# 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/zz/czz54ut75scrtht33wfs25yho3nmankwisvydcauscn44sw62gdt.py
# Topologically Sorted Source Nodes: [tanh, sub, mul, softplus, add, g], Original ATen: [aten.tanh, aten.sub, aten.mul, aten.softplus, aten.add]
# Source node to ATen node mapping:
#   add => add
#   g => mul_1
#   mul => mul
#   softplus => exp, gt, log1p, where
#   sub => sub
#   tanh => tanh
# Graph fragment:
#   %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%arg0_1,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, 0.6931471805599453), kwargs = {})
#   %mul : [num_users=3] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, -2), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%mul, 20), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%mul,), kwargs = {})
#   %log1p : [num_users=1] = call_function[target=torch.ops.aten.log1p.default](args = (%exp,), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %mul, %log1p), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub, %where), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, -2), kwargs = {})
triton_poi_fused_add_mul_softplus_sub_tanh_0 = async_compile.triton('triton_poi_fused_add_mul_softplus_sub_tanh_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_mul_softplus_sub_tanh_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_softplus_sub_tanh_0(in_ptr0, out_ptr0, out_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_ptr0 + (x0), xmask)
    tmp1 = libdevice.tanh(tmp0)
    tmp2 = 0.6931471805599453
    tmp3 = tmp0 - tmp2
    tmp4 = -2.0
    tmp5 = tmp0 * tmp4
    tmp6 = 20.0
    tmp7 = tmp5 > tmp6
    tmp8 = tl_math.exp(tmp5)
    tmp9 = libdevice.log1p(tmp8)
    tmp10 = tl.where(tmp7, tmp5, tmp9)
    tmp11 = tmp3 + tmp10
    tmp12 = tmp11 * tmp4
    tl.store(out_ptr0 + (x0), tmp1, xmask)
    tl.store(out_ptr1 + (x0), tmp12, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 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.float32)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [tanh, sub, mul, softplus, add, g], Original ATen: [aten.tanh, aten.sub, aten.mul, aten.softplus, aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_mul_softplus_sub_tanh_0.run(arg0_1, buf0, buf1, 256, grid=grid(256), stream=stream0)
        del arg0_1
    return (buf0, buf1, )


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)
    fn = lambda: call([arg0_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 math
import torch


class Tanh(torch.nn.Tanh):
    """
    Class that extends ``torch.nn.Tanh`` additionally computing the log diagonal
    blocks of the Jacobian.
    """

    def forward(self, inputs, grad: 'torch.Tensor'=None):
        """
        Parameters
        ----------
        inputs : ``torch.Tensor``, required.
            The input tensor.
        grad : ``torch.Tensor``, optional (default = None).
            The log diagonal blocks of the partial Jacobian of previous transformations.
        Returns
        -------
        The output tensor and the log diagonal blocks of the partial log-Jacobian of previous 
        transformations combined with this transformation.
        """
        g = -2 * (inputs - math.log(2) + torch.nn.functional.softplus(-2 *
            inputs))
        return torch.tanh(inputs), g.view(grad.shape
            ) + grad if grad is not None else g


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


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

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, math as tl_math
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_softplus_sub_tanh_0(in_ptr0, out_ptr0,
    out_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_ptr0 + x0, xmask)
    tmp1 = libdevice.tanh(tmp0)
    tmp2 = 0.6931471805599453
    tmp3 = tmp0 - tmp2
    tmp4 = -2.0
    tmp5 = tmp0 * tmp4
    tmp6 = 20.0
    tmp7 = tmp5 > tmp6
    tmp8 = tl_math.exp(tmp5)
    tmp9 = libdevice.log1p(tmp8)
    tmp10 = tl.where(tmp7, tmp5, tmp9)
    tmp11 = tmp3 + tmp10
    tmp12 = tmp11 * tmp4
    tl.store(out_ptr0 + x0, tmp1, xmask)
    tl.store(out_ptr1 + x0, tmp12, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 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.float32)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_softplus_sub_tanh_0[grid(256)](arg0_1,
            buf0, buf1, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
    return buf0, buf1


class TanhNew(torch.nn.Tanh):
    """
    Class that extends ``torch.nn.Tanh`` additionally computing the log diagonal
    blocks of the Jacobian.
    """

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0], output[1]
