# 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_6/inductor_cache/im/cimrryk5tlijxgch3lnrftbusuhrtt63nyisb4difipuywd7wx5o.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.constant_pad_nd]
# Source node to ATen node mapping:
#   x => constant_pad_nd
# Graph fragment:
#   %constant_pad_nd : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%primals_1, [0, 1]), kwargs = {})
triton_poi_fused_constant_pad_nd_0 = async_compile.triton('triton_poi_fused_constant_pad_nd_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=[128], 
    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_constant_pad_nd_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 5
    x1 = (xindex // 5)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 4, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = tl.load(in_ptr0 + (x0 + (4*x1)), tmp2 & xmask, other=0.0)
    tl.store(out_ptr0 + (x2), tmp3, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/kx/ckxt5ip3pdehe4uvtgnb7arkqq5gkapqc7o4qwwpbxdypvnq4zxb.py
# Topologically Sorted Source Nodes: [tanh, sigmoid, x_2], Original ATen: [aten.tanh, aten.sigmoid, aten.mul]
# Source node to ATen node mapping:
#   sigmoid => sigmoid
#   tanh => tanh
#   x_2 => mul
# Graph fragment:
#   %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%getitem,), kwargs = {})
#   %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_1,), kwargs = {})
#   %mul : [num_users=3] = call_function[target=torch.ops.aten.mul.Tensor](args = (%tanh, %sigmoid), kwargs = {})
triton_poi_fused_mul_sigmoid_tanh_1 = async_compile.triton('triton_poi_fused_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=[32], 
    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_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_mul_sigmoid_tanh_1(in_ptr0, in_ptr1, 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
    x2 = (xindex // 8)
    x4 = xindex % 8
    x1 = (xindex // 4) % 2
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x4 + (16*x2)), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (8 + x4 + (16*x2)), xmask)
    tmp5 = tl.load(in_ptr1 + (2 + 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 + (x3), tmp3, xmask)
    tl.store(out_ptr1 + (x3), tmp7, xmask)
    tl.store(out_ptr2 + (x3), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/lf/clf7hs52i4bd5d3e73uio27ntyjfqmszkbsw6dta3r6rzgeftva3.py
# Topologically Sorted Source Nodes: [s], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   s => convolution_1
# Graph fragment:
#   %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%mul, %primals_4, %primals_5, [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_6/inductor_cache/jg/cjgfre7znianqklg7g5ijua6n6nby7yvw4ljytb2viu2zu3gjmst.py
# Topologically Sorted Source Nodes: [x_3, add, sqrt, x_4], Original ATen: [aten.convolution, aten.add, aten.sqrt, aten.mul]
# Source node to ATen node mapping:
#   add => add
#   sqrt => full_default
#   x_3 => convolution_2
#   x_4 => 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 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_2, %primals_1), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.7071067690849304), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, %full_default), kwargs = {})
triton_poi_fused_add_convolution_mul_sqrt_3 = async_compile.triton('triton_poi_fused_add_convolution_mul_sqrt_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_mul_sqrt_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_mul_sqrt_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
    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
    tmp5 = 0.7071067690849304
    tmp6 = tmp4 * tmp5
    tl.store(in_out_ptr0 + (x3), tmp6, 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 = 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, 2, 1), (2, 1, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (4, 2, 1), (2, 1, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 5), (20, 5, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.constant_pad_nd]
        stream0 = get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0.run(primals_1, buf0, 80, grid=grid(80), stream=stream0)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,), padding=(1,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4), (16, 4, 1))
        buf2 = empty_strided_cuda((4, 2, 4), (8, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 2, 4), (8, 4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 2, 4), (8, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [tanh, sigmoid, x_2], Original ATen: [aten.tanh, aten.sigmoid, aten.mul]
        triton_poi_fused_mul_sigmoid_tanh_1.run(buf1, primals_3, buf2, buf3, buf4, 32, grid=grid(32), stream=stream0)
        del buf1
        del primals_3
        # Topologically Sorted Source Nodes: [s], Original ATen: [aten.convolution]
        buf5 = extern_kernels.convolution(buf4, primals_4, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf5, (4, 4, 4), (16, 4, 1))
        buf6 = buf5; del buf5  # reuse
        # Topologically Sorted Source Nodes: [s], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_2.run(buf6, primals_5, 64, grid=grid(64), stream=stream0)
        del primals_5
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.convolution]
        buf7 = extern_kernels.convolution(buf4, primals_6, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf7, (4, 4, 4), (16, 4, 1))
        buf8 = buf7; del buf7  # reuse
        # Topologically Sorted Source Nodes: [x_3, add, sqrt, x_4], Original ATen: [aten.convolution, aten.add, aten.sqrt, aten.mul]
        triton_poi_fused_add_convolution_mul_sqrt_3.run(buf8, primals_7, primals_1, 64, grid=grid(64), stream=stream0)
        del primals_1
        del primals_7
    return (buf8, buf6, primals_2, primals_4, primals_6, buf0, 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, 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, 2, 1), (2, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (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)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7])
    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 typing as T


