# 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_4/inductor_cache/rs/crsqcexxk6yerceqnf6qa4jqmipzzsvegtsy5g4honvx4bpify5w.py
# Topologically Sorted Source Nodes: [sub, exp_1, add, truediv, pow_1, sub_1, exp, add_1, truediv_1, k, mul, t, t_1], Original ATen: [aten.rsub, aten.exp, aten.add, aten.reciprocal, aten.mul, aten.pow, aten.hardtanh]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   exp => exp
#   exp_1 => exp_1
#   k => pow_2
#   mul => mul_2
#   pow_1 => pow_1
#   sub => sub
#   sub_1 => sub_1
#   t => add_2
#   t_1 => clamp_max, clamp_min
#   truediv => mul, reciprocal
#   truediv_1 => mul_1, reciprocal_1
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %rand), kwargs = {})
#   %exp_1 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%arg1_1,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%exp_1, 1e-08), kwargs = {})
#   %reciprocal : [num_users=1] = call_function[target=torch.ops.aten.reciprocal.default](args = (%add,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%reciprocal, 1), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Tensor](args = (%sub, %mul), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %pow_1), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%arg0_1,), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%exp, 1e-08), kwargs = {})
#   %reciprocal_1 : [num_users=1] = call_function[target=torch.ops.aten.reciprocal.default](args = (%add_1,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%reciprocal_1, 1), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Tensor](args = (%sub_1, %mul_1), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_2, 1.2000000000000002), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, -0.1), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%add_2, 0), kwargs = {})
#   %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 1), kwargs = {})
triton_poi_fused_add_exp_hardtanh_mul_pow_reciprocal_rsub_0 = async_compile.triton('triton_poi_fused_add_exp_hardtanh_mul_pow_reciprocal_rsub_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: '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_exp_hardtanh_mul_pow_reciprocal_rsub_0', '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_exp_hardtanh_mul_pow_reciprocal_rsub_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp3 = tl.load(in_ptr0 + (x0), xmask)
    tmp12 = tl.load(in_ptr1 + (x0), xmask)
    tmp1 = 1.0
    tmp2 = tmp1 - tmp0
    tmp4 = tl_math.exp(tmp3)
    tmp5 = 1e-08
    tmp6 = tmp4 + tmp5
    tmp7 = tl.full([1], 1, tl.int32)
    tmp8 = tmp7 / tmp6
    tmp9 = tmp8 * tmp1
    tmp10 = libdevice.pow(tmp2, tmp9)
    tmp11 = tmp1 - tmp10
    tmp13 = tl_math.exp(tmp12)
    tmp14 = tmp13 + tmp5
    tmp15 = tmp7 / tmp14
    tmp16 = tmp15 * tmp1
    tmp17 = libdevice.pow(tmp11, tmp16)
    tmp18 = 1.2000000000000002
    tmp19 = tmp17 * tmp18
    tmp20 = -0.1
    tmp21 = tmp19 + tmp20
    tmp22 = 0.0
    tmp23 = triton_helpers.maximum(tmp21, tmp22)
    tmp24 = triton_helpers.minimum(tmp23, tmp1)
    tl.store(in_out_ptr0 + (x0), tmp24, 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, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [u], Original ATen: [aten.rand_like]
        buf0 = torch.ops.aten.rand.default([4, 4, 4, 4], dtype=torch.float32, device=device(type='cuda', index=0), pin_memory=False)
        buf1 = buf0
        del buf0
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [sub, exp_1, add, truediv, pow_1, sub_1, exp, add_1, truediv_1, k, mul, t, t_1], Original ATen: [aten.rsub, aten.exp, aten.add, aten.reciprocal, aten.mul, aten.pow, aten.hardtanh]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_exp_hardtanh_mul_pow_reciprocal_rsub_0.run(buf2, arg1_1, arg0_1, 256, grid=grid(256), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf2, )


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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 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
import torch.optim


def kuma_reparametrization(a, b):
    u = torch.rand_like(a)
    k = (1 - (1 - u) ** (1 / (b + 1e-08))) ** (1 / (a + 1e-08))
    return k


class Rectifier(nn.Module):

    def __init__(self, l=-0.1, r=1.1):
        super().__init__()
        self.l = l
        self.r = r
        self.eps = 1e-07

    def forward(self, x, l=None, r=None):
        l = l if l is not None else self.l
        r = r if r is not None else self.r
        t = l + (r - l) * x
        t = torch.nn.functional.hardtanh(t, 0, 1)
        return t


class HardKumaBinarizer(nn.Module):

    def __init__(self, l=-0.1, r=1.1):
        super().__init__()
        self.rectifier = Rectifier(l, r)

    def forward(self, a, b, l=None, r=None):
        k = kuma_reparametrization(torch.exp(a), torch.exp(b))
        t = self.rectifier(k, l, r)
        return t


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


def get_init_inputs():
    return [[], {}]

import torch
from torch import device
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
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride


@triton.jit
def triton_poi_fused_add_exp_hardtanh_mul_pow_reciprocal_rsub_0(in_out_ptr0,
    in_ptr0, in_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp3 = tl.load(in_ptr0 + x0, xmask)
    tmp12 = tl.load(in_ptr1 + x0, xmask)
    tmp1 = 1.0
    tmp2 = tmp1 - tmp0
    tmp4 = tl_math.exp(tmp3)
    tmp5 = 1e-08
    tmp6 = tmp4 + tmp5
    tmp7 = tl.full([1], 1, tl.int32)
    tmp8 = tmp7 / tmp6
    tmp9 = tmp8 * tmp1
    tmp10 = libdevice.pow(tmp2, tmp9)
    tmp11 = tmp1 - tmp10
    tmp13 = tl_math.exp(tmp12)
    tmp14 = tmp13 + tmp5
    tmp15 = tmp7 / tmp14
    tmp16 = tmp15 * tmp1
    tmp17 = libdevice.pow(tmp11, tmp16)
    tmp18 = 1.2000000000000002
    tmp19 = tmp17 * tmp18
    tmp20 = -0.1
    tmp21 = tmp19 + tmp20
    tmp22 = 0.0
    tmp23 = triton_helpers.maximum(tmp21, tmp22)
    tmp24 = triton_helpers.minimum(tmp23, tmp1)
    tl.store(in_out_ptr0 + x0, tmp24, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = torch.ops.aten.rand.default([4, 4, 4, 4], dtype=torch.
            float32, device=device(type='cuda', index=0), pin_memory=False)
        buf1 = buf0
        del buf0
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_poi_fused_add_exp_hardtanh_mul_pow_reciprocal_rsub_0[grid(256)](
            buf2, arg1_1, arg0_1, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
    return buf2,


def kuma_reparametrization(a, b):
    u = torch.rand_like(a)
    k = (1 - (1 - u) ** (1 / (b + 1e-08))) ** (1 / (a + 1e-08))
    return k


class Rectifier(nn.Module):

    def __init__(self, l=-0.1, r=1.1):
        super().__init__()
        self.l = l
        self.r = r
        self.eps = 1e-07

    def forward(self, x, l=None, r=None):
        l = l if l is not None else self.l
        r = r if r is not None else self.r
        t = l + (r - l) * x
        t = torch.nn.functional.hardtanh(t, 0, 1)
        return t


class HardKumaBinarizerNew(nn.Module):

    def __init__(self, l=-0.1, r=1.1):
        super().__init__()
        self.rectifier = Rectifier(l, r)

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0]
