import torch
from torchvision.transforms import *


class ConvBlock(torch.nn.Module):

    def __init__(self, input_size, output_size, kernel_size=3, stride=1,
        padding=1, bias=True, activation='prelu', norm=None):
        super(ConvBlock, self).__init__()
        self.conv = torch.nn.Conv2d(input_size, output_size, kernel_size,
            stride, padding, bias=bias)
        self.norm = norm
        if self.norm == 'batch':
            self.bn = torch.nn.BatchNorm2d(output_size)
        elif self.norm == 'instance':
            self.bn = torch.nn.InstanceNorm2d(output_size)
        self.activation = activation
        if self.activation == 'relu':
            self.act = torch.nn.ReLU(True)
        elif self.activation == 'prelu':
            self.act = torch.nn.PReLU()
        elif self.activation == 'lrelu':
            self.act = torch.nn.LeakyReLU(0.2, True)
        elif self.activation == 'tanh':
            self.act = torch.nn.Tanh()
        elif self.activation == 'sigmoid':
            self.act = torch.nn.Sigmoid()

    def forward(self, x):
        if self.norm is not None:
            out = self.bn(self.conv(x))
        else:
            out = self.conv(x)
        if self.activation is not None:
            return self.act(out)
        else:
            return out


class DeconvBlock(torch.nn.Module):

    def __init__(self, input_size, output_size, kernel_size=4, stride=2,
        padding=1, bias=True, activation='prelu', norm=None):
        super(DeconvBlock, self).__init__()
        self.deconv = torch.nn.ConvTranspose2d(input_size, output_size,
            kernel_size, stride, padding, bias=bias)
        self.norm = norm
        if self.norm == 'batch':
            self.bn = torch.nn.BatchNorm2d(output_size)
        elif self.norm == 'instance':
            self.bn = torch.nn.InstanceNorm2d(output_size)
        self.activation = activation
        if self.activation == 'relu':
            self.act = torch.nn.ReLU(True)
        elif self.activation == 'prelu':
            self.act = torch.nn.PReLU()
        elif self.activation == 'lrelu':
            self.act = torch.nn.LeakyReLU(0.2, True)
        elif self.activation == 'tanh':
            self.act = torch.nn.Tanh()
        elif self.activation == 'sigmoid':
            self.act = torch.nn.Sigmoid()

    def forward(self, x):
        if self.norm is not None:
            out = self.bn(self.deconv(x))
        else:
            out = self.deconv(x)
        if self.activation is not None:
            return self.act(out)
        else:
            return out


class D_UpBlock(torch.nn.Module):

    def __init__(self, num_filter, kernel_size=8, stride=4, padding=2,
        num_stages=1, bias=True, activation='prelu', norm=None):
        super(D_UpBlock, self).__init__()
        self.conv = ConvBlock(num_filter * num_stages, num_filter, 1, 1, 0,
            activation, norm=None)
        self.up_conv1 = DeconvBlock(num_filter, num_filter, kernel_size,
            stride, padding, activation, norm=None)
        self.up_conv2 = ConvBlock(num_filter, num_filter, kernel_size,
            stride, padding, activation, norm=None)
        self.up_conv3 = DeconvBlock(num_filter, num_filter, kernel_size,
            stride, padding, activation, norm=None)

    def forward(self, x):
        x = self.conv(x)
        h0 = self.up_conv1(x)
        l0 = self.up_conv2(h0)
        h1 = self.up_conv3(l0 - x)
        return h1 + h0


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


def get_init_inputs():
    return [[], {'num_filter': 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 torchvision.transforms import *
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__prelu_kernel_convolution_0(in_out_ptr0, 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_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + 0)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(out_ptr0 + x3, tmp8, xmask)


@triton.jit
def triton_poi_fused__prelu_kernel_convolution_1(in_out_ptr0, in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 256 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + 0)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tl.store(in_out_ptr0 + x3, tmp2, None)
    tl.store(out_ptr0 + x3, tmp8, None)


@triton.jit
def triton_poi_fused__prelu_kernel_convolution_sub_2(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, 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_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + 0)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp9 = tl.load(in_ptr2 + x3, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tmp10 = tmp8 - tmp9
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(out_ptr0 + x3, tmp10, xmask)


