# 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/bx/cbxzgkjsxqhvj5hh2fd7fd25u7ojweicme3rmrputqt42bdpmj2t.py
# Topologically Sorted Source Nodes: [mul_1, sigma2, mul, sigma1, truediv, log, exp_2, sub, pow_1, add, exp_3, mul_2, truediv_1, add_1, kld, sum_1, mean], Original ATen: [aten.mul, aten.exp, aten.div, aten.log, aten.sub, aten.pow, aten.add, aten.sum, aten.mean]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   exp_2 => exp_2
#   exp_3 => exp_3
#   kld => sub_1
#   log => log
#   mean => mean
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   pow_1 => pow_1
#   sigma1 => exp
#   sigma2 => exp_1
#   sub => sub
#   sum_1 => sum_1
#   truediv => div
#   truediv_1 => div_1
# Graph fragment:
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg1_1, 0.5), kwargs = {})
#   %exp_1 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%mul_1,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, 0.5), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%mul,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp_1, %exp), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%div,), kwargs = {})
#   %exp_2 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%arg0_1,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg2_1, %arg3_1), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%exp_2, %pow_1), kwargs = {})
#   %exp_3 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%arg1_1,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%exp_3, 2), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add, %mul_2), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%log, %div_1), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_1, 0.5), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%sub_1, [-1]), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sum_1,), kwargs = {})
triton_per_fused_add_div_exp_log_mean_mul_pow_sub_sum_0 = async_compile.triton('triton_per_fused_add_div_exp_log_mean_mul_pow_sub_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=[1, 64],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: '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': {5: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 6), equal_to_1=(5,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_exp_log_mean_mul_pow_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 16, '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_log_mean_mul_pow_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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
    tmp0 = tl.load(in_ptr0 + (4*r0), None, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (4*r0), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr2 + (4*r0), None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr3 + (4*r0), None, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr0 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp24 = tl.load(in_ptr1 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr2 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr3 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp41 = tl.load(in_ptr0 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp44 = tl.load(in_ptr1 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp50 = tl.load(in_ptr2 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp51 = tl.load(in_ptr3 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp61 = tl.load(in_ptr0 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp64 = tl.load(in_ptr1 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp70 = tl.load(in_ptr2 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp71 = tl.load(in_ptr3 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = tl_math.exp(tmp2)
    tmp5 = tmp4 * tmp1
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tmp3 / tmp6
    tmp8 = tl_math.log(tmp7)
    tmp9 = tl_math.exp(tmp4)
    tmp12 = tmp10 - tmp11
    tmp13 = tmp12 * tmp12
    tmp14 = tmp9 + tmp13
    tmp15 = tl_math.exp(tmp0)
    tmp16 = 2.0
    tmp17 = tmp15 * tmp16
    tmp18 = tmp14 / tmp17
    tmp19 = tmp8 + tmp18
    tmp20 = tmp19 - tmp1
    tmp22 = tmp21 * tmp1
    tmp23 = tl_math.exp(tmp22)
    tmp25 = tmp24 * tmp1
    tmp26 = tl_math.exp(tmp25)
    tmp27 = tmp23 / tmp26
    tmp28 = tl_math.log(tmp27)
    tmp29 = tl_math.exp(tmp24)
    tmp32 = tmp30 - tmp31
    tmp33 = tmp32 * tmp32
    tmp34 = tmp29 + tmp33
    tmp35 = tl_math.exp(tmp21)
    tmp36 = tmp35 * tmp16
    tmp37 = tmp34 / tmp36
    tmp38 = tmp28 + tmp37
    tmp39 = tmp38 - tmp1
    tmp40 = tmp20 + tmp39
    tmp42 = tmp41 * tmp1
    tmp43 = tl_math.exp(tmp42)
    tmp45 = tmp44 * tmp1
    tmp46 = tl_math.exp(tmp45)
    tmp47 = tmp43 / tmp46
    tmp48 = tl_math.log(tmp47)
    tmp49 = tl_math.exp(tmp44)
    tmp52 = tmp50 - tmp51
    tmp53 = tmp52 * tmp52
    tmp54 = tmp49 + tmp53
    tmp55 = tl_math.exp(tmp41)
    tmp56 = tmp55 * tmp16
    tmp57 = tmp54 / tmp56
    tmp58 = tmp48 + tmp57
    tmp59 = tmp58 - tmp1
    tmp60 = tmp40 + tmp59
    tmp62 = tmp61 * tmp1
    tmp63 = tl_math.exp(tmp62)
    tmp65 = tmp64 * tmp1
    tmp66 = tl_math.exp(tmp65)
    tmp67 = tmp63 / tmp66
    tmp68 = tl_math.log(tmp67)
    tmp69 = tl_math.exp(tmp64)
    tmp72 = tmp70 - tmp71
    tmp73 = tmp72 * tmp72
    tmp74 = tmp69 + tmp73
    tmp75 = tl_math.exp(tmp61)
    tmp76 = tmp75 * tmp16
    tmp77 = tmp74 / tmp76
    tmp78 = tmp68 + tmp77
    tmp79 = tmp78 - tmp1
    tmp80 = tmp60 + tmp79
    tmp81 = tl.broadcast_to(tmp80, [XBLOCK, RBLOCK])
    tmp83 = tl.sum(tmp81, 1)[:, None]
    tmp84 = 64.0
    tmp85 = tmp83 / tmp84
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp85, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1, arg3_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))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg3_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: [mul_1, sigma2, mul, sigma1, truediv, log, exp_2, sub, pow_1, add, exp_3, mul_2, truediv_1, add_1, kld, sum_1, mean], Original ATen: [aten.mul, aten.exp, aten.div, aten.log, aten.sub, aten.pow, aten.add, aten.sum, aten.mean]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_div_exp_log_mean_mul_pow_sub_sum_0.run(buf2, arg1_1, arg0_1, arg2_1, arg3_1, 1, 64, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
        del arg2_1
        del arg3_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)
    arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg3_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_1, arg3_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.cuda
from torch import linalg as linalg


