta


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


def get_init_inputs():
    return [[], {'c_num': 4}]

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
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_add_mul_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x5 = xindex // 4
    x2 = xindex // 16 % 4
    tmp0 = tl.load(in_ptr0 + x4, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x5, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x5), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x5), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x5), xmask, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = 4.0
    tmp9 = tmp7 / tmp8
    tmp10 = tmp0 - tmp9
    tmp11 = tmp1 - tmp9
    tmp12 = tmp11 * tmp11
    tmp13 = tmp2 - tmp9
    tmp14 = tmp13 * tmp13
    tmp15 = tmp12 + tmp14
    tmp16 = tmp4 - tmp9
    tmp17 = tmp16 * tmp16
    tmp18 = tmp15 + tmp17
    tmp19 = tmp6 - tmp9
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = 3.0
    tmp23 = tmp21 / tmp22
    tmp24 = libdevice.sqrt(tmp23)
    tmp25 = 1e-10
    tmp26 = tmp24 + tmp25
    tmp27 = tmp10 / tmp26
    tmp29 = tmp27 * tmp28
    tmp31 = tmp29 + tmp30
    tl.store(out_ptr0 + x4, tmp31, 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, 1), (1, 1, 1))
    assert_size_stride(primals_3, (4, 1, 1), (1, 1, 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_mul_0[grid(256)](primals_1, primals_2,
            primals_3, buf0, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        del primals_3
    return buf0, primals_1


class GroupNormNew(nn.Module):

    def __init__(self, c_num, group_num=16, eps=1e-10):
        """
        The groupnorm layer from https://arxiv.org/abs/1803.08494
        Args:
            c_num (int): Number of input channels
            group_num (int): Number of group by which to divide the channels
            eps (float): Epsilon
        """
        super(GroupNormNew, self).__init__()
        self.group_num = group_num
        self.gamma = nn.Parameter(torch.ones(c_num, 1, 1))
        self.beta = nn.Parameter(torch.zeros(c_num, 1, 1))
        self.eps = eps

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