# 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/da/cdao3qbipnoxxf24fybsrcg3sjcyvg5eih7df5sdrbb7pghq2bgw.py
# Topologically Sorted Source Nodes: [stack], Original ATen: [aten.stack]
# Source node to ATen node mapping:
#   stack => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%sub_3, %sub_2, %add_5, %add_4], 2), kwargs = {})
triton_poi_fused_stack_0 = async_compile.triton('triton_poi_fused_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=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_stack_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 16, '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_stack_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = (xindex // 16)
    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 + (1 + (4*x0) + (16*x1)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + (3 + (4*x0) + (16*x1)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp7 = tl.load(in_ptr1 + (1 + (4*x0) + (16*x1)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp8 = tmp6 - tmp7
    tmp9 = tmp5 * tmp8
    tmp10 = tmp7 + tmp6
    tmp11 = 0.5
    tmp12 = tmp10 * tmp11
    tmp13 = tmp9 + tmp12
    tmp14 = tl.load(in_ptr0 + (3 + (4*x0) + (16*x1)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp15 = tl_math.exp(tmp14)
    tmp16 = tmp15 * tmp8
    tmp17 = tmp16 * tmp11
    tmp18 = tmp13 - tmp17
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp4, tmp18, tmp19)
    tmp21 = tmp0 >= tmp3
    tmp22 = tl.full([1], 8, tl.int64)
    tmp23 = tmp0 < tmp22
    tmp24 = tmp21 & tmp23
    tmp25 = tl.load(in_ptr0 + ((4*((-4) + x0)) + (16*x1)), tmp24 & xmask, eviction_policy='evict_last', other=0.0)
    tmp26 = tl.load(in_ptr1 + (2 + (4*((-4) + x0)) + (16*x1)), tmp24 & xmask, eviction_policy='evict_last', other=0.0)
    tmp27 = tl.load(in_ptr1 + ((4*((-4) + x0)) + (16*x1)), tmp24 & xmask, eviction_policy='evict_last', other=0.0)
    tmp28 = tmp26 - tmp27
    tmp29 = tmp25 * tmp28
    tmp30 = tmp27 + tmp26
    tmp31 = tmp30 * tmp11
    tmp32 = tmp29 + tmp31
    tmp33 = tl.load(in_ptr0 + (2 + (4*((-4) + x0)) + (16*x1)), tmp24 & xmask, eviction_policy='evict_last', other=0.0)
    tmp34 = tl_math.exp(tmp33)
    tmp35 = tmp34 * tmp28
    tmp36 = tmp35 * tmp11
    tmp37 = tmp32 - tmp36
    tmp38 = tl.full(tmp37.shape, 0.0, tmp37.dtype)
    tmp39 = tl.where(tmp24, tmp37, tmp38)
    tmp40 = tmp0 >= tmp22
    tmp41 = tl.full([1], 12, tl.int64)
    tmp42 = tmp0 < tmp41
    tmp43 = tmp40 & tmp42
