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.triton_helpers import libdevice
from torch import 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_mul_sigmoid_tanh_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    out_ptr1, out_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 2
    x2 = xindex // 8
    x4 = xindex % 8
    x3 = xindex
    tmp10 = tl.load(in_ptr0 + (x0 + 7 * x1 + 28 * x2), xmask)
    tmp11 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr0 + (14 + x0 + 7 * x1 + 28 * x2), xmask)
    tmp23 = tl.load(in_ptr1 + (2 + x1), xmask, eviction_policy='evict_last')
    tmp0 = x0
    tmp1 = tl.full([1], 4, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = tl.load(in_ptr0 + (x0 + 7 * x1 + 28 * x2), tmp2 & xmask, other=0.0)
    tmp4 = tl.load(in_ptr1 + x1, tmp2 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp5 = tmp3 + tmp4
    tmp6 = tl.load(in_ptr2 + (x4 + 16 * x2), tmp2 & xmask, other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp2, tmp7, tmp8)
    tmp12 = tmp10 + tmp11
    tmp13 = tl.where(tmp2, tmp9, tmp12)
    tmp14 = libdevice.tanh(tmp13)
    tmp15 = tl.load(in_ptr0 + (14 + x0 + 7 * x1 + 28 * x2), tmp2 & xmask,
        other=0.0)
    tmp16 = tl.load(in_ptr1 + (2 + x1), tmp2 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp17 = tmp15 + tmp16
    tmp18 = tl.load(in_ptr2 + (8 + x4 + 16 * x2), tmp2 & xmask, other=0.0)
    tmp19 = tmp17 + tmp18
    tmp20 = tl.full(tmp19.shape, 0.0, tmp19.dtype)
    tmp21 = tl.where(tmp2, tmp19, tmp20)
    tmp24 = tmp22 + tmp23
    tmp25 = tl.where(tmp2, tmp21, tmp24)
    tmp26 = tl.sigmoid(tmp25)
    tmp27 = tmp14 * tmp26
    tl.store(out_ptr0 + x3, tmp14, xmask)
    tl.store(out_ptr1 + x3, tmp26, xmask)
    tl.store(out_ptr2 + x3, tmp27, xmask)


@triton.jit
def triton_poi_fused_add_convolution_1(in_out_ptr0, in_ptr0, in_ptr1,
    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')
    tmp3 = tl.load(in_ptr1 + x3, xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tl.store(in_out_ptr0 + x3, tmp4, xmask)


@triton.jit
def triton_poi_fused_convolution_2(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, primals_4, primals_5, primals_6,
        primals_7, primals_8) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_5, (4, 2, 1), (2, 1, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4, 2, 1), (2, 1, 1))
    assert_size_stride(primals_8, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1,),
            padding=(3,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 7), (28, 7, 1))
        buf1 = empty_strided_cuda((4, 2, 4), (8, 4, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 2, 4), (8, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 2, 4), (8, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_sigmoid_tanh_0[grid(32)](buf0, primals_3,
            primals_4, buf1, buf2, buf3, 32, XBLOCK=32, num_warps=1,
            num_stages=1)
        del buf0
        del primals_3
        del primals_4
        buf4 = extern_kernels.convolution(buf3, primals_5, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf4, (4, 4, 4), (16, 4, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_add_convolution_1[grid(64)](buf5, primals_6,
            primals_1, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_6
        buf6 = extern_kernels.convolution(buf3, primals_7, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 4), (16, 4, 1))
        buf7 = buf6
        del buf6
        triton_poi_fused_convolution_2[grid(64)](buf7, primals_8, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_8
    return (buf5, buf7, primals_1, primals_2, primals_5, primals_7, buf1,
        buf2, buf3)


class ResidualBlockNew(nn.Module):

    def __init__(self, filter_size, dilation, residual_channels,
        dilated_channels, skip_channels):
        super().__init__()
        self.conv = nn.Conv1d(residual_channels, dilated_channels,
            kernel_size=filter_size, padding=dilation * (filter_size - 1),
            dilation=dilation)
        self.res = nn.Conv1d(dilated_channels // 2, residual_channels, 1)
        self.skip = nn.Conv1d(dilated_channels // 2, skip_channels, 1)
        self.filter_size = filter_size
        self.dilation = dilation
        self.residual_channels = residual_channels

    def initialize(self, n):
        self.queue = torch.zeros((n, self.residual_channels, self.dilation *
            (self.filter_size - 1) + 1), dtype=self.conv.weight.dtype)
        self.conv.padding = 0

    def pop(self, condition):
        return self(self.queue, condition)

    def push(self, x):
        self.queue = torch.cat((self.queue[:, :, 1:], x), dim=2)

    def forward(self, input_0, input_1):
        primals_1 = self.conv.weight
        primals_3 = self.conv.bias
        primals_5 = self.res.weight
        primals_6 = self.res.bias
        primals_7 = self.skip.weight
        primals_8 = self.skip.bias
        primals_2 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8])
        return output[0], output[1]


Measured latency_ms: 0.076278