# 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/sd/csdpxpt6qkkz7yt7aexzdtstgbnmu45cps6zaio7uh6d6ppkfs2l.py
# Topologically Sorted Source Nodes: [getitem_1, getitem_3, mul, value, sigmoid], Original ATen: [aten.index, aten.mul, aten.sum, aten.sigmoid]
# Source node to ATen node mapping:
#   getitem_1 => index
#   getitem_3 => index_1
#   mul => mul
#   sigmoid => sigmoid
#   value => sum_1
# Graph fragment:
#   %index : [num_users=1] = call_function[target=torch.ops.aten.index.Tensor](args = (%arg1_1, [%select]), kwargs = {})
#   %index_1 : [num_users=1] = call_function[target=torch.ops.aten.index.Tensor](args = (%arg1_1, [%select_1]), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%index, %index_1), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [1]), kwargs = {})
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%sum_1,), kwargs = {})
triton_poi_fused_index_mul_sigmoid_sum_0 = async_compile.triton('triton_poi_fused_index_mul_sigmoid_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=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '*i64', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_index_mul_sigmoid_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_index_mul_sigmoid_sum_0(in_ptr0, in_ptr1, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp7 = tl.load(in_ptr0 + (4 + x0), xmask)
    tmp1 = tl.full([XBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert(((0 <= tmp4) & (tmp4 < 4)) | ~(xmask), "index out of bounds: 0 <= tmp4 < 4")
    tmp6 = tl.load(in_ptr1 + (4*tmp4), xmask, eviction_policy='evict_last')
    tmp8 = tmp7 + tmp1
    tmp9 = tmp7 < 0
    tmp10 = tl.where(tmp9, tmp8, tmp7)
    tl.device_assert(((0 <= tmp10) & (tmp10 < 4)) | ~(xmask), "index out of bounds: 0 <= tmp10 < 4")
    tmp12 = tl.load(in_ptr1 + (4*tmp10), xmask, eviction_policy='evict_last')
    tmp13 = tmp6 * tmp12
    tmp14 = tl.load(in_ptr1 + (1 + (4*tmp4)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr1 + (1 + (4*tmp10)), xmask, eviction_policy='evict_last')
    tmp16 = tmp14 * tmp15
    tmp17 = tmp13 + tmp16
    tmp18 = tl.load(in_ptr1 + (2 + (4*tmp4)), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr1 + (2 + (4*tmp10)), xmask, eviction_policy='evict_last')
    tmp20 = tmp18 * tmp19
    tmp21 = tmp17 + tmp20
    tmp22 = tl.load(in_ptr1 + (3 + (4*tmp4)), xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr1 + (3 + (4*tmp10)), xmask, eviction_policy='evict_last')
    tmp24 = tmp22 * tmp23
    tmp25 = tmp21 + tmp24
    tmp26 = tmp25.to(tl.float32)
    tmp27 = tl.sigmoid(tmp26)
    tl.store(out_ptr1 + (x0), tmp27, 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, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((4, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [getitem_1, getitem_3, mul, value, sigmoid], Original ATen: [aten.index, aten.mul, aten.sum, aten.sigmoid]
        stream0 = get_raw_stream(0)
        triton_poi_fused_index_mul_sigmoid_sum_0.run(arg0_1, arg1_1, buf1, 4, grid=grid(4), stream=stream0)
        del arg0_1
        del arg1_1
    return (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, 1), device='cuda:0', dtype=torch.int64)
    arg1_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.int64)
    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.utils.data


class InnerProductDecoder(torch.nn.Module):
    """The inner product decoder from the `"Variational Graph Auto-Encoders"
    <https://arxiv.org/abs/1611.07308>`_ paper

    .. math::
        \\sigma(\\mathbf{Z}\\mathbf{Z}^{\\top})

    where :math:`\\mathbf{Z} \\in \\mathbb{R}^{N \\times d}` denotes the latent
    space produced by the encoder."""

    def forward(self, z, edge_index, sigmoid=True):
        """Decodes the latent variables :obj:`z` into edge probabilties for
        the given node-pairs :obj:`edge_index`.

