# 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/jl/cjl6gdowsefrajigrysj4wqaymcm2bsrwo2xgutm5v4zsn37efbb.py
# Topologically Sorted Source Nodes: [pow_1, e_square], Original ATen: [aten.pow, aten.sum]
# Source node to ATen node mapping:
#   e_square => sum_1
#   pow_1 => pow_1
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%view_1, 2), kwargs = {})
#   %sum_1 : [num_users=2] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [1]), kwargs = {})
triton_per_fused_pow_sum_0 = async_compile.triton('triton_per_fused_pow_sum_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=[4, 64],
    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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_pow_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_pow_sum_0(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (64*x0)), xmask, other=0.0)
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(xmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tl.store(out_ptr0 + (x0), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ap/cap6lykjc7jtwgmu47dbzfsoyufnnnc6q4klmqmepnt2iwmmbz37.py
# Topologically Sorted Source Nodes: [add, mul, sub, res, res_1], Original ATen: [aten.add, aten.mul, aten.sub, aten.clamp, aten.sqrt]
# Source node to ATen node mapping:
#   add => add
#   mul => mul
#   res => clamp_min
#   res_1 => sqrt
#   sub => sub
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%unsqueeze, %unsqueeze_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm, 2), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %mul), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub, 1e-12), kwargs = {})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%clamp_min,), kwargs = {})
triton_poi_fused_add_clamp_mul_sqrt_sub_1 = async_compile.triton('triton_poi_fused_add_clamp_mul_sqrt_sub_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=[16], 
    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_add_clamp_mul_sqrt_sub_1', 'mutated_arg_names': ['in_out_ptr0'], '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_add_clamp_mul_sqrt_sub_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4)
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = 2.0
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 - tmp5
    tmp7 = 1e-12
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp9 = libdevice.sqrt(tmp8)
    tl.store(in_out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/qs/cqsmgohon47rlgpds2ngf2yb2q2itiyxn4p5dl5rzavw2q36vv5i.py
# Topologically Sorted Source Nodes: [add, mul, sub, res, res_1, setitem], Original ATen: [aten.add, aten.mul, aten.sub, aten.clamp, aten.sqrt, aten.lift_fresh, aten.index_put]
# Source node to ATen node mapping:
#   add => add
#   mul => mul
#   res => clamp_min
#   res_1 => sqrt
#   setitem => full_default, index_put
#   sub => sub
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%unsqueeze, %unsqueeze_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm, 2), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %mul), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub, 1e-12), kwargs = {})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%clamp_min,), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %index_put : [num_users=2] = call_function[target=torch.ops.aten.index_put_.default](args = (%sqrt, [%lift_fresh_copy_1, %lift_fresh_copy_2], %full_default), kwargs = {})
triton_poi_fused_add_clamp_index_put_lift_fresh_mul_sqrt_sub_2 = async_compile.triton('triton_poi_fused_add_clamp_index_put_lift_fresh_mul_sqrt_sub_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=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '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,), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_clamp_index_put_lift_fresh_mul_sqrt_sub_2', 'mutated_arg_names': ['out_ptr0'], 'no_x_dim': False, 'num_load': 0, '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_add_clamp_index_put_lift_fresh_mul_sqrt_sub_2(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 2, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.full([1], 0, tl.int64)
    tmp6 = tl.where(tmp4, tmp5, tmp3)
    tmp7 = tl.full([1], 3, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.where(tmp8, tmp1, tmp7)
    tmp10 = tl.where(tmp2, tmp6, tmp9)
    tmp11 = 0.0
    tl.store(out_ptr0 + (tl.broadcast_to(5*tmp10, [XBLOCK])), tmp11, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/dw/cdwmn7qwnbjqpgeeo5kmewgnitdozmuz4ynpnfasfptynvyt4kyr.py
# Topologically Sorted Source Nodes: [gt], Original ATen: [aten.gt]
# Source node to ATen node mapping:
#   gt => gt
# Graph fragment:
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%index_put, 0), kwargs = {})
triton_poi_fused_gt_3 = async_compile.triton('triton_poi_fused_gt_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.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i1', 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_gt_3', '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_gt_3(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tl.store(out_ptr0 + (x0), tmp2, 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, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [pow_1, e_square], Original ATen: [aten.pow, aten.sum]
        stream0 = get_raw_stream(0)
        triton_per_fused_pow_sum_0.run(arg1_1, buf0, 4, 64, grid=grid(4), stream=stream0)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [prod], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(arg1_1, (4, 64), (64, 1), 0), reinterpret_tensor(arg1_1, (64, 4), (1, 64), 0), out=buf1)
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [add, mul, sub, res, res_1], Original ATen: [aten.add, aten.mul, aten.sub, aten.clamp, aten.sqrt]
        triton_poi_fused_add_clamp_mul_sqrt_sub_1.run(buf2, buf0, 16, grid=grid(16), stream=stream0)
        del buf0
        # Topologically Sorted Source Nodes: [add, mul, sub, res, res_1, setitem], Original ATen: [aten.add, aten.mul, aten.sub, aten.clamp, aten.sqrt, aten.lift_fresh, aten.index_put]
        triton_poi_fused_add_clamp_index_put_lift_fresh_mul_sqrt_sub_2.run(buf2, 4, grid=grid(4), stream=stream0)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [gt], Original ATen: [aten.gt]
        triton_poi_fused_gt_3.run(buf2, buf4, 16, grid=grid(16), stream=stream0)
    return (buf2, buf4, reinterpret_tensor(arg0_1, (4, 64), (64, 1), 0), reinterpret_tensor(arg1_1, (4, 64), (64, 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.functional as F
import torch.optim


