# 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_1/inductor_cache/vs/cvs4eu6m5jgj773xwkaazwcptgesom3eznf44ursaoowub57ko6g.py
# Topologically Sorted Source Nodes: [output_feat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   output_feat => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%view_1, %view_3, %view_5, %view_7], 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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '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, 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': 4, '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_cat_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4) % 16
    x0 = xindex % 4
    x2 = (xindex // 64)
    x3 = xindex
    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 + (4*x1) + (16*x2)), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + (4*((-4) + x1)) + (16*x2)), tmp9 & xmask, other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 12, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr2 + (x0 + (4*((-8) + x1)) + (16*x2)), tmp14 & xmask, other=0.0)
    tmp16 = tmp0 >= tmp12
    tmp17 = tl.full([1], 16, tl.int64)
    tmp18 = tmp0 < tmp17
    tmp19 = tl.load(in_ptr3 + (x0 + (4*((-12) + x1)) + (16*x2)), tmp16 & xmask, other=0.0)
    tmp20 = tl.where(tmp14, tmp15, tmp19)
    tmp21 = tl.where(tmp9, tmp10, tmp20)
    tmp22 = tl.where(tmp4, tmp5, tmp21)
    tl.store(out_ptr0 + (x3), tmp22, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12 = args
    args.clear()
    assert_size_stride(primals_1, (1, 4), (4, 1))
    assert_size_stride(primals_2, (1, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (1, 4), (4, 1))
    assert_size_stride(primals_5, (1, ), (1, ))
    assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_7, (1, 4), (4, 1))
    assert_size_stride(primals_8, (1, ), (1, ))
    assert_size_stride(primals_9, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_10, (1, 4), (4, 1))
    assert_size_stride(primals_11, (1, ), (1, ))
    assert_size_stride(primals_12, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [resized_obj], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf1)
        del primals_1
        del primals_2
        buf3 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [resized_embed], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_6, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf3)
        del primals_4
        del primals_5
        buf5 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [resized_box], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_8, reinterpret_tensor(primals_9, (64, 4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf5)
        del primals_7
        del primals_8
        buf7 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [resized_overlap], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_11, reinterpret_tensor(primals_12, (64, 4), (4, 1), 0), reinterpret_tensor(primals_10, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf7)
        del primals_10
        del primals_11
        buf8 = empty_strided_cuda((4, 16, 4, 1), (64, 4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output_feat], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(buf1, buf3, buf5, buf7, buf8, 256, grid=grid(256), stream=stream0)
        del buf1
        del buf3
        del buf5
        del buf7
    return (buf8, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_6, (64, 4), (4, 1), 0), reinterpret_tensor(primals_9, (64, 4), (4, 1), 0), reinterpret_tensor(primals_12, (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((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12])
    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
import torch.utils.data


class RLFeatPreprocessNet(nn.Module):
    """
    Preprocess Features
    1. visual feature
    2. label prediction embed feature
    3. box embed
    4. overlap embed
    """

    def __init__(self, feat_size, embed_size, bbox_size, overlap_size,
        output_size):
        super(RLFeatPreprocessNet, self).__init__()
        self.feature_size = feat_size
        self.embed_size = embed_size
        self.box_info_size = bbox_size
        self.overlap_info_size = overlap_size
        self.output_size = output_size
        self.resize_feat = nn.Linear(self.feature_size, int(output_size / 4))
        self.resize_embed = nn.Linear(self.embed_size, int(output_size / 4))
        self.resize_box = nn.Linear(self.box_info_size, int(output_size / 4))
        self.resize_overlap = nn.Linear(self.overlap_info_size, int(
            output_size / 4))
        self.resize_feat.weight.data.normal_(0, 0.001)
        self.resize_embed.weight.data.normal_(0, 0.01)
        self.resize_box.weight.data.normal_(0, 1)
        self.resize_overlap.weight.data.normal_(0, 1)
        self.resize_feat.bias.data.zero_()
        self.resize_embed.bias.data.zero_()
        self.resize_box.bias.data.zero_()
        self.resize_overlap.bias.data.zero_()

    def forward(self, obj_feat, obj_embed, box_info, overlap_info):
        resized_obj = self.resize_feat(obj_feat)
        resized_embed = self.resize_embed(obj_embed)
        resized_box = self.resize_box(box_info)
        resized_overlap = self.resize_overlap(overlap_info)
        output_feat = torch.cat((resized_obj, resized_embed, resized_box,
            resized_overlap), 1)
        return output_feat


