import torch
import torch.nn as nn
from torch.nn import Parameter


def l2normalize(v, eps=1e-12):
    return v / (v.norm() + eps)


class LayerNorm(nn.Module):

    def __init__(self, num_features, eps=1e-08, affine=True):
        super(LayerNorm, self).__init__()
        self.num_features = num_features
        self.affine = affine
        self.eps = eps
        if self.affine:
            self.gamma = Parameter(torch.Tensor(num_features).uniform_())
            self.beta = Parameter(torch.zeros(num_features))

    def forward(self, x):
        shape = [-1] + [1] * (x.dim() - 1)
        if x.size(0) == 1:
            mean = x.view(-1).mean().view(*shape)
            std = x.view(-1).std().view(*shape)
        else:
            mean = x.view(x.size(0), -1).mean(1).view(*shape)
            std = x.view(x.size(0), -1).std(1).view(*shape)
        x = (x - mean) / (std + self.eps)
        if self.affine:
            shape = [1, -1] + [1] * (x.dim() - 2)
            x = x * self.gamma.view(*shape) + self.beta.view(*shape)
        return x


class SpectralNorm(nn.Module):

    def __init__(self, module, name='weight', power_iterations=1):
        super(SpectralNorm, self).__init__()
        self.module = module
        self.name = name
        self.power_iterations = power_iterations
        if not self._made_params():
            self._make_params()

    def _update_u_v(self):
        u = getattr(self.module, self.name + '_u')
        v = getattr(self.module, self.name + '_v')
        w = getattr(self.module, self.name + '_bar')
        height = w.data.shape[0]
        for _ in range(self.power_iterations):
            v.data = l2normalize(torch.mv(torch.t(w.view(height, -1).data),
                u.data))
            u.data = l2normalize(torch.mv(w.view(height, -1).data, v.data))
        sigma = u.dot(w.view(height, -1).mv(v))
        setattr(self.module, self.name, w / sigma.expand_as(w))

    def _made_params(self):
        try:
            getattr(self.module, self.name + '_u')
            getattr(self.module, self.name + '_v')
            getattr(self.module, self.name + '_bar')
            return True
        except AttributeError:
            return False

    def _make_params(self):
        w = getattr(self.module, self.name)
        height = w.data.shape[0]
        width = w.view(height, -1).data.shape[1]
        u = Parameter(w.data.new(height).normal_(0, 1), requires_grad=False)
        v = Parameter(w.data.new(width).normal_(0, 1), requires_grad=False)
        u.data = l2normalize(u.data)
        v.data = l2normalize(v.data)
        w_bar = Parameter(w.data)
        del self.module._parameters[self.name]
        self.module.register_parameter(self.name + '_u', u)
        self.module.register_parameter(self.name + '_v', v)
        self.module.register_parameter(self.name + '_bar', w_bar)

    def forward(self, *args):
        self._update_u_v()
        return self.module.forward(*args)


class GatedConv2d(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        padding=0, dilation=1, pad_type='reflect', activation='elu', norm=
        'none', sn=False):
        super(GatedConv2d, self).__init__()
        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)
        if norm == 'bn':
            self.norm = nn.BatchNorm2d(out_channels)
        elif norm == 'in':
            self.norm = nn.InstanceNorm2d(out_channels)
        elif norm == 'ln':
            self.norm = LayerNorm(out_channels)
        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 == 'elu':
            self.activation = nn.ELU()
        elif activation == 'selu':
            self.activation = nn.SELU(inplace=True)
        elif activation == 'tanh':
            self.activation = nn.Tanh()
        elif activation == 'sigmoid':
            self.activation = nn.Sigmoid()
        elif activation == 'none':
            self.activation = None
        else:
            assert 0, 'Unsupported activation: {}'.format(activation)
        if sn:
            self.conv2d = SpectralNorm(nn.Conv2d(in_channels, out_channels,
                kernel_size, stride, padding=0, dilation=dilation))
            self.mask_conv2d = SpectralNorm(nn.Conv2d(in_channels,
                out_channels, kernel_size, stride, padding=0, dilation=
                dilation))
        else:
            self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size,
                stride, padding=0, dilation=dilation)
            self.mask_conv2d = nn.Conv2d(in_channels, out_channels,
                kernel_size, stride, padding=0, dilation=dilation)
        self.sigmoid = torch.nn.Sigmoid()

    def forward(self, x):
        x = self.pad(x)
        conv = self.conv2d(x)
        mask = self.mask_conv2d(x)
        gated_mask = self.sigmoid(mask)
        if self.activation:
            conv = self.activation(conv)
        x = conv * gated_mask
        return x


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 4}]

