# 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/uf/cufm2zulygopt6lu7sgnnhymbp23i2wzcm252jv4h4badnmn2ugw.py
# Topologically Sorted Source Nodes: [conv2d, prelu, sum_1, mean, sub, pow_1, sum_2, truediv_1, add, std, sub_1, x, mul, output], Original ATen: [aten.convolution, aten._prelu_kernel, aten.sum, aten.div, aten.sub, aten.pow, aten.add, aten.sqrt, aten.mul]
# Source node to ATen node mapping:
#   add => add
#   conv2d => convolution
#   mean => div
#   mul => mul_1
#   output => add_1
#   pow_1 => pow_1
#   prelu => gt, mul, where
#   std => sqrt
#   sub => sub
#   sub_1 => sub_1
#   sum_1 => sum_1
#   sum_2 => sum_2
#   truediv_1 => div_1
#   x => div_2
# Graph fragment:
#   %convolution : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution, 0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, %convolution), kwargs = {})
#   %where : [num_users=3] = call_function[target=torch.ops.aten.where.self](args = (%gt, %convolution, %mul), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%where, [3], True), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_1, 1), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%where, %div), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [3], True), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_2, 1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div_1, 1e-08), kwargs = {})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%add,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%where, %expand), kwargs = {})
#   %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub_1, %expand_1), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div_2, %expand_2), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %expand_3), kwargs = {})
triton_poi_fused__prelu_kernel_add_convolution_div_mul_pow_sqrt_sub_sum_0 = async_compile.triton('triton_poi_fused__prelu_kernel_add_convolution_div_mul_pow_sqrt_sub_sum_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__prelu_kernel_add_convolution_div_mul_pow_sqrt_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], '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__prelu_kernel_add_convolution_div_mul_pow_sqrt_sub_sum_0(in_out_ptr0, 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
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (0))
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp18 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr3 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tmp9 = 1.0
    tmp10 = tmp8 * tmp9
    tmp11 = tmp8 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tmp12 * tmp9
    tmp14 = 1e-08
    tmp15 = tmp13 + tmp14
    tmp16 = libdevice.sqrt(tmp15)
    tmp17 = tmp11 / tmp16
    tmp19 = tmp17 * tmp18
    tmp21 = tmp19 + tmp20
    tl.store(in_out_ptr0 + (x2), tmp2, xmask)
    tl.store(out_ptr0 + (x2), tmp21, 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (1, ), (1, ))
    assert_size_stride(primals_5, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_6, (1, 4, 1, 1), (4, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 1, 1), (4, 1, 1, 1))
        buf1 = buf0; del buf0  # reuse
        buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d, prelu, sum_1, mean, sub, pow_1, sum_2, truediv_1, add, std, sub_1, x, mul, output], Original ATen: [aten.convolution, aten._prelu_kernel, aten.sum, aten.div, aten.sub, aten.pow, aten.add, aten.sqrt, aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused__prelu_kernel_add_convolution_div_mul_pow_sqrt_sub_sum_0.run(buf1, primals_2, primals_4, primals_5, primals_6, buf2, 16, grid=grid(16), stream=stream0)
        del primals_2
        del primals_6
    return (buf2, primals_1, primals_3, primals_4, primals_5, buf1, )


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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((1, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((1, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6])
    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 numpy as np
import torch.nn as nn
from torch.autograd import Variable


class tLN(nn.Module):

    def __init__(self, dimension, eps=1e-08, trainable=True):
        super(tLN, self).__init__()
        self.eps = eps
        if trainable:
            self.gain = nn.Parameter(torch.ones(1, dimension, 1, 1))
            self.bias = nn.Parameter(torch.zeros(1, dimension, 1, 1))
        else:
            self.gain = Variable(torch.ones(1, dimension, 1, 1),
                requires_grad=False)
            self.bias = Variable(torch.zeros(1, dimension, 1, 1),
                requires_grad=False)

    def forward(self, inp):
        inp.size(0)
        mean = torch.sum(inp, 3, keepdim=True) / inp.shape[3]
        std = torch.sqrt(torch.sum((inp - mean) ** 2, 3, keepdim=True) /
            inp.shape[3] + self.eps)
        x = (inp - mean.expand_as(inp)) / std.expand_as(inp)
        return x * self.gain.expand_as(x).type(x.type()) + self.bias.expand_as(
            x).type(x.type())


class CausalConv2d(torch.nn.Conv2d):

    def __init__(self, in_channels, out_channels, kernel_size, stride=(1, 1
        ), dilation=(1, 1), groups=1, bias=True):
        _pad = int(np.log2((kernel_size[1] - 1) / 2))
        padding_2 = int(2 ** (np.log2(dilation[1]) + _pad))
        self.__padding = (kernel_size[0] - 1) * dilation[0], padding_2
        super(CausalConv2d, 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(CausalConv2d, self).forward(input)
        if self.__padding[0] != 0:
            return result[:, :, :-self.__padding[0]]
        return result


