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
import torch.onnx
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__softmax_0(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x5 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    x4 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x5, 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')
    tmp9 = tl.load(in_ptr0 + 4 * x4, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (1 + 4 * x4), xmask, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr0 + (2 + 4 * x4), xmask, eviction_policy='evict_last'
        )
    tmp14 = tl.load(in_ptr0 + (3 + 4 * x4), 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
    tmp11 = triton_helpers.maximum(tmp9, tmp10)
    tmp13 = triton_helpers.maximum(tmp11, tmp12)
    tmp15 = triton_helpers.maximum(tmp13, tmp14)
    tmp16 = tmp0 - tmp15
    tmp17 = tl_math.exp(tmp16)
    tl.store(out_ptr0 + x5, tmp8, xmask)
    tl.store(out_ptr1 + x5, tmp17, xmask)


@triton.jit
def triton_per_fused__log_softmax__softmax_index_mean_mul_neg_pow_rsub_1(
    in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, 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)
    r2 = rindex // 16
    r4 = rindex % 16
    r1 = rindex // 4 % 4
    tmp11 = tl.load(in_ptr0 + r2, None, eviction_policy='evict_last')
    tmp0 = r2
    tmp1 = tl.full([1, 1], 2, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = tl.full([1, 1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.full([1, 1], 0, tl.int64)
    tmp6 = tl.where(tmp4, tmp5, tmp3)
    tmp7 = tl.full([1, 1], 3, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.where(tmp8, tmp1, tmp7)
    tmp10 = tl.where(tmp2, tmp6, tmp9)
    tmp12 = tl.full([XBLOCK, RBLOCK], 4, tl.int32)
    tmp13 = tmp11 + tmp12
    tmp14 = tmp11 < 0
    tmp15 = tl.where(tmp14, tmp13, tmp11)
    tl.device_assert((0 <= tmp15) & (tmp15 < 4),
        'index out of bounds: 0 <= tmp15 < 4')
    tmp17 = tl.load(in_ptr1 + (r4 + 16 * tmp15 + 64 * tmp10), None)
    tmp18 = tl.load(in_ptr1 + (r4 + 64 * tmp10), None)
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tl.load(in_ptr1 + (16 + r4 + 64 * tmp10), None)
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp19 + tmp21
    tmp23 = tl.load(in_ptr1 + (32 + r4 + 64 * tmp10), None)
    tmp24 = tl_math.exp(tmp23)
    tmp25 = tmp22 + tmp24
    tmp26 = tl.load(in_ptr1 + (48 + r4 + 64 * tmp10), None)
    tmp27 = tl_math.exp(tmp26)
    tmp28 = tmp25 + tmp27
    tmp29 = tl_math.log(tmp28)
    tmp30 = tmp17 - tmp29
    tmp31 = -tmp30
    tmp32 = 1.0
    tmp33 = tmp31 * tmp32
    tmp34 = tl.load(in_ptr2 + (r4 + 16 * tmp15 + 64 * tmp10), None)
    tmp35 = tl.load(in_ptr2 + (4 * r1 + 16 * tmp15 + 64 * tmp10), None,
        eviction_policy='evict_last')
    tmp36 = tl.load(in_ptr2 + (1 + 4 * r1 + 16 * tmp15 + 64 * tmp10), None,
        eviction_policy='evict_last')
    tmp37 = tmp35 + tmp36
    tmp38 = tl.load(in_ptr2 + (2 + 4 * r1 + 16 * tmp15 + 64 * tmp10), None,
        eviction_policy='evict_last')
    tmp39 = tmp37 + tmp38
    tmp40 = tl.load(in_ptr2 + (3 + 4 * r1 + 16 * tmp15 + 64 * tmp10), None,
        eviction_policy='evict_last')
    tmp41 = tmp39 + tmp40
    tmp42 = tmp34 / tmp41
    tmp32 - tmp42
    tmp44 = tmp33 * tmp32
    tmp45 = tl.broadcast_to(tmp44, [XBLOCK, RBLOCK])
    tmp47 = tl.sum(tmp45, 1)[:, None]
    tmp48 = 64.0
    tmp49 = tmp47 / tmp48
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp49, 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,), (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)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__log_softmax__softmax_0[grid(256)](arg0_1, buf0,
            buf1, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf3
        del buf3
        triton_per_fused__log_softmax__softmax_index_mean_mul_neg_pow_rsub_1[
            grid(1)](buf4, arg1_1, buf0, buf1, 1, 64, XBLOCK=1, num_warps=2,
            num_stages=1)
        del arg1_1
        del buf0
        del buf1
    return buf4,


class FocalLossNew(nn.Module):

    def __init__(self, alpha=1, gamma=0):
        super(FocalLossNew, self).__init__()
        self.gamma = gamma
        self.alpha = alpha

    def get_attention(self, input, target):
        prob = F.softmax(input, dim=-1)
        prob = prob[range(target.shape[0]), target]
        prob = 1 - prob
        prob = prob ** self.gamma
        return prob

    def get_celoss(self, input, target):
        ce_loss = F.log_softmax(input, dim=1)
        ce_loss = -ce_loss[range(target.shape[0]), target]
        return ce_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: 0.032581