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.nn.functional as F
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_mean_mul_neg_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)
    tmp2 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp5 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp8 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp13 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
    tmp16 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
    tmp20 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
    tmp24 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
    tmp1 = tl_math.exp(tmp0)
    tmp3 = tl_math.exp(tmp2)
    tmp4 = tmp1 + tmp3
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tmp4 + tmp6
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tmp7 + tmp9
    tmp11 = tl_math.log(tmp10)
    tmp12 = tmp0 - tmp11
    tmp14 = tmp12 * tmp13
    tmp15 = tmp2 - tmp11
    tmp17 = tmp15 * tmp16
    tmp18 = tmp14 + tmp17
    tmp19 = tmp5 - tmp11
    tmp21 = tmp19 * tmp20
    tmp22 = tmp18 + tmp21
    tmp23 = tmp8 - tmp11
    tmp25 = tmp23 * tmp24
    tmp26 = tmp22 + tmp25
    tmp27 = -tmp26
    tmp28 = tl.broadcast_to(tmp27, [XBLOCK, RBLOCK])
    tmp30 = tl.sum(tmp28, 1)[:, None]
    tmp31 = 64.0
    tmp32 = tmp30 / tmp31
    tmp33 = 1.0
    tmp34 = tmp32 * tmp33
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp34, 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)](arg0_1, buf0, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        triton_per_fused__log_softmax_mean_mul_neg_sum_1[grid(1)](buf2,
            buf0, arg1_1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg1_1
        del buf0
    return buf2,


def reduce_loss(loss, reduction):
    """Reduce loss as specified.

    Args:
        loss (Tensor): Elementwise loss tensor.
        reduction (str): Options are "none", "mean" and "sum".
    Return:
        Tensor: Reduced loss tensor.
    """
    reduction_enum = F._Reduction.get_enum(reduction)
    if reduction_enum == 0:
        return loss
    elif reduction_enum == 1:
        return loss.mean()
    elif reduction_enum == 2:
        return loss.sum()


def weight_reduce_loss(loss, weight=None, reduction='mean', avg_factor=None):
    """Apply element-wise weight and reduce loss.

    Args:
        loss (Tensor): Element-wise loss.
        weight (Tensor): Element-wise weights.
        reduction (str): Same as built-in losses of PyTorch.
        avg_factor (float): Average factor when computing the mean of losses.
    Returns:
        Tensor: Processed loss values.
    """
    if weight is not None:
        loss = loss * weight
    if avg_factor is None:
        loss = reduce_loss(loss, reduction)
    elif reduction == 'mean':
        loss = loss.sum() / avg_factor
    elif reduction != 'none':
        raise ValueError('avg_factor can not be used with reduction="sum"')
    return loss


def binary_cross_entropy(pred, label, weight=None, reduction='mean',
    avg_factor=None, class_weight=None, pos_weight=None):
    """Calculate the binary CrossEntropy loss with logits.
    Args:
        pred (torch.Tensor): The prediction with shape (N, \\*).
        label (torch.Tensor): The gt label with shape (N, \\*).
        weight (torch.Tensor, optional): Element-wise weight of loss with shape
            (N, ). Defaults to None.
        reduction (str): The method used to reduce the loss.
            Options are "none", "mean" and "sum". If reduction is 'none' , loss
            is same shape as pred and label. Defaults to 'mean'.
        avg_factor (int, optional): Average factor that is used to average
            the loss. Defaults to None.
        class_weight (torch.Tensor, optional): The weight for each class with
            shape (C), C is the number of classes. Default None.
        pos_weight (torch.Tensor, optional): The positive weight for each
            class with shape (C), C is the number of classes. Default None.
    Returns:
        torch.Tensor: The calculated loss
    """
    assert pred.dim() == label.dim()
    if class_weight is not None:
        N = pred.size()[0]
        class_weight = class_weight.repeat(N, 1)
    loss = F.binary_cross_entropy_with_logits(pred, label, weight=
        class_weight, pos_weight=pos_weight, reduction='none')
    if weight is not None:
        assert weight.dim() == 1
        weight = weight.float()
        if pred.dim() > 1:
            weight = weight.reshape(-1, 1)
    loss = weight_reduce_loss(loss, weight=weight, reduction=reduction,
        avg_factor=avg_factor)
    return loss


def cross_entropy(pred, label, weight=None, reduction='mean', avg_factor=
    None, class_weight=None):
    """Calculate the CrossEntropy loss.

