# 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/32/c32kocizrzjutc3dhrpv2d43l62w7u3435wi3ivkawpblwfbly6h.py
# Topologically Sorted Source Nodes: [sub, l2_loss, mul, sub_1, mul_1, exp, add, shrink_loss, mean], Original ATen: [aten.sub, aten.linalg_vector_norm, aten.mul, aten.rsub, aten.exp, aten.add, aten.div, aten.mean]
# Source node to ATen node mapping:
#   add => add
#   exp => exp
#   l2_loss => pow_1, pow_2, sum_1
#   mean => mean
#   mul => mul
#   mul_1 => mul_1
#   shrink_loss => div
#   sub => sub
#   sub_1 => sub_1
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %arg1_1), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [1]), kwargs = {})
#   %pow_2 : [num_users=2] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_1, 0.5), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_2, %pow_2), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (0.2, %pow_2), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, 10.0), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%mul_1,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%exp, 1.0), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul, %add), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%div,), kwargs = {})
triton_per_fused_add_div_exp_linalg_vector_norm_mean_mul_rsub_sub_0 = async_compile.triton('triton_per_fused_add_div_exp_linalg_vector_norm_mean_mul_rsub_sub_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.persistent_reduction(
    size_hints=[1, 64],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: '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': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_exp_linalg_vector_norm_mean_mul_rsub_sub_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 8, '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_add_div_exp_linalg_vector_norm_mean_mul_rsub_sub_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    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 = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex % 16
    r1 = (rindex // 16)
    r2 = rindex
    tmp0 = tl.load(in_ptr0 + (r0 + (64*r1)), None)
    tmp1 = tl.load(in_ptr1 + (r0 + (64*r1)), None)
    tmp4 = tl.load(in_ptr0 + (16 + r0 + (64*r1)), None)
    tmp5 = tl.load(in_ptr1 + (16 + r0 + (64*r1)), None)
    tmp9 = tl.load(in_ptr0 + (32 + r0 + (64*r1)), None)
    tmp10 = tl.load(in_ptr1 + (32 + r0 + (64*r1)), None)
    tmp14 = tl.load(in_ptr0 + (48 + r0 + (64*r1)), None)
    tmp15 = tl.load(in_ptr1 + (48 + r0 + (64*r1)), None)
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp6 = tmp4 - tmp5
    tmp7 = tmp6 * tmp6
    tmp8 = tmp3 + tmp7
    tmp11 = tmp9 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tmp8 + tmp12
    tmp16 = tmp14 - tmp15
    tmp17 = tmp16 * tmp16
    tmp18 = tmp13 + tmp17
    tmp19 = libdevice.sqrt(tmp18)
    tmp20 = tmp19 * tmp19
    tmp21 = 0.2
    tmp22 = tmp21 - tmp19
    tmp23 = 10.0
    tmp24 = tmp22 * tmp23
    tmp25 = tl_math.exp(tmp24)
    tmp26 = 1.0
    tmp27 = tmp25 + tmp26
    tmp28 = tmp20 / tmp27
    tmp29 = tl.broadcast_to(tmp28, [XBLOCK, RBLOCK])
    tmp31 = tl.sum(tmp29, 1)[:, None]
    tmp32 = 64.0
    tmp33 = tmp31 / tmp32
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp33, None)
''', 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)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [sub, l2_loss, mul, sub_1, mul_1, exp, add, shrink_loss, mean], Original ATen: [aten.sub, aten.linalg_vector_norm, aten.mul, aten.rsub, aten.exp, aten.add, aten.div, aten.mean]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_div_exp_linalg_vector_norm_mean_mul_rsub_sub_0.run(buf2, arg0_1, arg1_1, 1, 64, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf2, )


