# 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/ve/cvekibnibp2jzomh3kuv73u2liqkd3ap4mdsxspqzvopkso2ubyb.py
# Topologically Sorted Source Nodes: [ge, float_1, add, mul, add_1, mul_1, lt, float_2, gt, float_3, mul_2, mul_3, add_2, le, float_4, add_3, mul_4, add_4, mul_5, add_5], Original ATen: [aten.ge, aten._to_copy, aten.add, aten.mul, aten.lt, aten.gt, aten.le]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   add_2 => add_2
#   add_3 => add_3
#   add_4 => add_4
#   add_5 => add_5
#   float_1 => convert_element_type
#   float_2 => convert_element_type_1
#   float_3 => convert_element_type_2
#   float_4 => convert_element_type_3
#   ge => ge
#   gt => gt
#   le => le
#   lt => lt
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   mul_3 => mul_3
#   mul_4 => mul_4
#   mul_5 => mul_5
# Graph fragment:
#   %ge : [num_users=2] = call_function[target=torch.ops.aten.ge.Tensor](args = (%primals_2, %primals_1), kwargs = {})
#   %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%ge, torch.float32), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_2, %primals_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_3, %add), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_1, %mul), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convert_element_type, %add_1), kwargs = {})
#   %lt : [num_users=1] = call_function[target=torch.ops.aten.lt.Tensor](args = (%primals_2, %primals_1), kwargs = {})
#   %convert_element_type_1 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%lt, torch.float32), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Tensor](args = (%primals_2, %primals_4), kwargs = {})
#   %convert_element_type_2 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%gt, torch.float32), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convert_element_type_1, %convert_element_type_2), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_2, %primals_2), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %mul_3), kwargs = {})
#   %le : [num_users=2] = call_function[target=torch.ops.aten.le.Tensor](args = (%primals_2, %primals_4), kwargs = {})
#   %convert_element_type_3 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%le, torch.float32), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_2, %primals_4), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_5, %add_3), kwargs = {})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_4, %mul_4), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convert_element_type_3, %add_4), kwargs = {})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %mul_5), kwargs = {})
triton_poi_fused__to_copy_add_ge_gt_le_lt_mul_0 = async_compile.triton('triton_poi_fused__to_copy_add_ge_gt_le_lt_mul_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*i1', 6: '*i1', 7: '*fp32', 8: '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, 8), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_ge_gt_le_lt_mul_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__to_copy_add_ge_gt_le_lt_mul_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr3 + (x0), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr4 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 >= tmp1
    tmp4 = tmp0 <= tmp3
    tmp5 = tmp2.to(tl.float32)
    tmp7 = tmp0 + tmp1
    tmp8 = tmp6 * tmp7
    tmp9 = tmp1 + tmp8
    tmp10 = tmp5 * tmp9
    tmp11 = tmp0 < tmp1
    tmp12 = tmp11.to(tl.float32)
    tmp13 = tmp0 > tmp3
    tmp14 = tmp13.to(tl.float32)
    tmp15 = tmp12 * tmp14
    tmp16 = tmp15 * tmp0
    tmp17 = tmp10 + tmp16
    tmp18 = tmp4.to(tl.float32)
    tmp20 = tmp0 + tmp3
    tmp21 = tmp19 * tmp20
    tmp22 = tmp3 + tmp21
    tmp23 = tmp18 * tmp22
    tmp24 = tmp17 + tmp23
    tl.store(out_ptr0 + (x2), tmp2, xmask)
    tl.store(out_ptr1 + (x2), tmp4, xmask)
    tl.store(out_ptr2 + (x2), tmp24, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, ), (1, ))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, ), (1, ))
    assert_size_stride(primals_5, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [ge, float_1, add, mul, add_1, mul_1, lt, float_2, gt, float_3, mul_2, mul_3, add_2, le, float_4, add_3, mul_4, add_4, mul_5, add_5], Original ATen: [aten.ge, aten._to_copy, aten.add, aten.mul, aten.lt, aten.gt, aten.le]
        stream0 = get_raw_stream(0)
        triton_poi_fused__to_copy_add_ge_gt_le_lt_mul_0.run(primals_2, primals_1, primals_4, primals_3, primals_5, buf0, buf1, buf2, 256, grid=grid(256), stream=stream0)
    return (buf2, primals_1, primals_2, primals_3, primals_4, primals_5, buf0, 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, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4, 4), (64, 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, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    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
from torch.nn.parameter import Parameter


class SReLU(nn.Module):
    """
    SReLU (S-shaped Rectified Linear Activation Unit): a combination of three linear functions, which perform mapping R → R with the following formulation:

    .. math::

        h(x_i) = \\left\\{\\begin{matrix} t_i^r + a_i^r(x_i - t_i^r), x_i \\geq t_i^r \\\\  x_i, t_i^r > x_i > t_i^l\\\\  t_i^l + a_i^l(x_i - t_i^l), x_i \\leq  t_i^l \\\\ \\end{matrix}\\right.

    with 4 trainable parameters.

    Shape:
        - Input: (N, *) where * means, any number of additional
          dimensions
        - Output: (N, *), same shape as the input

    Parameters:

        .. math:: \\{t_i^r, a_i^r, t_i^l, a_i^l\\}

    4 trainable parameters, which model an individual SReLU activation unit. The subscript i indicates that we allow SReLU to vary in different channels. Parameters can be initialized manually or randomly.

