# 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_8/inductor_cache/fg/cfg3taj7uccsafc6ucwwdzgk2r5epy4w6xugopt4x4g4ig66hvas.py
# Topologically Sorted Source Nodes: [h_1, y__1, mul, sub, mul_1, y__2], Original ATen: [aten.sigmoid, aten.relu, aten.mul, aten.rsub, aten.add]
# Source node to ATen node mapping:
#   h_1 => sigmoid
#   mul => mul
#   mul_1 => mul_1
#   sub => sub
#   y__1 => relu
#   y__2 => add
# Graph fragment:
#   %sigmoid : [num_users=3] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem,), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%getitem_1,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %relu), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %primals_3), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
triton_poi_fused_add_mul_relu_rsub_sigmoid_0 = async_compile.triton('triton_poi_fused_add_mul_relu_rsub_sigmoid_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: '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_relu_rsub_sigmoid_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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_relu_rsub_sigmoid_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4) % 4
    x2 = (xindex // 16)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (5*x1) + (40*x2)), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (20 + x0 + (5*x1) + (40*x2)), xmask)
    tmp5 = tl.load(in_ptr1 + (4 + x1), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr2 + (x3), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tmp6 = tmp4 + tmp5
    tmp7 = tl.full([1], 0, tl.int32)
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tmp9 = tmp3 * tmp8
    tmp10 = 1.0
    tmp11 = tmp10 - tmp3
    tmp13 = tmp11 * tmp12
    tmp14 = tmp9 + tmp13
    tl.store(out_ptr0 + (x3), tmp3, xmask)
    tl.store(out_ptr1 + (x3), tmp8, xmask)
    tl.store(out_ptr2 + (x3), tmp14, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (8, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (8, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [y], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,), padding=(2,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf0, (4, 8, 5), (40, 5, 1))
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [h_1, y__1, mul, sub, mul_1, y__2], Original ATen: [aten.sigmoid, aten.relu, aten.mul, aten.rsub, aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_mul_relu_rsub_sigmoid_0.run(buf0, primals_2, primals_3, buf1, buf2, buf3, 64, grid=grid(64), stream=stream0)
        del buf0
        del primals_2
    return (buf3, primals_1, primals_3, buf1, buf2, )


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((8, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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
import torch.utils.data


class Conv1d(nn.Conv1d):
    """
    :param in_channels: Scalar
    :param out_channels: Scalar
    :param kernel_size: Scalar
    :param activation_fn: activation function
    :param drop_rate: Scalar. dropout rate
    :param stride: Scalar
    :param padding: padding type
    :param dilation: Scalar
    :param groups: Scalar
    :param bias: Boolean.
    :param bn: Boolean. whether it uses batch normalization

    """

    def __init__(self, in_channels, out_channels, kernel_size,
        activation_fn=None, drop_rate=0.0, stride=1, padding='same',
        dilation=1, groups=1, bias=True, bn=False):
        self.activation_fn = activation_fn
        self.drop_rate = drop_rate
        if padding == 'same':
            padding = kernel_size // 2 * dilation
            self.even_kernel = not bool(kernel_size % 2)
        super(Conv1d, self).__init__(in_channels, out_channels, kernel_size,
            stride=stride, padding=padding, dilation=dilation, groups=
            groups, bias=bias)
        self.drop_out = nn.Dropout(drop_rate) if drop_rate > 0 else None
        self.batch_norm = nn.BatchNorm1d(out_channels, eps=0.001, momentum=
            0.001) if bn else None

    def forward(self, x):
        """
        :param x: (N, C_in, T) Tensor.

        Returns:
            y: (N, C_out, T) Tensor.

        """
        y = super(Conv1d, self).forward(x)
        y = self.batch_norm(y) if self.batch_norm is not None else y
        y = self.activation_fn(y) if self.activation_fn is not None else y
        y = self.drop_out(y) if self.drop_out is not None else y
        y = y[:, :, :-1] if self.even_kernel else y
        return y


class HighwayConv1d(Conv1d):
    """
    :param in_channels: Scalar
    :param out_channels: Scalar
    :param kernel_size: Scalar
    :param drop_rate: Scalar. dropout rate
    :param stride: Scalar
    :param padding: padding type
    :param dilation: Scalar
    :param groups: Scalar
    :param bias: Boolean.