class RKDLoss(nn.Module):
    """Relational Knowledge Disitllation, CVPR2019"""

    def __init__(self, w_d=25, w_a=50):
        super(RKDLoss, self).__init__()
        self.w_d = w_d
        self.w_a = w_a

    def forward(self, f_s, f_t):
        student = f_s.view(f_s.shape[0], -1)
        teacher = f_t.view(f_t.shape[0], -1)
        with torch.no_grad():
            t_d = self.pdist(teacher, squared=False)
            mean_td = t_d[t_d > 0].mean()
            t_d = t_d / mean_td
        d = self.pdist(student, squared=False)
        mean_d = d[d > 0].mean()
        d = d / mean_d
        loss_d = F.smooth_l1_loss(d, t_d)
        with torch.no_grad():
            td = teacher.unsqueeze(0) - teacher.unsqueeze(1)
            norm_td = F.normalize(td, p=2, dim=2)
            t_angle = torch.bmm(norm_td, norm_td.transpose(1, 2)).view(-1)
        sd = student.unsqueeze(0) - student.unsqueeze(1)
        norm_sd = F.normalize(sd, p=2, dim=2)
        s_angle = torch.bmm(norm_sd, norm_sd.transpose(1, 2)).view(-1)
        loss_a = F.smooth_l1_loss(s_angle, t_angle)
        loss = self.w_d * loss_d + self.w_a * loss_a
        return loss

    @staticmethod
    def pdist(e, squared=False, eps=1e-12):
        e_square = e.pow(2).sum(dim=1)
        prod = e @ e.t()
        res = (e_square.unsqueeze(1) + e_square.unsqueeze(0) - 2 * prod).clamp(
            min=eps)
        if not squared:
            res = res.sqrt()
        res = res.clone()
        res[range(len(e)), range(len(e))] = 0
        return res


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 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
import torch.nn as nn
import torch.optim
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_per_fused_pow_sum_0(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(xmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp5, xmask)


@triton.jit
def triton_poi_fused_add_clamp_mul_sqrt_sub_1(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_out_ptr0 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = 2.0
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 - tmp5
    tmp7 = 1e-12
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp9 = libdevice.sqrt(tmp8)
    tl.store(in_out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused_add_clamp_index_put_lift_fresh_mul_sqrt_sub_2(out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 2, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.full([1], 0, tl.int64)
    tmp6 = tl.where(tmp4, tmp5, tmp3)
    tmp7 = tl.full([1], 3, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.where(tmp8, tmp1, tmp7)
    tmp10 = tl.where(tmp2, tmp6, tmp9)
    tmp11 = 0.0
    tl.store(out_ptr0 + tl.broadcast_to(5 * tmp10, [XBLOCK]), tmp11, xmask)


@triton.jit
def triton_poi_fused_gt_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tl.store(out_ptr0 + x0, tmp2, 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,), (1,), torch.float32)
        get_raw_stream(0)
        triton_per_fused_pow_sum_0[grid(4)](arg1_1, buf0, 4, 64, XBLOCK=1,
            num_warps=2, num_stages=1)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(arg1_1, (4, 64), (64, 1), 0),
            reinterpret_tensor(arg1_1, (64, 4), (1, 64), 0), out=buf1)
        buf2 = buf1
        del buf1
        triton_poi_fused_add_clamp_mul_sqrt_sub_1[grid(16)](buf2, buf0, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del buf0
        triton_poi_fused_add_clamp_index_put_lift_fresh_mul_sqrt_sub_2[grid(4)
            ](buf2, 4, XBLOCK=4, num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        triton_poi_fused_gt_3[grid(16)](buf2, buf4, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
    return buf2, buf4, reinterpret_tensor(arg0_1, (4, 64), (64, 1), 0
        ), reinterpret_tensor(arg1_1, (4, 64), (64, 1), 0)


class RKDLossNew(nn.Module):
    """Relational Knowledge Disitllation, CVPR2019"""

    def __init__(self, w_d=25, w_a=50):
        super(RKDLossNew, self).__init__()
        self.w_d = w_d
        self.w_a = w_a

    @staticmethod
    def pdist(e, squared=False, eps=1e-12):
        e_square = e.pow(2).sum(dim=1)
        prod = e @ e.t()
        res = (e_square.unsqueeze(1) + e_square.unsqueeze(0) - 2 * prod).clamp(
            min=eps)
        if not squared:
            res = res.sqrt()
        res = res.clone()
        res[range(len(e)), range(len(e))] = 0
        return res

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