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
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_0(in_out_ptr0,
    in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, 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, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(4, 
            4), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 1, 1), (4, 1, 1, 1))
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_threshold_backward_0[grid(16)](buf1,
            primals_2, buf2, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_2
    return buf1, primals_1, primals_3, buf2


class AdaptiveInstanceLayerNorm2d(nn.Module):

    def __init__(self, num_features, eps=1e-05, momentum=0.9,
        using_moving_average=True, using_bn=False):
        super(AdaptiveInstanceLayerNorm2d, self).__init__()
        self.eps = eps
        self.momentum = momentum
        self.using_moving_average = using_moving_average
        self.using_bn = using_bn
        self.num_features = num_features
        if self.using_bn:
            self.rho = nn.Parameter(torch.Tensor(1, num_features, 3))
            self.rho[:, :, 0].data.fill_(3)
            self.rho[:, :, 1].data.fill_(1)
            self.rho[:, :, 2].data.fill_(1)
            self.register_buffer('running_mean', torch.zeros(1,
                num_features, 1, 1))
            self.register_buffer('running_var', torch.zeros(1, num_features,
                1, 1))
            self.running_mean.zero_()
            self.running_var.zero_()
        else:
            self.rho = nn.Parameter(torch.Tensor(1, num_features, 2))
            self.rho[:, :, 0].data.fill_(3.2)
            self.rho[:, :, 1].data.fill_(1)
        self.weight = None
        self.bias = None

    def forward(self, input):
        assert self.weight is not None and self.bias is not None, 'Please assign AdaILN weight first'
        in_mean, in_var = torch.mean(input, dim=[2, 3], keepdim=True
            ), torch.var(input, dim=[2, 3], keepdim=True)
        out_in = (input - in_mean) / torch.sqrt(in_var + self.eps)
        ln_mean, ln_var = torch.mean(input, dim=[1, 2, 3], keepdim=True
            ), torch.var(input, dim=[1, 2, 3], keepdim=True)
        out_ln = (input - ln_mean) / torch.sqrt(ln_var + self.eps)
        softmax = nn.Softmax(2)
        rho = softmax(self.rho)
        if self.using_bn:
            if self.training:
                bn_mean, bn_var = torch.mean(input, dim=[0, 2, 3], keepdim=True
                    ), torch.var(input, dim=[0, 2, 3], keepdim=True)
                if self.using_moving_average:
                    self.running_mean.mul_(self.momentum)
                    self.running_mean.add_((1 - self.momentum) * bn_mean.data)
                    self.running_var.mul_(self.momentum)
                    self.running_var.add_((1 - self.momentum) * bn_var.data)
                else:
                    self.running_mean.add_(bn_mean.data)
                    self.running_var.add_(bn_mean.data ** 2 + bn_var.data)
            else:
                bn_mean = torch.autograd.Variable(self.running_mean)
                bn_var = torch.autograd.Variable(self.running_var)
            out_bn = (input - bn_mean) / torch.sqrt(bn_var + self.eps)
            rho_0 = rho[:, :, 0]
            rho_1 = rho[:, :, 1]
            rho_2 = rho[:, :, 2]
            rho_0 = rho_0.view(1, self.num_features, 1, 1)
            rho_1 = rho_1.view(1, self.num_features, 1, 1)
            rho_2 = rho_2.view(1, self.num_features, 1, 1)
            rho_0 = rho_0.expand(input.shape[0], -1, -1, -1)
            rho_1 = rho_1.expand(input.shape[0], -1, -1, -1)
            rho_2 = rho_2.expand(input.shape[0], -1, -1, -1)
            out = rho_0 * out_in + rho_1 * out_ln + rho_2 * out_bn
        else:
            rho_0 = rho[:, :, 0]
            rho_1 = rho[:, :, 1]
            rho_0 = rho_0.view(1, self.num_features, 1, 1)
            rho_1 = rho_1.view(1, self.num_features, 1, 1)
            rho_0 = rho_0.expand(input.shape[0], -1, -1, -1)
            rho_1 = rho_1.expand(input.shape[0], -1, -1, -1)
            out = rho_0 * out_in + rho_1 * out_ln
        out = out * self.weight.unsqueeze(2).unsqueeze(3
            ) + self.bias.unsqueeze(2).unsqueeze(3)
        return out


class AdaptiveInstanceNorm2d(nn.Module):

    def __init__(self, num_features, eps=1e-05, momentum=0.1):
        super(AdaptiveInstanceNorm2d, self).__init__()
        self.num_features = num_features
        self.eps = eps
        self.momentum = momentum
        self.weight = None
        self.bias = None
        self.register_buffer('running_mean', torch.zeros(num_features))
        self.register_buffer('running_var', torch.ones(num_features))

    def forward(self, x):
        assert self.weight is not None and self.bias is not None, 'Please assign AdaIN weight first'
        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, self.
            weight, self.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 Identity(nn.Module):

    def forward(self, x):
        return x


class InstanceLayerNorm2d(nn.Module):

