# 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/kh/ckhtqfawq2kdlfjxi7ht5pfiebeork7wrcmpyo755duufkdrsqhm.py
# Topologically Sorted Source Nodes: [a, b], Original ATen: [aten.stack]
# Source node to ATen node mapping:
#   a => cat
#   b => cat_1
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%cos, %cos_1], 1), kwargs = {})
#   %cat_1 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%cos_2, %cos_3], 1), 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=[32], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_stack_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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_stack_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 24
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = (xindex // 8)
    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 + (12 + (8*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr0 + (4 + (8*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp7 = -tmp6
    tmp8 = tmp5 + tmp7
    tmp9 = tl.load(in_ptr0 + (8 + (8*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = tl.load(in_ptr0 + ((8*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp11 = -tmp10
    tmp12 = tmp9 + tmp11
    tmp13 = libdevice.atan2(tmp8, tmp12)
    tmp14 = 1.5707963
    tmp15 = libdevice.fmod(tmp13, tmp14)
    tmp16 = tl_math.abs(tmp15)
    tmp17 = tl_math.cos(tmp16)
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp4, tmp17, tmp18)
    tmp20 = tmp0 >= tmp3
    tmp21 = tl.full([1], 8, tl.int64)
    tmp22 = tmp0 < tmp21
    tmp23 = tl.load(in_ptr0 + (12 + (8*x1) + ((-4) + x0)), tmp20 & xmask, eviction_policy='evict_last', other=0.0)
    tmp24 = tl.load(in_ptr0 + (4 + (8*x1) + ((-4) + x0)), tmp20 & xmask, eviction_policy='evict_last', other=0.0)
    tmp25 = -tmp24
    tmp26 = tmp23 + tmp25
    tmp27 = tl.load(in_ptr0 + (8 + (8*x1) + ((-4) + x0)), tmp20 & xmask, eviction_policy='evict_last', other=0.0)
    tmp28 = tl.load(in_ptr0 + ((8*x1) + ((-4) + x0)), tmp20 & xmask, eviction_policy='evict_last', other=0.0)
    tmp29 = -tmp28
    tmp30 = tmp27 + tmp29
    tmp31 = libdevice.atan2(tmp26, tmp30)
    tmp32 = libdevice.fmod(tmp31, tmp14)
    tmp33 = tl_math.abs(tmp32)
    tmp34 = tmp33 - tmp14
    tmp35 = tl_math.cos(tmp34)
    tmp36 = tl.full(tmp35.shape, 0.0, tmp35.dtype)
    tmp37 = tl.where(tmp20, tmp35, tmp36)
    tmp38 = tl.where(tmp4, tmp19, tmp37)
    tmp39 = tmp16 + tmp14
    tmp40 = tl_math.cos(tmp39)
    tmp41 = tl.full(tmp40.shape, 0.0, tmp40.dtype)
    tmp42 = tl.where(tmp4, tmp40, tmp41)
    tmp43 = tl_math.cos(tmp33)
    tmp44 = tl.full(tmp43.shape, 0.0, tmp43.dtype)
    tmp45 = tl.where(tmp20, tmp43, tmp44)
    tmp46 = tl.where(tmp4, tmp42, tmp45)
    tl.store(out_ptr0 + (x2), tmp38, xmask)
    tl.store(out_ptr1 + (x2), tmp46, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/q4/cq4m4vhr5g5n2fs4y4nu64bh5neuox6dalmxljp6fzzrnfl56umx.py
# Topologically Sorted Source Nodes: [R_tensor], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   R_tensor => cat_2
# Graph fragment:
#   %cat_2 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%unsqueeze, %unsqueeze_1], 1), kwargs = {})
triton_poi_fused_cat_1 = async_compile.triton('triton_poi_fused_cat_1', '''
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: '*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_cat_1', '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_cat_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 48
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 8) % 2
    x0 = xindex % 8
    x2 = (xindex // 16)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (8*x2)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 2, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (x0 + (8*x2)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + (x3), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ju/cjuomzxntkde2voilgp5hg4xn7wi5ku73hgurst4ze6bfozlugyt.py
# Topologically Sorted Source Nodes: [rot_points], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   rot_points => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand_1,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_2 = async_compile.triton('triton_poi_fused_clone_2', '''
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=[128], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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_clone_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_clone_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 96
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4) % 4
    x2 = (xindex // 16) % 2
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (4*x2) + (8*x1)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x4), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ju/cjucyy734rzs3nnc55tq6fjkyiudqyv5dccouryil7yjdqun37oj.py
# Topologically Sorted Source Nodes: [sub_1, sub_2, areas, min_2], Original ATen: [aten.sub, aten.mul, aten.min]
# Source node to ATen node mapping:
#   areas => mul
#   min_2 => min_2
#   sub_1 => sub_1
#   sub_2 => sub_2
# Graph fragment:
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%select_2, %select_3), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%select_4, %select_5), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %sub_2), kwargs = {})
#   %min_2 : [num_users=1] = call_function[target=torch.ops.aten.min.default](args = (%mul,), kwargs = {})
triton_per_fused_min_mul_sub_3 = async_compile.triton('triton_per_fused_min_mul_sub_3', '''
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.persistent_reduction(
    size_hints=[1, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_min_mul_sub_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused_min_mul_sub_3(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 12
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = rindex < rnumel
    r0 = rindex % 4
    r1 = (rindex // 4)
    tmp0 = tl.load(in_ptr0 + (r0 + (16*r1)), rmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + (4 + r0 + (16*r1)), rmask, other=0.0)
    tmp5 = tl.load(in_ptr0 + (8 + r0 + (16*r1)), rmask, other=0.0)
    tmp6 = tl.load(in_ptr0 + (12 + r0 + (16*r1)), rmask, other=0.0)
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = triton_helpers.minimum(tmp0, tmp1)
    tmp4 = tmp2 - tmp3
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = triton_helpers.minimum(tmp5, tmp6)
    tmp9 = tmp7 - tmp8
    tmp10 = tmp4 * tmp9
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp13 = tl.where(rmask, tmp11, float("inf"))
    tmp14 = triton_helpers.min2(tmp13, 1)[:, None]
    tl.store(out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp14, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 2, 4), (8, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((3, 8), (8, 1), torch.float32)
        buf1 = empty_strided_cuda((3, 8), (8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [a, b], Original ATen: [aten.stack]
        stream0 = get_raw_stream(0)
        triton_poi_fused_stack_0.run(arg0_1, buf0, buf1, 24, grid=grid(24), stream=stream0)
        buf2 = empty_strided_cuda((3, 2, 2, 4), (16, 8, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [R_tensor], Original ATen: [aten.cat]
        triton_poi_fused_cat_1.run(buf0, buf1, buf2, 48, grid=grid(48), stream=stream0)
        del buf0
        del buf1
        buf3 = empty_strided_cuda((3, 2, 4, 4), (32, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [rot_points], Original ATen: [aten.clone]
        triton_poi_fused_clone_2.run(arg0_1, buf3, 96, grid=grid(96), stream=stream0)
        del arg0_1
        buf4 = empty_strided_cuda((6, 2, 4), (8, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [rot_points], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf2, (6, 2, 4), (8, 4, 1), 0), reinterpret_tensor(buf3, (6, 4, 4), (16, 4, 1), 0), out=buf4)
        del buf2
        del buf3
        buf5 = empty_strided_cuda((), (), torch.float32)
        # Topologically Sorted Source Nodes: [sub_1, sub_2, areas, min_2], Original ATen: [aten.sub, aten.mul, aten.min]
        triton_per_fused_min_mul_sub_3.run(buf4, buf5, 1, 12, grid=grid(1), stream=stream0)
        del buf4
    return (buf5, )