@triton.jit
def triton_poi_fused__prelu_kernel_add_convolution_3(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 256 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + 0)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp9 = tl.load(in_ptr2 + x3, None)
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tmp10 = tmp8 + tmp9
    tl.store(in_out_ptr0 + x3, tmp2, None)
    tl.store(out_ptr0 + x3, tmp10, None)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (1,), (1,))
    assert_size_stride(primals_5, (4, 4, 8, 8), (256, 64, 8, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (1,), (1,))
    assert_size_stride(primals_8, (4, 4, 8, 8), (256, 64, 8, 1))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (1,), (1,))
    assert_size_stride(primals_11, (4, 4, 8, 8), (256, 64, 8, 1))
    assert_size_stride(primals_12, (4,), (1,))
    assert_size_stride(primals_13, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__prelu_kernel_convolution_0[grid(256)](buf1,
            primals_2, primals_4, buf2, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_2
        buf3 = extern_kernels.convolution(buf2, primals_5, stride=(4, 4),
            padding=(2, 2), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 16, 16), (1024, 256, 16, 1))
        buf4 = buf3
        del buf3
        buf5 = empty_strided_cuda((4, 4, 16, 16), (1024, 256, 16, 1), torch
            .float32)
        triton_poi_fused__prelu_kernel_convolution_1[grid(4096)](buf4,
            primals_6, primals_7, buf5, 4096, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_6
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(4, 4),
            padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 4, 4), (64, 16, 4, 1))
        buf7 = buf6
        del buf6
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__prelu_kernel_convolution_sub_2[grid(256)](buf7,
            primals_9, primals_10, buf2, buf8, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_9
        buf9 = extern_kernels.convolution(buf8, primals_11, stride=(4, 4),
            padding=(2, 2), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf9, (4, 4, 16, 16), (1024, 256, 16, 1))
        buf10 = buf9
        del buf9
        buf11 = empty_strided_cuda((4, 4, 16, 16), (1024, 256, 16, 1),
            torch.float32)
        triton_poi_fused__prelu_kernel_add_convolution_3[grid(4096)](buf10,
            primals_12, primals_13, buf5, buf11, 4096, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_12
    return (buf11, primals_1, primals_3, primals_4, primals_5, primals_7,
        primals_8, primals_10, primals_11, primals_13, buf1, buf2, buf4,
        buf5, buf7, buf8, buf10)


class ConvBlock(torch.nn.Module):

    def __init__(self, input_size, output_size, kernel_size=3, stride=1,
        padding=1, bias=True, activation='prelu', norm=None):
        super(ConvBlock, self).__init__()
        self.conv = torch.nn.Conv2d(input_size, output_size, kernel_size,
            stride, padding, bias=bias)
        self.norm = norm
        if self.norm == 'batch':
            self.bn = torch.nn.BatchNorm2d(output_size)
        elif self.norm == 'instance':
            self.bn = torch.nn.InstanceNorm2d(output_size)
        self.activation = activation
        if self.activation == 'relu':
            self.act = torch.nn.ReLU(True)
        elif self.activation == 'prelu':
            self.act = torch.nn.PReLU()
        elif self.activation == 'lrelu':
            self.act = torch.nn.LeakyReLU(0.2, True)
        elif self.activation == 'tanh':
            self.act = torch.nn.Tanh()
        elif self.activation == 'sigmoid':
            self.act = torch.nn.Sigmoid()

    def forward(self, x):
        if self.norm is not None:
            out = self.bn(self.conv(x))
        else:
            out = self.conv(x)
        if self.activation is not None:
            return self.act(out)
        else:
            return out


class DeconvBlock(torch.nn.Module):

    def __init__(self, input_size, output_size, kernel_size=4, stride=2,
        padding=1, bias=True, activation='prelu', norm=None):
        super(DeconvBlock, self).__init__()
        self.deconv = torch.nn.ConvTranspose2d(input_size, output_size,
            kernel_size, stride, padding, bias=bias)
        self.norm = norm
        if self.norm == 'batch':
            self.bn = torch.nn.BatchNorm2d(output_size)
        elif self.norm == 'instance':
            self.bn = torch.nn.InstanceNorm2d(output_size)
        self.activation = activation
        if self.activation == 'relu':
            self.act = torch.nn.ReLU(True)
        elif self.activation == 'prelu':
            self.act = torch.nn.PReLU()
        elif self.activation == 'lrelu':
            self.act = torch.nn.LeakyReLU(0.2, True)
        elif self.activation == 'tanh':
            self.act = torch.nn.Tanh()
        elif self.activation == 'sigmoid':
            self.act = torch.nn.Sigmoid()

    def forward(self, x):
        if self.norm is not None:
            out = self.bn(self.deconv(x))
        else:
            out = self.deconv(x)
        if self.activation is not None:
            return self.act(out)
        else:
            return out


class D_UpBlockNew(torch.nn.Module):

    def __init__(self, num_filter, kernel_size=8, stride=4, padding=2,
        num_stages=1, bias=True, activation='prelu', norm=None):
        super(D_UpBlockNew, self).__init__()
        self.conv = ConvBlock(num_filter * num_stages, num_filter, 1, 1, 0,
            activation, norm=None)
        self.up_conv1 = DeconvBlock(num_filter, num_filter, kernel_size,
            stride, padding, activation, norm=None)
        self.up_conv2 = ConvBlock(num_filter, num_filter, kernel_size,
            stride, padding, activation, norm=None)
        self.up_conv3 = DeconvBlock(num_filter, num_filter, kernel_size,
            stride, padding, activation, norm=None)

    def forward(self, input_0):
        primals_1 = self.conv.conv.weight
        primals_2 = self.conv.conv.bias
        primals_4 = self.conv.act.weight
        primals_5 = self.up_conv1.deconv.weight
        primals_6 = self.up_conv1.deconv.bias
        primals_7 = self.up_conv1.act.weight
        primals_8 = self.up_conv2.conv.weight
        primals_9 = self.up_conv2.conv.bias
        primals_10 = self.up_conv2.act.weight
        primals_11 = self.up_conv3.deconv.weight
        primals_12 = self.up_conv3.deconv.bias
        primals_13 = self.up_conv3.act.weight
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13])
        return output[0]