        Args:
            z (Tensor): The latent space :math:`\\mathbf{Z}`.
            sigmoid (bool, optional): If set to :obj:`False`, does not apply
                the logistic sigmoid function to the output.
                (default: :obj:`True`)
        """
        value = (z[edge_index[0]] * z[edge_index[1]]).sum(dim=1)
        return torch.sigmoid(value) if sigmoid else value

    def forward_all(self, z, sigmoid=True):
        """Decodes the latent variables :obj:`z` into a probabilistic dense
        adjacency matrix.

        Args:
            z (Tensor): The latent space :math:`\\mathbf{Z}`.
            sigmoid (bool, optional): If set to :obj:`False`, does not apply
                the logistic sigmoid function to the output.
                (default: :obj:`True`)
        """
        adj = torch.matmul(z, z.t())
        return torch.sigmoid(adj) if sigmoid else adj


def get_inputs():
    return [torch.ones([4, 4], dtype=torch.int64), torch.ones([4, 4], dtype
        =torch.int64)]


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
import torch.utils.data
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_index_mul_sigmoid_sum_0(in_ptr0, in_ptr1, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp7 = tl.load(in_ptr0 + (4 + x0), xmask)
    tmp1 = tl.full([XBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert((0 <= tmp4) & (tmp4 < 4) | ~xmask,
        'index out of bounds: 0 <= tmp4 < 4')
    tmp6 = tl.load(in_ptr1 + 4 * tmp4, xmask, eviction_policy='evict_last')
    tmp8 = tmp7 + tmp1
    tmp9 = tmp7 < 0
    tmp10 = tl.where(tmp9, tmp8, tmp7)
    tl.device_assert((0 <= tmp10) & (tmp10 < 4) | ~xmask,
        'index out of bounds: 0 <= tmp10 < 4')
    tmp12 = tl.load(in_ptr1 + 4 * tmp10, xmask, eviction_policy='evict_last')
    tmp13 = tmp6 * tmp12
    tmp14 = tl.load(in_ptr1 + (1 + 4 * tmp4), xmask, eviction_policy=
        'evict_last')
    tmp15 = tl.load(in_ptr1 + (1 + 4 * tmp10), xmask, eviction_policy=
        'evict_last')
    tmp16 = tmp14 * tmp15
    tmp17 = tmp13 + tmp16
    tmp18 = tl.load(in_ptr1 + (2 + 4 * tmp4), xmask, eviction_policy=
        'evict_last')
    tmp19 = tl.load(in_ptr1 + (2 + 4 * tmp10), xmask, eviction_policy=
        'evict_last')
    tmp20 = tmp18 * tmp19
    tmp21 = tmp17 + tmp20
    tmp22 = tl.load(in_ptr1 + (3 + 4 * tmp4), xmask, eviction_policy=
        'evict_last')
    tmp23 = tl.load(in_ptr1 + (3 + 4 * tmp10), xmask, eviction_policy=
        'evict_last')
    tmp24 = tmp22 * tmp23
    tmp25 = tmp21 + tmp24
    tmp26 = tmp25.to(tl.float32)
    tmp27 = tl.sigmoid(tmp26)
    tl.store(out_ptr1 + x0, tmp27, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((4,), (1,), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_index_mul_sigmoid_sum_0[grid(4)](arg0_1, arg1_1,
            buf1, 4, XBLOCK=4, num_warps=1, num_stages=1)
        del arg0_1
        del arg1_1
    return buf1,


class InnerProductDecoderNew(torch.nn.Module):
    """The inner product decoder from the `"Variational Graph Auto-Encoders"
    <https://arxiv.org/abs/1611.07308>`_ paper

    .. math::
        \\sigma(\\mathbf{Z}\\mathbf{Z}^{\\top})

    where :math:`\\mathbf{Z} \\in \\mathbb{R}^{N \\times d}` denotes the latent
    space produced by the encoder."""

    def forward_all(self, z, sigmoid=True):
        """Decodes the latent variables :obj:`z` into a probabilistic dense
        adjacency matrix.

        Args:
            z (Tensor): The latent space :math:`\\mathbf{Z}`.
            sigmoid (bool, optional): If set to :obj:`False`, does not apply
                the logistic sigmoid function to the output.
                (default: :obj:`True`)
        """
        adj = torch.matmul(z, z.t())
        return torch.sigmoid(adj) if sigmoid else adj

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