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
from torch.nn import functional as F
from torch import nn
from torch.nn.utils import spectral_norm
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_poi_fused_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 6 % 6
    x0 = xindex % 6
    x2 = xindex // 36
    x4 = xindex
    tmp0 = -1 + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = -1 + x0
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + (-5 + x0 + 4 * x1 + 16 * x2), tmp10 & xmask,
        other=0.0)
    tl.store(out_ptr0 + x4, tmp11, xmask)


@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_1(in_out_ptr0,
    in_out_ptr1, in_ptr0, out_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)
    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)
    tl.store(in_out_ptr0 + (r2 + 16 * x3), tmp2, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x3, tmp23, xmask)
    tl.store(out_ptr0 + x3, tmp12, xmask)


@triton.jit
def triton_poi_fused_constant_pad_nd_elu_2(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 6 % 6
    x0 = xindex % 6
    x2 = xindex // 36
    x4 = xindex
    tmp0 = -1 + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = -1 + x0
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + (-5 + x0 + 4 * x1 + 16 * x2), tmp10 & xmask,
        other=0.0)
    tmp12 = tl.load(in_ptr1 + x2, tmp10 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp13 = tmp11 - tmp12
    tmp14 = tl.load(in_ptr2 + x2, tmp10 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp15 = tmp13 * tmp14
    tmp16 = 0.0
    tmp17 = tmp15 > tmp16
    tmp18 = 1.0
    tmp19 = tmp15 * tmp18
    tmp20 = libdevice.expm1(tmp19)
    tmp21 = tmp20 * tmp18
    tmp22 = tl.where(tmp17, tmp19, tmp21)
    tmp23 = tl.full(tmp22.shape, 0.0, tmp22.dtype)
    tmp24 = tl.where(tmp10, tmp22, tmp23)
    tl.store(out_ptr0 + x4, tmp24, xmask)


@triton.jit
def triton_per_fused__native_batch_norm_legit_add_convolution_3(in_out_ptr0,
    in_ptr0, in_ptr1, out_ptr0, out_ptr2, out_ptr3, 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')
    tmp26 = tl.load(in_ptr1 + (r2 + 16 * x3), xmask, other=0.0)
    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 = tmp2 - tmp12
    tmp20 = 16.0
    tmp21 = tmp18 / tmp20
    tmp22 = 1e-05
    tmp23 = tmp21 + tmp22
    tmp24 = libdevice.rsqrt(tmp23)
    tmp25 = tmp19 * tmp24
    tmp27 = tmp25 + tmp26
    tl.store(in_out_ptr0 + (r2 + 16 * x3), tmp2, xmask)
    tl.store(out_ptr2 + (r2 + 16 * x3), tmp27, xmask)
    tl.store(out_ptr3 + x3, tmp24, xmask)
    tl.store(out_ptr0 + x3, 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, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0[grid(576)](primals_1, buf0, 576,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
        buf2 = buf1
        del buf1
        buf3 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
        buf4 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
            )
        buf6 = reinterpret_tensor(buf4, (1, 16, 1, 1), (16, 1, 1, 1), 0)
        del buf4
        triton_per_fused__native_batch_norm_legit_convolution_1[grid(16)](buf2,
            buf6, primals_3, buf3, 16, 16, XBLOCK=8, num_warps=2, num_stages=1)
        del primals_3
        buf7 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
        triton_poi_fused_constant_pad_nd_elu_2[grid(576)](buf2, buf3, buf6,
            buf7, 576, XBLOCK=256, num_warps=4, num_stages=1)
        buf8 = extern_kernels.convolution(buf7, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 4, 4, 4), (64, 16, 4, 1))
        buf9 = buf8
        del buf8
        buf10 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.
            float32)
        buf14 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf13 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.
            float32)
        triton_per_fused__native_batch_norm_legit_add_convolution_3[grid(16)](
            buf9, primals_5, primals_1, buf10, buf14, buf13, 16, 16, XBLOCK
            =1, num_warps=2, num_stages=1)
        del primals_1
        del primals_5
    return (buf14, primals_2, primals_4, buf0, buf2, buf3, buf6, buf7, buf9,
        reinterpret_tensor(buf13, (16,), (1,), 0), reinterpret_tensor(buf10,
        (1, 16, 1, 1), (16, 1, 1, 1), 0))


