# 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_7/inductor_cache/du/cduy525phknxid22xvrpd35o2jiixe65loi2ohvwk3yx3jy5g7bq.py
# Topologically Sorted Source Nodes: [result], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   result => convolution
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%unsqueeze_2, %primals_6, %primals_7, [1], [0], [1], False, [0], 1), kwargs = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_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=[4], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_0', '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_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr0 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tl.store(in_out_ptr0 + (x0), tmp3, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/af/cafogcqt5bgvyoyqkmlhxmyji2y2vje7lkiscqited7cmcnauc4m.py
# Topologically Sorted Source Nodes: [add, z_f, add_1, z_g, z], Original ATen: [aten.add, aten.tanh, aten.sigmoid, aten.mul]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   z => mul
#   z_f => tanh
#   z_g => sigmoid
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%squeeze, %unsqueeze), kwargs = {})
#   %tanh : [num_users=1] = 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 = (%squeeze_1, %unsqueeze_1), kwargs = {})
#   %sigmoid : [num_users=1] = 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_1 = async_compile.triton('triton_poi_fused_add_mul_sigmoid_tanh_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_sigmoid_tanh_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tmp6 = tmp4 + tmp5
    tmp7 = tl.sigmoid(tmp6)
    tmp8 = tmp3 * tmp7
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/lf/clf7hs52i4bd5d3e73uio27ntyjfqmszkbsw6dta3r6rzgeftva3.py
# Topologically Sorted Source Nodes: [skip], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   skip => convolution_2
# Graph fragment:
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%mul, %primals_11, %primals_12, [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')


# kernel path: runs/run_shard_7/inductor_cache/2m/c2m5vlzbvchnauaygo2yeciztzul33nwy65znzuw6hpjrsnewebe.py
# Topologically Sorted Source Nodes: [conv1d_3, residual], Original ATen: [aten.convolution, aten.add]
# Source node to ATen node mapping:
#   conv1d_3 => convolution_3
#   residual => add_2
# Graph fragment:
#   %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%mul, %primals_13, %primals_14, [1], [0], [1], False, [0], 1), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_8, %convolution_3), kwargs = {})
triton_poi_fused_add_convolution_3 = async_compile.triton('triton_poi_fused_add_convolution_3', '''
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_3', '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_3(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 % 16
    x4 = xindex
    x1 = (xindex // 4) % 4
    tmp0 = tl.load(in_ptr0 + (x3), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_out_ptr0 + (x4), xmask)
    tmp2 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tl.store(in_out_ptr0 + (x4), tmp4, 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, primals_10, primals_11, primals_12, primals_13, primals_14 = args
    args.clear()
    assert_size_stride(primals_1, (1, 4), (4, 1))
    assert_size_stride(primals_2, (1, ), (1, ))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (1, 4), (4, 1))
    assert_size_stride(primals_5, (1, ), (1, ))
    assert_size_stride(primals_6, (1, 4, 1), (4, 1, 1))
    assert_size_stride(primals_7, (1, ), (1, ))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (1, 4, 1), (4, 1, 1))
    assert_size_stride(primals_10, (1, ), (1, ))
    assert_size_stride(primals_11, (4, 1, 1), (1, 1, 1))
    assert_size_stride(primals_12, (4, ), (1, ))
    assert_size_stride(primals_13, (4, 1, 1), (1, 1, 1))
    assert_size_stride(primals_14, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_2, primals_3, reinterpret_tensor(primals_1, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf1)
        del primals_1
        del primals_2
        buf3 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, primals_3, reinterpret_tensor(primals_4, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf3)
        del primals_4
        del primals_5
        # Topologically Sorted Source Nodes: [result], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(reinterpret_tensor(primals_8, (1, 4, 4), (16, 4, 1), 0), primals_6, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf4, (1, 1, 4), (4, 4, 1))
        buf5 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [result], Original ATen: [aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_0.run(buf5, primals_7, 4, grid=grid(4), stream=stream0)
        del primals_7
        # Topologically Sorted Source Nodes: [result_1], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(reinterpret_tensor(primals_8, (1, 4, 4), (16, 4, 1), 0), primals_9, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf6, (1, 1, 4), (4, 4, 1))
        buf7 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [result_1], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_0.run(buf7, primals_10, 4, grid=grid(4), stream=stream0)
        del primals_10
        buf8 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [add, z_f, add_1, z_g, z], Original ATen: [aten.add, aten.tanh, aten.sigmoid, aten.mul]
        triton_poi_fused_add_mul_sigmoid_tanh_1.run(buf5, buf1, buf7, buf3, buf8, 16, grid=grid(16), stream=stream0)
        # Topologically Sorted Source Nodes: [skip], Original ATen: [aten.convolution]
        buf9 = extern_kernels.convolution(buf8, primals_11, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf9, (4, 4, 4), (16, 4, 1))
        buf10 = buf9; del buf9  # reuse
        # Topologically Sorted Source Nodes: [skip], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_2.run(buf10, primals_12, 64, grid=grid(64), stream=stream0)
        del primals_12
        # Topologically Sorted Source Nodes: [conv1d_3], Original ATen: [aten.convolution]
        buf11 = extern_kernels.convolution(buf8, primals_13, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf11, (4, 4, 4), (16, 4, 1))
        buf12 = buf11; del buf11  # reuse
        # Topologically Sorted Source Nodes: [conv1d_3, residual], Original ATen: [aten.convolution, aten.add]
        triton_poi_fused_add_convolution_3.run(buf12, primals_8, primals_14, 64, grid=grid(64), stream=stream0)
        del primals_14
    return (buf10, buf12, primals_3, primals_6, primals_9, primals_11, primals_13, buf1, buf3, reinterpret_tensor(primals_8, (1, 4, 4), (16, 4, 1), 0), buf5, buf7, buf8, )


