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]


Measured latency_ms: 0.017225