import torch
import torch.nn.functional as F
from torch import nn


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 Conv2dBlock(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):
        super(Conv2dBlock, self).__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 == '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=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)
        self.conv = nn.Conv2d(in_dim, out_dim, ks, st, bias=self.use_bias)

    def forward(self, x):
        if self.activation_first:
            if self.activation:
                x = self.activation(x)
            x = self.conv(self.pad(x))
            if self.norm:
                x = self.norm(x)
        else:
            x = self.conv(self.pad(x))
            if self.norm:
                x = self.norm(x)
            if self.activation:
                x = self.activation(x)
        return x


class ActFirstResBlock(nn.Module):

    def __init__(self, fin, fout, fhid=None, activation='lrelu', norm='none'):
        super().__init__()
        self.learned_shortcut = fin != fout
        self.fin = fin
        self.fout = fout
        self.fhid = min(fin, fout) if fhid is None else fhid
        self.conv_0 = Conv2dBlock(self.fin, self.fhid, 3, 1, padding=1,
            pad_type='reflect', norm=norm, activation=activation,
            activation_first=True)
        self.conv_1 = Conv2dBlock(self.fhid, self.fout, 3, 1, padding=1,
            pad_type='reflect', norm=norm, activation=activation,
            activation_first=True)
        if self.learned_shortcut:
            self.conv_s = Conv2dBlock(self.fin, self.fout, 1, 1, activation
                ='none', use_bias=False)

    def forward(self, x):
        x_s = self.conv_s(x) if self.learned_shortcut else x
        dx = self.conv_0(x)
        dx = self.conv_1(dx)
        out = x_s + dx
        return out


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


def get_init_inputs():
    return [[], {'fin': 4, 'fout': 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 math as tl_math
import torch.nn.functional as F
from torch import nn
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_leaky_relu_reflection_pad2d_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
    x0 = xindex % 6
    x1 = xindex // 6 % 6
    x2 = xindex // 36
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
        x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2),
        xmask, eviction_policy='evict_last')
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + x3, tmp5, xmask)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_1(in_ptr0, in_ptr1, out_ptr0,
    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_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tl.store(out_ptr0 + x3, tmp4, xmask)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_reflection_pad2d_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
    x0 = xindex % 6
    x1 = xindex // 6 % 6
    x4 = xindex // 36
    x2 = xindex // 36 % 4
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
        x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x4),
        xmask, eviction_policy='evict_last').to(tl.int1)
    tmp1 = tl.load(in_ptr1 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
        x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x4),
        xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = 0.2
    tmp5 = tmp3 * tmp4
    tmp6 = tl.where(tmp0, tmp3, tmp5)
    tl.store(out_ptr0 + x5, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_convolution_3(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)
        buf0 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_leaky_relu_reflection_pad2d_0[grid(576)](primals_1,
            buf0, 576, XBLOCK=256, 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 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_convolution_leaky_relu_1[grid(256)](buf1,
            primals_3, buf2, 256, XBLOCK=128, num_warps=4, num_stages=1)
        buf3 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
        triton_poi_fused_convolution_leaky_relu_reflection_pad2d_2[grid(576)](
            buf2, buf1, primals_3, buf3, 576, XBLOCK=128, num_warps=4,
            num_stages=1)
        del buf1
        del primals_3
        buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1),
            padding=(0, 0), 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
        triton_poi_fused_add_convolution_3[grid(256)](buf5, primals_1,
            primals_5, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        del primals_5
    return buf5, primals_2, primals_4, buf0, buf2, buf3


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 Conv2dBlock(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):
        super(Conv2dBlock, self).__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 == '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=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)
        self.conv = nn.Conv2d(in_dim, out_dim, ks, st, bias=self.use_bias)

    def forward(self, x):
        if self.activation_first:
            if self.activation:
                x = self.activation(x)
            x = self.conv(self.pad(x))
            if self.norm:
                x = self.norm(x)
        else:
            x = self.conv(self.pad(x))
            if self.norm:
                x = self.norm(x)
            if self.activation:
                x = self.activation(x)
        return x


class ActFirstResBlockNew(nn.Module):

    def __init__(self, fin, fout, fhid=None, activation='lrelu', norm='none'):
        super().__init__()
        self.learned_shortcut = fin != fout
        self.fin = fin
        self.fout = fout
        self.fhid = min(fin, fout) if fhid is None else fhid
        self.conv_0 = Conv2dBlock(self.fin, self.fhid, 3, 1, padding=1,
            pad_type='reflect', norm=norm, activation=activation,
            activation_first=True)
        self.conv_1 = Conv2dBlock(self.fhid, self.fout, 3, 1, padding=1,
            pad_type='reflect', norm=norm, activation=activation,
            activation_first=True)
        if self.learned_shortcut:
            self.conv_s = Conv2dBlock(self.fin, self.fout, 1, 1, activation
                ='none', use_bias=False)

    def forward(self, input_0):
        primals_2 = self.conv_0.conv.weight
        primals_3 = self.conv_0.conv.bias
        primals_4 = self.conv_1.conv.weight
        primals_5 = self.conv_1.conv.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