    """

    def __init__(self, in_channels, out_channels, kernel_size, drop_rate=
        0.0, stride=1, padding='same', dilation=1, groups=1, bias=True):
        self.drop_rate = drop_rate
        super(HighwayConv1d, self).__init__(in_channels, out_channels * 2,
            kernel_size, activation_fn=None, stride=stride, padding=padding,
            dilation=dilation, groups=groups, bias=bias)
        self.drop_out = nn.Dropout(self.drop_rate) if drop_rate > 0 else None

    def forward(self, x):
        """
        :param x: (N, C_in, T) Tensor.

        Returns:
            y: (N, C_out, T) Tensor.
            
        """
        y = super(HighwayConv1d, self).forward(x)
        h, y_ = y.chunk(2, dim=1)
        h = torch.sigmoid(h)
        y_ = torch.relu(y_)
        y_ = h * y_ + (1 - h) * x
        y_ = self.drop_out(y_) if self.drop_out is not None else y_
        return y_


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_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 import triton_helpers
import torch.nn as nn
import torch.utils.data
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_relu_rsub_sigmoid_0(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 5 * x1 + 40 * x2), xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (20 + x0 + 5 * x1 + 40 * x2), xmask)
    tmp5 = tl.load(in_ptr1 + (4 + x1), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr2 + x3, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tmp6 = tmp4 + tmp5
    tmp7 = tl.full([1], 0, tl.int32)
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tmp9 = tmp3 * tmp8
    tmp10 = 1.0
    tmp11 = tmp10 - tmp3
    tmp13 = tmp11 * tmp12
    tmp14 = tmp9 + tmp13
    tl.store(out_ptr0 + x3, tmp3, xmask)
    tl.store(out_ptr1 + x3, tmp8, xmask)
    tl.store(out_ptr2 + x3, tmp14, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (8, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (8,), (1,))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,),
            padding=(2,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf0, (4, 8, 5), (40, 5, 1))
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_relu_rsub_sigmoid_0[grid(64)](buf0,
            primals_2, primals_3, buf1, buf2, buf3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf0
        del primals_2
    return buf3, primals_1, primals_3, buf1, buf2


class Conv1d(nn.Conv1d):
    """
    :param in_channels: Scalar
    :param out_channels: Scalar
    :param kernel_size: Scalar
    :param activation_fn: activation function
    :param drop_rate: Scalar. dropout rate
    :param stride: Scalar
    :param padding: padding type
    :param dilation: Scalar
    :param groups: Scalar
    :param bias: Boolean.
    :param bn: Boolean. whether it uses batch normalization

    """

    def __init__(self, in_channels, out_channels, kernel_size,
        activation_fn=None, drop_rate=0.0, stride=1, padding='same',
        dilation=1, groups=1, bias=True, bn=False):
        self.activation_fn = activation_fn
        self.drop_rate = drop_rate
        if padding == 'same':
            padding = kernel_size // 2 * dilation
            self.even_kernel = not bool(kernel_size % 2)
        super(Conv1d, self).__init__(in_channels, out_channels, kernel_size,
            stride=stride, padding=padding, dilation=dilation, groups=
            groups, bias=bias)
        self.drop_out = nn.Dropout(drop_rate) if drop_rate > 0 else None
        self.batch_norm = nn.BatchNorm1d(out_channels, eps=0.001, momentum=
            0.001) if bn else None

    def forward(self, x):
        """
        :param x: (N, C_in, T) Tensor.

        Returns:
            y: (N, C_out, T) Tensor.

        """
        y = super(Conv1d, self).forward(x)
        y = self.batch_norm(y) if self.batch_norm is not None else y
        y = self.activation_fn(y) if self.activation_fn is not None else y
        y = self.drop_out(y) if self.drop_out is not None else y
        y = y[:, :, :-1] if self.even_kernel else y
        return y


class HighwayConv1dNew(Conv1d):
    """
    :param in_channels: Scalar
    :param out_channels: Scalar
    :param kernel_size: Scalar
    :param drop_rate: Scalar. dropout rate
    :param stride: Scalar
    :param padding: padding type
    :param dilation: Scalar
    :param groups: Scalar
    :param bias: Boolean.

    """

    def __init__(self, in_channels, out_channels, kernel_size, drop_rate=
        0.0, stride=1, padding='same', dilation=1, groups=1, bias=True):
        self.drop_rate = drop_rate
        super(HighwayConv1dNew, self).__init__(in_channels, out_channels * 
            2, kernel_size, activation_fn=None, stride=stride, padding=
            padding, dilation=dilation, groups=groups, bias=bias)
        self.drop_out = nn.Dropout(self.drop_rate) if drop_rate > 0 else None

    def forward(self, input_0):
        primals_1 = self.weight
        primals_2 = self.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
