# 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_1/inductor_cache/nk/cnkibmnzi3rlt4bmqnorm7bwmtvv5pf6bj4xuqiaxc54v74paago.py
# Topologically Sorted Source Nodes: [x, add, mul, sigmoid, sub_1, mul_1, sigmoid_1, x_1, x_2], Original ATen: [aten.sub, aten.add, aten.mul, aten.sigmoid, aten.sum]
# Source node to ATen node mapping:
#   add => add
#   mul => mul
#   mul_1 => mul_1
#   sigmoid => sigmoid
#   sigmoid_1 => sigmoid_1
#   sub_1 => sub_1
#   x => sub
#   x_1 => sub_2
#   x_2 => sum_1
# Graph fragment:
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%unsqueeze, %unsqueeze_1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub, 0.0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, 4), kwargs = {})
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%mul,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub, 0.0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, 4), kwargs = {})
#   %sigmoid_1 : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%mul_1,), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sigmoid, %sigmoid_1), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%sub_2, [1]), kwargs = {})
triton_poi_fused_add_mul_sigmoid_sub_sum_0 = async_compile.triton('triton_poi_fused_add_mul_sigmoid_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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_sigmoid_sub_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_sigmoid_sub_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 4) % 4
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (64 + x3), xmask)
    tmp22 = tl.load(in_ptr0 + (128 + x3), xmask)
    tmp32 = tl.load(in_ptr0 + (192 + x3), xmask)
    tmp2 = tmp0 - tmp1
    tmp3 = 0.0
    tmp4 = tmp2 + tmp3
    tmp5 = 4.0
    tmp6 = tmp4 * tmp5
    tmp7 = tl.sigmoid(tmp6)
    tmp8 = tmp2 - tmp3
    tmp9 = tmp8 * tmp5
    tmp10 = tl.sigmoid(tmp9)
    tmp11 = tmp7 - tmp10
    tmp13 = tmp12 - tmp1
    tmp14 = tmp13 + tmp3
    tmp15 = tmp14 * tmp5
    tmp16 = tl.sigmoid(tmp15)
    tmp17 = tmp13 - tmp3
    tmp18 = tmp17 * tmp5
    tmp19 = tl.sigmoid(tmp18)
    tmp20 = tmp16 - tmp19
    tmp21 = tmp11 + tmp20
    tmp23 = tmp22 - tmp1
    tmp24 = tmp23 + tmp3
    tmp25 = tmp24 * tmp5
    tmp26 = tl.sigmoid(tmp25)
    tmp27 = tmp23 - tmp3
    tmp28 = tmp27 * tmp5
    tmp29 = tl.sigmoid(tmp28)
    tmp30 = tmp26 - tmp29
    tmp31 = tmp21 + tmp30
    tmp33 = tmp32 - tmp1
    tmp34 = tmp33 + tmp3
    tmp35 = tmp34 * tmp5
    tmp36 = tl.sigmoid(tmp35)
    tmp37 = tmp33 - tmp3
    tmp38 = tmp37 * tmp5
    tmp39 = tl.sigmoid(tmp38)
    tmp40 = tmp36 - tmp39
    tmp41 = tmp31 + tmp40
    tl.store(out_ptr0 + (x3), tmp41, 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, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x, add, mul, sigmoid, sub_1, mul_1, sigmoid_1, x_1, x_2], Original ATen: [aten.sub, aten.add, aten.mul, aten.sigmoid, aten.sum]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_mul_sigmoid_sub_sum_0.run(arg0_1, arg1_1, buf0, 64, grid=grid(64), 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, 4), (64, 16, 4, 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


class SoftHistogram(torch.nn.Module):
    """
    Motivated by https://discuss.pytorch.org/t/differentiable-torch-histc/25865/3
    """

    def __init__(self, bins, min_bin_edge, max_bin_edge, sigma):
        super(SoftHistogram, self).__init__()
        self.sigma = sigma
        self.delta = float(max_bin_edge - min_bin_edge) / float(bins)
        self.centers = float(min_bin_edge) + self.delta * (torch.arange(
            bins).float() + 0.5)
        self.centers = torch.nn.Parameter(self.centers, requires_grad=False)

    def forward(self, x):
        x = torch.unsqueeze(x, 0) - torch.unsqueeze(self.centers, 1)
        x = torch.sigmoid(self.sigma * (x + self.delta / 2)) - torch.sigmoid(
            self.sigma * (x - self.delta / 2))
        x = x.sum(dim=1)
        return x


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


def get_init_inputs():
    return [[], {'bins': 4, 'min_bin_edge': 4, 'max_bin_edge': 4, 'sigma': 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
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_sigmoid_sub_sum_0(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (64 + x3), xmask)
    tmp22 = tl.load(in_ptr0 + (128 + x3), xmask)
    tmp32 = tl.load(in_ptr0 + (192 + x3), xmask)
    tmp2 = tmp0 - tmp1
    tmp3 = 0.0
    tmp4 = tmp2 + tmp3
    tmp5 = 4.0
    tmp6 = tmp4 * tmp5
    tmp7 = tl.sigmoid(tmp6)
    tmp8 = tmp2 - tmp3
    tmp9 = tmp8 * tmp5
    tmp10 = tl.sigmoid(tmp9)
    tmp11 = tmp7 - tmp10
    tmp13 = tmp12 - tmp1
    tmp14 = tmp13 + tmp3
    tmp15 = tmp14 * tmp5
    tmp16 = tl.sigmoid(tmp15)
    tmp17 = tmp13 - tmp3
    tmp18 = tmp17 * tmp5
    tmp19 = tl.sigmoid(tmp18)
    tmp20 = tmp16 - tmp19
    tmp21 = tmp11 + tmp20
    tmp23 = tmp22 - tmp1
    tmp24 = tmp23 + tmp3
    tmp25 = tmp24 * tmp5
    tmp26 = tl.sigmoid(tmp25)
    tmp27 = tmp23 - tmp3
    tmp28 = tmp27 * tmp5
    tmp29 = tl.sigmoid(tmp28)
    tmp30 = tmp26 - tmp29
    tmp31 = tmp21 + tmp30
    tmp33 = tmp32 - tmp1
    tmp34 = tmp33 + tmp3
    tmp35 = tmp34 * tmp5
    tmp36 = tl.sigmoid(tmp35)
    tmp37 = tmp33 - tmp3
    tmp38 = tmp37 * tmp5
    tmp39 = tl.sigmoid(tmp38)
    tmp40 = tmp36 - tmp39
    tmp41 = tmp31 + tmp40
    tl.store(out_ptr0 + x3, tmp41, 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,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_sigmoid_sub_sum_0[grid(64)](arg0_1, arg1_1,
            buf0, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del arg0_1
        del arg1_1
    return buf0,


class SoftHistogramNew(torch.nn.Module):
    """
    Motivated by https://discuss.pytorch.org/t/differentiable-torch-histc/25865/3
    """

    def __init__(self, bins, min_bin_edge, max_bin_edge, sigma):
        super(SoftHistogramNew, self).__init__()
        self.sigma = sigma
        self.delta = float(max_bin_edge - min_bin_edge) / float(bins)
        self.centers = float(min_bin_edge) + self.delta * (torch.arange(
            bins).float() + 0.5)
        self.centers = torch.nn.Parameter(self.centers, requires_grad=False)

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