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
from torch.nn.modules.loss import _WeightedLoss
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 = 256
    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_gather_index_put_lift_fresh_neg_1(in_ptr0, in_ptr1,
    out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp11 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp16 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp19 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp1 = tl.full([1], -100, tl.int64)
    tmp2 = tmp0 == tmp1
    tmp3 = tl.full([1], 0, tl.int64)
    tmp4 = tl.where(tmp2, tmp3, tmp0)
    tmp5 = tl.full([XBLOCK], 4, tl.int32)
    tmp6 = tmp4 + tmp5
    tmp7 = tmp4 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp4)
    tl.device_assert((0 <= tmp8) & (tmp8 < 4) | ~xmask,
        'index out of bounds: 0 <= tmp8 < 4')
    tmp10 = tl.load(in_ptr1 + (tmp8 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp12 = tl_math.exp(tmp11)
    tmp14 = tl_math.exp(tmp13)
    tmp15 = tmp12 + tmp14
    tmp17 = tl_math.exp(tmp16)
    tmp18 = tmp15 + tmp17
    tmp20 = tl_math.exp(tmp19)
    tmp21 = tmp18 + tmp20
    tmp22 = tl_math.log(tmp21)
    tmp23 = tmp10 - tmp22
    tmp24 = -tmp23
    tl.store(out_ptr0 + x0, tmp4, xmask)
    tl.store(out_ptr1 + x0, tmp24, xmask)


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,), (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=256, num_warps=4, num_stages=1)
        del arg0_1
        buf3 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        triton_poi_fused_gather_index_put_lift_fresh_neg_1[grid(4)](arg1_1,
            buf0, arg1_1, buf3, 4, XBLOCK=4, num_warps=1, num_stages=1)
        del arg1_1
        del buf0
    return reinterpret_tensor(buf3, (4,), (1,), 0),


class MaskedCrossEntropyCriterionNew(_WeightedLoss):

    def __init__(self, ignore_index=[-100], reduce=None):
        super(MaskedCrossEntropyCriterionNew, self).__init__()
        self.padding_idx = ignore_index
        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]


Measured latency_ms: 0.028613