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, 2, 4), (8, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_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


class mbr_convex_hull(nn.Module):
    """
        Miminum Bounding Rectangle (MBR)
        Algorithm core: The orientation of the MBR is the same as the one of one of the edges of the point cloud convex hull, which means
        the result rectangle must overlap with at least one of the edges.
    """

    def _init_(self, hull_points_2d):
        super(mbr_convex_hull, self)._init_()
        self.hull_points_2d = hull_points_2d
        return

    def forward(ctx, hull_points_2d):
        N = hull_points_2d.shape[0]
        edges = hull_points_2d[1:N, :].add(-hull_points_2d[0:N - 1, :])
        edge_angles = torch.atan2(edges[:, 1], edges[:, 0])
        edge_angles = torch.fmod(edge_angles, 3.1415926 / 2.0)
        edge_angles = torch.abs(edge_angles)
        a = torch.stack((torch.cos(edge_angles), torch.cos(edge_angles - 
            3.1415926 / 2.0)), 1)
        a = torch.unsqueeze(a, 1)
        b = torch.stack((torch.cos(edge_angles + 3.1415926 / 2.0), torch.
            cos(edge_angles)), 1)
        b = torch.unsqueeze(b, 1)
        R_tensor = torch.cat((a, b), 1)
        hull_points_2d_ = torch.unsqueeze(torch.transpose(hull_points_2d, 0,
            1), 0)
        rot_points = R_tensor.matmul(hull_points_2d_)
        min_x = torch.min(rot_points, 2)[0]
        max_x = torch.max(rot_points, 2)[0]
        areas = (max_x[:, 0] - min_x[:, 0]).mul(max_x[:, 1] - min_x[:, 1])
        return torch.min(areas)


def get_inputs():
    return [torch.rand([4, 2, 4])]


def get_init_inputs():
    return [[], {}]