    tmp44 = tl.load(in_ptr0 + (1 + (4*((-8) + x0)) + (16*x1)), tmp43 & xmask, eviction_policy='evict_last', other=0.0)
    tmp45 = tl.load(in_ptr1 + (3 + (4*((-8) + x0)) + (16*x1)), tmp43 & xmask, eviction_policy='evict_last', other=0.0)
    tmp46 = tl.load(in_ptr1 + (1 + (4*((-8) + x0)) + (16*x1)), tmp43 & xmask, eviction_policy='evict_last', other=0.0)
    tmp47 = tmp45 - tmp46
    tmp48 = tmp44 * tmp47
    tmp49 = tmp46 + tmp45
    tmp50 = tmp49 * tmp11
    tmp51 = tmp48 + tmp50
    tmp52 = tl.load(in_ptr0 + (3 + (4*((-8) + x0)) + (16*x1)), tmp43 & xmask, eviction_policy='evict_last', other=0.0)
    tmp53 = tl_math.exp(tmp52)
    tmp54 = tmp53 * tmp47
    tmp55 = tmp54 * tmp11
    tmp56 = tmp51 + tmp55
    tmp57 = tl.full(tmp56.shape, 0.0, tmp56.dtype)
    tmp58 = tl.where(tmp43, tmp56, tmp57)
    tmp59 = tmp0 >= tmp41
    tmp60 = tl.full([1], 16, tl.int64)
    tmp61 = tmp0 < tmp60
    tmp62 = tl.load(in_ptr0 + ((4*((-12) + x0)) + (16*x1)), tmp59 & xmask, eviction_policy='evict_last', other=0.0)
    tmp63 = tl.load(in_ptr1 + (2 + (4*((-12) + x0)) + (16*x1)), tmp59 & xmask, eviction_policy='evict_last', other=0.0)
    tmp64 = tl.load(in_ptr1 + ((4*((-12) + x0)) + (16*x1)), tmp59 & xmask, eviction_policy='evict_last', other=0.0)
    tmp65 = tmp63 - tmp64
    tmp66 = tmp62 * tmp65
    tmp67 = tmp64 + tmp63
    tmp68 = tmp67 * tmp11
    tmp69 = tmp66 + tmp68
    tmp70 = tl.load(in_ptr0 + (2 + (4*((-12) + x0)) + (16*x1)), tmp59 & xmask, eviction_policy='evict_last', other=0.0)
    tmp71 = tl_math.exp(tmp70)
    tmp72 = tmp71 * tmp65
    tmp73 = tmp72 * tmp11
    tmp74 = tmp69 + tmp73
    tmp75 = tl.full(tmp74.shape, 0.0, tmp74.dtype)
    tmp76 = tl.where(tmp59, tmp74, tmp75)
    tmp77 = tl.where(tmp43, tmp58, tmp76)
    tmp78 = tl.where(tmp24, tmp39, tmp77)
    tmp79 = tl.where(tmp4, tmp20, tmp78)
    tl.store(out_ptr0 + (x2), tmp79, 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, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [stack], Original ATen: [aten.stack]
        stream0 = get_raw_stream(0)
        triton_poi_fused_stack_0.run(arg1_1, arg0_1, buf0, 256, grid=grid(256), stream=stream0)
        del arg0_1
        del arg1_1
    return (reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 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
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 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
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed


