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
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused__unsafe_index_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 % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tmp5 = tl.load(in_ptr0 + (tmp4 + 4 * x1), None, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + x2, tmp5, None)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, in_ptr1, 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 % 8
    x3 = xindex // 8
    x1 = xindex // 8 % 64
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 9 * x3), None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + x4, tmp2, None)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 64, 4), (256, 4, 1))
    assert_size_stride(primals_2, (64, 64, 10), (640, 10, 1))
    assert_size_stride(primals_3, (64,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 64, 8), (512, 8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__unsafe_index_0[grid(2048)](primals_1, buf0, 2048,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,),
            padding=(4,), dilation=(1,), transposed=True, output_padding=(0
            ,), groups=1, bias=None)
        assert_size_stride(buf1, (4, 64, 9), (576, 9, 1))
        buf2 = empty_strided_cuda((4, 64, 8), (512, 8, 1), torch.float32)
        triton_poi_fused_clone_1[grid(2048)](buf1, primals_3, buf2, 2048,
            XBLOCK=256, num_warps=4, num_stages=1)
        del buf1
        del primals_3
    return reinterpret_tensor(buf2, (4, 512), (512, 1), 0), primals_2, buf0


class TAE_decoderNew(nn.Module):
    """
    Class for temporal autoencoder decoder.
    filter_1 : filter size of the first convolution layer
    filter_lstm : hidden size of the lstm.
    """

    def __init__(self, n_hidden=64, pooling=8):
        super().__init__()
        self.pooling = pooling
        self.n_hidden = n_hidden
        self.up_layer = nn.Upsample(size=pooling)
        self.deconv_layer = nn.ConvTranspose1d(in_channels=self.n_hidden,
            out_channels=self.n_hidden, kernel_size=10, stride=1, padding=
            self.pooling // 2)

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


Measured latency_ms: 0.039574