import torch
from torch import nn
from torch.nn import init


class SpatialGroupEnhance(nn.Module):

    def __init__(self, groups):
        super().__init__()
        self.groups = groups
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.weight = nn.Parameter(torch.zeros(1, groups, 1, 1))
        self.bias = nn.Parameter(torch.zeros(1, groups, 1, 1))
        self.sig = nn.Sigmoid()
        self.init_weights()

    def init_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                init.kaiming_normal_(m.weight, mode='fan_out')
                if m.bias is not None:
                    init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                init.constant_(m.weight, 1)
                init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                init.normal_(m.weight, std=0.001)
                if m.bias is not None:
                    init.constant_(m.bias, 0)

    def forward(self, x):
        b, c, h, w = x.shape
        x = x.view(b * self.groups, -1, h, w)
        xn = x * self.avg_pool(x)
        xn = xn.sum(dim=1, keepdim=True)
        t = xn.view(b * self.groups, -1)
        t = t - t.mean(dim=1, keepdim=True)
        std = t.std(dim=1, keepdim=True) + 1e-05
        t = t / std
        t = t.view(b, self.groups, h, w)
        t = t * self.weight + self.bias
        t = t.view(b * self.groups, 1, h, w)
        x = x * self.sig(t)
        x = x.view(b, c, h, w)
        return x


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


def get_init_inputs():
    return [[], {'groups': 1}]

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
from torch import nn
from torch.nn import init
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_mean_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    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 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp5 = 16.0
    tmp6 = tmp4 / tmp5
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp6, xmask)


@triton.jit
def triton_per_fused_add_mean_mul_sigmoid_std_sub_sum_1(in_out_ptr0,
    in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 16
    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 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + r1 + 64 * x0), xmask, other=0.0)
    tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (32 + r1 + 64 * x0), xmask, other=0.0)
    tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (48 + r1 + 64 * x0), xmask, other=0.0)
    tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp44 = tl.load(in_ptr2 + 0)
    tmp45 = tl.broadcast_to(tmp44, [XBLOCK, RBLOCK])
    tmp47 = tl.load(in_ptr3 + 0)
    tmp48 = tl.broadcast_to(tmp47, [XBLOCK, RBLOCK])
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = 16.0
    tmp20 = tmp18 / tmp19
    tmp21 = tmp14 - tmp20
    tmp22 = tl.broadcast_to(tmp21, [XBLOCK, RBLOCK])
    tl.where(xmask, tmp22, 0)
    tmp25 = tl.broadcast_to(tmp22, [XBLOCK, RBLOCK])
    tmp27 = tl.where(xmask, tmp25, 0)
    tmp28 = tl.sum(tmp27, 1)[:, None]
    tmp29 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp30 = tmp29.to(tl.float32)
    tmp31 = tmp28 / tmp30
    tmp32 = tmp22 - tmp31
    tmp33 = tmp32 * tmp32
    tmp34 = tl.broadcast_to(tmp33, [XBLOCK, RBLOCK])
    tmp36 = tl.where(xmask, tmp34, 0)
    tmp37 = tl.sum(tmp36, 1)[:, None]
    tmp38 = 15.0
    tmp39 = tmp37 / tmp38
    tmp40 = libdevice.sqrt(tmp39)
    tmp41 = 1e-05
    tmp42 = tmp40 + tmp41
    tmp43 = tmp21 / tmp42
    tmp46 = tmp43 * tmp45
    tmp49 = tmp46 + tmp48
    tmp50 = tl.sigmoid(tmp49)
    tl.store(out_ptr0 + (r1 + 16 * x0), tmp14, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp20, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x0, tmp42, xmask)
    tl.store(out_ptr1 + (r1 + 16 * x0), tmp50, xmask)


@triton.jit
def triton_poi_fused_add_mul_sigmoid_2(in_ptr0, in_ptr1, 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_ptr1 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x3, 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, (1, 1, 1, 1), (1, 1, 1, 1))
    assert_size_stride(primals_3, (1, 1, 1, 1), (1, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 4, 1, 1), (4, 1, 1, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_per_fused_mean_0[grid(16)](buf1, primals_1, 16, 16, XBLOCK=1,
            num_warps=2, num_stages=1)
        buf2 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf4 = reinterpret_tensor(buf3, (4, 1), (1, 1), 0)
        del buf3
        buf6 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf8 = reinterpret_tensor(buf6, (4, 1), (1, 1), 0)
        del buf6
        buf9 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        triton_per_fused_add_mean_mul_sigmoid_std_sub_sum_1[grid(4)](buf4,
            buf8, primals_1, buf1, primals_2, primals_3, buf2, buf9, 4, 16,
            XBLOCK=1, num_warps=2, num_stages=1)
        del buf2
        buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_sigmoid_2[grid(256)](primals_1, buf9,
            buf10, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf9
    return buf10, primals_1, primals_2, primals_3, buf1, buf4, buf8


class SpatialGroupEnhanceNew(nn.Module):

    def __init__(self, groups):
        super().__init__()
        self.groups = groups
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.weight = nn.Parameter(torch.zeros(1, groups, 1, 1))
        self.bias = nn.Parameter(torch.zeros(1, groups, 1, 1))
        self.sig = nn.Sigmoid()
        self.init_weights()

    def init_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                init.kaiming_normal_(m.weight, mode='fan_out')
                if m.bias is not None:
                    init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                init.constant_(m.weight, 1)
                init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                init.normal_(m.weight, std=0.001)
                if m.bias is not None:
                    init.constant_(m.bias, 0)

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