import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data


class Decoder(nn.Module):
    """ VAE decoder """

    def __init__(self, img_channels, latent_size):
        super(Decoder, self).__init__()
        self.latent_size = latent_size
        self.img_channels = img_channels
        self.fc1 = nn.Linear(latent_size, 1024)
        self.deconv1 = nn.ConvTranspose2d(1024, 128, 5, stride=2)
        self.deconv2 = nn.ConvTranspose2d(128, 64, 5, stride=2)
        self.deconv3 = nn.ConvTranspose2d(64, 32, 6, stride=2)
        self.deconv4 = nn.ConvTranspose2d(32, img_channels, 6, stride=2)

    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = x.unsqueeze(-1).unsqueeze(-1)
        x = F.relu(self.deconv1(x))
        x = F.relu(self.deconv2(x))
        x = F.relu(self.deconv3(x))
        reconstruction = F.sigmoid(self.deconv4(x))
        return reconstruction


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


def get_init_inputs():
    return [[], {'img_channels': 4, 'latent_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 import triton_helpers
import torch.nn as nn
import torch.utils.data
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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 25
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)
        ) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 128
    y1 = yindex // 128
    tmp0 = tl.load(in_ptr0 + (x2 + 25 * y3), xmask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 128 * x2 + 3200 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 25
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = yindex // 64
    tmp0 = tl.load(in_ptr0 + (x2 + 25 * y3), xmask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 64 * x2 + 1600 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 36
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 32
    y1 = yindex // 32
    tmp0 = tl.load(in_ptr0 + (x2 + 36 * y3), xmask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 32 * x2 + 1152 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 128
    xnumel = 36
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 4
    y1 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (x2 + 36 * y3), xmask & ymask, eviction_policy
        ='evict_last')
    tl.store(out_ptr0 + (y0 + 4 * x2 + 144 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_4(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 1024
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, 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, None)
    tl.store(out_ptr0 + x2, tmp6, None)


@triton.jit
def triton_poi_fused_convolution_relu_5(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 12800
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 128
    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)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_6(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 43264
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    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)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_7(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 115200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 32
    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)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_convolution_sigmoid_8(in_ptr0, in_ptr1, out_ptr0,
    ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16384 * y1), ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tl.store(out_ptr0 + (x2 + 4096 * y3), tmp3, ymask)


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) = args
    args.clear()
    assert_size_stride(primals_1, (1024, 4), (4, 1))
    assert_size_stride(primals_2, (1024,), (1,))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (1024, 128, 5, 5), (3200, 25, 5, 1))
    assert_size_stride(primals_5, (128,), (1,))
    assert_size_stride(primals_6, (128, 64, 5, 5), (1600, 25, 5, 1))
    assert_size_stride(primals_7, (64,), (1,))
    assert_size_stride(primals_8, (64, 32, 6, 6), (1152, 36, 6, 1))
    assert_size_stride(primals_9, (32,), (1,))
    assert_size_stride(primals_10, (32, 4, 6, 6), (144, 36, 6, 1))
    assert_size_stride(primals_11, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1024, 128, 5, 5), (3200, 1, 640, 128),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(131072, 25)](primals_4, buf0, 131072, 25,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_4
        buf1 = empty_strided_cuda((128, 64, 5, 5), (1600, 1, 320, 64),
            torch.float32)
        triton_poi_fused_1[grid(8192, 25)](primals_6, buf1, 8192, 25,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_6
        buf2 = empty_strided_cuda((64, 32, 6, 6), (1152, 1, 192, 32), torch
            .float32)
        triton_poi_fused_2[grid(2048, 36)](primals_8, buf2, 2048, 36,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_8
        buf3 = empty_strided_cuda((32, 4, 6, 6), (144, 1, 24, 4), torch.float32
            )
        triton_poi_fused_3[grid(128, 36)](primals_10, buf3, 128, 36, XBLOCK
            =32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_10
        buf4 = empty_strided_cuda((4, 1024), (1024, 1), torch.float32)
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_1, (4, 1024
            ), (1, 4), 0), out=buf4)
        del primals_1
        buf5 = buf4
        del buf4
        buf14 = empty_strided_cuda((4, 1024), (1024, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_4[grid(4096)](buf5,
            primals_2, buf14, 4096, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
        buf6 = extern_kernels.convolution(reinterpret_tensor(buf5, (4, 1024,
            1, 1), (1024, 1, 0, 0), 0), buf0, stride=(2, 2), padding=(0, 0),
            dilation=(1, 1), transposed=True, output_padding=(0, 0), groups
            =1, bias=None)
        assert_size_stride(buf6, (4, 128, 5, 5), (3200, 1, 640, 128))
        buf7 = buf6
        del buf6
        triton_poi_fused_convolution_relu_5[grid(12800)](buf7, primals_5, 
            12800, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_5
        buf8 = extern_kernels.convolution(buf7, buf1, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 64, 13, 13), (10816, 1, 832, 64))
        buf9 = buf8
        del buf8
        triton_poi_fused_convolution_relu_6[grid(43264)](buf9, primals_7, 
            43264, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_7
        buf10 = extern_kernels.convolution(buf9, buf2, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 32, 30, 30), (28800, 1, 960, 32))
        buf11 = buf10
        del buf10
        triton_poi_fused_convolution_relu_7[grid(115200)](buf11, primals_9,
            115200, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_9
        buf12 = extern_kernels.convolution(buf11, buf3, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 4, 64, 64), (16384, 1, 256, 4))
        buf13 = empty_strided_cuda((4, 4, 64, 64), (16384, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_convolution_sigmoid_8[grid(16, 4096)](buf12,
            primals_11, buf13, 16, 4096, XBLOCK=32, YBLOCK=16, num_warps=4,
            num_stages=1)
        del buf12
        del primals_11
    return buf13, primals_3, buf0, buf1, buf2, buf3, reinterpret_tensor(buf5,
        (4, 1024, 1, 1), (1024, 1, 1, 1), 0), buf7, buf9, buf11, buf13, buf14


class DecoderNew(nn.Module):
    """ VAE decoder """

    def __init__(self, img_channels, latent_size):
        super(DecoderNew, self).__init__()
        self.latent_size = latent_size
        self.img_channels = img_channels
        self.fc1 = nn.Linear(latent_size, 1024)
        self.deconv1 = nn.ConvTranspose2d(1024, 128, 5, stride=2)
        self.deconv2 = nn.ConvTranspose2d(128, 64, 5, stride=2)
        self.deconv3 = nn.ConvTranspose2d(64, 32, 6, stride=2)
        self.deconv4 = nn.ConvTranspose2d(32, img_channels, 6, stride=2)

    def forward(self, input_0):
        primals_1 = self.fc1.weight
        primals_2 = self.fc1.bias
        primals_4 = self.deconv1.weight
        primals_5 = self.deconv1.bias
        primals_6 = self.deconv2.weight
        primals_7 = self.deconv2.bias
        primals_8 = self.deconv3.weight
        primals_9 = self.deconv3.bias
        primals_10 = self.deconv4.weight
        primals_11 = self.deconv4.bias
        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])
        return output[0]