    Args:
        pred (torch.Tensor): The prediction with shape (N, C), C is the number
            of classes.
        label (torch.Tensor): The gt label of the prediction.
        weight (torch.Tensor, optional): Sample-wise loss weight.
        reduction (str): The method used to reduce the loss.
        avg_factor (int, optional): Average factor that is used to average
            the loss. Defaults to None.
        class_weight (torch.Tensor, optional): The weight for each class with
            shape (C), C is the number of classes. Default None.
    Returns:
        torch.Tensor: The calculated loss
    """
    loss = F.cross_entropy(pred, label, weight=class_weight, reduction='none')
    if weight is not None:
        weight = weight.float()
    loss = weight_reduce_loss(loss, weight=weight, reduction=reduction,
        avg_factor=avg_factor)
    return loss


def soft_cross_entropy(pred, label, weight=None, reduction='mean',
    class_weight=None, avg_factor=None):
    """Calculate the Soft CrossEntropy loss.

    The label can be float.
    Args:
        pred (torch.Tensor): The prediction with shape (N, C), C is the number
            of classes.
        label (torch.Tensor): The gt label of the prediction with shape (N, C).
            When using "mixup", the label can be float.
        weight (torch.Tensor, optional): Sample-wise loss weight.
        reduction (str): The method used to reduce the loss.
        avg_factor (int, optional): Average factor that is used to average
            the loss. Defaults to None.
        class_weight (torch.Tensor, optional): The weight for each class with
            shape (C), C is the number of classes. Default None.
    Returns:
        torch.Tensor: The calculated loss
    """
    loss = -label * F.log_softmax(pred, dim=-1)
    if class_weight is not None:
        loss *= class_weight
    loss = loss.sum(dim=-1)
    if weight is not None:
        weight = weight.float()
    loss = weight_reduce_loss(loss, weight=weight, reduction=reduction,
        avg_factor=avg_factor)
    return loss


class CrossEntropyLossNew(nn.Module):
    """Cross entropy loss.

    Args:
        use_sigmoid (bool): Whether the prediction uses sigmoid
            of softmax. Defaults to False.
        use_soft (bool): Whether to use the soft version of CrossEntropyLoss.
            Defaults to False.
        reduction (str): The method used to reduce the loss.
            Options are "none", "mean" and "sum". Defaults to 'mean'.
        loss_weight (float):  Weight of the loss. Defaults to 1.0.
        class_weight (List[float], optional): The weight for each class with
            shape (C), C is the number of classes. Default None.
        pos_weight (List[float], optional): The positive weight for each
            class with shape (C), C is the number of classes. Only enabled in
            BCE loss when ``use_sigmoid`` is True. Default None.
    """

    def __init__(self, use_sigmoid=False, use_soft=False, reduction='mean',
        loss_weight=1.0, class_weight=None, pos_weight=None):
        super(CrossEntropyLossNew, self).__init__()
        self.use_sigmoid = use_sigmoid
        self.use_soft = use_soft
        assert not (self.use_soft and self.use_sigmoid
            ), 'use_sigmoid and use_soft could not be set simultaneously'
        self.reduction = reduction
        self.loss_weight = loss_weight
        self.class_weight = class_weight
        self.pos_weight = pos_weight
        if self.use_sigmoid:
            self.cls_criterion = binary_cross_entropy
        elif self.use_soft:
            self.cls_criterion = soft_cross_entropy
        else:
            self.cls_criterion = cross_entropy

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


Measured latency_ms: 0.015389