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((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((1, 4, 1), (4, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((1, 4, 1), (4, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((4, 1, 1), (1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((4, 1, 1), (1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_14 = 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, primals_10, primals_11, primals_12, primals_13, primals_14])
    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
import torch.nn as nn


class CausalConv1d(torch.nn.Conv1d):
    """Causal 1d convolution"""

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        dilation=1, groups=1, bias=True):
        self.__padding = (kernel_size - 1) * dilation
        super(CausalConv1d, self).__init__(in_channels, out_channels,
            kernel_size=kernel_size, stride=stride, padding=self.__padding,
            dilation=dilation, groups=groups, bias=bias)

    def forward(self, input):
        result = super(CausalConv1d, self).forward(input)
        if self.__padding != 0:
            return result[:, :, :-self.__padding]
        return result


class ResidualBlock(nn.Module):
    """Residual block of WaveNet architecture"""

    def __init__(self, residual_channels, dilation_channels, skip_channels,
        n_globals, kernel_size=1, dilation=1):
        super(ResidualBlock, self).__init__()
        self.residual_channels = residual_channels
        self.dilation_channels = dilation_channels
        self.skip_channels = skip_channels
        self.n_globals = n_globals
        self.kernel_size = kernel_size
        self.dilation = dilation
        self.filter_conv = CausalConv1d(in_channels=self.residual_channels,
            out_channels=self.dilation_channels, kernel_size=self.
            kernel_size, dilation=self.dilation)
        self.gated_conv = CausalConv1d(in_channels=self.residual_channels,
            out_channels=self.dilation_channels, kernel_size=self.
            kernel_size, dilation=self.dilation)
        self.filter_linear = nn.Linear(in_features=self.n_globals,
            out_features=self.dilation_channels)
        self.gated_linear = nn.Linear(in_features=self.n_globals,
            out_features=self.dilation_channels)
        self._1x1_conv_res = nn.Conv1d(in_channels=self.dilation_channels,
            out_channels=self.residual_channels, kernel_size=1, dilation=1)
        self._1x1_conv_skip = nn.Conv1d(in_channels=self.dilation_channels,
            out_channels=self.skip_channels, kernel_size=1, dilation=1)

    def forward(self, x, h):
        h_f = self.filter_linear(h).unsqueeze(2)
        h_g = self.gated_linear(h).unsqueeze(2)
        x_f = self.filter_conv(x)
        x_g = self.gated_conv(x)
        z_f = torch.tanh(x_f + h_f)
        z_g = torch.sigmoid(x_g + h_g)
        z = torch.mul(z_f, z_g)
        skip = self._1x1_conv_skip(z)
        residual = x + self._1x1_conv_res(z)
        return skip, residual


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


def get_init_inputs():
    return [[], {'residual_channels': 4, 'dilation_channels': 1,
        'skip_channels': 4, 'n_globals': 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
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tl.store(in_out_ptr0 + x0, tmp3, xmask)


@triton.jit
def triton_poi_fused_add_mul_sigmoid_tanh_1(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tmp6 = tmp4 + tmp5
    tmp7 = tl.sigmoid(tmp6)
    tmp8 = tmp3 * tmp7
    tl.store(out_ptr0 + x2, tmp8, 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)


