# 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_7/inductor_cache/lr/clrzmli4fy4ajmp3wzncjxv3xkjt4z2ekwzzziz53vbldnwg3hoq.py
# Topologically Sorted Source Nodes: [featmap_3], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   featmap_3 => amax_1, exp_1, sub_1, sum_2
# Graph fragment:
#   %amax_1 : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_1, [-1], True), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_1, %amax_1), kwargs = {})
#   %exp_1 : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_1,), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp_1, [-1], True), kwargs = {})
triton_per_fused__softmax_0 = async_compile.triton('triton_per_fused__softmax_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=[16, 16],
    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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__softmax_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 2, '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__softmax_0(in_ptr0, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    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 + (16*x0)), xmask, other=0.0)
    tmp1 = 0.25
    tmp2 = tmp0 * tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(xmask, tmp3, float("-inf"))
    tmp6 = triton_helpers.max2(tmp5, 1)[:, None]
    tmp7 = tmp2 - tmp6
    tmp8 = tl_math.exp(tmp7)
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp11 = tl.where(xmask, tmp9, 0)
    tmp12 = tl.sum(tmp11, 1)[:, None]
    tl.store(out_ptr0 + (x0), tmp6, xmask)
    tl.store(out_ptr1 + (x0), tmp12, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/zw/czwwgyy7z6lgo726yr4ug2w6bbl7fdgks2j4xnkmknic5ndivfz6.py
# Topologically Sorted Source Nodes: [featmap_3, loss, featmap_1, norm_s, loss_1, mul], Original ATen: [aten._softmax, aten.xlogy, aten.log, aten.mul, aten.sub, aten.sum, aten.div]
# Source node to ATen node mapping:
#   featmap_1 => div_1, exp, sub
#   featmap_3 => div_3, exp_1, sub_1
#   loss => eq, full_default, full_default_1, isnan, log_1, mul, mul_1, sub_2, sum_3, where, where_1
#   loss_1 => div_4
#   mul => mul_2
#   norm_s => log
# Graph fragment:
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_1, %amax_1), kwargs = {})
#   %exp_1 : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_1,), kwargs = {})
#   %div_3 : [num_users=5] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp_1, %sum_2), kwargs = {})
#   %isnan : [num_users=1] = call_function[target=torch.ops.aten.isnan.default](args = (%div_3,), kwargs = {})
#   %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], nan), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%div_3, 0), 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})
#   %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%div_3,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div_3, %log_1), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%eq, %full_default, %mul_1), kwargs = {})
#   %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%isnan, %full_default_1, %where), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%div_1,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div_3, %log), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%where_1, %mul), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%sub_2,), kwargs = {})
#   %div_4 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_3, 16), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div_4, 16.0), kwargs = {})
triton_per_fused__softmax_div_log_mul_sub_sum_xlogy_1 = async_compile.triton('triton_per_fused__softmax_div_log_mul_sub_sum_xlogy_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.persistent_reduction(
    size_hints=[1, 256],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: 'i32', 8: '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': {7: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 8), equal_to_1=(7,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__softmax_div_log_mul_sub_sum_xlogy_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 6, '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__softmax_div_log_mul_sub_sum_xlogy_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, xnumel, rnumel):
    xnumel = 1
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r2 = rindex
    r1 = (rindex // 16)
    tmp0 = tl.load(in_ptr0 + (r2), None)
    tmp3 = tl.load(in_ptr1 + (r1), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr2 + (r1), None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr3 + (r2), None)
    tmp18 = tl.load(in_ptr4 + (r1), None, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr5 + (r1), None, eviction_policy='evict_last')
    tmp1 = 0.25
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp7 = tmp5 / tmp6
    tmp8 = libdevice.isnan(tmp7).to(tl.int1)
    tmp9 = 0.0
    tmp10 = tmp7 == tmp9
    tmp11 = tl_math.log(tmp7)
    tmp12 = tmp7 * tmp11
    tmp13 = tl.where(tmp10, tmp9, tmp12)
    tmp14 = float("nan")
    tmp15 = tl.where(tmp8, tmp14, tmp13)
    tmp17 = tmp16 * tmp1
    tmp19 = tmp17 - tmp18
    tmp20 = tl_math.exp(tmp19)
    tmp22 = tmp20 / tmp21
    tmp23 = tl_math.log(tmp22)
    tmp24 = tmp7 * tmp23
    tmp25 = tmp15 - tmp24
    tmp26 = tl.broadcast_to(tmp25, [RBLOCK])
    tmp28 = triton_helpers.promote_to_tensor(tl.sum(tmp26, 0))
    tmp29 = 0.0625
    tmp30 = tmp28 * tmp29
    tmp31 = 16.0
    tmp32 = tmp30 * tmp31
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp32, 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)
        buf0 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        # Topologically Sorted Source Nodes: [featmap_3], Original ATen: [aten._softmax]
        stream0 = get_raw_stream(0)
        triton_per_fused__softmax_0.run(arg1_1, buf0, buf1, 16, 16, grid=grid(16), stream=stream0)
        buf2 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        # Topologically Sorted Source Nodes: [featmap_1], Original ATen: [aten._softmax]
        triton_per_fused__softmax_0.run(arg0_1, buf2, buf3, 16, 16, grid=grid(16), stream=stream0)
        buf4 = empty_strided_cuda((), (), torch.float32)
        buf5 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [featmap_3, loss, featmap_1, norm_s, loss_1, mul], Original ATen: [aten._softmax, aten.xlogy, aten.log, aten.mul, aten.sub, aten.sum, aten.div]
        triton_per_fused__softmax_div_log_mul_sub_sum_xlogy_1.run(buf5, arg1_1, buf0, buf1, arg0_1, buf2, buf3, 1, 256, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
        del buf0
        del buf1
        del buf2
        del buf3
    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, 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._utils
import torch.optim


