# AOT ID: ['0_inference']
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/ez/cezo3qeqowipxahtpm7rhn2jnvy6no5gdlebcssicgia3nr4zaon.py
# Topologically Sorted Source Nodes: [mag_1, add_2, lt, tensor, output], Original ATen: [aten.cat, aten.add, aten.lt, aten.lift_fresh, aten.where]
# Source node to ATen node mapping:
#   add_2 => add_2
#   lt => lt
#   mag_1 => clone
#   output => where
#   tensor => full_default
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%view,), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%clone, %expand_1), kwargs = {})
#   %lt : [num_users=1] = call_function[target=torch.ops.aten.lt.Scalar](args = (%add_2, 0.3), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%lt, %full_default, %arg0_1), kwargs = {})
triton_poi_fused_add_cat_lift_fresh_lt_where_0 = async_compile.triton('triton_poi_fused_add_cat_lift_fresh_lt_where_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: '*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_cat_lift_fresh_lt_where_0', '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_cat_lift_fresh_lt_where_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = (xindex // 2)
    x2 = (xindex // 8) % 4
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (2*x4), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (2*x4)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (x5), xmask)
    tmp1 = tmp0 * tmp0
    tmp3 = tmp2 * tmp2
    tmp4 = tmp1 + tmp3
    tmp5 = libdevice.sqrt(tmp4)
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp9 = tmp7 + tmp8
    tmp10 = 0.3
    tmp11 = tmp9 < tmp10
    tmp13 = 0.0
    tmp14 = tl.where(tmp11, tmp13, tmp12)
    tl.store(out_ptr0 + (x5), tmp14, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 2), (32, 8, 2, 1))
    assert_size_stride(arg1_1, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 2), (32, 8, 2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mag_1, add_2, lt, tensor, output], Original ATen: [aten.cat, aten.add, aten.lt, aten.lift_fresh, aten.where]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_cat_lift_fresh_lt_where_0.run(arg0_1, arg1_1, buf0, 128, grid=grid(128), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf0, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 2), (32, 8, 2, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1])
    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
from torch.nn.parameter import Parameter


def magnitude(input):
    if input.ndimension() == 4:
        return (input[:, :, :, 0] ** 2 + input[:, :, :, 1] ** 2) ** 0.5
    elif input.ndimension() == 5:
        return (input[:, :, :, :, 0] ** 2 + input[:, :, :, :, 1] ** 2) ** 0.5
    elif input.ndimension() == 6:
        return (input[:, :, :, :, :, 0] ** 2 + input[:, :, :, :, :, 1] ** 2
            ) ** 0.5


class ModReLU(nn.Module):

    def __init__(self, in_channels, inplace=True):
        super(ModReLU, self).__init__()
        self.inplace = inplace
        self.in_channels = in_channels
        self.b = Parameter(torch.Tensor(in_channels), requires_grad=True)
        self.reset_parameters()
        self.relu = nn.ReLU(self.inplace)

    def reset_parameters(self):
        self.b.data.uniform_(-0.01, 0.0)

    def forward(self, input):
        eps = 1e-05
        ndims = input.ndimension()
        mag = magnitude(input).unsqueeze(-1) + eps
        mag = torch.cat([mag, mag], ndims - 1)
        if ndims == 4:
            brdcst_b = self.b.unsqueeze(0).unsqueeze(2).unsqueeze(3).expand_as(
                mag)
        elif ndims == 5:
            brdcst_b = self.b.unsqueeze(0).unsqueeze(2).unsqueeze(3).unsqueeze(
                4).expand_as(mag)
        elif ndims == 6:
            brdcst_b = self.b.unsqueeze(0).unsqueeze(2).unsqueeze(3).unsqueeze(
                4).unsqueeze(5).expand_as(mag)
        output = torch.where(mag + brdcst_b < 0.3, torch.tensor(0.0), input)
        return output


def get_inputs():
    return [torch.rand([4, 4, 4, 2])]


def get_init_inputs():
    return [[], {'in_channels': 4}]

import torch
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
from torch.nn.parameter import Parameter
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_cat_lift_fresh_lt_where_0(in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex // 2
    x2 = xindex // 8 % 4
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + 2 * x4, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 2 * x4), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + x5, xmask)
    tmp1 = tmp0 * tmp0
    tmp3 = tmp2 * tmp2
    tmp4 = tmp1 + tmp3
    tmp5 = libdevice.sqrt(tmp4)
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp9 = tmp7 + tmp8
    tmp10 = 0.3
    tmp11 = tmp9 < tmp10
    tmp13 = 0.0
    tmp14 = tl.where(tmp11, tmp13, tmp12)
    tl.store(out_ptr0 + x5, tmp14, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 2), (32, 8, 2, 1))
    assert_size_stride(arg1_1, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 2), (32, 8, 2, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_cat_lift_fresh_lt_where_0[grid(128)](arg0_1,
            arg1_1, buf0, 128, XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
    return buf0,


def magnitude(input):
    if input.ndimension() == 4:
        return (input[:, :, :, 0] ** 2 + input[:, :, :, 1] ** 2) ** 0.5
    elif input.ndimension() == 5:
        return (input[:, :, :, :, 0] ** 2 + input[:, :, :, :, 1] ** 2) ** 0.5
    elif input.ndimension() == 6:
        return (input[:, :, :, :, :, 0] ** 2 + input[:, :, :, :, :, 1] ** 2
            ) ** 0.5


class ModReLUNew(nn.Module):

    def __init__(self, in_channels, inplace=True):
        super(ModReLUNew, self).__init__()
        self.inplace = inplace
        self.in_channels = in_channels
        self.b = Parameter(torch.Tensor(in_channels), requires_grad=True)
        self.reset_parameters()
        self.relu = nn.ReLU(self.inplace)

    def reset_parameters(self):
        self.b.data.uniform_(-0.01, 0.0)

    def forward(self, input_0):
        arg1_1 = self.b
        arg0_1 = input_0
        output = call([arg0_1, arg1_1])
        return output[0]