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


def get_init_inputs():
    return [[], {'feat_size': 4, 'embed_size': 4, 'bbox_size': 4,
        'overlap_size': 4, 'output_size': 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
import torch.utils.data
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_cat_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 16
    x0 = xindex % 4
    x2 = xindex // 64
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 4 * x1 + 16 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + 4 * (-4 + x1) + 16 * x2), tmp9 & xmask,
        other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 12, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr2 + (x0 + 4 * (-8 + x1) + 16 * x2), tmp14 & xmask,
        other=0.0)
    tmp16 = tmp0 >= tmp12
    tl.full([1], 16, tl.int64)
    tmp19 = tl.load(in_ptr3 + (x0 + 4 * (-12 + x1) + 16 * x2), tmp16 &
        xmask, other=0.0)
    tmp20 = tl.where(tmp14, tmp15, tmp19)
    tmp21 = tl.where(tmp9, tmp10, tmp20)
    tmp22 = tl.where(tmp4, tmp5, tmp21)
    tl.store(out_ptr0 + x3, tmp22, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12
        ) = args
    args.clear()
    assert_size_stride(primals_1, (1, 4), (4, 1))
    assert_size_stride(primals_2, (1,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (1, 4), (4, 1))
    assert_size_stride(primals_5, (1,), (1,))
    assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_7, (1, 4), (4, 1))
    assert_size_stride(primals_8, (1,), (1,))
    assert_size_stride(primals_9, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_10, (1, 4), (4, 1))
    assert_size_stride(primals_11, (1,), (1,))
    assert_size_stride(primals_12, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 1), (1, 4), 0
            ), alpha=1, beta=1, out=buf1)
        del primals_1
        del primals_2
        buf3 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_6, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 1), (1, 4), 0
            ), alpha=1, beta=1, out=buf3)
        del primals_4
        del primals_5
        buf5 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_8, reinterpret_tensor(primals_9, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 1), (1, 4), 0
            ), alpha=1, beta=1, out=buf5)
        del primals_7
        del primals_8
        buf7 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_11, reinterpret_tensor(primals_12, (64,
            4), (4, 1), 0), reinterpret_tensor(primals_10, (4, 1), (1, 4), 
            0), alpha=1, beta=1, out=buf7)
        del primals_10
        del primals_11
        buf8 = empty_strided_cuda((4, 16, 4, 1), (64, 4, 1, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(256)](buf1, buf3, buf5, buf7, buf8, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del buf1
        del buf3
        del buf5
        del buf7
    return buf8, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), reinterpret_tensor(primals_6, (64, 4), (4, 1), 0
        ), reinterpret_tensor(primals_9, (64, 4), (4, 1), 0
        ), reinterpret_tensor(primals_12, (64, 4), (4, 1), 0)


class RLFeatPreprocessNetNew(nn.Module):
    """
    Preprocess Features
    1. visual feature
    2. label prediction embed feature
    3. box embed
    4. overlap embed
    """

    def __init__(self, feat_size, embed_size, bbox_size, overlap_size,
        output_size):
        super(RLFeatPreprocessNetNew, self).__init__()
        self.feature_size = feat_size
        self.embed_size = embed_size
        self.box_info_size = bbox_size
        self.overlap_info_size = overlap_size
        self.output_size = output_size
        self.resize_feat = nn.Linear(self.feature_size, int(output_size / 4))
        self.resize_embed = nn.Linear(self.embed_size, int(output_size / 4))
        self.resize_box = nn.Linear(self.box_info_size, int(output_size / 4))
        self.resize_overlap = nn.Linear(self.overlap_info_size, int(
            output_size / 4))
        self.resize_feat.weight.data.normal_(0, 0.001)
        self.resize_embed.weight.data.normal_(0, 0.01)
        self.resize_box.weight.data.normal_(0, 1)
        self.resize_overlap.weight.data.normal_(0, 1)
        self.resize_feat.bias.data.zero_()
        self.resize_embed.bias.data.zero_()
        self.resize_box.bias.data.zero_()
        self.resize_overlap.bias.data.zero_()

    def forward(self, input_0, input_1, input_2, input_3):
        primals_1 = self.resize_feat.weight
        primals_2 = self.resize_feat.bias
        primals_4 = self.resize_embed.weight
        primals_5 = self.resize_embed.bias
        primals_7 = self.resize_box.weight
        primals_8 = self.resize_box.bias
        primals_10 = self.resize_overlap.weight
        primals_11 = self.resize_overlap.bias
        primals_3 = input_0
        primals_6 = input_1
        primals_9 = input_2
        primals_12 = input_3
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12])
        return output[0]