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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
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_stack_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 24
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8
    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 + (12 + 8 * x1 + x0), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr0 + (4 + 8 * x1 + x0), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp7 = -tmp6
    tmp8 = tmp5 + tmp7
    tmp9 = tl.load(in_ptr0 + (8 + 8 * x1 + x0), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.load(in_ptr0 + (8 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp11 = -tmp10
    tmp12 = tmp9 + tmp11
    tmp13 = libdevice.atan2(tmp8, tmp12)
    tmp14 = 1.5707963
    tmp15 = libdevice.fmod(tmp13, tmp14)
    tmp16 = tl_math.abs(tmp15)
    tmp17 = tl_math.cos(tmp16)
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp4, tmp17, tmp18)
    tmp20 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp23 = tl.load(in_ptr0 + (12 + 8 * x1 + (-4 + x0)), tmp20 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp24 = tl.load(in_ptr0 + (4 + 8 * x1 + (-4 + x0)), tmp20 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp25 = -tmp24
    tmp26 = tmp23 + tmp25
    tmp27 = tl.load(in_ptr0 + (8 + 8 * x1 + (-4 + x0)), tmp20 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp28 = tl.load(in_ptr0 + (8 * x1 + (-4 + x0)), tmp20 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp29 = -tmp28
    tmp30 = tmp27 + tmp29
    tmp31 = libdevice.atan2(tmp26, tmp30)
    tmp32 = libdevice.fmod(tmp31, tmp14)
    tmp33 = tl_math.abs(tmp32)
    tmp34 = tmp33 - tmp14
    tmp35 = tl_math.cos(tmp34)
    tmp36 = tl.full(tmp35.shape, 0.0, tmp35.dtype)
    tmp37 = tl.where(tmp20, tmp35, tmp36)
    tmp38 = tl.where(tmp4, tmp19, tmp37)
    tmp39 = tmp16 + tmp14
    tmp40 = tl_math.cos(tmp39)
    tmp41 = tl.full(tmp40.shape, 0.0, tmp40.dtype)
    tmp42 = tl.where(tmp4, tmp40, tmp41)
    tmp43 = tl_math.cos(tmp33)
    tmp44 = tl.full(tmp43.shape, 0.0, tmp43.dtype)
    tmp45 = tl.where(tmp20, tmp43, tmp44)
    tmp46 = tl.where(tmp4, tmp42, tmp45)
    tl.store(out_ptr0 + x2, tmp38, xmask)
    tl.store(out_ptr1 + x2, tmp46, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 48
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 8 % 2
    x0 = xindex % 8
    x2 = xindex // 16
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 8 * x2), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 2, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 8 * x2), tmp6 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x3, tmp10, xmask)


@triton.jit
def triton_poi_fused_clone_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 96
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16 % 2
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 8 * x1), xmask, eviction_policy
        ='evict_last')
    tl.store(out_ptr0 + x4, tmp0, xmask)


@triton.jit
def triton_per_fused_min_mul_sub_3(in_ptr0, out_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    rnumel = 12
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r0 = rindex % 4
    r1 = rindex // 4
    tmp0 = tl.load(in_ptr0 + (r0 + 16 * r1), rmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + (4 + r0 + 16 * r1), rmask, other=0.0)
    tmp5 = tl.load(in_ptr0 + (8 + r0 + 16 * r1), rmask, other=0.0)
    tmp6 = tl.load(in_ptr0 + (12 + r0 + 16 * r1), rmask, other=0.0)
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = triton_helpers.minimum(tmp0, tmp1)
    tmp4 = tmp2 - tmp3
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = triton_helpers.minimum(tmp5, tmp6)
    tmp9 = tmp7 - tmp8
    tmp10 = tmp4 * tmp9
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp13 = tl.where(rmask, tmp11, float('inf'))
    tmp14 = triton_helpers.min2(tmp13, 1)[:, None]
    tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp14, None)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 2, 4), (8, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((3, 8), (8, 1), torch.float32)
        buf1 = empty_strided_cuda((3, 8), (8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_stack_0[grid(24)](arg0_1, buf0, buf1, 24, XBLOCK=
            32, num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((3, 2, 2, 4), (16, 8, 4, 1), torch.float32)
        triton_poi_fused_cat_1[grid(48)](buf0, buf1, buf2, 48, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf0
        del buf1
        buf3 = empty_strided_cuda((3, 2, 4, 4), (32, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_2[grid(96)](arg0_1, buf3, 96, XBLOCK=128,
            num_warps=4, num_stages=1)
        del arg0_1
        buf4 = empty_strided_cuda((6, 2, 4), (8, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf2, (6, 2, 4), (8, 4, 1), 0
            ), reinterpret_tensor(buf3, (6, 4, 4), (16, 4, 1), 0), out=buf4)
        del buf2
        del buf3
        buf5 = empty_strided_cuda((), (), torch.float32)
        triton_per_fused_min_mul_sub_3[grid(1)](buf4, buf5, 1, 12, XBLOCK=1,
            num_warps=2, num_stages=1)
        del buf4
    return buf5,


class mbr_convex_hullNew(nn.Module):
    """
        Miminum Bounding Rectangle (MBR)
        Algorithm core: The orientation of the MBR is the same as the one of one of the edges of the point cloud convex hull, which means
        the result rectangle must overlap with at least one of the edges.
    """

    def _init_(self, hull_points_2d):
        super(mbr_convex_hullNew, self)._init_()
        self.hull_points_2d = hull_points_2d
        return

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