class BBoxTransform(nn.Module):

    def forward(self, anchors, regression):
        """
        decode_box_outputs adapted from https://github.com/google/automl/blob/master/efficientdet/anchors.py

        Args:
            anchors: [batchsize, boxes, (y1, x1, y2, x2)]
            regression: [batchsize, boxes, (dy, dx, dh, dw)]

        Returns:

        """
        y_centers_a = (anchors[..., 0] + anchors[..., 2]) / 2
        x_centers_a = (anchors[..., 1] + anchors[..., 3]) / 2
        ha = anchors[..., 2] - anchors[..., 0]
        wa = anchors[..., 3] - anchors[..., 1]
        w = regression[..., 3].exp() * wa
        h = regression[..., 2].exp() * ha
        y_centers = regression[..., 0] * ha + y_centers_a
        x_centers = regression[..., 1] * wa + x_centers_a
        ymin = y_centers - h / 2.0
        xmin = x_centers - w / 2.0
        ymax = y_centers + h / 2.0
        xmax = x_centers + w / 2.0
        return torch.stack([xmin, ymin, xmax, ymax], dim=2)


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


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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
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_stack_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    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 + (1 + 4 * x0 + 16 * x1), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + (3 + 4 * x0 + 16 * x1), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp7 = tl.load(in_ptr1 + (1 + 4 * x0 + 16 * x1), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp8 = tmp6 - tmp7
    tmp9 = tmp5 * tmp8
    tmp10 = tmp7 + tmp6
    tmp11 = 0.5
    tmp12 = tmp10 * tmp11
    tmp13 = tmp9 + tmp12
    tmp14 = tl.load(in_ptr0 + (3 + 4 * x0 + 16 * x1), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp15 = tl_math.exp(tmp14)
    tmp16 = tmp15 * tmp8
    tmp17 = tmp16 * tmp11
    tmp18 = tmp13 - tmp17
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp4, tmp18, tmp19)
    tmp21 = tmp0 >= tmp3
    tmp22 = tl.full([1], 8, tl.int64)
    tmp23 = tmp0 < tmp22
    tmp24 = tmp21 & tmp23
    tmp25 = tl.load(in_ptr0 + (4 * (-4 + x0) + 16 * x1), tmp24 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp26 = tl.load(in_ptr1 + (2 + 4 * (-4 + x0) + 16 * x1), tmp24 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp27 = tl.load(in_ptr1 + (4 * (-4 + x0) + 16 * x1), tmp24 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp28 = tmp26 - tmp27
    tmp29 = tmp25 * tmp28
    tmp30 = tmp27 + tmp26
    tmp31 = tmp30 * tmp11
    tmp32 = tmp29 + tmp31
    tmp33 = tl.load(in_ptr0 + (2 + 4 * (-4 + x0) + 16 * x1), tmp24 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp34 = tl_math.exp(tmp33)
    tmp35 = tmp34 * tmp28
    tmp36 = tmp35 * tmp11
    tmp37 = tmp32 - tmp36
    tmp38 = tl.full(tmp37.shape, 0.0, tmp37.dtype)
    tmp39 = tl.where(tmp24, tmp37, tmp38)
    tmp40 = tmp0 >= tmp22
    tmp41 = tl.full([1], 12, tl.int64)
    tmp42 = tmp0 < tmp41
    tmp43 = tmp40 & tmp42
    tmp44 = tl.load(in_ptr0 + (1 + 4 * (-8 + x0) + 16 * x1), tmp43 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp45 = tl.load(in_ptr1 + (3 + 4 * (-8 + x0) + 16 * x1), tmp43 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp46 = tl.load(in_ptr1 + (1 + 4 * (-8 + x0) + 16 * x1), tmp43 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp47 = tmp45 - tmp46
    tmp48 = tmp44 * tmp47
    tmp49 = tmp46 + tmp45
    tmp50 = tmp49 * tmp11
    tmp51 = tmp48 + tmp50
    tmp52 = tl.load(in_ptr0 + (3 + 4 * (-8 + x0) + 16 * x1), tmp43 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp53 = tl_math.exp(tmp52)
    tmp54 = tmp53 * tmp47
    tmp55 = tmp54 * tmp11
    tmp56 = tmp51 + tmp55
    tmp57 = tl.full(tmp56.shape, 0.0, tmp56.dtype)
    tmp58 = tl.where(tmp43, tmp56, tmp57)
    tmp59 = tmp0 >= tmp41
    tl.full([1], 16, tl.int64)
    tmp62 = tl.load(in_ptr0 + (4 * (-12 + x0) + 16 * x1), tmp59 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp63 = tl.load(in_ptr1 + (2 + 4 * (-12 + x0) + 16 * x1), tmp59 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp64 = tl.load(in_ptr1 + (4 * (-12 + x0) + 16 * x1), tmp59 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp65 = tmp63 - tmp64
    tmp66 = tmp62 * tmp65
    tmp67 = tmp64 + tmp63
    tmp68 = tmp67 * tmp11
    tmp69 = tmp66 + tmp68
    tmp70 = tl.load(in_ptr0 + (2 + 4 * (-12 + x0) + 16 * x1), tmp59 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp71 = tl_math.exp(tmp70)
    tmp72 = tmp71 * tmp65
    tmp73 = tmp72 * tmp11
    tmp74 = tmp69 + tmp73
    tmp75 = tl.full(tmp74.shape, 0.0, tmp74.dtype)
    tmp76 = tl.where(tmp59, tmp74, tmp75)
    tmp77 = tl.where(tmp43, tmp58, tmp76)
    tmp78 = tl.where(tmp24, tmp39, tmp77)
    tmp79 = tl.where(tmp4, tmp20, tmp78)
    tl.store(out_ptr0 + x2, tmp79, 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, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_stack_0[grid(256)](arg1_1, arg0_1, buf0, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
    return reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0),


class BBoxTransformNew(nn.Module):

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