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
import torch.nn as nn
from functools import partial
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__native_batch_norm_legit_elu_0(in_ptr0, out_ptr2,
    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])
    tl.where(xmask, tmp1, 0)
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.where(xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tmp17 = tmp0 - tmp10
    tmp18 = 16.0
    tmp19 = tmp16 / tmp18
    tmp20 = 1e-05
    tmp21 = tmp19 + tmp20
    tmp22 = libdevice.rsqrt(tmp21)
    tmp23 = tmp17 * tmp22
    tmp24 = 0.0
    tmp25 = tmp23 > tmp24
    tmp26 = 1.0
    tmp27 = tmp23 * tmp26
    tmp28 = libdevice.expm1(tmp27)
    tmp29 = tmp28 * tmp26
    tmp30 = tl.where(tmp25, tmp27, tmp29)
    tl.store(out_ptr2 + (r1 + 16 * x0), tmp30, xmask)


@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_elu_1(in_out_ptr0,
    in_out_ptr1, in_ptr0, out_ptr0, out_ptr1, 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)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (r2 + 16 * x3), xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + x0, xmask, 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], 16, 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 = 16.0
    tmp20 = tmp18 / tmp19
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tmp24 = tmp2 - tmp12
    tmp25 = tmp24 * tmp23
    tmp26 = 0.0
    tmp27 = tmp25 > tmp26
    tmp28 = 1.0
    tmp29 = tmp25 * tmp28
    tmp30 = libdevice.expm1(tmp29)
    tmp31 = tmp30 * tmp28
    tmp32 = tl.where(tmp27, tmp29, tmp31)
    tl.store(in_out_ptr0 + (r2 + 16 * x3), tmp2, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x3, tmp23, xmask)
    tl.store(out_ptr1 + (r2 + 16 * x3), tmp32, xmask)
    tl.store(out_ptr0 + x3, tmp12, xmask)


@triton.jit
def triton_poi_fused_add_convolution_2(in_out_ptr0, in_ptr0, in_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_out_ptr0 + x3, xmask)
    tmp2 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tl.store(in_out_ptr0 + x3, tmp4, 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, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused__native_batch_norm_legit_elu_0[grid(16)](primals_1,
            buf3, 16, 16, XBLOCK=1, num_warps=2, num_stages=1)
        buf4 = extern_kernels.convolution(buf3, primals_2, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 4, 4, 4), (64, 16, 4, 1))
        buf5 = buf4
        del buf4
        buf6 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
        buf7 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
            )
        buf9 = reinterpret_tensor(buf7, (1, 16, 1, 1), (16, 1, 1, 1), 0)
        del buf7
        buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_per_fused__native_batch_norm_legit_convolution_elu_1[grid(16)](
            buf5, buf9, primals_3, buf6, buf10, 16, 16, XBLOCK=1, num_warps
            =2, num_stages=1)
        del primals_3
        buf11 = extern_kernels.convolution(buf10, primals_4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 4, 4, 4), (64, 16, 4, 1))
        buf12 = buf11
        del buf11
        triton_poi_fused_add_convolution_2[grid(256)](buf12, primals_1,
            primals_5, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        del primals_5
    return buf12, primals_2, primals_4, buf3, buf5, buf6, buf9, buf10


def ncsn_conv3x3(in_planes, out_planes, stride=1, bias=True, dilation=1,
    init_scale=1.0, padding=1):
    """3x3 convolution with PyTorch initialization. Same as NCSNv1/NCSNv2."""
    init_scale = 1e-10 if init_scale == 0 else init_scale
    conv = nn.Conv2d(in_planes, out_planes, stride=stride, bias=bias,
        dilation=dilation, padding=padding, kernel_size=3)
    conv.weight.data *= init_scale
    conv.bias.data *= init_scale
    return conv


def ncsn_conv1x1(in_planes, out_planes, stride=1, bias=True, dilation=1,
    init_scale=1.0, padding=0):
    """1x1 convolution. Same as NCSNv1/v2."""
    conv = nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride,
        bias=bias, dilation=dilation, padding=padding)
    init_scale = 1e-10 if init_scale == 0 else init_scale
    conv.weight.data *= init_scale
    conv.bias.data *= init_scale
    return conv


class ConvMeanPool(nn.Module):

    def __init__(self, input_dim, output_dim, kernel_size=3, biases=True,
        adjust_padding=False):
        super().__init__()
        if not adjust_padding:
            conv = nn.Conv2d(input_dim, output_dim, kernel_size, stride=1,
                padding=kernel_size // 2, bias=biases)
            self.conv = conv
        else:
            conv = nn.Conv2d(input_dim, output_dim, kernel_size, stride=1,
                padding=kernel_size // 2, bias=biases)
            self.conv = nn.Sequential(nn.ZeroPad2d((1, 0, 1, 0)), conv)

    def forward(self, inputs):
        output = self.conv(inputs)
        output = sum([output[:, :, ::2, ::2], output[:, :, 1::2, ::2],
            output[:, :, ::2, 1::2], output[:, :, 1::2, 1::2]]) / 4.0
        return output


class ResidualBlockNew(nn.Module):

    def __init__(self, input_dim, output_dim, resample=None, act=nn.ELU(),
        normalization=nn.InstanceNorm2d, adjust_padding=False, dilation=1):
        super().__init__()
        self.non_linearity = act
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.resample = resample
        self.normalization = normalization
        if resample == 'down':
            if dilation > 1:
                self.conv1 = ncsn_conv3x3(input_dim, input_dim, dilation=
                    dilation)
                self.normalize2 = normalization(input_dim)
                self.conv2 = ncsn_conv3x3(input_dim, output_dim, dilation=
                    dilation)
                conv_shortcut = partial(ncsn_conv3x3, dilation=dilation)
            else:
                self.conv1 = ncsn_conv3x3(input_dim, input_dim)
                self.normalize2 = normalization(input_dim)
                self.conv2 = ConvMeanPool(input_dim, output_dim, 3,
                    adjust_padding=adjust_padding)
                conv_shortcut = partial(ConvMeanPool, kernel_size=1,
                    adjust_padding=adjust_padding)
        elif resample is None:
            if dilation > 1:
                conv_shortcut = partial(ncsn_conv3x3, dilation=dilation)
                self.conv1 = ncsn_conv3x3(input_dim, output_dim, dilation=
                    dilation)
                self.normalize2 = normalization(output_dim)
                self.conv2 = ncsn_conv3x3(output_dim, output_dim, dilation=
                    dilation)
            else:
                conv_shortcut = partial(ncsn_conv1x1)
                self.conv1 = ncsn_conv3x3(input_dim, output_dim)
                self.normalize2 = normalization(output_dim)
                self.conv2 = ncsn_conv3x3(output_dim, output_dim)
        else:
            raise Exception('invalid resample value')
        if output_dim != input_dim or resample is not None:
            self.shortcut = conv_shortcut(input_dim, output_dim)
        self.normalize1 = normalization(input_dim)

    def forward(self, input_0):
        primals_2 = self.conv1.weight
        primals_3 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.035231