class BaseMeasure(nn.Module):
    """
    """
    NAME: 'str' = NotImplemented
    REFERENCE: 'str' = None
    BIGGER_IS_BETTER = False
    OPT_VALUE = 0.0

    def __init__(self, device):
        """
        Args:
            device ():
        """
        super(BaseMeasure, self).__init__()
        self.device = device
        self

    def forward(self, pred: 'torch.Tensor', target: 'torch.Tensor'):
        """
        Args:
            pred ():
            target ():
        Returns:
        """
        if pred.ndim != 5 or target.ndim != 5:
            raise ValueError(f'{self.NAME} expects 5-D inputs!')
        value = self.criterion(pred, target)
        return value.sum(dim=(4, 3, 2)).mean(dim=1).mean(dim=0)

    def reshape_clamp(self, pred: 'torch.Tensor', target: 'torch.Tensor'):
        """
        Args:
            pred ():
            target ():
        Returns:
        """
        if pred.ndim != 5 or target.ndim != 5:
            raise ValueError(f'{self.NAME} expects 5-D inputs!')
        pred = pred.reshape(-1, *pred.shape[2:])
        pred = ((pred + 1) / 2).clamp_(min=0.0, max=1.0)
        target = target.reshape(-1, *target.shape[2:])
        target = ((target + 1) / 2).clamp_(min=0.0, max=1.0)
        return pred, target

    @classmethod
    def to_display(cls, x):
        """
        Args:
            x ():
        Returns:
        """
        return x


class KLLoss(BaseMeasure):
    """
    KL-Divergence loss function
    """
    NAME = 'KL-Divergence (KL)'

    def __init__(self, device):
        super(KLLoss, self).__init__(device)

    def criterion(self, mu1, logvar1, mu2, logvar2):
        """ Computing the KL-Divergence between two Gaussian distributions """
        sigma1 = logvar1.mul(0.5).exp()
        sigma2 = logvar2.mul(0.5).exp()
        kld = torch.log(sigma2 / sigma1) + (torch.exp(logvar1) + (mu1 - mu2
            ) ** 2) / (2 * torch.exp(logvar2)) - 1 / 2
        return kld

    def forward(self, mu1, logvar1, mu2, logvar2):
        """ Computing the KL-Divergence between two Gaussian distributions """
        value = self.criterion(mu1, logvar1, mu2, logvar2)
        return value.sum(dim=-1).mean()


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


def get_init_inputs():
    return [[], {'device': 0}]

