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


@triton.jit
def triton_poi_fused_constant_pad_nd_0(in_ptr0, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 7
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x1 = xindex
    y0 = yindex
    tmp0 = x1
    tmp1 = tl.full([1, 1], 4, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = tl.load(in_ptr0 + (y0 + 16 * x1), tmp2 & xmask & ymask,
        eviction_policy='evict_last', other=0.0)
    tl.store(out_ptr0 + (x1 + 7 * y0), tmp3, xmask & ymask)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 4
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x1), xmask & ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (x1 + 16 * y0), tmp0, xmask & ymask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 1, 4), (4, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 7), (28, 7, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0[grid(16, 7)](primals_1, buf0, 16,
            7, XBLOCK=8, YBLOCK=16, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=4, bias=None)
        assert_size_stride(buf1, (4, 4, 4), (16, 4, 1))
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_clone_1[grid(4, 16)](buf1, buf2, 4, 16, XBLOCK=16,
            YBLOCK=4, num_warps=1, num_stages=1)
        del buf1
    return buf2, primals_2, buf0


class LookaheadNew(nn.Module):

    def __init__(self, n_features, context):
        super(LookaheadNew, self).__init__()
        assert context > 0
        self.context = context
        self.n_features = n_features
        self.pad = 0, self.context - 1
        self.conv = nn.Conv1d(self.n_features, self.n_features, kernel_size
            =self.context, stride=1, groups=self.n_features, padding=0,
            bias=None)

    def __repr__(self):
        return self.__class__.__name__ + '(' + 'n_features=' + str(self.
            n_features) + ', context=' + str(self.context) + ')'

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


Measured latency_ms: 0.035379