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
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.nn.init as init
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_convolution_relu_0(in_out_ptr0, in_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 // 1024 % 32
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, 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 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_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 % 32
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, 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 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_convolution_relu_2(in_out_ptr0, in_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 // 64 % 64
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, 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 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_3(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)
    x3 = xindex
    x1 = xindex // 16 % 64
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, 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 + x3, tmp4, None)
    tl.store(out_ptr0 + x3, tmp6, None)


@triton.jit
def triton_poi_fused_relu_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 512
    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_exp_sqrt_5(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 40
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 10
    x1 = xindex // 10
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (10 + x0 + 20 * x1), xmask)
    tmp1 = tl_math.exp(tmp0)
    tmp2 = libdevice.sqrt(tmp1)
    tl.store(out_ptr0 + x2, tmp2, xmask)


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, (32, 1, 4, 4), (16, 16, 4, 1))
    assert_size_stride(primals_2, (32,), (1,))
    assert_size_stride(primals_3, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_4, (32, 32, 4, 4), (512, 16, 4, 1))
    assert_size_stride(primals_5, (32,), (1,))
    assert_size_stride(primals_6, (64, 32, 4, 4), (512, 16, 4, 1))
    assert_size_stride(primals_7, (64,), (1,))
    assert_size_stride(primals_8, (64, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_9, (64,), (1,))
    assert_size_stride(primals_10, (128, 1024), (1024, 1))
    assert_size_stride(primals_11, (128,), (1,))
    assert_size_stride(primals_12, (20, 128), (128, 1))
    assert_size_stride(primals_13, (20,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(2, 
            2), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 32, 32, 32), (32768, 1024, 32, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(131072)](buf1, primals_2, 
            131072, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_2
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 32, 16, 16), (8192, 256, 16, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_relu_1[grid(32768)](buf3, primals_5, 
            32768, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_5
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 64, 8, 8), (4096, 64, 8, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_relu_2[grid(16384)](buf5, primals_7, 
            16384, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_7
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 64, 4, 4), (1024, 16, 4, 1))
        buf7 = buf6
        del buf6
        buf12 = empty_strided_cuda((4, 64, 4, 4), (1024, 16, 4, 1), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_3[grid(4096)](buf7
            , primals_9, buf12, 4096, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_9
        buf8 = empty_strided_cuda((4, 128), (128, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf7, (4, 1024), (1024, 1), 0),
            reinterpret_tensor(primals_10, (1024, 128), (1, 1024), 0), out=buf8
            )
        buf9 = buf8
        del buf8
        triton_poi_fused_relu_4[grid(512)](buf9, primals_11, 512, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_11
        buf10 = empty_strided_cuda((4, 20), (20, 1), torch.float32)
        extern_kernels.addmm(primals_13, buf9, reinterpret_tensor(
            primals_12, (128, 20), (1, 128), 0), alpha=1, beta=1, out=buf10)
        del primals_13
        buf11 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
        triton_poi_fused_exp_sqrt_5[grid(40)](buf10, buf11, 40, XBLOCK=64,
            num_warps=1, num_stages=1)
    return (reinterpret_tensor(buf10, (4, 10), (20, 1), 0), buf11,
        reinterpret_tensor(buf10, (4, 10), (20, 1), 10), buf10, primals_1,
        primals_3, primals_4, primals_6, primals_8, buf1, buf3, buf5,
        reinterpret_tensor(buf7, (4, 1024), (1024, 1), 0), buf9,
        reinterpret_tensor(buf10, (4, 10), (20, 1), 10), buf11, primals_12,
        primals_10, buf12)


def kaiming_init(m):
    if isinstance(m, (nn.Linear, nn.Conv2d)):
        init.kaiming_normal_(m.weight)
        if m.bias is not None:
            m.bias.data.fill_(0)
    elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d)):
        m.weight.data.fill_(1)
        if m.bias is not None:
            m.bias.data.fill_(0)


def normal_init(m):
    if isinstance(m, (nn.Linear, nn.Conv2d)):
        init.normal_(m.weight, 0, 0.02)
        if m.bias is not None:
            m.bias.data.fill_(0)
    elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d)):
        m.weight.data.fill_(1)
        if m.bias is not None:
            m.bias.data.fill_(0)


class Encoder1New(nn.Module):
    """
    encoder architecture for the "dsprites" data
    """

    def __init__(self, z_dim=10):
        super(Encoder1New, self).__init__()
        self.z_dim = z_dim
        self.conv1 = nn.Conv2d(1, 32, 4, 2, 1)
        self.conv2 = nn.Conv2d(32, 32, 4, 2, 1)
        self.conv3 = nn.Conv2d(32, 64, 4, 2, 1)
        self.conv4 = nn.Conv2d(64, 64, 4, 2, 1)
        self.fc5 = nn.Linear(64 * 4 * 4, 128)
        self.fc6 = nn.Linear(128, 2 * z_dim)
        self.weight_init()

    def weight_init(self, mode='normal'):
        if mode == 'kaiming':
            initializer = kaiming_init
        elif mode == 'normal':
            initializer = normal_init
        for m in self._modules:
            initializer(self._modules[m])

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_2 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv2.bias
        primals_6 = self.conv3.weight
        primals_7 = self.conv3.bias
        primals_8 = self.conv4.weight
        primals_9 = self.conv4.bias
        primals_10 = self.fc5.weight
        primals_11 = self.fc5.bias
        primals_12 = self.fc6.weight
        primals_13 = self.fc6.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, primals_12, primals_13])
        return output[0], output[1], output[2]


Measured latency_ms: 0.014303