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


class ShrinkageLoss(nn.Module):
    """ ShrinkageLoss class.
        Modified version of shrinkage loss tailored to images:
        http://openaccess.thecvf.com/content_ECCV_2018/papers/Xiankai_Lu_Deep_Regression_Tracking_ECCV_2018_paper.pdf
        It basically computes a point-wise shrinkage loss.
    """

    def __init__(self, speed=10.0, loc=0.2, verbose=False):
        """ Initialize ShrinkageLoss class with user-defined parameters.
        Arguments:
            shrink_speed (float): Shrinkage speed, i.e., weight assigned to hard samples.
            shrink_loc (float):   Shrinkage localization, i.e., threshold for hard mining.
            verbose (bool):       Whether  the log will be shown in the shell.
        """
        nn.Module.__init__(self)
        self.shrink_speed = speed
        self.shrink_loc = loc

    def forward(self, estimate, ground_truth):
        """ Calculate shrinkage loss between the estimate and grount truth, if any.
            Otherwise, the loss is computed using the estimate, which is already
            the difference to the ground truth or the parameters.
        Arguments:
            estimate (tensor):     Estimate or delta (MxC, where M, C are
                                   the number of points and channels, respectively).
            ground_truth (tensor): Ground truth (optional). MxC, where M, C are
                                   the number of points and channels, respectively
        Return:
            Mean per-point shrinkage loss (float)
        """
        l2_loss = torch.norm(estimate - ground_truth, p=2, dim=1)
        shrink_loss = torch.mul(l2_loss, l2_loss) / (1.0 + torch.exp(self.
            shrink_speed * (self.shrink_loc - l2_loss)))
        return torch.mean(shrink_loss)


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 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


@triton.jit
def triton_per_fused_add_div_exp_linalg_vector_norm_mean_mul_rsub_sub_0(
    in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    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, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex % 16
    r1 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
    tmp1 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
    tmp4 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp5 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
    tmp9 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp10 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
    tmp14 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp15 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp6 = tmp4 - tmp5
    tmp7 = tmp6 * tmp6
    tmp8 = tmp3 + tmp7
    tmp11 = tmp9 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tmp8 + tmp12
    tmp16 = tmp14 - tmp15
    tmp17 = tmp16 * tmp16
    tmp18 = tmp13 + tmp17
    tmp19 = libdevice.sqrt(tmp18)
    tmp20 = tmp19 * tmp19
    tmp21 = 0.2
    tmp22 = tmp21 - tmp19
    tmp23 = 10.0
    tmp24 = tmp22 * tmp23
    tmp25 = tl_math.exp(tmp24)
    tmp26 = 1.0
    tmp27 = tmp25 + tmp26
    tmp28 = tmp20 / tmp27
    tmp29 = tl.broadcast_to(tmp28, [XBLOCK, RBLOCK])
    tmp31 = tl.sum(tmp29, 1)[:, None]
    tmp32 = 64.0
    tmp33 = tmp31 / tmp32
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp33, None)


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)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_per_fused_add_div_exp_linalg_vector_norm_mean_mul_rsub_sub_0[
            grid(1)](buf2, arg0_1, arg1_1, 1, 64, XBLOCK=1, num_warps=2,
            num_stages=1)
        del arg0_1
        del arg1_1
    return buf2,


class ShrinkageLossNew(nn.Module):
    """ ShrinkageLoss class.
        Modified version of shrinkage loss tailored to images:
        http://openaccess.thecvf.com/content_ECCV_2018/papers/Xiankai_Lu_Deep_Regression_Tracking_ECCV_2018_paper.pdf
        It basically computes a point-wise shrinkage loss.
    """

    def __init__(self, speed=10.0, loc=0.2, verbose=False):
        """ Initialize ShrinkageLoss class with user-defined parameters.
        Arguments:
            shrink_speed (float): Shrinkage speed, i.e., weight assigned to hard samples.
            shrink_loc (float):   Shrinkage localization, i.e., threshold for hard mining.
            verbose (bool):       Whether  the log will be shown in the shell.
        """
        nn.Module.__init__(self)
        self.shrink_speed = speed
        self.shrink_loc = loc

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