# 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/dj/cdjqugkpff4tp7afrb4qlbsirht4efxjqwxqceb52cjctslqk6kh.py
# Topologically Sorted Source Nodes: [add, audios_Tanh_1, add_1, audios_Sigmoid_1, audios], Original ATen: [aten.add, aten.tanh, aten.sigmoid, aten.mul]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   audios => mul
#   audios_Sigmoid_1 => sigmoid
#   audios_Tanh_1 => tanh
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, %getitem_2), kwargs = {})
#   %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%add,), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_1, %getitem_3), kwargs = {})
#   %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%add_1,), kwargs = {})
#   %mul : [num_users=3] = call_function[target=torch.ops.aten.mul.Tensor](args = (%tanh, %sigmoid), kwargs = {})
triton_poi_fused_add_mul_sigmoid_tanh_0 = async_compile.triton('triton_poi_fused_add_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=[16], 
    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_add_mul_sigmoid_tanh_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, '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_mul_sigmoid_tanh_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 2) % 2
    x2 = (xindex // 4)
    x4 = xindex % 4
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x1 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x4 + (8*x2)), xmask)
    tmp6 = tl.load(in_ptr0 + (2 + x1 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (2 + x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + (4 + x4 + (8*x2)), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = libdevice.tanh(tmp4)
    tmp8 = tmp6 + tmp7
    tmp10 = tmp8 + tmp9
    tmp11 = tl.sigmoid(tmp10)
    tmp12 = tmp5 * tmp11
    tl.store(out_ptr0 + (x3), tmp5, xmask)
    tl.store(out_ptr1 + (x3), tmp11, xmask)
    tl.store(out_ptr2 + (x3), tmp12, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/63/c63djmmppemy4hyvqoirh5fk7yoo44o6c2vt2yx5eyv5sr2e2xhk.py
# Topologically Sorted Source Nodes: [conv1d_2, add_2, outs], Original ATen: [aten.convolution, aten.add, aten.mul]
# Source node to ATen node mapping:
#   add_2 => add_2
#   conv1d_2 => convolution_2
#   outs => mul_1
# Graph fragment:
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%mul, %primals_6, %primals_7, [1], [0], [1], False, [0], 1), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_2, %primals_1), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_2, 0.7071067811865476), kwargs = {})
triton_poi_fused_add_convolution_mul_1 = async_compile.triton('triton_poi_fused_add_convolution_mul_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=[32], 
    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_mul_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_mul_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 2) % 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
    tmp5 = 0.7071067811865476
    tmp6 = tmp4 * tmp5
    tl.store(in_out_ptr0 + (x3), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/75/c75wqywl5zsbpbtkqhssilbaw6iunsfor5ujzl532mwbz5rcetho.py
# Topologically Sorted Source Nodes: [skips], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   skips => convolution_3
# Graph fragment:
#   %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%mul, %primals_8, %primals_9, [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=[32], 
    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 = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 2) % 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, primals_9 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 2), (8, 2, 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, 1), (4, 1, 1))
    assert_size_stride(primals_5, (4, 4, 2), (8, 2, 1))
    assert_size_stride(primals_6, (4, 2, 1), (2, 1, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (4, 2, 1), (2, 1, 1))
    assert_size_stride(primals_9, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [input_2], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1,), padding=(1,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 1), (4, 1, 1))
        # Topologically Sorted Source Nodes: [auxs], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(primals_5, primals_4, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 2), (8, 2, 1))
        buf2 = empty_strided_cuda((4, 2, 2), (4, 2, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 2, 2), (4, 2, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 2, 2), (4, 2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [add, audios_Tanh_1, add_1, audios_Sigmoid_1, audios], Original ATen: [aten.add, aten.tanh, aten.sigmoid, aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_mul_sigmoid_tanh_0.run(buf0, primals_3, buf1, buf2, buf3, buf4, 16, grid=grid(16), stream=stream0)
        del buf0
        del buf1
        del primals_3
        # Topologically Sorted Source Nodes: [conv1d_2], Original ATen: [aten.convolution]
        buf5 = extern_kernels.convolution(buf4, primals_6, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf5, (4, 4, 2), (8, 2, 1))
        buf6 = buf5; del buf5  # reuse
        # Topologically Sorted Source Nodes: [conv1d_2, add_2, outs], Original ATen: [aten.convolution, aten.add, aten.mul]
        triton_poi_fused_add_convolution_mul_1.run(buf6, primals_7, primals_1, 32, grid=grid(32), stream=stream0)
        del primals_7
        # Topologically Sorted Source Nodes: [skips], Original ATen: [aten.convolution]
        buf7 = extern_kernels.convolution(buf4, primals_8, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf7, (4, 4, 2), (8, 2, 1))
        buf8 = buf7; del buf7  # reuse
        # Topologically Sorted Source Nodes: [skips], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_2.run(buf8, primals_9, 32, grid=grid(32), stream=stream0)
        del primals_9
    return (buf6, buf8, primals_1, primals_2, primals_4, primals_5, primals_6, primals_8, buf2, buf3, buf4, )


