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 numpy as np
import torch as th
import torch.nn as nn
import torch.utils.data.distributed
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_div_mul_neg_sum_1(in_out_ptr0, in_ptr0,
    in_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r3 = rindex
    r0 = rindex % 16
    r2 = rindex // 64
    tmp0 = tl.load(in_ptr0 + r3, None)
    tmp2 = tl.load(in_ptr1 + r3, None)
    tmp3 = tl.load(in_ptr1 + (r0 + 64 * r2), None, eviction_policy='evict_last'
        )
    tmp5 = tl.load(in_ptr1 + (16 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp8 = tl.load(in_ptr1 + (32 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp11 = tl.load(in_ptr1 + (48 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp1 = -tmp0
    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 = tmp2 - tmp14
    tmp16 = tmp1 * tmp15
    tmp17 = tl.broadcast_to(tmp16, [RBLOCK])
    tmp19 = triton_helpers.promote_to_tensor(tl.sum(tmp17, 0))
    tmp20 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp22 = triton_helpers.promote_to_tensor(tl.sum(tmp20, 0))
    tmp23 = tmp19 / tmp22
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp23, 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
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf1
        del buf1
        triton_per_fused__log_softmax_div_mul_neg_sum_1[grid(1)](buf3,
            arg0_1, buf0, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del buf0
    return buf3,


class MultiSoftmaxCrossEntropyLossNew(nn.Module):

    def __init__(self, class_weight=None, label_smoothing_value=0):
        super(MultiSoftmaxCrossEntropyLossNew, self).__init__()
        self.class_weight = class_weight
        if self.class_weight is not None:
            self.class_weight = self.class_weight
        self.logsoftmax = nn.LogSoftmax(dim=1)
        self.label_smoothing_value = label_smoothing_value

    def cross_entropy(self, pred, soft_targets, class_weight=None):
        if class_weight is not None:
            class_weight_matrix = class_weight.expand_as(soft_targets)
            used_class_weights = th.where(soft_targets > 0,
                class_weight_matrix, soft_targets)
            samples_weight = th.max(used_class_weights, dim=1, keepdim=True)[0]
            loss = th.mean(th.sum(-samples_weight * soft_targets * self.
                logsoftmax(pred), 1), 0)
        else:
            if self.label_smoothing_value > 0:
                batch_size, total_classes_count = soft_targets.size()
                for sample_index in range(batch_size):
                    pos_indices = np.where(soft_targets[sample_index, :] > 0)
                    pos_classes_count = len(pos_indices[0])
                    if pos_classes_count > 0:
                        neg_p = self.label_smoothing_value / float(
                            total_classes_count - pos_classes_count)
                        pos_p = self.label_smoothing_value / pos_classes_count
                        soft_targets[sample_index, :] += neg_p
                        soft_targets[sample_index, pos_indices[0]
                            ] = soft_targets[sample_index, pos_indices[0]
                            ] - pos_p - neg_p
            loss = th.sum(-soft_targets * self.logsoftmax(pred))
            loss = loss / soft_targets.sum()
        return loss

    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: 0.028088