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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_per_fused_convolution_native_group_norm_relu_threshold_backward_0(
    in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr2, out_ptr3,
    out_ptr4, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 64
    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)
    r3 = rindex
    x0 = xindex
    r2 = rindex // 16
    tmp0 = tl.load(in_out_ptr0 + (r3 + 64 * x0), xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + r2, None, eviction_policy='evict_last')
    tmp26 = tl.load(in_ptr1 + r2, None, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr2 + r2, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tl.where(xmask, tmp3, 0)
    tmp6 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 64, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp3 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = tmp2 - tmp12
    tmp20 = 64.0
    tmp21 = tmp18 / tmp20
    tmp22 = 1e-05
    tmp23 = tmp21 + tmp22
    tmp24 = libdevice.rsqrt(tmp23)
    tmp25 = tmp19 * tmp24
    tmp27 = tmp25 * tmp26
    tmp29 = tmp27 + tmp28
    tmp30 = tl.full([1, 1], 0, tl.int32)
    tmp31 = triton_helpers.maximum(tmp30, tmp29)
    tmp32 = 0.0
    tmp33 = tmp31 <= tmp32
    tl.store(in_out_ptr0 + (r3 + 64 * x0), tmp2, xmask)
    tl.store(out_ptr2 + (r3 + 64 * x0), tmp31, xmask)
    tl.store(out_ptr3 + (r3 + 64 * x0), tmp33, xmask)
    tl.store(out_ptr4 + x0, tmp24, xmask)
    tl.store(out_ptr0 + x0, tmp12, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        buf5 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        get_raw_stream(0)
        triton_per_fused_convolution_native_group_norm_relu_threshold_backward_0[
            grid(4)](buf1, primals_3, primals_4, primals_5, buf2, buf6,
            buf7, buf5, 4, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del primals_3
        del primals_5
    return buf6, primals_1, primals_2, primals_4, buf1, reinterpret_tensor(buf2
        , (4, 1), (1, 1), 0), reinterpret_tensor(buf5, (4, 1), (1, 1), 0), buf7


class Conv2dNormActivNew(nn.Module):
    """
    Module for one Conv2d + an Activation (e.g. ReLU, leakyReLU)
    Assumption: odd kernel_size
    """

    def __init__(self, in_ch, out_ch, k_size=3, stride=1, norm=nn.GroupNorm,
        activation=nn.ReLU, dilation=1, padding=None):
        super(Conv2dNormActivNew, self).__init__()
        if k_size % 2 == 0:
            raise ValueError('k_size has to be odd')
        if not padding:
            padding = k_size // 2
            if k_size < 2:
                padding = 0
        self.conv = nn.Conv2d(in_ch, out_ch, kernel_size=k_size, stride=
            stride, padding=padding, dilation=dilation)
        if norm is None:
            self.norm = norm
        elif norm is nn.BatchNorm2d:
            self.norm = norm(out_ch)
        elif norm is nn.GroupNorm:
            self.norm = norm(num_groups=int(out_ch / 4), num_channels=out_ch)
        else:
            raise NotImplementedError
        self.activ = activation(inplace=True
            ) if activation is not None else None
        self.in_ch = in_ch
        self.out_ch = out_ch

    def __str__(self):
        norm_str = 'None' if self.norm is None else str(self.norm)
        if type(self.norm) == nn.BatchNorm2d:
            norm_str = 'BatchNorm2d'
        elif type(self.norm) == nn.GroupNorm:
            norm_str = 'GroupNorm'
        act_str = 'None' if self.activ is None else str(self.activ)
        if type(self.activ) == nn.ReLU:
            act_str = 'ReLU'
        elif type(self.activ) == nn.LeakyReLU:
            act_str = 'LeakyReLU'
        return (
            f'Conv: ({self.in_ch}, {self.out_ch}), Norm: {norm_str}, Act: {act_str}'
            )

    def forward(self, input_0):
        primals_2 = self.conv.weight
        primals_3 = self.conv.bias
        primals_4 = self.norm.weight
        primals_5 = self.norm.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.030287