@triton.jit
def triton_poi_fused_add_convolution_3(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 % 16
    x4 = xindex
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_out_ptr0 + x4, xmask)
    tmp2 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tl.store(in_out_ptr0 + x4, tmp4, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14) = args
    args.clear()
    assert_size_stride(primals_1, (1, 4), (4, 1))
    assert_size_stride(primals_2, (1,), (1,))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (1, 4), (4, 1))
    assert_size_stride(primals_5, (1,), (1,))
    assert_size_stride(primals_6, (1, 4, 1), (4, 1, 1))
    assert_size_stride(primals_7, (1,), (1,))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (1, 4, 1), (4, 1, 1))
    assert_size_stride(primals_10, (1,), (1,))
    assert_size_stride(primals_11, (4, 1, 1), (1, 1, 1))
    assert_size_stride(primals_12, (4,), (1,))
    assert_size_stride(primals_13, (4, 1, 1), (1, 1, 1))
    assert_size_stride(primals_14, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_2, primals_3, reinterpret_tensor(
            primals_1, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf1)
        del primals_1
        del primals_2
        buf3 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_5, primals_3, reinterpret_tensor(
            primals_4, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf3)
        del primals_4
        del primals_5
        buf4 = extern_kernels.convolution(reinterpret_tensor(primals_8, (1,
            4, 4), (16, 4, 1), 0), primals_6, stride=(1,), padding=(0,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf4, (1, 1, 4), (4, 4, 1))
        buf5 = buf4
        del buf4
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(4)](buf5, primals_7, 4, XBLOCK=
            4, num_warps=1, num_stages=1)
        del primals_7
        buf6 = extern_kernels.convolution(reinterpret_tensor(primals_8, (1,
            4, 4), (16, 4, 1), 0), primals_9, stride=(1,), padding=(0,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf6, (1, 1, 4), (4, 4, 1))
        buf7 = buf6
        del buf6
        triton_poi_fused_convolution_0[grid(4)](buf7, primals_10, 4, XBLOCK
            =4, num_warps=1, num_stages=1)
        del primals_10
        buf8 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32)
        triton_poi_fused_add_mul_sigmoid_tanh_1[grid(16)](buf5, buf1, buf7,
            buf3, buf8, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf9 = extern_kernels.convolution(buf8, primals_11, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf9, (4, 4, 4), (16, 4, 1))
        buf10 = buf9
        del buf9
        triton_poi_fused_convolution_2[grid(64)](buf10, primals_12, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_12
        buf11 = extern_kernels.convolution(buf8, primals_13, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf11, (4, 4, 4), (16, 4, 1))
        buf12 = buf11
        del buf11
        triton_poi_fused_add_convolution_3[grid(64)](buf12, primals_8,
            primals_14, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_14
    return (buf10, buf12, primals_3, primals_6, primals_9, primals_11,
        primals_13, buf1, buf3, reinterpret_tensor(primals_8, (1, 4, 4), (
        16, 4, 1), 0), buf5, buf7, buf8)


class CausalConv1d(torch.nn.Conv1d):
    """Causal 1d convolution"""

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        dilation=1, groups=1, bias=True):
        self.__padding = (kernel_size - 1) * dilation
        super(CausalConv1d, self).__init__(in_channels, out_channels,
            kernel_size=kernel_size, stride=stride, padding=self.__padding,
            dilation=dilation, groups=groups, bias=bias)

    def forward(self, input):
        result = super(CausalConv1d, self).forward(input)
        if self.__padding != 0:
            return result[:, :, :-self.__padding]
        return result


class ResidualBlockNew(nn.Module):
    """Residual block of WaveNet architecture"""

    def __init__(self, residual_channels, dilation_channels, skip_channels,
        n_globals, kernel_size=1, dilation=1):
        super(ResidualBlockNew, self).__init__()
        self.residual_channels = residual_channels
        self.dilation_channels = dilation_channels
        self.skip_channels = skip_channels
        self.n_globals = n_globals
        self.kernel_size = kernel_size
        self.dilation = dilation
        self.filter_conv = CausalConv1d(in_channels=self.residual_channels,
            out_channels=self.dilation_channels, kernel_size=self.
            kernel_size, dilation=self.dilation)
        self.gated_conv = CausalConv1d(in_channels=self.residual_channels,
            out_channels=self.dilation_channels, kernel_size=self.
            kernel_size, dilation=self.dilation)
        self.filter_linear = nn.Linear(in_features=self.n_globals,
            out_features=self.dilation_channels)
        self.gated_linear = nn.Linear(in_features=self.n_globals,
            out_features=self.dilation_channels)
        self._1x1_conv_res = nn.Conv1d(in_channels=self.dilation_channels,
            out_channels=self.residual_channels, kernel_size=1, dilation=1)
        self._1x1_conv_skip = nn.Conv1d(in_channels=self.dilation_channels,
            out_channels=self.skip_channels, kernel_size=1, dilation=1)

    def forward(self, input_0, input_1):
        primals_6 = self.filter_conv.weight
        primals_2 = self.filter_conv.bias
        primals_9 = self.gated_conv.weight
        primals_5 = self.gated_conv.bias
        primals_1 = self.filter_linear.weight
        primals_7 = self.filter_linear.bias
        primals_4 = self.gated_linear.weight
        primals_10 = self.gated_linear.bias
        primals_11 = self._1x1_conv_res.weight
        primals_12 = self._1x1_conv_res.bias
        primals_13 = self._1x1_conv_skip.weight
        primals_14 = self._1x1_conv_skip.bias
        primals_3 = input_0
        primals_8 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14])
        return output[0], output[1]
