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
from torchaudio import transforms
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_replication_pad1d_0(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
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (4 * (x1 % 4) + 16 * (x1 // 16) + 64 * (x1 // 
        4 % 4) + (3 * (3 <= 0 * (0 >= -2 + x0) + (-2 + x0) * (-2 + x0 > 0)) +
        (0 * (0 >= -2 + x0) + (-2 + x0) * (-2 + x0 > 0)) * (0 * (0 >= -2 +
        x0) + (-2 + x0) * (-2 + x0 > 0) < 3))), xmask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_arange_repeat_1(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 320
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 5
    x2 = xindex
    tmp0 = -2 + x0
    tmp1 = tmp0.to(tl.float32)
    tl.store(out_ptr0 + x2, tmp1, xmask)


@triton.jit
def triton_poi_fused_replication_pad1d_2(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
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (4 * x1 + (3 * (3 <= 0 * (0 >= -2 + x0) + (-2 +
        x0) * (-2 + x0 > 0)) + (0 * (0 >= -2 + x0) + (-2 + x0) * (-2 + x0 >
        0)) * (0 * (0 >= -2 + x0) + (-2 + x0) * (-2 + x0 > 0) < 3))), xmask,
        eviction_policy='evict_last')
    tmp1 = 0.1
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x2, tmp2, xmask)


@triton.jit
def triton_poi_fused_cat_3(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 12
    x4 = xindex // 12
    x1 = xindex // 12 % 4
    x2 = xindex // 48 % 4
    x3 = xindex // 192
    x5 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x4 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (4 * x1 + 16 * x3 + 64 * x2 + (-4 + x0)), 
        tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp11 = 0.1
    tmp12 = tmp10 * tmp11
    tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
    tmp14 = tl.where(tmp9, tmp12, tmp13)
    tmp15 = tmp0 >= tmp7
    tl.full([1], 12, tl.int64)
    tmp18 = tl.load(in_ptr2 + (4 * x1 + 16 * x3 + 64 * x2 + (-8 + x0)), 
        tmp15 & xmask, eviction_policy='evict_last', other=0.0)
    tmp19 = tmp18 * tmp11
    tmp20 = tl.full(tmp19.shape, 0.0, tmp19.dtype)
    tmp21 = tl.where(tmp15, tmp19, tmp20)
    tmp22 = tl.where(tmp9, tmp14, tmp21)
    tmp23 = tl.where(tmp4, tmp5, tmp22)
    tl.store(out_ptr0 + x5, tmp23, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, 64, 8), (512, 8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_replication_pad1d_0[grid(512)](arg0_1, buf0, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf1 = empty_strided_cuda((64, 1, 5), (5, 5, 1), torch.float32)
        triton_poi_fused_arange_repeat_1[grid(320)](buf1, 320, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf2 = extern_kernels.convolution(buf0, buf1, stride=(1,), padding=
            (0,), dilation=(1,), transposed=False, output_padding=(0,),
            groups=64, bias=None)
        assert_size_stride(buf2, (1, 64, 4), (256, 4, 1))
        buf3 = buf0
        del buf0
        triton_poi_fused_replication_pad1d_2[grid(512)](buf2, buf3, 512,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf4 = buf1
        del buf1
        triton_poi_fused_arange_repeat_1[grid(320)](buf4, 320, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf5 = extern_kernels.convolution(buf3, buf4, stride=(1,), padding=
            (0,), dilation=(1,), transposed=False, output_padding=(0,),
            groups=64, bias=None)
        assert_size_stride(buf5, (1, 64, 4), (256, 4, 1))
        del buf3
        del buf4
        buf6 = empty_strided_cuda((4, 4, 4, 12), (192, 48, 12, 1), torch.
            float32)
        triton_poi_fused_cat_3[grid(768)](arg0_1, buf2, buf5, buf6, 768,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        del buf2
        del buf5
    return buf6,


class DeltaNew(nn.Module):

    def __init__(self, order=2, **kwargs):
        super(DeltaNew, self).__init__()
        self.order = order
        self.compute_delta = transforms.ComputeDeltas(**kwargs)

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0]


Measured latency_ms: 0.024734