    def __init__(self, num_features, eps=1e-05, momentum=0.9,
        using_moving_average=True, using_bn=False):
        super(InstanceLayerNorm2d, self).__init__()
        self.eps = eps
        self.momentum = momentum
        self.using_moving_average = using_moving_average
        self.using_bn = using_bn
        self.num_features = num_features
        if self.using_bn:
            self.rho = nn.Parameter(torch.Tensor(1, num_features, 3))
            self.rho[:, :, 0].data.fill_(1)
            self.rho[:, :, 1].data.fill_(3)
            self.rho[:, :, 2].data.fill_(3)
            self.register_buffer('running_mean', torch.zeros(1,
                num_features, 1, 1))
            self.register_buffer('running_var', torch.zeros(1, num_features,
                1, 1))
            self.running_mean.zero_()
            self.running_var.zero_()
        else:
            self.rho = nn.Parameter(torch.Tensor(1, num_features, 2))
            self.rho[:, :, 0].data.fill_(1)
            self.rho[:, :, 1].data.fill_(3.2)
        self.gamma = nn.Parameter(torch.Tensor(1, num_features, 1, 1))
        self.beta = nn.Parameter(torch.Tensor(1, num_features, 1, 1))
        self.gamma.data.fill_(1.0)
        self.beta.data.fill_(0.0)

    def forward(self, input):
        in_mean, in_var = torch.mean(input, dim=[2, 3], keepdim=True
            ), torch.var(input, dim=[2, 3], keepdim=True)
        out_in = (input - in_mean) / torch.sqrt(in_var + self.eps)
        ln_mean, ln_var = torch.mean(input, dim=[1, 2, 3], keepdim=True
            ), torch.var(input, dim=[1, 2, 3], keepdim=True)
        out_ln = (input - ln_mean) / torch.sqrt(ln_var + self.eps)
        softmax = nn.Softmax(2)
        rho = softmax(self.rho)
        if self.using_bn:
            if self.training:
                bn_mean, bn_var = torch.mean(input, dim=[0, 2, 3], keepdim=True
                    ), torch.var(input, dim=[0, 2, 3], keepdim=True)
                if self.using_moving_average:
                    self.running_mean.mul_(self.momentum)
                    self.running_mean.add_((1 - self.momentum) * bn_mean.data)
                    self.running_var.mul_(self.momentum)
                    self.running_var.add_((1 - self.momentum) * bn_var.data)
                else:
                    self.running_mean.add_(bn_mean.data)
                    self.running_var.add_(bn_mean.data ** 2 + bn_var.data)
            else:
                bn_mean = torch.autograd.Variable(self.running_mean)
                bn_var = torch.autograd.Variable(self.running_var)
            out_bn = (input - bn_mean) / torch.sqrt(bn_var + self.eps)
            rho_0 = rho[:, :, 0]
            rho_1 = rho[:, :, 1]
            rho_2 = rho[:, :, 2]
            rho_0 = rho_0.view(1, self.num_features, 1, 1)
            rho_1 = rho_1.view(1, self.num_features, 1, 1)
            rho_2 = rho_2.view(1, self.num_features, 1, 1)
            rho_0 = rho_0.expand(input.shape[0], -1, -1, -1)
            rho_1 = rho_1.expand(input.shape[0], -1, -1, -1)
            rho_2 = rho_2.expand(input.shape[0], -1, -1, -1)
            out = rho_0 * out_in + rho_1 * out_ln + rho_2 * out_bn
        else:
            rho_0 = rho[:, :, 0]
            rho_1 = rho[:, :, 1]
            rho_0 = rho_0.view(1, self.num_features, 1, 1)
            rho_1 = rho_1.view(1, self.num_features, 1, 1)
            rho_0 = rho_0.expand(input.shape[0], -1, -1, -1)
            rho_1 = rho_1.expand(input.shape[0], -1, -1, -1)
            out = rho_0 * out_in + rho_1 * out_ln
        out = out * self.gamma.expand(input.shape[0], -1, -1, -1
            ) + self.beta.expand(input.shape[0], -1, -1, -1)
        return out


class Conv2dBlockNew(nn.Module):

    def __init__(self, in_dim, out_dim, ks, st, padding=0, norm='none',
        activation='relu', pad_type='zero', use_bias=True, activation_first
        =False, groups=1, sn=False):
        super(Conv2dBlockNew, self).__init__()
        self.use_bias = use_bias
        self.activation_first = activation_first
        if padding == 0:
            self.pad = Identity()
        elif 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 == 'gn':
            self.norm = nn.GroupNorm(4, norm_dim, 0.8)
        elif norm == 'in':
            self.norm = nn.InstanceNorm2d(norm_dim)
        elif norm == 'adain':
            self.norm = AdaptiveInstanceNorm2d(norm_dim)
        elif norm == 'iln':
            self.norm = InstanceLayerNorm2d(norm_dim)
        elif norm == 'adailn':
            self.norm = AdaptiveInstanceLayerNorm2d(norm_dim)
        elif norm == 'none':
            self.norm = None
        else:
            assert 0, 'Unsupported normalization: {}'.format(norm)
        if activation == 'relu':
            self.activation = nn.ReLU(inplace=False)
        elif activation == 'lrelu':
            self.activation = nn.LeakyReLU(0.2, inplace=False)
        elif activation == 'tanh':
            self.activation = nn.Tanh()
        elif activation == 'none':
            self.activation = None
        else:
            assert 0, 'Unsupported activation: {}'.format(activation)
        if sn:
            self.conv = nn.utils.spectral_norm(nn.Conv2d(in_dim, out_dim,
                ks, st, bias=self.use_bias, groups=groups))
        else:
            self.conv = nn.Conv2d(in_dim, out_dim, ks, st, bias=self.
                use_bias, groups=groups)

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


Measured latency_ms: 0.0485