class AdaptiveInstanceNorm2d(nn.Module):

    def __init__(self, num_features, eps=1e-05, momentum=0.1):
        super().__init__()
        self.num_features = num_features
        self.eps = eps
        self.momentum = momentum
        self.register_buffer('running_mean', torch.zeros(num_features))
        self.register_buffer('running_var', torch.ones(num_features))

    def forward(self, x, adain_params):
        b, c = x.size(0), x.size(1)
        running_mean = self.running_mean.repeat(b)
        running_var = self.running_var.repeat(b)
        x_reshaped = x.contiguous().view(1, b * c, *x.size()[2:])
        out = F.batch_norm(x_reshaped, running_mean, running_var,
            adain_params['weight'], adain_params['bias'], True, self.
            momentum, self.eps)
        return out.view(b, c, *x.size()[2:])

    def __repr__(self):
        return self.__class__.__name__ + '(' + str(self.num_features) + ')'


class Conv2dBlock(nn.Module):

    def __init__(self, in_dim, out_dim, ks, st, padding=0, norm='none',
        activation='elu', pad_type='zero', use_bias=True, activation_first=
        False, snorm=False):
        super().__init__()
        self.use_bias = use_bias
        self.activation_first = activation_first
        if pad_type == 'reflect':
            self.pad = nn.ReflectionPad2d(padding)
        elif pad_type == 'replicate':
            self.pad = nn.ReplicationPad2d(padding)
        elif pad_type == 'zero':
            self.pad = nn.ZeroPad2d(padding)
        else:
            assert 0, 'Unsupported padding type: {}'.format(pad_type)
        norm_dim = out_dim
        if norm == 'bn':
            self.norm = nn.BatchNorm2d(norm_dim)
        elif norm == 'in':
            self.norm = nn.InstanceNorm2d(norm_dim)
        elif norm == 'group':
            self.norm = nn.GroupNorm(num_channels=norm_dim, num_groups=16)
        elif norm == 'adain':
            self.norm = AdaptiveInstanceNorm2d(norm_dim)
        elif norm == 'none':
            self.norm = None
        else:
            assert 0, 'Unsupported normalization: {}'.format(norm)
        if activation == 'relu':
            self.activation = nn.ReLU(inplace=True)
        elif activation == 'lrelu':
            self.activation = nn.LeakyReLU(0.2, inplace=True)
        elif activation == 'tanh':
            self.activation = nn.Tanh()
        elif activation == 'elu':
            self.activation = nn.ELU()
        elif activation == 'none':
            self.activation = None
        else:
            assert 0, 'Unsupported activation: {}'.format(activation)
        if snorm:
            self.conv = spectral_norm(nn.Conv2d(in_dim, out_dim, ks, st,
                bias=self.use_bias))
        else:
            self.conv = nn.Conv2d(in_dim, out_dim, ks, st, bias=self.use_bias)

    def forward(self, x, adain_params=None):
        if self.activation_first:
            if self.activation:
                x = self.activation(x)
            x = self.conv(self.pad(x))
            if self.norm and not isinstance(self.norm, AdaptiveInstanceNorm2d):
                x = self.norm(x)
            elif isinstance(self.norm, AdaptiveInstanceNorm2d):
                x = self.norm(x, adain_params)
        else:
            x = self.conv(self.pad(x))
            if self.norm and not isinstance(self.norm, AdaptiveInstanceNorm2d):
                x = self.norm(x)
            elif isinstance(self.norm, AdaptiveInstanceNorm2d):
                x = self.norm(x, adain_params)
            if self.activation:
                x = self.activation(x)
        return x


