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 import 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


@triton.jit
def triton_poi_fused_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 5
    x1 = xindex // 5
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 4, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = tl.load(in_ptr0 + (x0 + 4 * x1), tmp2 & xmask, other=0.0)
    tl.store(out_ptr0 + x2, tmp3, xmask)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 5), (20, 5, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0[grid(80)](primals_3, buf0, 80,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf1 = extern_kernels.convolution(buf0, primals_1, stride=(1,),
            padding=(1,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4), (16, 4, 1))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_1[grid(64)](buf2, primals_2, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_2
    return buf2, primals_1, buf0


def conv1d_same_padding(input, weight, bias, stride, dilation, groups):
    kernel, dilation, stride = weight.size(2), dilation[0], stride[0]
    l_out = l_in = input.size(2)
    padding = (l_out - 1) * stride - l_in + dilation * (kernel - 1) + 1
    if padding % 2 != 0:
        input = F.pad(input, [0, 1])
    return F.conv1d(input=input, weight=weight, bias=bias, stride=stride,
        padding=padding // 2, dilation=dilation, groups=groups)


class Conv1dSamePaddingNew(nn.Conv1d):
    """Represents the "Same" padding functionality from Tensorflow.
    See: https://github.com/pytorch/pytorch/issues/3867
    Note that the padding argument in the initializer doesn't do anything now
    """

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


Measured latency_ms: 0.035129