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, 2), (8, 2, 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, 1), (4, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 4, 2), (8, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 2, 1), (2, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 2, 1), (2, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = 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, primals_9])
    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 math
import torch


class Conv1d(torch.nn.Conv1d):

    def __init__(self, *args, **kwargs):
        super(Conv1d, self).__init__(*args, **kwargs)

    def reset_parameters(self):
        torch.nn.init.kaiming_normal_(self.weight, nonlinearity='relu')
        if self.bias is not None:
            torch.nn.init.zeros_(self.bias)


class Conv1d1x1(Conv1d):

    def __init__(self, in_channels, out_channels, bias):
        super(Conv1d1x1, self).__init__(in_channels=in_channels,
            out_channels=out_channels, kernel_size=1, padding=0, dilation=1,
            bias=bias)


class ResConvGLU(torch.nn.Module):

    def __init__(self, residual_channels, gate_channels, skip_channels,
        aux_channels, kernel_size, dilation=1, dropout=0.0, bias=True):
        super(ResConvGLU, self).__init__()
        self.layer_Dict = torch.nn.ModuleDict()
        self.layer_Dict['Conv1d'] = torch.nn.Sequential()
        self.layer_Dict['Conv1d'].add_module('Dropout', torch.nn.Dropout(p=
            dropout))
        self.layer_Dict['Conv1d'].add_module('Conv1d', Conv1d(in_channels=
            residual_channels, out_channels=gate_channels, kernel_size=
            kernel_size, padding=(kernel_size - 1) // 2 * dilation,
            dilation=dilation, bias=bias))
        self.layer_Dict['Aux'] = Conv1d1x1(in_channels=aux_channels,
            out_channels=gate_channels, bias=False)
        self.layer_Dict['Out'] = Conv1d1x1(in_channels=gate_channels // 2,
            out_channels=residual_channels, bias=bias)
        self.layer_Dict['Skip'] = Conv1d1x1(in_channels=gate_channels // 2,
            out_channels=skip_channels, bias=bias)

    def forward(self, audios, auxs):
        residuals = audios
        audios = self.layer_Dict['Conv1d'](audios)
        audios_Tanh, audios_Sigmoid = audios.split(audios.size(1) // 2, dim=1)
        auxs = self.layer_Dict['Aux'](auxs)
        auxs_Tanh, auxs_Sigmoid = auxs.split(auxs.size(1) // 2, dim=1)
        audios_Tanh = torch.tanh(audios_Tanh + auxs_Tanh)
        audios_Sigmoid = torch.sigmoid(audios_Sigmoid + auxs_Sigmoid)
        audios = audios_Tanh * audios_Sigmoid
        outs = (self.layer_Dict['Out'](audios) + residuals) * math.sqrt(0.5)
        skips = self.layer_Dict['Skip'](audios)
        return outs, skips


def get_inputs():
    return [torch.rand([4, 4, 2]), torch.rand([4, 4, 2])]


def get_init_inputs():
    return [[], {'residual_channels': 4, 'gate_channels': 4,
        'skip_channels': 4, 'aux_channels': 4, 'kernel_size': 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
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_add_mul_sigmoid_tanh_0(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 2 % 2
    x2 = xindex // 4
    x4 = xindex % 4
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x1 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x4 + 8 * x2), xmask)
    tmp6 = tl.load(in_ptr0 + (2 + x1 + 4 * x2), xmask, eviction_policy=
        'evict_last')
    tmp7 = tl.load(in_ptr1 + (2 + x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + (4 + x4 + 8 * x2), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = libdevice.tanh(tmp4)
    tmp8 = tmp6 + tmp7
    tmp10 = tmp8 + tmp9
    tmp11 = tl.sigmoid(tmp10)
    tmp12 = tmp5 * tmp11
    tl.store(out_ptr0 + x3, tmp5, xmask)
    tl.store(out_ptr1 + x3, tmp11, xmask)
    tl.store(out_ptr2 + x3, tmp12, xmask)


