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 matplotlib import cm as cm
import torch.nn as nn
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_0(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)
    x0 = xindex % 4096
    x1 = xindex // 4096 % 3
    x2 = xindex // 12288 % 3
    x3 = xindex // 36864
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4096 * x2 + 12288 * x1 + 36864 * x3), None)
    tl.store(out_ptr0 + x4, tmp0, 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 // 3072 % 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 // 512 % 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_3(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 // 128 % 128
    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_4(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 // 16 % 256
    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_5(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 // 4 % 512
    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_mean_relu_6(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)
    x2 = xindex
    x0 = xindex % 512
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 1.0
    tmp4 = tmp2 / tmp3
    tmp5 = tl.full([1], 0, tl.int32)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tl.store(in_out_ptr0 + x2, tmp6, 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, primals_14, primals_15) = args
    args.clear()
    assert_size_stride(primals_1, (4, 3, 3, 64, 64), (36864, 12288, 4096, 
        64, 1))
    assert_size_stride(primals_2, (32, 3, 3, 3, 3), (81, 27, 9, 3, 1))
    assert_size_stride(primals_3, (32,), (1,))
    assert_size_stride(primals_4, (64, 32, 3, 3, 3), (864, 27, 9, 3, 1))
    assert_size_stride(primals_5, (64,), (1,))
    assert_size_stride(primals_6, (128, 64, 3, 3, 3), (1728, 27, 9, 3, 1))
    assert_size_stride(primals_7, (128,), (1,))
    assert_size_stride(primals_8, (256, 128, 3, 3, 3), (3456, 27, 9, 3, 1))
    assert_size_stride(primals_9, (256,), (1,))
    assert_size_stride(primals_10, (512, 256, 3, 3, 3), (6912, 27, 9, 3, 1))
    assert_size_stride(primals_11, (512,), (1,))
    assert_size_stride(primals_12, (512, 512, 3, 3, 3), (13824, 27, 9, 3, 1))
    assert_size_stride(primals_13, (512,), (1,))
    assert_size_stride(primals_14, (128, 512), (512, 1))
    assert_size_stride(primals_15, (128,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 3, 3, 64, 64), (36864, 12288, 4096, 
            64, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(147456)](primals_1, buf0, 
            147456, XBLOCK=1024, num_warps=4, num_stages=1)
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 2, 2),
            padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 32, 3, 32, 32), (98304, 3072, 1024, 32, 1)
            )
        del buf0
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_relu_1[grid(393216)](buf2, primals_3, 
            393216, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_3
        buf3 = extern_kernels.convolution(buf2, primals_4, stride=(2, 2, 2),
            padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 64, 2, 16, 16), (32768, 512, 256, 16, 1))
        buf4 = buf3
        del buf3
        triton_poi_fused_convolution_relu_2[grid(131072)](buf4, primals_5, 
            131072, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_5
        buf5 = extern_kernels.convolution(buf4, primals_6, stride=(1, 2, 2),
            padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 128, 2, 8, 8), (16384, 128, 64, 8, 1))
        buf6 = buf5
        del buf5
        triton_poi_fused_convolution_relu_3[grid(65536)](buf6, primals_7, 
            65536, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_7
        buf7 = extern_kernels.convolution(buf6, primals_8, stride=(2, 2, 2),
            padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 256, 1, 4, 4), (4096, 16, 16, 4, 1))
        buf8 = buf7
        del buf7
        triton_poi_fused_convolution_relu_4[grid(16384)](buf8, primals_9, 
            16384, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_9
        buf9 = extern_kernels.convolution(buf8, primals_10, stride=(2, 2, 2
            ), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf9, (4, 512, 1, 2, 2), (2048, 4, 4, 2, 1))
        buf10 = buf9
        del buf9
        triton_poi_fused_convolution_relu_5[grid(8192)](buf10, primals_11, 
            8192, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_11
        buf11 = extern_kernels.convolution(buf10, primals_12, stride=(2, 2,
            2), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 512, 1, 1, 1), (512, 1, 1, 1, 1))
        buf12 = reinterpret_tensor(buf11, (4, 512), (512, 1), 0)
        del buf11
        triton_poi_fused_mean_relu_6[grid(2048)](buf12, primals_13, 2048,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_13
        buf13 = empty_strided_cuda((4, 128), (128, 1), torch.float32)
        extern_kernels.addmm(primals_15, buf12, reinterpret_tensor(
            primals_14, (512, 128), (1, 512), 0), alpha=1, beta=1, out=buf13)
        del primals_15
    return (buf13, primals_2, primals_4, primals_6, primals_8, primals_10,
        primals_12, reinterpret_tensor(primals_1, (4, 3, 3, 64, 64), (36864,
        4096, 12288, 64, 1), 0), buf2, buf4, buf6, buf8, buf10, buf12,
        primals_14)


class ConvEncoder3DNew(nn.Module):
    """ Simple convolutional conditioning network.

    It consists of 6 convolutional layers, each downsampling the input by a
    factor of 2, and a final fully-connected layer projecting the output to
    c_dim dimensions.
    """

    def __init__(self, c_dim=128, hidden_dim=32, **kwargs):
        """ Initialisation.

        Args:
            c_dim (int): output dimension of the latent embedding
        """
        super().__init__()
        self.conv0 = nn.Conv3d(3, hidden_dim, 3, stride=(1, 2, 2), padding=1)
        self.conv1 = nn.Conv3d(hidden_dim, hidden_dim * 2, 3, stride=(2, 2,
            2), padding=1)
        self.conv2 = nn.Conv3d(hidden_dim * 2, hidden_dim * 4, 3, stride=(1,
            2, 2), padding=1)
        self.conv3 = nn.Conv3d(hidden_dim * 4, hidden_dim * 8, 3, stride=(2,
            2, 2), padding=1)
        self.conv4 = nn.Conv3d(hidden_dim * 8, hidden_dim * 16, 3, stride=(
            2, 2, 2), padding=1)
        self.conv5 = nn.Conv3d(hidden_dim * 16, hidden_dim * 16, 3, stride=
            (2, 2, 2), padding=1)
        self.fc_out = nn.Linear(hidden_dim * 16, c_dim)
        self.actvn = nn.ReLU()

    def forward(self, input_0):
        primals_2 = self.conv0.weight
        primals_3 = self.conv0.bias
        primals_4 = self.conv1.weight
        primals_5 = self.conv1.bias
        primals_6 = self.conv2.weight
        primals_7 = self.conv2.bias
        primals_8 = self.conv3.weight
        primals_9 = self.conv3.bias
        primals_10 = self.conv4.weight
        primals_11 = self.conv4.bias
        primals_12 = self.conv5.weight
        primals_13 = self.conv5.bias
        primals_14 = self.fc_out.weight
        primals_15 = self.fc_out.bias
        primals_1 = 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, primals_14,
            primals_15])
        return output[0]


Measured latency_ms: 0.377898