class ChannelNorm(nn.Module):

    def __init__(self):
        super(ChannelNorm, self).__init__()

    def forward(self, featmap):
        n, c, _h, _w = featmap.shape
        featmap = featmap.reshape((n, c, -1))
        featmap = featmap.softmax(dim=-1)
        return featmap


class CriterionCWD(nn.Module):

    def __init__(self, norm_type='channel', divergence='kl', temperature=4.0):
        super(CriterionCWD, self).__init__()
        if norm_type == 'channel':
            self.normalize = ChannelNorm()
        elif norm_type == 'spatial':
            self.normalize = nn.Softmax(dim=1)
        elif norm_type == 'channel_mean':
            self.normalize = lambda x: x.view(x.size(0), x.size(1), -1).mean(-1
                )
        else:
            self.normalize = None
        self.norm_type = norm_type
        self.temperature = 1.0
        if divergence == 'mse':
            self.criterion = nn.MSELoss(reduction='sum')
        elif divergence == 'kl':
            self.criterion = nn.KLDivLoss(reduction='sum')
            self.temperature = temperature
        self.divergence = divergence

    def forward(self, preds_S, preds_T):
        n, c, h, w = preds_S.shape
        if self.normalize is not None:
            norm_s = self.normalize(preds_S / self.temperature)
            norm_t = self.normalize(preds_T.detach() / self.temperature)
        else:
            norm_s = preds_S
            norm_t = preds_T.detach()
        if self.divergence == 'kl':
            norm_s = norm_s.log()
        loss = self.criterion(norm_s, norm_t)
        if self.norm_type == 'channel' or self.norm_type == 'channel_mean':
            loss /= n * c
        else:
            loss /= n * h * w
        return loss * self.temperature ** 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch._utils
import torch.optim
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__softmax_0(in_ptr0, out_ptr0, out_ptr1, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    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 + 16 * x0), xmask, other=0.0)
    tmp1 = 0.25
    tmp2 = tmp0 * tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(xmask, tmp3, float('-inf'))
    tmp6 = triton_helpers.max2(tmp5, 1)[:, None]
    tmp7 = tmp2 - tmp6
    tmp8 = tl_math.exp(tmp7)
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp11 = tl.where(xmask, tmp9, 0)
    tmp12 = tl.sum(tmp11, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp6, xmask)
    tl.store(out_ptr1 + x0, tmp12, xmask)