@triton.jit
def triton_poi_fused_add_convolution_mul_1(in_out_ptr0, in_ptr0, in_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 2 % 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
    tmp5 = 0.7071067811865476
    tmp6 = tmp4 * tmp5
    tl.store(in_out_ptr0 + x3, tmp6, xmask)


@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 2 % 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, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 2), (8, 2, 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, 1), (4, 1, 1))
    assert_size_stride(primals_5, (4, 4, 2), (8, 2, 1))
    assert_size_stride(primals_6, (4, 2, 1), (2, 1, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 2, 1), (2, 1, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1,),
            padding=(1,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 1), (4, 1, 1))
        buf1 = extern_kernels.convolution(primals_5, primals_4, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 2), (8, 2, 1))
        buf2 = empty_strided_cuda((4, 2, 2), (4, 2, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 2, 2), (4, 2, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 2, 2), (4, 2, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_sigmoid_tanh_0[grid(16)](buf0, primals_3,
            buf1, buf2, buf3, buf4, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del buf0
        del buf1
        del primals_3
        buf5 = extern_kernels.convolution(buf4, primals_6, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf5, (4, 4, 2), (8, 2, 1))
        buf6 = buf5
        del buf5
        triton_poi_fused_add_convolution_mul_1[grid(32)](buf6, primals_7,
            primals_1, 32, XBLOCK=32, num_warps=1, num_stages=1)
        del primals_7
        buf7 = extern_kernels.convolution(buf4, primals_8, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf7, (4, 4, 2), (8, 2, 1))
        buf8 = buf7
        del buf7
        triton_poi_fused_convolution_2[grid(32)](buf8, primals_9, 32,
            XBLOCK=32, num_warps=1, num_stages=1)
        del primals_9
    return (buf6, buf8, primals_1, primals_2, primals_4, primals_5,
        primals_6, primals_8, buf2, buf3, buf4)


class Conv1d(torch.nn.Conv1d):

    def __init__(self, *args, **kwargs):
        super(Conv1d, self).__init__(*args, **kwargs)

    def reset_parameters(self):
        torch.nn.init.kaiming_normal_(self.weight, nonlinearity='relu')
        if self.bias is not None:
            torch.nn.init.zeros_(self.bias)


class Conv1d1x1(Conv1d):

    def __init__(self, in_channels, out_channels, bias):
        super(Conv1d1x1, self).__init__(in_channels=in_channels,
            out_channels=out_channels, kernel_size=1, padding=0, dilation=1,
            bias=bias)


class ResConvGLUNew(torch.nn.Module):

    def __init__(self, residual_channels, gate_channels, skip_channels,
        aux_channels, kernel_size, dilation=1, dropout=0.0, bias=True):
        super(ResConvGLUNew, self).__init__()
        self.layer_Dict = torch.nn.ModuleDict()
        self.layer_Dict['Conv1d'] = torch.nn.Sequential()
        self.layer_Dict['Conv1d'].add_module('Dropout', torch.nn.Dropout(p=
            dropout))
        self.layer_Dict['Conv1d'].add_module('Conv1d', Conv1d(in_channels=
            residual_channels, out_channels=gate_channels, kernel_size=
            kernel_size, padding=(kernel_size - 1) // 2 * dilation,
            dilation=dilation, bias=bias))
        self.layer_Dict['Aux'] = Conv1d1x1(in_channels=aux_channels,
            out_channels=gate_channels, bias=False)
        self.layer_Dict['Out'] = Conv1d1x1(in_channels=gate_channels // 2,
            out_channels=residual_channels, bias=bias)
        self.layer_Dict['Skip'] = Conv1d1x1(in_channels=gate_channels // 2,
            out_channels=skip_channels, bias=bias)

    def forward(self, input_0, input_1):
        primals_2 = self.layer_Dict.Conv1d.Conv1d.weight
        primals_3 = self.layer_Dict.Conv1d.Conv1d.bias
        primals_4 = self.layer_Dict.Aux.weight
        primals_6 = self.layer_Dict.Out.weight
        primals_7 = self.layer_Dict.Out.bias
        primals_8 = self.layer_Dict.Skip.weight
        primals_9 = self.layer_Dict.Skip.bias
        primals_1 = input_0
        primals_5 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0], output[1]
