import torch
import torch.nn.functional as F


def non_saturating_loss(logits, targets):
    probs = logits.softmax(1)
    log_prob = torch.log(1 - probs + 1e-12)
    if targets.ndim == 2:
        return -(targets * log_prob).sum(1).mean()
    else:
        return F.nll_loss(log_prob, targets)


class NonSaturatingLoss(torch.nn.Module):

    def __init__(self, epsilon=0):
        super().__init__()
        self.epsilon = epsilon

    def forward(self, logits, targets):
        if self.epsilon > 0:
            n_classes = logits.shape[1]
            onehot_targets = F.one_hot(targets, n_classes).float()
            targets = (1 - self.epsilon
                ) * onehot_targets + self.epsilon / n_classes
        return non_saturating_loss(logits, targets)


def get_inputs():
    return [torch.rand([4, 4]), torch.rand([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.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__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    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
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_per_fused__softmax_add_log_mean_mul_neg_rsub_sum_1(in_out_ptr0,
    in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    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')
    tmp1 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr1 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp29 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp1 / tmp7
    tmp9 = 1.0
    tmp10 = tmp9 - tmp8
    tmp11 = 1e-12
    tmp12 = tmp10 + tmp11
    tmp13 = tl_math.log(tmp12)
    tmp14 = tmp0 * tmp13
    tmp16 = tmp2 / tmp7
    tmp17 = tmp9 - tmp16
    tmp18 = tmp17 + tmp11
    tmp19 = tl_math.log(tmp18)
    tmp20 = tmp15 * tmp19
    tmp21 = tmp14 + tmp20
    tmp23 = tmp4 / tmp7
    tmp24 = tmp9 - tmp23
    tmp25 = tmp24 + tmp11
    tmp26 = tl_math.log(tmp25)
    tmp27 = tmp22 * tmp26
    tmp28 = tmp21 + tmp27
    tmp30 = tmp6 / tmp7
    tmp31 = tmp9 - tmp30
    tmp32 = tmp31 + tmp11
    tmp33 = tl_math.log(tmp32)
    tmp34 = tmp29 * tmp33
    tmp35 = tmp28 + tmp34
    tmp36 = tl.broadcast_to(tmp35, [XBLOCK, RBLOCK])
    tmp38 = tl.sum(tmp36, 1)[:, None]
    tmp39 = 4.0
    tmp40 = tmp38 / tmp39
    tmp41 = -tmp40
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp41, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(16)](arg0_1, buf0, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del arg0_1
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf2
        del buf2
        triton_per_fused__softmax_add_log_mean_mul_neg_rsub_sum_1[grid(1)](buf3
            , arg1_1, buf0, 1, 4, XBLOCK=1, num_warps=2, num_stages=1)
        del arg1_1
        del buf0
    return buf3,


def non_saturating_loss(logits, targets):
    probs = logits.softmax(1)
    log_prob = torch.log(1 - probs + 1e-12)
    if targets.ndim == 2:
        return -(targets * log_prob).sum(1).mean()
    else:
        return F.nll_loss(log_prob, targets)


class NonSaturatingLossNew(torch.nn.Module):

    def __init__(self, epsilon=0):
        super().__init__()
        self.epsilon = epsilon

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