# 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/qg/cqg3e6qbl457uja37r5t7mwrhshp5howpejrpt2vy5kg7sjg7kkn.py
# Topologically Sorted Source Nodes: [conv1d_1, add, out_tanh, conv1d_3, add_1, out_gate, mul], Original ATen: [aten.convolution, aten.add, aten.tanh, aten.sigmoid, aten.mul]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   conv1d_1 => convolution_1
#   conv1d_3 => convolution_3
#   mul => mul
#   out_gate => sigmoid
#   out_tanh => tanh
# Graph fragment:
#   %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_5, %primals_3, %primals_4, [1], [0], [1], False, [0], 1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%slice_3, %convolution_1), kwargs = {})
#   %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%add,), kwargs = {})
#   %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_5, %primals_7, %primals_8, [1], [0], [1], False, [0], 1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%slice_6, %convolution_3), kwargs = {})
#   %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%add_1,), kwargs = {})
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%tanh, %sigmoid), kwargs = {})
triton_poi_fused_add_convolution_mul_sigmoid_tanh_0 = async_compile.triton('triton_poi_fused_add_convolution_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '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, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_mul_sigmoid_tanh_0', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], '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_convolution_mul_sigmoid_tanh_0(in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x4 = (xindex // 4)
    x3 = xindex
    x1 = (xindex // 4) % 4
    tmp0 = tl.load(in_ptr0 + (x0 + (7*x4)), xmask)
    tmp1 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp2 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr2 + (x0 + (7*x4)), xmask)
    tmp7 = tl.load(in_out_ptr1 + (x3), xmask)
    tmp8 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tmp5 = libdevice.tanh(tmp4)
    tmp9 = tmp7 + tmp8
    tmp10 = tmp6 + tmp9
    tmp11 = tl.sigmoid(tmp10)
    tmp12 = tmp5 * tmp11
    tl.store(in_out_ptr0 + (x3), tmp5, xmask)
    tl.store(in_out_ptr1 + (x3), tmp11, xmask)
    tl.store(out_ptr0 + (x3), tmp12, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/us/custepuaxcgmeci6okpmedesxdvzu45qqfj6xlln2yvyowwq2gle.py
# Topologically Sorted Source Nodes: [conv1d_4, output], Original ATen: [aten.convolution, aten.add]
# Source node to ATen node mapping:
#   conv1d_4 => convolution_4
#   output => add_2
# Graph fragment:
#   %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%mul, %primals_9, %primals_10, [1], [0], [1], False, [0], 1), kwargs = {})
#   %add_2 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_4, %primals_2), 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], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   skip => convolution_5
# Graph fragment:
#   %convolution_5 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%add_2, %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')


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 = 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, 4, 1), (4, 1, 1))
    assert_size_stride(primals_4, (4, ), (1, ))
    assert_size_stride(primals_5, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_7, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_8, (4, ), (1, ))
    assert_size_stride(primals_9, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_10, (4, ), (1, ))
    assert_size_stride(primals_11, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_12, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_2, primals_1, 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))
        # Topologically Sorted Source Nodes: [conv1d_1], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(primals_5, primals_3, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4), (16, 4, 1))
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(primals_2, primals_6, stride=(1,), padding=(3,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 7), (28, 7, 1))
        # Topologically Sorted Source Nodes: [conv1d_3], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(primals_5, primals_7, 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))
        buf2 = buf1; del buf1  # reuse
        buf5 = buf4; del buf4  # reuse
        buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv1d_1, add, out_tanh, conv1d_3, add_1, out_gate, mul], Original ATen: [aten.convolution, aten.add, aten.tanh, aten.sigmoid, aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_convolution_mul_sigmoid_tanh_0.run(buf2, buf5, buf0, primals_4, buf3, primals_8, buf6, 64, grid=grid(64), stream=stream0)
        del buf0
        del buf3
        del primals_4
        del primals_8
        # Topologically Sorted Source Nodes: [conv1d_4], Original ATen: [aten.convolution]
        buf7 = extern_kernels.convolution(buf6, primals_9, 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: [conv1d_4, output], Original ATen: [aten.convolution, aten.add]
        triton_poi_fused_add_convolution_1.run(buf8, primals_10, primals_2, 64, grid=grid(64), stream=stream0)
        del primals_10
        # 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
    return (buf8, buf10, primals_1, primals_2, primals_3, primals_5, primals_6, primals_7, primals_9, primals_11, buf2, buf5, buf6, 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((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, 4, 1), (4, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, 4, 1), (4, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, 4, 1), (4, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((4, 4, 1), (4, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_12 = 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])
    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 CausalConv1d(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, dilation=1,
        bias=False):
        super(CausalConv1d, self).__init__()
        self.padding = padding = (kernel_size - 1) * dilation
        self.conv = nn.Conv1d(in_channels, out_channels, kernel_size,
            padding=padding, dilation=dilation, bias=bias)

    def forward(self, x):
        x = self.conv(x)
        if self.padding != 0:
            x = x[:, :, :-self.padding]
        return x


class ResBlock(nn.Module):

