import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.optim


def kl_divergence(y, target, mask=None, reduce=True):
    loss = (target * torch.log(target) - target * F.log_softmax(y, 1)).sum(1)
    if mask is not None:
        loss = mask * loss
    if reduce:
        return loss.mean()
    else:
        return loss


class KLDivergence(nn.Module):

    def __init__(self, reduce):
        super().__init__()
        self.reduce = reduce

    def forward(self, y, target, mask=None, *args, **kwargs):
        return kl_divergence(y, target.detach(), mask, self.reduce)


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


def get_init_inputs():
    return [[], {'reduce': 4}]

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 math as tl_math
import torch.nn.functional as F
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


@triton.jit
def triton_poi_fused__log_softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tl.store(out_ptr0 + x3, tmp8, xmask)


@triton.jit
def triton_per_fused__log_softmax_log_mean_mul_sub_sum_1(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)
    tmp3 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
    tmp5 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
    tmp8 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
    tmp11 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
    tmp18 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp25 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp32 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp1 = tl_math.log(tmp0)
    tmp2 = tmp0 * tmp1
    tmp4 = tl_math.exp(tmp3)
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tmp4 + tmp6
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tmp7 + tmp9
    tmp12 = tl_math.exp(tmp11)
    tmp13 = tmp10 + tmp12
    tmp14 = tl_math.log(tmp13)
    tmp15 = tmp3 - tmp14
    tmp16 = tmp0 * tmp15
    tmp17 = tmp2 - tmp16
    tmp19 = tl_math.log(tmp18)
    tmp20 = tmp18 * tmp19
    tmp21 = tmp5 - tmp14
    tmp22 = tmp18 * tmp21
    tmp23 = tmp20 - tmp22
    tmp24 = tmp17 + tmp23
    tmp26 = tl_math.log(tmp25)
    tmp27 = tmp25 * tmp26
    tmp28 = tmp8 - tmp14
    tmp29 = tmp25 * tmp28
    tmp30 = tmp27 - tmp29
    tmp31 = tmp24 + tmp30
    tmp33 = tl_math.log(tmp32)
    tmp34 = tmp32 * tmp33
    tmp35 = tmp11 - tmp14
    tmp36 = tmp32 * tmp35
    tmp37 = tmp34 - tmp36
    tmp38 = tmp31 + tmp37
    tmp39 = tl.broadcast_to(tmp38, [XBLOCK, RBLOCK])
    tmp41 = tl.sum(tmp39, 1)[:, None]
    tmp42 = 64.0
    tmp43 = tmp41 / tmp42
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp43, 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, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__log_softmax_0[grid(256)](arg1_1, buf0, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del arg1_1
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf2
        del buf2
        triton_per_fused__log_softmax_log_mean_mul_sub_sum_1[grid(1)](buf3,
            arg0_1, buf0, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del buf0
    return buf3,


def kl_divergence(y, target, mask=None, reduce=True):
    loss = (target * torch.log(target) - target * F.log_softmax(y, 1)).sum(1)
    if mask is not None:
        loss = mask * loss
    if reduce:
        return loss.mean()
    else:
        return loss


class KLDivergenceNew(nn.Module):

    def __init__(self, reduce):
        super().__init__()
        self.reduce = reduce

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