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, math as tl_math
import torch.nn as nn
from torch.nn import Parameter
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_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + x0) + -4 * tl_math
        .abs(-3 + x1) + 16 * x2), xmask)
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_convolution_elu_mul_sigmoid_1(in_out_ptr0, in_out_ptr1,
    in_ptr0, in_ptr1, 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')
    tmp3 = tl.load(in_out_ptr1 + x2, xmask)
    tmp4 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = 0.0
    tmp7 = tmp2 > tmp6
    tmp8 = 1.0
    tmp9 = tmp2 * tmp8
    tmp10 = libdevice.expm1(tmp9)
    tmp11 = tmp10 * tmp8
    tmp12 = tl.where(tmp7, tmp9, tmp11)
    tmp13 = tl.sigmoid(tmp5)
    tmp14 = tmp12 * tmp13
    tl.store(in_out_ptr0 + x2, tmp2, xmask)
    tl.store(in_out_ptr1 + x2, tmp5, xmask)
    tl.store(out_ptr0 + x2, tmp14, 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, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_reflection_pad2d_0[grid(256)](primals_1, buf0, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_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, 1, 1), (4, 1, 1, 1))
        buf3 = extern_kernels.convolution(buf0, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 1, 1), (4, 1, 1, 1))
        buf2 = buf1
        del buf1
        buf4 = buf3
        del buf3
        buf5 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        triton_poi_fused_convolution_elu_mul_sigmoid_1[grid(16)](buf2, buf4,
            primals_3, primals_5, buf5, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        del primals_3
        del primals_5
    return buf5, primals_2, primals_4, buf0, buf2, buf4


def l2normalize(v, eps=1e-12):
    return v / (v.norm() + eps)


class LayerNorm(nn.Module):

    def __init__(self, num_features, eps=1e-08, affine=True):
        super(LayerNorm, self).__init__()
        self.num_features = num_features
        self.affine = affine
        self.eps = eps
        if self.affine:
            self.gamma = Parameter(torch.Tensor(num_features).uniform_())
            self.beta = Parameter(torch.zeros(num_features))

    def forward(self, x):
        shape = [-1] + [1] * (x.dim() - 1)
        if x.size(0) == 1:
            mean = x.view(-1).mean().view(*shape)
            std = x.view(-1).std().view(*shape)
        else:
            mean = x.view(x.size(0), -1).mean(1).view(*shape)
            std = x.view(x.size(0), -1).std(1).view(*shape)
        x = (x - mean) / (std + self.eps)
        if self.affine:
            shape = [1, -1] + [1] * (x.dim() - 2)
            x = x * self.gamma.view(*shape) + self.beta.view(*shape)
        return x


class SpectralNorm(nn.Module):

    def __init__(self, module, name='weight', power_iterations=1):
        super(SpectralNorm, self).__init__()
        self.module = module
        self.name = name
        self.power_iterations = power_iterations
        if not self._made_params():
            self._make_params()

    def _update_u_v(self):
        u = getattr(self.module, self.name + '_u')
        v = getattr(self.module, self.name + '_v')
        w = getattr(self.module, self.name + '_bar')
        height = w.data.shape[0]
        for _ in range(self.power_iterations):
            v.data = l2normalize(torch.mv(torch.t(w.view(height, -1).data),
                u.data))
            u.data = l2normalize(torch.mv(w.view(height, -1).data, v.data))
        sigma = u.dot(w.view(height, -1).mv(v))
        setattr(self.module, self.name, w / sigma.expand_as(w))

    def _made_params(self):
        try:
            getattr(self.module, self.name + '_u')
            getattr(self.module, self.name + '_v')
            getattr(self.module, self.name + '_bar')
            return True
        except AttributeError:
            return False

    def _make_params(self):
        w = getattr(self.module, self.name)
        height = w.data.shape[0]
        width = w.view(height, -1).data.shape[1]
        u = Parameter(w.data.new(height).normal_(0, 1), requires_grad=False)
        v = Parameter(w.data.new(width).normal_(0, 1), requires_grad=False)
        u.data = l2normalize(u.data)
        v.data = l2normalize(v.data)
        w_bar = Parameter(w.data)
        del self.module._parameters[self.name]
        self.module.register_parameter(self.name + '_u', u)
        self.module.register_parameter(self.name + '_v', v)
        self.module.register_parameter(self.name + '_bar', w_bar)

    def forward(self, *args):
        self._update_u_v()
        return self.module.forward(*args)


class GatedConv2dNew(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        padding=0, dilation=1, pad_type='reflect', activation='elu', norm=
        'none', sn=False):
        super(GatedConv2dNew, self).__init__()
        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)
        if norm == 'bn':
            self.norm = nn.BatchNorm2d(out_channels)
        elif norm == 'in':
            self.norm = nn.InstanceNorm2d(out_channels)
        elif norm == 'ln':
            self.norm = LayerNorm(out_channels)
        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 == 'elu':
            self.activation = nn.ELU()
        elif activation == 'selu':
            self.activation = nn.SELU(inplace=True)
        elif activation == 'tanh':
            self.activation = nn.Tanh()
        elif activation == 'sigmoid':
            self.activation = nn.Sigmoid()
        elif activation == 'none':
            self.activation = None
        else:
            assert 0, 'Unsupported activation: {}'.format(activation)
        if sn:
            self.conv2d = SpectralNorm(nn.Conv2d(in_channels, out_channels,
                kernel_size, stride, padding=0, dilation=dilation))
            self.mask_conv2d = SpectralNorm(nn.Conv2d(in_channels,
                out_channels, kernel_size, stride, padding=0, dilation=
                dilation))
        else:
            self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size,
                stride, padding=0, dilation=dilation)
            self.mask_conv2d = nn.Conv2d(in_channels, out_channels,
                kernel_size, stride, padding=0, dilation=dilation)
        self.sigmoid = torch.nn.Sigmoid()

    def forward(self, input_0):
        primals_1 = self.conv2d.weight
        primals_3 = self.conv2d.bias
        primals_2 = self.mask_conv2d.weight
        primals_5 = self.mask_conv2d.bias
        primals_4 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