    def __init__(self, dilation, aux_channels, n_channels, skip_channels,
        kernel_size, fast_inference=False):
        super(ResBlock, self).__init__()
        conv_dilation = 1 if fast_inference else dilation
        self.filter = CausalConv1d(n_channels, n_channels, kernel_size=
            kernel_size, dilation=conv_dilation)
        self.gate = CausalConv1d(n_channels, n_channels, kernel_size=
            kernel_size, dilation=conv_dilation)
        self.aux_filter = nn.Conv1d(aux_channels, n_channels, kernel_size=1)
        self.aux_gate = nn.Conv1d(aux_channels, n_channels, kernel_size=1)
        if fast_inference:
            self.queue = None
            self.buffer_size = conv_dilation * 2 * (kernel_size - 1)
        self.fast_inference = fast_inference
        self.permute = nn.Conv1d(n_channels, n_channels, kernel_size=1)
        self.skip = nn.Conv1d(n_channels, skip_channels, kernel_size=1)

    def forward(self, x, h):
        if self.fast_inference:
            pass
        else:
            out_tanh = torch.tanh(self.filter(x) + self.aux_filter(h))
            out_gate = torch.sigmoid(self.gate(x) + self.aux_gate(h))
            output = self.permute(out_tanh * out_gate) + x
            skip = self.skip(output)
            return output, skip

    def clear(self):
        self.queue = None


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


def get_init_inputs():
    return [[], {'dilation': 1, 'aux_channels': 4, 'n_channels': 4,
        'skip_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
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_add_convolution_mul_sigmoid_tanh_0(in_out_ptr0,
    in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x4 = xindex // 4
    x3 = xindex
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_ptr0 + (x0 + 7 * x4), xmask)
    tmp1 = tl.load(in_out_ptr0 + x3, xmask)
    tmp2 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr2 + (x0 + 7 * x4), xmask)
    tmp7 = tl.load(in_out_ptr1 + x3, xmask)
    tmp8 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tmp5 = libdevice.tanh(tmp4)
    tmp9 = tmp7 + tmp8
    tmp10 = tmp6 + tmp9
    tmp11 = tl.sigmoid(tmp10)
    tmp12 = tmp5 * tmp11
    tl.store(in_out_ptr0 + x3, tmp5, xmask)
    tl.store(in_out_ptr1 + x3, tmp11, xmask)
    tl.store(out_ptr0 + x3, tmp12, 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, primals_9, primals_10, primals_11, primals_12
        ) = 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, 4, 1), (4, 1, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_7, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_10, (4,), (1,))
    assert_size_stride(primals_11, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_12, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_2, primals_1, 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 = extern_kernels.convolution(primals_5, primals_3, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4), (16, 4, 1))
        buf3 = extern_kernels.convolution(primals_2, primals_6, stride=(1,),
            padding=(3,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 7), (28, 7, 1))
        buf4 = extern_kernels.convolution(primals_5, primals_7, 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))
        buf2 = buf1
        del buf1
        buf5 = buf4
        del buf4
        buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_convolution_mul_sigmoid_tanh_0[grid(64)](buf2,
            buf5, buf0, primals_4, buf3, primals_8, buf6, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf0
        del buf3
        del primals_4
        del primals_8
        buf7 = extern_kernels.convolution(buf6, primals_9, 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_1[grid(64)](buf8, primals_10,
            primals_2, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_10
        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
    return (buf8, buf10, primals_1, primals_2, primals_3, primals_5,
        primals_6, primals_7, primals_9, primals_11, buf2, buf5, buf6, buf8)


class CausalConv1d(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, dilation=1,
        bias=False):
        super(CausalConv1d, self).__init__()
        self.padding = padding = (kernel_size - 1) * dilation
        self.conv = nn.Conv1d(in_channels, out_channels, kernel_size,
            padding=padding, dilation=dilation, bias=bias)

    def forward(self, x):
        x = self.conv(x)
        if self.padding != 0:
            x = x[:, :, :-self.padding]
        return x


class ResBlockNew(nn.Module):

    def __init__(self, dilation, aux_channels, n_channels, skip_channels,
        kernel_size, fast_inference=False):
        super(ResBlockNew, self).__init__()
        conv_dilation = 1 if fast_inference else dilation
        self.filter = CausalConv1d(n_channels, n_channels, kernel_size=
            kernel_size, dilation=conv_dilation)
        self.gate = CausalConv1d(n_channels, n_channels, kernel_size=
            kernel_size, dilation=conv_dilation)
        self.aux_filter = nn.Conv1d(aux_channels, n_channels, kernel_size=1)
        self.aux_gate = nn.Conv1d(aux_channels, n_channels, kernel_size=1)
        if fast_inference:
            self.queue = None
            self.buffer_size = conv_dilation * 2 * (kernel_size - 1)
        self.fast_inference = fast_inference
        self.permute = nn.Conv1d(n_channels, n_channels, kernel_size=1)
        self.skip = nn.Conv1d(n_channels, skip_channels, kernel_size=1)

    def clear(self):
        self.queue = None

    def forward(self, input_0, input_1):
        primals_1 = self.filter.conv.weight
        primals_2 = self.gate.conv.weight
        primals_3 = self.aux_filter.weight
        primals_4 = self.aux_filter.bias
        primals_7 = self.aux_gate.weight
        primals_8 = self.aux_gate.bias
        primals_9 = self.permute.weight
        primals_10 = self.permute.bias
        primals_11 = self.skip.weight
        primals_12 = self.skip.bias
        primals_5 = input_0
        primals_6 = 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])
        return output[0], output[1]
