# 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/4w/c4wnsgoc33twicaodq5y5dd6anhipb3zir4gnwilzg5rby6lukqe.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%index, %index_1, %arg3_1], 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_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: '*i64', 1: '*i64', 2: '*i64', 3: '*fp32', 4: '*fp32', 5: '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, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], '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_cat_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 48
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 12
    x1 = (xindex // 12)
    x2 = xindex
    tmp0 = x0
    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 + (x1), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.full([XBLOCK], 4, tl.int32)
    tmp7 = tmp5 + tmp6
    tmp8 = tmp5 < 0
    tmp9 = tl.where(tmp8, tmp7, tmp5)
    tl.device_assert(((0 <= tl.broadcast_to(tmp9, [XBLOCK])) & (tl.broadcast_to(tmp9, [XBLOCK]) < 4)) | ~(tmp4 & xmask), "index out of bounds: 0 <= tl.broadcast_to(tmp9, [XBLOCK]) < 4")
    tmp11 = tl.load(in_ptr1 + ((4*tmp9) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp12 = tmp11.to(tl.float32)
    tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
    tmp14 = tl.where(tmp4, tmp12, tmp13)
    tmp15 = tmp0 >= tmp3
    tmp16 = tl.full([1], 8, tl.int64)
    tmp17 = tmp0 < tmp16
    tmp18 = tmp15 & tmp17
    tmp19 = tl.load(in_ptr2 + (x1), tmp18 & xmask, eviction_policy='evict_last', other=0.0)
    tmp20 = tmp19 + tmp6
    tmp21 = tmp19 < 0
    tmp22 = tl.where(tmp21, tmp20, tmp19)
    tl.device_assert(((0 <= tl.broadcast_to(tmp22, [XBLOCK])) & (tl.broadcast_to(tmp22, [XBLOCK]) < 4)) | ~(tmp18 & xmask), "index out of bounds: 0 <= tl.broadcast_to(tmp22, [XBLOCK]) < 4")
    tmp24 = tl.load(in_ptr1 + ((4*tmp22) + ((-4) + x0)), tmp18 & xmask, eviction_policy='evict_last', other=0.0)
    tmp25 = tmp24.to(tl.float32)
    tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
    tmp27 = tl.where(tmp18, tmp25, tmp26)
    tmp28 = tmp0 >= tmp16
    tmp29 = tl.full([1], 12, tl.int64)
    tmp30 = tmp0 < tmp29
    tmp31 = tl.load(in_ptr3 + ((4*x1) + ((-8) + x0)), tmp28 & xmask, eviction_policy='evict_last', other=0.0)
    tmp32 = tl.where(tmp18, tmp27, tmp31)
    tmp33 = tl.where(tmp4, tmp14, tmp32)
    tl.store(out_ptr0 + (x2), tmp33, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, ), (1, ))
    assert_size_stride(arg1_1, (4, ), (1, ))
    assert_size_stride(arg2_1, (4, 4), (4, 1))
    assert_size_stride(arg3_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 12), (12, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(arg0_1, arg2_1, arg1_1, arg3_1, buf0, 48, grid=grid(48), stream=stream0)
        del arg0_1
        del arg1_1
        del arg2_1
        del arg3_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, ), (1, ), device='cuda:0', dtype=torch.int64)
    arg1_1 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.int64)
    arg2_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.int64)
    arg3_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_1, arg3_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 Join(torch.nn.Module):
    """Join layer
    """

    def forward(self, unary: 'torch.Tensor', binary: 'torch.Tensor', index1:
        'torch.Tensor', index2: 'torch.Tensor'):
        """Join the unary and binary tensors.
        :param unary: [u, |U|] the tensor with unary predicates pre-activations
        :param binary: [b, |B|] the tensor with binary predicates pre-activations
        :param index1: [b] a vector containing the indices of the first object
        of the pair referred by binary tensor
        :param index1: [b] a vector containing the indices of the second object
        of the pair referred by binary tensor
        :returns [b, 2|U| + |B|]
        """
        index1 = torch.squeeze(index1)
        index2 = torch.squeeze(index2)
        if index1.ndim == 0 and index2.ndim == 0:
            index1 = torch.unsqueeze(index1, 0)
            index2 = torch.unsqueeze(index2, 0)
        u1 = unary[index1]
        u2 = unary[index2]
        return torch.cat([u1, u2, binary], dim=1)


def get_inputs():
    return [torch.ones([4, 4], dtype=torch.int64), torch.rand([4, 4]),
        torch.ones([4], dtype=torch.int64), torch.ones([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
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_cat_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 48
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 12
    x1 = xindex // 12
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + x1, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp6 = tl.full([XBLOCK], 4, tl.int32)
    tmp7 = tmp5 + tmp6
    tmp8 = tmp5 < 0
    tmp9 = tl.where(tmp8, tmp7, tmp5)
    tl.device_assert((0 <= tl.broadcast_to(tmp9, [XBLOCK])) & (tl.
        broadcast_to(tmp9, [XBLOCK]) < 4) | ~(tmp4 & xmask),
        'index out of bounds: 0 <= tl.broadcast_to(tmp9, [XBLOCK]) < 4')
    tmp11 = tl.load(in_ptr1 + (4 * tmp9 + x0), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp12 = tmp11.to(tl.float32)
    tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
    tmp14 = tl.where(tmp4, tmp12, tmp13)
    tmp15 = tmp0 >= tmp3
    tmp16 = tl.full([1], 8, tl.int64)
    tmp17 = tmp0 < tmp16
    tmp18 = tmp15 & tmp17
    tmp19 = tl.load(in_ptr2 + x1, tmp18 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp20 = tmp19 + tmp6
    tmp21 = tmp19 < 0
    tmp22 = tl.where(tmp21, tmp20, tmp19)
    tl.device_assert((0 <= tl.broadcast_to(tmp22, [XBLOCK])) & (tl.
        broadcast_to(tmp22, [XBLOCK]) < 4) | ~(tmp18 & xmask),
        'index out of bounds: 0 <= tl.broadcast_to(tmp22, [XBLOCK]) < 4')
    tmp24 = tl.load(in_ptr1 + (4 * tmp22 + (-4 + x0)), tmp18 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp25 = tmp24.to(tl.float32)
    tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
    tmp27 = tl.where(tmp18, tmp25, tmp26)
    tmp28 = tmp0 >= tmp16
    tl.full([1], 12, tl.int64)
    tmp31 = tl.load(in_ptr3 + (4 * x1 + (-8 + x0)), tmp28 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp32 = tl.where(tmp18, tmp27, tmp31)
    tmp33 = tl.where(tmp4, tmp14, tmp32)
    tl.store(out_ptr0 + x2, tmp33, xmask)


def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4,), (1,))
    assert_size_stride(arg1_1, (4,), (1,))
    assert_size_stride(arg2_1, (4, 4), (4, 1))
    assert_size_stride(arg3_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 12), (12, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(48)](arg0_1, arg2_1, arg1_1, arg3_1,
            buf0, 48, XBLOCK=64, num_warps=1, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
        del arg3_1
    return buf0,


class JoinNew(torch.nn.Module):
    """Join layer
    """

    def forward(self, input_0, input_1, input_2, input_3):
        arg2_1 = input_0
        arg3_1 = input_1
        arg0_1 = input_2
        arg1_1 = input_3
        output = call([arg0_1, arg1_1, arg2_1, arg3_1])
        return output[0]