class Conv2dTransposeBlock(nn.Module):

    def __init__(self, in_dim, out_dim, ks, st, padding=0, norm='none',
        activation='elu', use_bias=True, activation_first=False, snorm=False):
        super().__init__()
        self.use_bias = use_bias
        self.activation_first = activation_first
        norm_dim = out_dim
        if norm == 'bn':
            self.norm = nn.BatchNorm2d(norm_dim)
        elif norm == 'in':
            self.norm = nn.InstanceNorm2d(norm_dim)
        elif norm == 'group':
            self.norm = nn.GroupNorm(num_channels=norm_dim, num_groups=16)
        elif norm == 'adain':
            self.norm = AdaptiveInstanceNorm2d(norm_dim)
        elif norm == 'none':
            self.norm = None
        else:
            assert 0, 'Unsupported normalization: {}'.format(norm)
        if activation == 'elu':
            self.activation = nn.ReLU(inplace=True)
        elif activation == 'lrelu':
            self.activation = nn.LeakyReLU(0.2, inplace=True)
        elif activation == 'tanh':
            self.activation = nn.Tanh()
        elif activation == 'none':
            self.activation = None
        else:
            assert 0, 'Unsupported activation: {}'.format(activation)
        if snorm:
            self.conv = spectral_norm(nn.ConvTranspose2d(in_dim, out_dim,
                ks, st, bias=self.use_bias, padding=padding, output_padding
                =padding))
        else:
            self.conv = nn.ConvTranspose2d(in_dim, out_dim, ks, st, bias=
                self.use_bias, padding=padding, output_padding=padding)

    def forward(self, x, adain_params=None):
        if self.activation_first:
            if self.activation:
                x = self.activation(x)
            x = self.conv(x)
            if self.norm and not isinstance(self.norm, AdaptiveInstanceNorm2d):
                x = self.norm(x)
            elif isinstance(self.norm, AdaptiveInstanceNorm2d):
                x = self.norm(x, adain_params)
        else:
            x = self.conv(x)
            if self.norm and not isinstance(self.norm, AdaptiveInstanceNorm2d):
                x = self.norm(x)
            elif isinstance(self.norm, AdaptiveInstanceNorm2d):
                x = self.norm(x, adain_params)
            if self.activation:
                x = self.activation(x)
        return x


class ResBlockNew(nn.Module):

    def __init__(self, dim_in, dim_out, norm='in', activation='elu',
        pad_type='zero', upsampling=False, stride=1, snorm=False):
        super(ResBlockNew, self).__init__()
        self.norm = norm
        self.model = nn.ModuleList()
        if upsampling:
            self.conv1 = Conv2dTransposeBlock(dim_in, dim_out, 3, 2, 1,
                norm=self.norm, activation=activation, snorm=snorm)
            self.conv2 = Conv2dBlock(dim_out, dim_out, 3, 1, 1, norm=self.
                norm, activation='none', pad_type=pad_type, snorm=snorm)
        else:
            self.conv1 = Conv2dBlock(dim_in, dim_out, 3, stride, 1, norm=
                self.norm, activation=activation, pad_type=pad_type, snorm=
                snorm)
            self.conv2 = Conv2dBlock(dim_out, dim_out, 3, 1, 1, norm=self.
                norm, activation='none', pad_type=pad_type, snorm=snorm)
        self.convolve_res = dim_in != dim_out or upsampling or stride != 1
        if self.convolve_res:
            if not upsampling:
                self.res_conv = Conv2dBlock(dim_in, dim_out, 3, stride, 1,
                    norm='in', activation=activation, pad_type=pad_type,
                    snorm=snorm)
            else:
                self.res_conv = Conv2dTransposeBlock(dim_in, dim_out, 3, 2,
                    1, norm='in', activation=activation, snorm=snorm)

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


Measured latency_ms: 0.02506