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.cuda
from torch import linalg as linalg
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_log_mean_mul_pow_sub_sum_0(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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
    tmp0 = tl.load(in_ptr0 + 4 * r0, None, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr2 + 4 * r0, None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr3 + 4 * r0, None, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp24 = tl.load(in_ptr1 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr2 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr3 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp41 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp44 = tl.load(in_ptr1 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp50 = tl.load(in_ptr2 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp51 = tl.load(in_ptr3 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp61 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp64 = tl.load(in_ptr1 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp70 = tl.load(in_ptr2 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp71 = tl.load(in_ptr3 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = tl_math.exp(tmp2)
    tmp5 = tmp4 * tmp1
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tmp3 / tmp6
    tmp8 = tl_math.log(tmp7)
    tmp9 = tl_math.exp(tmp4)
    tmp12 = tmp10 - tmp11
    tmp13 = tmp12 * tmp12
    tmp14 = tmp9 + tmp13
    tmp15 = tl_math.exp(tmp0)
    tmp16 = 2.0
    tmp17 = tmp15 * tmp16
    tmp18 = tmp14 / tmp17
    tmp19 = tmp8 + tmp18
    tmp20 = tmp19 - tmp1
    tmp22 = tmp21 * tmp1
    tmp23 = tl_math.exp(tmp22)
    tmp25 = tmp24 * tmp1
    tmp26 = tl_math.exp(tmp25)
    tmp27 = tmp23 / tmp26
    tmp28 = tl_math.log(tmp27)
    tmp29 = tl_math.exp(tmp24)
    tmp32 = tmp30 - tmp31
    tmp33 = tmp32 * tmp32
    tmp34 = tmp29 + tmp33
    tmp35 = tl_math.exp(tmp21)
    tmp36 = tmp35 * tmp16
    tmp37 = tmp34 / tmp36
    tmp38 = tmp28 + tmp37
    tmp39 = tmp38 - tmp1
    tmp40 = tmp20 + tmp39
    tmp42 = tmp41 * tmp1
    tmp43 = tl_math.exp(tmp42)
    tmp45 = tmp44 * tmp1
    tmp46 = tl_math.exp(tmp45)
    tmp47 = tmp43 / tmp46
    tmp48 = tl_math.log(tmp47)
    tmp49 = tl_math.exp(tmp44)
    tmp52 = tmp50 - tmp51
    tmp53 = tmp52 * tmp52
    tmp54 = tmp49 + tmp53
    tmp55 = tl_math.exp(tmp41)
    tmp56 = tmp55 * tmp16
    tmp57 = tmp54 / tmp56
    tmp58 = tmp48 + tmp57
    tmp59 = tmp58 - tmp1
    tmp60 = tmp40 + tmp59
    tmp62 = tmp61 * tmp1
    tmp63 = tl_math.exp(tmp62)
    tmp65 = tmp64 * tmp1
    tmp66 = tl_math.exp(tmp65)
    tmp67 = tmp63 / tmp66
    tmp68 = tl_math.log(tmp67)
    tmp69 = tl_math.exp(tmp64)
    tmp72 = tmp70 - tmp71
    tmp73 = tmp72 * tmp72
    tmp74 = tmp69 + tmp73
    tmp75 = tl_math.exp(tmp61)
    tmp76 = tmp75 * tmp16
    tmp77 = tmp74 / tmp76
    tmp78 = tmp68 + tmp77
    tmp79 = tmp78 - tmp1
    tmp80 = tmp60 + tmp79
    tmp81 = tl.broadcast_to(tmp80, [XBLOCK, RBLOCK])
    tmp83 = tl.sum(tmp81, 1)[:, None]
    tmp84 = 64.0
    tmp85 = tmp83 / tmp84
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp85, None)


def call(args):
    arg0_1, arg1_1, arg2_1, arg3_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))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg3_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_log_mean_mul_pow_sub_sum_0[grid(1)](buf2,
            arg1_1, arg0_1, arg2_1, arg3_1, 1, 64, XBLOCK=1, num_warps=2,
            num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
        del arg3_1
    return buf2,


class BaseMeasure(nn.Module):
    """
    """
    NAME: 'str' = NotImplemented
    REFERENCE: 'str' = None
    BIGGER_IS_BETTER = False
    OPT_VALUE = 0.0

    def __init__(self, device):
        """
        Args:
            device ():
        """
        super(BaseMeasure, self).__init__()
        self.device = device
        self

    def forward(self, pred: 'torch.Tensor', target: 'torch.Tensor'):
        """
        Args:
            pred ():
            target ():
        Returns:
        """
        if pred.ndim != 5 or target.ndim != 5:
            raise ValueError(f'{self.NAME} expects 5-D inputs!')
        value = self.criterion(pred, target)
        return value.sum(dim=(4, 3, 2)).mean(dim=1).mean(dim=0)

    def reshape_clamp(self, pred: 'torch.Tensor', target: 'torch.Tensor'):
        """
        Args:
            pred ():
            target ():
        Returns:
        """
        if pred.ndim != 5 or target.ndim != 5:
            raise ValueError(f'{self.NAME} expects 5-D inputs!')
        pred = pred.reshape(-1, *pred.shape[2:])
        pred = ((pred + 1) / 2).clamp_(min=0.0, max=1.0)
        target = target.reshape(-1, *target.shape[2:])
        target = ((target + 1) / 2).clamp_(min=0.0, max=1.0)
        return pred, target

    @classmethod
    def to_display(cls, x):
        """
        Args:
            x ():
        Returns:
        """
        return x


class KLLossNew(BaseMeasure):
    """
    KL-Divergence loss function
    """
    NAME = 'KL-Divergence (KL)'

    def __init__(self, device):
        super(KLLossNew, self).__init__(device)

    def criterion(self, mu1, logvar1, mu2, logvar2):
        """ Computing the KL-Divergence between two Gaussian distributions """
        sigma1 = logvar1.mul(0.5).exp()
        sigma2 = logvar2.mul(0.5).exp()
        kld = torch.log(sigma2 / sigma1) + (torch.exp(logvar1) + (mu1 - mu2
            ) ** 2) / (2 * torch.exp(logvar2)) - 1 / 2
        return kld

    def forward(self, input_0, input_1, input_2, input_3):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        arg3_1 = input_3
        output = call([arg0_1, arg1_1, arg2_1, arg3_1])
        return output[0]