@triton.jit
def triton_per_fused__softmax_div_log_mul_sub_sum_xlogy_1(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r2 = rindex
    r1 = rindex // 16
    tmp0 = tl.load(in_ptr0 + r2, None)
    tmp3 = tl.load(in_ptr1 + r1, None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr2 + r1, None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr3 + r2, None)
    tmp18 = tl.load(in_ptr4 + r1, None, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr5 + r1, None, eviction_policy='evict_last')
    tmp1 = 0.25
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp7 = tmp5 / tmp6
    tmp8 = libdevice.isnan(tmp7).to(tl.int1)
    tmp9 = 0.0
    tmp10 = tmp7 == tmp9
    tmp11 = tl_math.log(tmp7)
    tmp12 = tmp7 * tmp11
    tmp13 = tl.where(tmp10, tmp9, tmp12)
    tmp14 = float('nan')
    tmp15 = tl.where(tmp8, tmp14, tmp13)
    tmp17 = tmp16 * tmp1
    tmp19 = tmp17 - tmp18
    tmp20 = tl_math.exp(tmp19)
    tmp22 = tmp20 / tmp21
    tmp23 = tl_math.log(tmp22)
    tmp24 = tmp7 * tmp23
    tmp25 = tmp15 - tmp24
    tmp26 = tl.broadcast_to(tmp25, [RBLOCK])
    tmp28 = triton_helpers.promote_to_tensor(tl.sum(tmp26, 0))
    tmp29 = 0.0625
    tmp30 = tmp28 * tmp29
    tmp31 = 16.0
    tmp32 = tmp30 * tmp31
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp32, 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)
        buf0 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        get_raw_stream(0)
        triton_per_fused__softmax_0[grid(16)](arg1_1, buf0, buf1, 16, 16,
            XBLOCK=1, num_warps=2, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        triton_per_fused__softmax_0[grid(16)](arg0_1, buf2, buf3, 16, 16,
            XBLOCK=1, num_warps=2, num_stages=1)
        buf4 = empty_strided_cuda((), (), torch.float32)
        buf5 = buf4
        del buf4
        triton_per_fused__softmax_div_log_mul_sub_sum_xlogy_1[grid(1)](buf5,
            arg1_1, buf0, buf1, arg0_1, buf2, buf3, 1, 256, num_warps=2,
            num_stages=1)
        del arg0_1
        del arg1_1
        del buf0
        del buf1
        del buf2
        del buf3
    return buf5,


class ChannelNorm(nn.Module):

    def __init__(self):
        super(ChannelNorm, self).__init__()

    def forward(self, featmap):
        n, c, _h, _w = featmap.shape
        featmap = featmap.reshape((n, c, -1))
        featmap = featmap.softmax(dim=-1)
        return featmap


class CriterionCWDNew(nn.Module):

    def __init__(self, norm_type='channel', divergence='kl', temperature=4.0):
        super(CriterionCWDNew, self).__init__()
        if norm_type == 'channel':
            self.normalize = ChannelNorm()
        elif norm_type == 'spatial':
            self.normalize = nn.Softmax(dim=1)
        elif norm_type == 'channel_mean':
            self.normalize = lambda x: x.view(x.size(0), x.size(1), -1).mean(-1
                )
        else:
            self.normalize = None
        self.norm_type = norm_type
        self.temperature = 1.0
        if divergence == 'mse':
            self.criterion = nn.MSELoss(reduction='sum')
        elif divergence == 'kl':
            self.criterion = nn.KLDivLoss(reduction='sum')
            self.temperature = temperature
        self.divergence = divergence

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