import torch
import torch.nn as nn
import torch.utils.data
import torch.nn.functional as F
import torch.autograd


class WeightedCrossEntropyLoss(nn.Module):
    """
    Transform input to fit the fomation of PyTorch offical cross entropy loss
    with anchor-wise weighting.
    """

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

    def forward(self, input: 'torch.Tensor', target: 'torch.Tensor',
        weights: 'torch.Tensor'):
        """
        Args:
            input: (B, #anchors, #classes) float tensor.
                Predited logits for each class.
            target: (B, #anchors, #classes) float tensor.
                One-hot classification targets.
            weights: (B, #anchors) float tensor.
                Anchor-wise weights.

        Returns:
            loss: (B, #anchors) float tensor.
                Weighted cross entropy loss without reduction
        """
        input = input.permute(0, 2, 1)
        target = target.argmax(dim=-1)
        loss = F.cross_entropy(input, target, reduction='none') * weights
        return loss


def get_inputs():
    return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4]), torch.rand([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 math as tl_math
import torch.nn as nn
import torch.utils.data
import torch.autograd
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_poi_fused__log_softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), 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 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_argmax_nll_loss2d_forward_1(in_ptr0, in_ptr1, out_ptr1,
    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 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp32 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp56 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp58 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp61 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp64 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 > tmp1
    tmp3 = tmp0 == tmp1
    tmp4 = tmp0 != tmp0
    tmp5 = tmp1 != tmp1
    tmp6 = tmp4 > tmp5
    tmp7 = tmp2 | tmp6
    tmp8 = tmp4 & tmp5
    tmp9 = tmp3 | tmp8
    tmp10 = tl.full([1], 0, tl.int64)
    tmp11 = tl.full([1], 1, tl.int64)
    tmp12 = tmp10 < tmp11
    tmp13 = tmp9 & tmp12
    tmp14 = tmp7 | tmp13
    tmp15 = tl.where(tmp14, tmp0, tmp1)
    tmp16 = tl.where(tmp14, tmp10, tmp11)
    tmp18 = tmp15 > tmp17
    tmp19 = tmp15 == tmp17
    tmp20 = tmp15 != tmp15
    tmp21 = tmp17 != tmp17
    tmp22 = tmp20 > tmp21
    tmp23 = tmp18 | tmp22
    tmp24 = tmp20 & tmp21
    tmp25 = tmp19 | tmp24
    tmp26 = tl.full([1], 2, tl.int64)
    tmp27 = tmp16 < tmp26
    tmp28 = tmp25 & tmp27
    tmp29 = tmp23 | tmp28
    tmp30 = tl.where(tmp29, tmp15, tmp17)
    tmp31 = tl.where(tmp29, tmp16, tmp26)
    tmp33 = tmp30 > tmp32
    tmp34 = tmp30 == tmp32
    tmp35 = tmp30 != tmp30
    tmp36 = tmp32 != tmp32
    tmp37 = tmp35 > tmp36
    tmp38 = tmp33 | tmp37
    tmp39 = tmp35 & tmp36
    tmp40 = tmp34 | tmp39
    tmp41 = tl.full([1], 3, tl.int64)
    tmp42 = tmp31 < tmp41
    tmp43 = tmp40 & tmp42
    tmp44 = tmp38 | tmp43
    tl.where(tmp44, tmp30, tmp32)
    tmp46 = tl.where(tmp44, tmp31, tmp41)
    tmp47 = tl.full([1], -100, tl.int64)
    tmp48 = tmp46 != tmp47
    tmp49 = tl.where(tmp48, tmp46, tmp10)
    tmp50 = tl.full([XBLOCK], 4, tl.int32)
    tmp51 = tmp49 + tmp50
    tmp52 = tmp49 < 0
    tmp53 = tl.where(tmp52, tmp51, tmp49)
    tl.device_assert((0 <= tmp53) & (tmp53 < 4) | ~xmask,
        'index out of bounds: 0 <= tmp53 < 4')
    tmp55 = tl.load(in_ptr1 + (tmp53 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp57 = tl_math.exp(tmp56)
    tmp59 = tl_math.exp(tmp58)
    tmp60 = tmp57 + tmp59
    tmp62 = tl_math.exp(tmp61)
    tmp63 = tmp60 + tmp62
    tmp65 = tl_math.exp(tmp64)
    tmp66 = tmp63 + tmp65
    tmp67 = tl_math.log(tmp66)
    tmp68 = tmp55 - tmp67
    tmp69 = -tmp68
    tmp70 = 0.0
    tmp71 = tl.where(tmp48, tmp69, tmp70)
    tl.store(out_ptr1 + x0, tmp71, xmask)


@triton.jit
def triton_poi_fused_mul_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x2, tmp2, xmask)


def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(arg2_1, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((4, 4, 4), (16, 1, 4), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__log_softmax_0[grid(64)](arg0_1, buf1, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
        del arg0_1
        buf2 = empty_strided_cuda((4, 1, 4), (4, 16, 1), torch.float32)
        triton_poi_fused_argmax_nll_loss2d_forward_1[grid(16)](arg1_1, buf1,
            buf2, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del arg1_1
        buf3 = reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0)
        del buf1
        triton_poi_fused_mul_2[grid(64)](buf2, arg2_1, buf3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del arg2_1
        del buf2
    return buf3,


class WeightedCrossEntropyLossNew(nn.Module):
    """
    Transform input to fit the fomation of PyTorch offical cross entropy loss
    with anchor-wise weighting.
    """

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

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