# 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_7/inductor_cache/4m/c4mb3evaolfrjioe7mq5eta334htzcfywer2x4df2y4knwf5l2pc.py
# Topologically Sorted Source Nodes: [relation_feature, truediv, add_4], Original ATen: [aten.stack, aten.div, aten.add]
# Source node to ATen node mapping:
#   add_4 => add_4
#   relation_feature => cat
#   truediv => div
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%add, %add_1, %add_2, %add_3],), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%view_2, 2), kwargs = {})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div, %primals_2), kwargs = {})
triton_poi_fused_add_div_stack_0 = async_compile.triton('triton_poi_fused_add_div_stack_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_div_stack_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 9, '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_div_stack_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
    x1 = (xindex // 16)
    x0 = xindex % 16
    x2 = xindex
    tmp41 = tl.load(in_ptr1 + (x2), xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (16*x1)), tmp4 & xmask, other=0.0)
    tmp6 = tl.load(in_ptr0 + (64 + x0 + (16*x1)), tmp4 & xmask, other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tmp11 = tl.full([1], 8, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tmp10 & tmp12
    tmp14 = tl.load(in_ptr0 + (x0 + (16*((-4) + x1))), tmp13 & xmask, other=0.0)
    tmp15 = tl.load(in_ptr0 + (128 + x0 + (16*((-4) + x1))), tmp13 & xmask, other=0.0)
    tmp16 = tmp14 + tmp15
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp13, tmp16, tmp17)
    tmp19 = tmp0 >= tmp11
    tmp20 = tl.full([1], 12, tl.int64)
    tmp21 = tmp0 < tmp20
    tmp22 = tmp19 & tmp21
    tmp23 = tl.load(in_ptr0 + (64 + x0 + (16*((-8) + x1))), tmp22 & xmask, other=0.0)
    tmp24 = tl.load(in_ptr0 + (192 + x0 + (16*((-8) + x1))), tmp22 & xmask, other=0.0)
    tmp25 = tmp23 + tmp24
    tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
    tmp27 = tl.where(tmp22, tmp25, tmp26)
    tmp28 = tmp0 >= tmp20
    tmp29 = tl.full([1], 16, tl.int64)
    tmp30 = tmp0 < tmp29
    tmp31 = tl.load(in_ptr0 + (128 + x0 + (16*((-12) + x1))), tmp28 & xmask, other=0.0)
    tmp32 = tl.load(in_ptr0 + (192 + x0 + (16*((-12) + x1))), tmp28 & xmask, other=0.0)
    tmp33 = tmp31 + tmp32
    tmp34 = tl.full(tmp33.shape, 0.0, tmp33.dtype)
    tmp35 = tl.where(tmp28, tmp33, tmp34)
    tmp36 = tl.where(tmp22, tmp27, tmp35)
    tmp37 = tl.where(tmp13, tmp18, tmp36)
    tmp38 = tl.where(tmp4, tmp9, tmp37)
    tmp39 = 0.5
    tmp40 = tmp38 * tmp39
    tmp42 = tmp40 + tmp41
    tl.store(in_out_ptr0 + (x2), tmp42, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [att_feats], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        buf2 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [relation_feature, truediv, add_4], Original ATen: [aten.stack, aten.div, aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_div_stack_0.run(buf2, buf0, primals_2, 256, grid=grid(256), stream=stream0)
        del buf0
    return (buf2, reinterpret_tensor(primals_2, (64, 4), (4, 1), 0), )


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, 4), (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)
    fn = lambda: call([primals_1, primals_2])
    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


class TemporalRelation(nn.Module):

    def __init__(self, feat_dim, time_window=1):
        super(TemporalRelation, self).__init__()
        self.time_window = time_window
        self.feat_dim = feat_dim
        self.WT = nn.Linear(self.feat_dim, self.feat_dim, bias=False)

    def forward(self, feats):
        relation_feature = []
        att_feats = self.WT(feats)
        for t in range(0, att_feats.size()[0], 1):
            if t < self.time_window:
                prev = att_feats[0, :, :]
            else:
                prev = att_feats[t - 1, :, :]
            if t == att_feats.size()[0] - 1:
                next = att_feats[t, :, :]
            else:
                next = att_feats[t + 1, :, :]
            relation_feature.append(prev + next)
        relation_feature = torch.stack(relation_feature, dim=0)
        return relation_feature / 2 + feats


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


def get_init_inputs():
    return [[], {'feat_dim': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
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.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_add_div_stack_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
    x1 = xindex // 16
    x0 = xindex % 16
    x2 = xindex
    tmp41 = tl.load(in_ptr1 + x2, xmask)
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1), tmp4 & xmask, other=0.0)
    tmp6 = tl.load(in_ptr0 + (64 + x0 + 16 * x1), tmp4 & xmask, other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tmp11 = tl.full([1], 8, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tmp10 & tmp12
    tmp14 = tl.load(in_ptr0 + (x0 + 16 * (-4 + x1)), tmp13 & xmask, other=0.0)
    tmp15 = tl.load(in_ptr0 + (128 + x0 + 16 * (-4 + x1)), tmp13 & xmask,
        other=0.0)
    tmp16 = tmp14 + tmp15
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp13, tmp16, tmp17)
    tmp19 = tmp0 >= tmp11
    tmp20 = tl.full([1], 12, tl.int64)
    tmp21 = tmp0 < tmp20
    tmp22 = tmp19 & tmp21
    tmp23 = tl.load(in_ptr0 + (64 + x0 + 16 * (-8 + x1)), tmp22 & xmask,
        other=0.0)
    tmp24 = tl.load(in_ptr0 + (192 + x0 + 16 * (-8 + x1)), tmp22 & xmask,
        other=0.0)
    tmp25 = tmp23 + tmp24
    tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
    tmp27 = tl.where(tmp22, tmp25, tmp26)
    tmp28 = tmp0 >= tmp20
    tl.full([1], 16, tl.int64)
    tmp31 = tl.load(in_ptr0 + (128 + x0 + 16 * (-12 + x1)), tmp28 & xmask,
        other=0.0)
    tmp32 = tl.load(in_ptr0 + (192 + x0 + 16 * (-12 + x1)), tmp28 & xmask,
        other=0.0)
    tmp33 = tmp31 + tmp32
    tmp34 = tl.full(tmp33.shape, 0.0, tmp33.dtype)
    tmp35 = tl.where(tmp28, tmp33, tmp34)
    tmp36 = tl.where(tmp22, tmp27, tmp35)
    tmp37 = tl.where(tmp13, tmp18, tmp36)
    tmp38 = tl.where(tmp4, tmp9, tmp37)
    tmp39 = 0.5
    tmp40 = tmp38 * tmp39
    tmp42 = tmp40 + tmp41
    tl.store(in_out_ptr0 + x2, tmp42, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        buf2 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf1
        get_raw_stream(0)
        triton_poi_fused_add_div_stack_0[grid(256)](buf2, buf0, primals_2, 
            256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf0
    return buf2, reinterpret_tensor(primals_2, (64, 4), (4, 1), 0)


class TemporalRelationNew(nn.Module):

    def __init__(self, feat_dim, time_window=1):
        super(TemporalRelationNew, self).__init__()
        self.time_window = time_window
        self.feat_dim = feat_dim
        self.WT = nn.Linear(self.feat_dim, self.feat_dim, bias=False)

    def forward(self, input_0):
        primals_1 = self.WT.weight
        primals_2 = input_0
        output = call([primals_1, primals_2])
        return output[0]