class DepthConv2dv2(nn.Module):

    def __init__(self, input_channel, hidden_channel, kernel, dilation=(1, 
        1), stride=(1, 1), padding=(0, 0), causal=False):
        super(DepthConv2dv2, self).__init__()
        self.padding = padding
        if causal:
            self.conv1d = CausalConv2d(input_channel, hidden_channel,
                kernel, stride=stride, dilation=dilation)
        else:
            self.conv1d = nn.Conv2d(input_channel, hidden_channel, kernel,
                stride=stride, padding=self.padding, dilation=dilation)
        self.nonlinearity1 = nn.PReLU()
        self.reg1 = tLN(hidden_channel)

    def forward(self, input):
        output = self.reg1(self.nonlinearity1(self.conv1d(input)))
        return output


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


def get_init_inputs():
    return [[], {'input_channel': 4, 'hidden_channel': 4, 'kernel': 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 numpy as np
import torch.nn as nn
from torch.autograd import Variable
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__prelu_kernel_add_convolution_div_mul_pow_sqrt_sub_sum_0(
    in_out_ptr0, 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
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + 0)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp18 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tmp9 = 1.0
    tmp10 = tmp8 * tmp9
    tmp11 = tmp8 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tmp12 * tmp9
    tmp14 = 1e-08
    tmp15 = tmp13 + tmp14
    tmp16 = libdevice.sqrt(tmp15)
    tmp17 = tmp11 / tmp16
    tmp19 = tmp17 * tmp18
    tmp21 = tmp19 + tmp20
    tl.store(in_out_ptr0 + x2, tmp2, xmask)
    tl.store(out_ptr0 + x2, tmp21, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (1,), (1,))
    assert_size_stride(primals_5, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_6, (1, 4, 1, 1), (4, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 1, 1), (4, 1, 1, 1))
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__prelu_kernel_add_convolution_div_mul_pow_sqrt_sub_sum_0[
            grid(16)](buf1, primals_2, primals_4, primals_5, primals_6,
            buf2, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_2
        del primals_6
    return buf2, primals_1, primals_3, primals_4, primals_5, buf1


class tLN(nn.Module):

    def __init__(self, dimension, eps=1e-08, trainable=True):
        super(tLN, self).__init__()
        self.eps = eps
        if trainable:
            self.gain = nn.Parameter(torch.ones(1, dimension, 1, 1))
            self.bias = nn.Parameter(torch.zeros(1, dimension, 1, 1))
        else:
            self.gain = Variable(torch.ones(1, dimension, 1, 1),
                requires_grad=False)
            self.bias = Variable(torch.zeros(1, dimension, 1, 1),
                requires_grad=False)

    def forward(self, inp):
        inp.size(0)
        mean = torch.sum(inp, 3, keepdim=True) / inp.shape[3]
        std = torch.sqrt(torch.sum((inp - mean) ** 2, 3, keepdim=True) /
            inp.shape[3] + self.eps)
        x = (inp - mean.expand_as(inp)) / std.expand_as(inp)
        return x * self.gain.expand_as(x).type(x.type()) + self.bias.expand_as(
            x).type(x.type())


class CausalConv2d(torch.nn.Conv2d):

    def __init__(self, in_channels, out_channels, kernel_size, stride=(1, 1
        ), dilation=(1, 1), groups=1, bias=True):
        _pad = int(np.log2((kernel_size[1] - 1) / 2))
        padding_2 = int(2 ** (np.log2(dilation[1]) + _pad))
        self.__padding = (kernel_size[0] - 1) * dilation[0], padding_2
        super(CausalConv2d, 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(CausalConv2d, self).forward(input)
        if self.__padding[0] != 0:
            return result[:, :, :-self.__padding[0]]
        return result


class DepthConv2dv2New(nn.Module):

    def __init__(self, input_channel, hidden_channel, kernel, dilation=(1, 
        1), stride=(1, 1), padding=(0, 0), causal=False):
        super(DepthConv2dv2New, self).__init__()
        self.padding = padding
        if causal:
            self.conv1d = CausalConv2d(input_channel, hidden_channel,
                kernel, stride=stride, dilation=dilation)
        else:
            self.conv1d = nn.Conv2d(input_channel, hidden_channel, kernel,
                stride=stride, padding=self.padding, dilation=dilation)
        self.nonlinearity1 = nn.PReLU()
        self.reg1 = tLN(hidden_channel)

    def forward(self, input_0):
        primals_1 = self.conv1d.weight
        primals_2 = self.conv1d.bias
        primals_4 = self.nonlinearity1.weight
        primals_5 = self.reg1.gain
        primals_6 = self.reg1.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]
