# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_4/inductor_cache/wx/cwxggww5mfxi3uxu4pue4rwqhhp7r7zav5gn74a5qqtrbtm3ooj5.py
# Topologically Sorted Source Nodes: [tanh, sigmoid, z], Original ATen: [aten.tanh, aten.sigmoid, aten.mul]
# Source node to ATen node mapping:
#   sigmoid => sigmoid
#   tanh => tanh
#   z => mul
# Graph fragment:
#   %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%getitem_2,), kwargs = {})
#   %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_5,), kwargs = {})
#   %mul : [num_users=3] = call_function[target=torch.ops.aten.mul.Tensor](args = (%tanh, %sigmoid), kwargs = {})
triton_poi_fused_mul_sigmoid_tanh_0 = async_compile.triton('triton_poi_fused_mul_sigmoid_tanh_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[32], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_sigmoid_tanh_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 10, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@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)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/us/custepuaxcgmeci6okpmedesxdvzu45qqfj6xlln2yvyowwq2gle.py
# Topologically Sorted Source Nodes: [conv1d_1, residual], Original ATen: [aten.convolution, aten.add]
# Source node to ATen node mapping:
#   conv1d_1 => convolution_1
#   residual => add_1
# Graph fragment:
#   %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%mul, %primals_5, %primals_6, [1], [0], [1], False, [0], 1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_1, %primals_1), kwargs = {})
triton_poi_fused_add_convolution_1 = async_compile.triton('triton_poi_fused_add_convolution_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@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)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/lf/clf7hs52i4bd5d3e73uio27ntyjfqmszkbsw6dta3r6rzgeftva3.py
# Topologically Sorted Source Nodes: [skip_connection], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   skip_connection => convolution_2
# Graph fragment:
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%mul, %primals_7, %primals_8, [1], [0], [1], False, [0], 1), kwargs = {})
triton_poi_fused_convolution_2 = async_compile.triton('triton_poi_fused_convolution_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@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)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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)
        # Topologically Sorted Source Nodes: [h], Original ATen: [aten.convolution]
        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)
        # Topologically Sorted Source Nodes: [tanh, sigmoid, z], Original ATen: [aten.tanh, aten.sigmoid, aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mul_sigmoid_tanh_0.run(buf0, primals_3, primals_4, buf1, buf2, buf3, 32, grid=grid(32), stream=stream0)
        del buf0
        del primals_3
        del primals_4
        # Topologically Sorted Source Nodes: [conv1d_1], Original ATen: [aten.convolution]
        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  # reuse
        # Topologically Sorted Source Nodes: [conv1d_1, residual], Original ATen: [aten.convolution, aten.add]
        triton_poi_fused_add_convolution_1.run(buf5, primals_6, primals_1, 64, grid=grid(64), stream=stream0)
        del primals_6
        # Topologically Sorted Source Nodes: [skip_connection], Original ATen: [aten.convolution]
        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  # reuse
        # Topologically Sorted Source Nodes: [skip_connection], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_2.run(buf7, primals_8, 64, grid=grid(64), stream=stream0)
        del primals_8
    return (buf5, buf7, primals_1, primals_2, primals_5, primals_7, buf1, buf2, buf3, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 2, 1), (2, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, 2, 1), (2, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
from torch import nn


class ResidualBlock(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 forward(self, x, condition):
        length = x.shape[2]
        h = self.conv(x)
        h = h[:, :, :length]
        h += condition
        tanh_z, sig_z = torch.split(h, h.size(1) // 2, dim=1)
        z = torch.tanh(tanh_z) * torch.sigmoid(sig_z)
        if x.shape[2] == z.shape[2]:
            residual = self.res(z) + x
        else:
            residual = self.res(z) + x[:, :, -1:]
        skip_connection = self.skip(z)
        return residual, skip_connection

    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 get_inputs():
    return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4])]


def get_init_inputs():
    return [[], {'filter_size': 4, 'dilation': 1, 'residual_channels': 4,
        'dilated_channels': 4, 'skip_channels': 4}]

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]
