import torch
import torch.nn as nn


class ModMBStddevLayer(nn.Module):
    """Modified MiniBatch Stddev Layer.

    This layer is modified from ``MiniBatchStddevLayer`` used in PGGAN. In
    StyleGAN2, the authors add a new feature, `channel_groups`, into this
    layer.
    """

    def __init__(self, group_size=4, channel_groups=1, sync_groups=None,
        eps=1e-08):
        super(ModMBStddevLayer, self).__init__()
        self.group_size = group_size
        self.eps = eps
        self.channel_groups = channel_groups
        self.sync_groups = group_size if sync_groups is None else sync_groups

    def forward(self, x):
        assert x.shape[0] <= self.group_size or x.shape[0
            ] % self.group_size == 0, f'Batch size be smaller than or equal to group size. Otherwise, batch size should be divisible by the group size.But got batch size {x.shape[0]}, group size {self.group_size}'
        assert x.shape[1
            ] % self.channel_groups == 0, f'"channel_groups" must be divided by the feature channels. channel_groups: {self.channel_groups}, feature channels: {x.shape[1]}'
        n, c, h, w = x.shape
        group_size = min(n, self.group_size)
        y = torch.reshape(x, (group_size, -1, self.channel_groups, c //
            self.channel_groups, h, w))
        y = torch.var(y, dim=0, unbiased=False)
        y = torch.sqrt(y + self.eps)
        y = y.mean(dim=(2, 3, 4), keepdim=True).squeeze(2)
        y = y.repeat(group_size, 1, h, w)
        return torch.cat([x, y], dim=1)


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


def get_init_inputs():
    return [[], {}]

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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_per_fused_add_mean_repeat_sqrt_var_0(in_ptr0, out_ptr1, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    r1 = rindex % 16
    r2 = rindex // 16
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.load(in_ptr0 + (64 + r0), None)
    tmp3 = tl.load(in_ptr0 + (128 + r0), None)
    tmp5 = tl.load(in_ptr0 + (192 + r0), None)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-08
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.sqrt(tmp22)
    tmp24 = tl.broadcast_to(tmp23, [XBLOCK, RBLOCK])
    tmp26 = tl.sum(tmp24, 1)[:, None]
    tmp27 = 64.0
    tmp28 = tmp26 / tmp27
    tl.store(out_ptr1 + tl.broadcast_to(r1 + 80 * r2, [XBLOCK, RBLOCK]),
        tmp28, None)


@triton.jit
def triton_poi_fused_cat_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
    x2 = xindex
    x0 = xindex % 64
    x1 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tl.store(out_ptr0 + (x0 + 80 * x1), tmp0, 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)
        buf3 = empty_strided_cuda((4, 5, 4, 4), (80, 16, 4, 1), torch.float32)
        buf2 = reinterpret_tensor(buf3, (4, 1, 4, 4), (80, 16, 4, 1), 64)
        get_raw_stream(0)
        triton_per_fused_add_mean_repeat_sqrt_var_0[grid(1)](arg0_1, buf2, 
            1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        buf1 = reinterpret_tensor(buf3, (4, 4, 4, 4), (80, 16, 4, 1), 0)
        triton_poi_fused_cat_1[grid(256)](arg0_1, buf1, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del arg0_1
    return buf3,


class ModMBStddevLayerNew(nn.Module):
    """Modified MiniBatch Stddev Layer.

    This layer is modified from ``MiniBatchStddevLayer`` used in PGGAN. In
    StyleGAN2, the authors add a new feature, `channel_groups`, into this
    layer.
    """

    def __init__(self, group_size=4, channel_groups=1, sync_groups=None,
        eps=1e-08):
        super(ModMBStddevLayerNew, self).__init__()
        self.group_size = group_size
        self.eps = eps
        self.channel_groups = channel_groups
        self.sync_groups = group_size if sync_groups is None else sync_groups

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