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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_convolution_leaky_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 // 32768 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + x3, tmp7, None)


@triton.jit
def triton_poi_fused_convolution_leaky_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 // 4096 % 8
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + x3, tmp7, None)


@triton.jit
def triton_poi_fused_convolution_leaky_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 % 16
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + x3, tmp7, None)


@triton.jit
def triton_poi_fused_convolution_leaky_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 // 64 % 32
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + x3, tmp7, None)


@triton.jit
def triton_poi_fused_sigmoid_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 128
    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.sigmoid(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, 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) = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 4, 4, 4), (64, 64, 16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 1, 64, 64, 64), (262144, 262144, 4096,
        64, 1))
    assert_size_stride(primals_4, (8, 4, 4, 4, 4), (256, 64, 16, 4, 1))
    assert_size_stride(primals_5, (8,), (1,))
    assert_size_stride(primals_6, (16, 8, 4, 4, 4), (512, 64, 16, 4, 1))
    assert_size_stride(primals_7, (16,), (1,))
    assert_size_stride(primals_8, (32, 16, 4, 4, 4), (1024, 64, 16, 4, 1))
    assert_size_stride(primals_9, (32,), (1,))
    assert_size_stride(primals_10, (32, 32, 4, 4, 4), (2048, 64, 16, 4, 1))
    assert_size_stride(primals_11, (32,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, 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(buf0, (4, 4, 32, 32, 32), (131072, 32768, 1024, 
            32, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_leaky_relu_0[grid(524288)](buf1,
            primals_2, 524288, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_2
        buf2 = extern_kernels.convolution(buf1, 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(buf2, (4, 8, 16, 16, 16), (32768, 4096, 256, 16, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_leaky_relu_1[grid(131072)](buf3,
            primals_5, 131072, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_5
        buf4 = extern_kernels.convolution(buf3, primals_6, 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(buf4, (4, 16, 8, 8, 8), (8192, 512, 64, 8, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_leaky_relu_2[grid(32768)](buf5,
            primals_7, 32768, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_7
        buf6 = extern_kernels.convolution(buf5, 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(buf6, (4, 32, 4, 4, 4), (2048, 64, 16, 4, 1))
        buf7 = buf6
        del buf6
        triton_poi_fused_convolution_leaky_relu_3[grid(8192)](buf7,
            primals_9, 8192, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_9
        buf8 = extern_kernels.convolution(buf7, primals_10, stride=(1, 1, 1
            ), padding=(0, 0, 0), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 32, 1, 1, 1), (32, 1, 1, 1, 1))
        buf9 = reinterpret_tensor(buf8, (4, 32), (32, 1), 0)
        del buf8
        triton_poi_fused_sigmoid_4[grid(128)](buf9, primals_11, 128, XBLOCK
            =128, num_warps=4, num_stages=1)
        del primals_11
    return (buf9, primals_1, primals_3, primals_4, primals_6, primals_8,
        primals_10, buf1, buf3, buf5, buf7, buf9)


class encoderNew(nn.Module):

    def __init__(self, ef_dim):
        super(encoderNew, self).__init__()
        self.ef_dim = ef_dim
        self.conv_1 = nn.Conv3d(1, self.ef_dim, 4, stride=2, padding=1,
            bias=True)
        self.conv_2 = nn.Conv3d(self.ef_dim, self.ef_dim * 2, 4, stride=2,
            padding=1, bias=True)
        self.conv_3 = nn.Conv3d(self.ef_dim * 2, self.ef_dim * 4, 4, stride
            =2, padding=1, bias=True)
        self.conv_4 = nn.Conv3d(self.ef_dim * 4, self.ef_dim * 8, 4, stride
            =2, padding=1, bias=True)
        self.conv_5 = nn.Conv3d(self.ef_dim * 8, self.ef_dim * 8, 4, stride
            =1, padding=0, bias=True)
        nn.init.xavier_uniform_(self.conv_1.weight)
        nn.init.constant_(self.conv_1.bias, 0)
        nn.init.xavier_uniform_(self.conv_2.weight)
        nn.init.constant_(self.conv_2.bias, 0)
        nn.init.xavier_uniform_(self.conv_3.weight)
        nn.init.constant_(self.conv_3.bias, 0)
        nn.init.xavier_uniform_(self.conv_4.weight)
        nn.init.constant_(self.conv_4.bias, 0)
        nn.init.xavier_uniform_(self.conv_5.weight)
        nn.init.constant_(self.conv_5.bias, 0)

    def forward(self, input_0):
        primals_1 = self.conv_1.weight
        primals_2 = self.conv_1.bias
        primals_4 = self.conv_2.weight
        primals_5 = self.conv_2.bias
        primals_6 = self.conv_3.weight
        primals_7 = self.conv_3.bias
        primals_8 = self.conv_4.weight
        primals_9 = self.conv_4.bias
        primals_10 = self.conv_5.weight
        primals_11 = self.conv_5.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]


Measured latency_ms: 0.041502