    References:
        - See SReLU paper:
        https://arxiv.org/pdf/1512.07030.pdf

    Examples:
        >>> srelu_activation = srelu((2,2))
        >>> t = torch.randn((2,2), dtype=torch.float, requires_grad = True)
        >>> output = srelu_activation(t)
    """

    def __init__(self, in_features, parameters=None):
        """
        Initialization.
        INPUT:
            - in_features: shape of the input
            - parameters: (tr, tl, ar, al) parameters for manual initialization, default value is None. If None is passed, parameters are initialized randomly.
        """
        super(SReLU, self).__init__()
        self.in_features = in_features
        if parameters is None:
            self.tr = Parameter(torch.randn(in_features, dtype=torch.float,
                requires_grad=True))
            self.tl = Parameter(torch.randn(in_features, dtype=torch.float,
                requires_grad=True))
            self.ar = Parameter(torch.randn(in_features, dtype=torch.float,
                requires_grad=True))
            self.al = Parameter(torch.randn(in_features, dtype=torch.float,
                requires_grad=True))
        else:
            self.tr, self.tl, self.ar, self.al = parameters

    def forward(self, x):
        """
        Forward pass of the function
        """
        return (x >= self.tr).float() * (self.tr + self.ar * (x + self.tr)) + (
            x < self.tr).float() * (x > self.tl).float() * x + (x <= self.tl
            ).float() * (self.tl + self.al * (x + self.tl))


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


def get_init_inputs():
    return [[], {'in_features': 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 import 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__to_copy_add_ge_gt_le_lt_mul_0(in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, in_ptr4, out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 >= tmp1
    tmp4 = tmp0 <= tmp3
    tmp5 = tmp2.to(tl.float32)
    tmp7 = tmp0 + tmp1
    tmp8 = tmp6 * tmp7
    tmp9 = tmp1 + tmp8
    tmp10 = tmp5 * tmp9
    tmp11 = tmp0 < tmp1
    tmp12 = tmp11.to(tl.float32)
    tmp13 = tmp0 > tmp3
    tmp14 = tmp13.to(tl.float32)
    tmp15 = tmp12 * tmp14
    tmp16 = tmp15 * tmp0
    tmp17 = tmp10 + tmp16
    tmp18 = tmp4.to(tl.float32)
    tmp20 = tmp0 + tmp3
    tmp21 = tmp19 * tmp20
    tmp22 = tmp3 + tmp21
    tmp23 = tmp18 * tmp22
    tmp24 = tmp17 + tmp23
    tl.store(out_ptr0 + x2, tmp2, xmask)
    tl.store(out_ptr1 + x2, tmp4, xmask)
    tl.store(out_ptr2 + x2, tmp24, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4,), (1,))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__to_copy_add_ge_gt_le_lt_mul_0[grid(256)](primals_2,
            primals_1, primals_4, primals_3, primals_5, buf0, buf1, buf2, 
            256, XBLOCK=128, num_warps=4, num_stages=1)
    return (buf2, primals_1, primals_2, primals_3, primals_4, primals_5,
        buf0, buf1)


class SReLUNew(nn.Module):
    """
    SReLU (S-shaped Rectified Linear Activation Unit): a combination of three linear functions, which perform mapping R → R with the following formulation:

    .. math::

        h(x_i) = \\left\\{\\begin{matrix} t_i^r + a_i^r(x_i - t_i^r), x_i \\geq t_i^r \\\\  x_i, t_i^r > x_i > t_i^l\\\\  t_i^l + a_i^l(x_i - t_i^l), x_i \\leq  t_i^l \\\\ \\end{matrix}\\right.

    with 4 trainable parameters.

    Shape:
        - Input: (N, *) where * means, any number of additional
          dimensions
        - Output: (N, *), same shape as the input

    Parameters:

        .. math:: \\{t_i^r, a_i^r, t_i^l, a_i^l\\}

    4 trainable parameters, which model an individual SReLU activation unit. The subscript i indicates that we allow SReLU to vary in different channels. Parameters can be initialized manually or randomly.

    References:
        - See SReLU paper:
        https://arxiv.org/pdf/1512.07030.pdf

    Examples:
        >>> srelu_activation = srelu((2,2))
        >>> t = torch.randn((2,2), dtype=torch.float, requires_grad = True)
        >>> output = srelu_activation(t)
    """

    def __init__(self, in_features, parameters=None):
        """
        Initialization.
        INPUT:
            - in_features: shape of the input
            - parameters: (tr, tl, ar, al) parameters for manual initialization, default value is None. If None is passed, parameters are initialized randomly.
        """
        super(SReLUNew, self).__init__()
        self.in_features = in_features
        if parameters is None:
            self.tr = Parameter(torch.randn(in_features, dtype=torch.float,
                requires_grad=True))
            self.tl = Parameter(torch.randn(in_features, dtype=torch.float,
                requires_grad=True))
            self.ar = Parameter(torch.randn(in_features, dtype=torch.float,
                requires_grad=True))
            self.al = Parameter(torch.randn(in_features, dtype=torch.float,
                requires_grad=True))
        else:
            self.tr, self.tl, self.ar, self.al = parameters

    def forward(self, input_0):
        primals_1 = self.tr
        primals_3 = self.tl
        primals_4 = self.ar
        primals_5 = self.al
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
