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.nn.functional as F
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_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 128
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, 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, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 3, 3, 3), (108, 27, 9, 3, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_3, (1,
            4, 4, 4, 4), (256, 64, 16, 4, 1), 0), primals_1, stride=(2, 1, 
            1), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=True,
            output_padding=(1, 0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (1, 4, 8, 4, 4), (512, 128, 16, 4, 1))
        buf1 = reinterpret_tensor(buf0, (4, 8, 4, 4), (128, 16, 4, 1), 0)
        del buf0
        buf2 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(512)](buf1,
            primals_2, buf2, 512, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
    return buf1, primals_1, reinterpret_tensor(primals_3, (1, 4, 4, 4, 4),
        (256, 64, 16, 4, 1), 0), buf2


class TransposedConv3dNew(nn.Module):

    def __init__(self, in_channels, output_channels, kernel_shape=(3, 3, 3),
        stride=(2, 1, 1), padding=(1, 1, 1), output_padding=(1, 0, 0),
        activation_fn=F.relu, use_batch_norm=False, use_bias=True):
        super(TransposedConv3dNew, self).__init__()
        self._use_batch_norm = use_batch_norm
        self._activation_fn = activation_fn
        self.transposed_conv3d = nn.ConvTranspose3d(in_channels,
            output_channels, kernel_shape, stride, padding=padding,
            output_padding=output_padding, bias=use_bias)
        if self._use_batch_norm:
            self.bn = nn.BatchNorm3d(self._output_channels, eps=0.001,
                momentum=0.01)

    def forward(self, input_0):
        primals_1 = self.transposed_conv3d.weight
        primals_2 = self.transposed_conv3d.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.023334