class WaveNetLayer(torch.nn.Module):
    """a single gated residual wavenet layer"""

    def __init__(self, channels: 'int', kernel_size: 'int', dilation: 'int'):
        super().__init__()
        self._conv = torch.nn.Conv1d(in_channels=channels, out_channels=
            channels, kernel_size=kernel_size, padding='same', dilation=
            dilation)
        self._conv_skip = torch.nn.Conv1d(in_channels=channels // 2,
            out_channels=channels, kernel_size=1)
        self._conv_out = torch.nn.Conv1d(in_channels=channels // 2,
            out_channels=channels, kernel_size=1)

    def forward(self, x: 'torch.Tensor') ->T.Tuple[torch.Tensor, torch.Tensor]:
        r = x
        x = self._conv(x)
        x, g = x.split(x.size(1) // 2, dim=1)
        x = torch.tanh(x) * torch.sigmoid(g)
        s = self._conv_skip(x)
        x = self._conv_out(x)
        x = (x + r) * torch.tensor(0.5).sqrt()
        return x, s


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


def get_init_inputs():
    return [[], {'channels': 4, 'kernel_size': 4, 'dilation': 1}]

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_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 5
    x1 = xindex // 5
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 4, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = tl.load(in_ptr0 + (x0 + 4 * x1), tmp2 & xmask, other=0.0)
    tl.store(out_ptr0 + x2, tmp3, xmask)


@triton.jit
def triton_poi_fused_mul_sigmoid_tanh_1(in_ptr0, in_ptr1, 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
    x2 = xindex // 8
    x4 = xindex % 8
    x1 = xindex // 4 % 2
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x4 + 16 * x2), xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (8 + x4 + 16 * x2), xmask)
    tmp5 = tl.load(in_ptr1 + (2 + 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 + x3, tmp3, xmask)
    tl.store(out_ptr1 + x3, tmp7, xmask)
    tl.store(out_ptr2 + x3, 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_mul_sqrt_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
    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
    tmp5 = 0.7071067690849304
    tmp6 = tmp4 * tmp5
    tl.store(in_out_ptr0 + x3, tmp6, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = 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, 2, 1), (2, 1, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 2, 1), (2, 1, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 5), (20, 5, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0[grid(80)](primals_1, buf0, 80,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,),
            padding=(1,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4), (16, 4, 1))
        buf2 = empty_strided_cuda((4, 2, 4), (8, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 2, 4), (8, 4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 2, 4), (8, 4, 1), torch.float32)
        triton_poi_fused_mul_sigmoid_tanh_1[grid(32)](buf1, primals_3, buf2,
            buf3, buf4, 32, XBLOCK=32, num_warps=1, num_stages=1)
        del buf1
        del primals_3
        buf5 = extern_kernels.convolution(buf4, primals_4, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf5, (4, 4, 4), (16, 4, 1))
        buf6 = buf5
        del buf5
        triton_poi_fused_convolution_2[grid(64)](buf6, primals_5, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_5
        buf7 = extern_kernels.convolution(buf4, primals_6, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf7, (4, 4, 4), (16, 4, 1))
        buf8 = buf7
        del buf7
        triton_poi_fused_add_convolution_mul_sqrt_3[grid(64)](buf8,
            primals_7, primals_1, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_1
        del primals_7
    return buf8, buf6, primals_2, primals_4, primals_6, buf0, buf2, buf3, buf4


class WaveNetLayerNew(torch.nn.Module):
    """a single gated residual wavenet layer"""

    def __init__(self, channels: 'int', kernel_size: 'int', dilation: 'int'):
        super().__init__()
        self._conv = torch.nn.Conv1d(in_channels=channels, out_channels=
            channels, kernel_size=kernel_size, padding='same', dilation=
            dilation)
        self._conv_skip = torch.nn.Conv1d(in_channels=channels // 2,
            out_channels=channels, kernel_size=1)
        self._conv_out = torch.nn.Conv1d(in_channels=channels // 2,
            out_channels=channels, kernel_size=1)

    def forward(self, input_0):
        primals_1 = self._conv.weight
        primals_3 = self._conv.bias
        primals_4 = self._conv_skip.weight
        primals_5 = self._conv_skip.bias
        primals_6 = self._conv_out.weight
        primals_7 = self._conv_out.bias
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0], output[1]
