import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data


def one_hot(val: 'torch.LongTensor', num: 'int', num_first: 'bool'=False
    ) ->torch.FloatTensor:
    """
    Overview:
        Convert a ``torch.LongTensor`` to one hot encoding.
        This implementation can be slightly faster than ``torch.nn.functional.one_hot``
    Arguments:
        - val (:obj:`torch.LongTensor`): each element contains the state to be encoded, the range should be [0, num-1]
        - num (:obj:`int`): number of states of the one hot encoding
        - num_first (:obj:`bool`): If ``num_first`` is False, the one hot encoding is added as the last; \\
            Otherwise as the first dimension.
    Returns:
        - one_hot (:obj:`torch.FloatTensor`)
    Example:
        >>> one_hot(2*torch.ones([2,2]).long(),3)
        tensor([[[0., 0., 1.],
                 [0., 0., 1.]],
                [[0., 0., 1.],
                 [0., 0., 1.]]])
        >>> one_hot(2*torch.ones([2,2]).long(),3,num_first=True)
        tensor([[[0., 0.], [1., 0.]],
                [[0., 1.], [0., 0.]],
                [[1., 0.], [0., 1.]]])
    """
    assert isinstance(val, torch.Tensor), type(val)
    assert val.dtype == torch.long
    assert len(val.shape) >= 1
    old_shape = val.shape
    val_reshape = val.reshape(-1, 1)
    ret = torch.zeros(val_reshape.shape[0], num, device=val.device)
    index_neg_one = torch.eq(val_reshape, -1).long()
    if index_neg_one.sum() != 0:
        val_reshape = torch.where(val_reshape != -1, val_reshape, torch.
            zeros(val_reshape.shape, device=val.device).long())
    try:
        ret.scatter_(1, val_reshape, 1)
        if index_neg_one.sum() != 0:
            ret = ret * (1 - index_neg_one)
    except RuntimeError:
        raise RuntimeError('value: {}\nnum: {}\t:val_shape: {}\n'.format(
            val_reshape, num, val_reshape.shape))
    if num_first:
        return ret.permute(1, 0).reshape(num, *old_shape)
    else:
        return ret.reshape(*old_shape, num)


class LabelSmoothCELoss(nn.Module):
    """
    Overview:
        Label smooth cross entropy loss.
    Interfaces:
        forward
    """

    def __init__(self, ratio: 'float') ->None:
        super().__init__()
        self.ratio = ratio

    def forward(self, logits: 'torch.Tensor', labels: 'torch.LongTensor'
        ) ->torch.Tensor:
        """
        Overview:
            Calculate label smooth cross entropy loss.
        Arguments:
            - logits (:obj:`torch.Tensor`): Predicted logits.
            - labels (:obj:`torch.LongTensor`): Ground truth.
        Returns:
            - loss (:obj:`torch.Tensor`): Calculated loss.
        """
        B, N = logits.shape
        val = float(self.ratio) / (N - 1)
        one_hot = torch.full_like(logits, val)
        one_hot.scatter_(1, labels.unsqueeze(1), 1 - val)
        logits = F.log_softmax(logits, dim=1)
        return -torch.sum(logits * one_hot.detach()) / B


def get_inputs():
    return [torch.rand([4, 4]), torch.ones([4], dtype=torch.int64)]


def get_init_inputs():
    return [[], {'ratio': 4}]

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.utils.data
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 = 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
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_per_fused__log_softmax_div_mul_neg_scatter_sum_1(in_out_ptr0,
    in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 16
    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
    r1 = rindex // 4
    r0 = rindex % 4
    tmp0 = tl.load(in_ptr0 + r2, None)
    tmp1 = tl.load(in_ptr0 + 4 * r1, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * r1), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * r1), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * r1), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr1 + r1, None, eviction_policy='evict_last')
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tl_math.exp(tmp3)
    tmp5 = tmp2 + tmp4
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp5 + tmp7
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tmp8 + tmp10
    tmp12 = tl_math.log(tmp11)
    tmp13 = tmp0 - tmp12
    tmp15 = r0
    tmp16 = tmp14 == tmp15
    tmp17 = -0.33333333333333326
    tmp18 = 1.3333333333333333
    tmp19 = tl.where(tmp16, tmp17, tmp18)
    tmp20 = tmp13 * tmp19
    tmp21 = tl.broadcast_to(tmp20, [XBLOCK, RBLOCK])
    tmp23 = tl.sum(tmp21, 1)[:, None]
    tmp24 = -tmp23
    tmp25 = 0.25
    tmp26 = tmp24 * tmp25
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp26, 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,), (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__log_softmax_0[grid(16)](arg0_1, buf0, 16, XBLOCK=
            16, num_warps=1, num_stages=1)
        del arg0_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        triton_per_fused__log_softmax_div_mul_neg_scatter_sum_1[grid(1)](buf2,
            buf0, arg1_1, 1, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del arg1_1
        del buf0
    return buf2,


def one_hot(val: 'torch.LongTensor', num: 'int', num_first: 'bool'=False
    ) ->torch.FloatTensor:
    """
    Overview:
        Convert a ``torch.LongTensor`` to one hot encoding.
        This implementation can be slightly faster than ``torch.nn.functional.one_hot``
    Arguments:
        - val (:obj:`torch.LongTensor`): each element contains the state to be encoded, the range should be [0, num-1]
        - num (:obj:`int`): number of states of the one hot encoding
        - num_first (:obj:`bool`): If ``num_first`` is False, the one hot encoding is added as the last; \\
            Otherwise as the first dimension.
    Returns:
        - one_hot (:obj:`torch.FloatTensor`)
    Example:
        >>> one_hot(2*torch.ones([2,2]).long(),3)
        tensor([[[0., 0., 1.],
                 [0., 0., 1.]],
                [[0., 0., 1.],
                 [0., 0., 1.]]])
        >>> one_hot(2*torch.ones([2,2]).long(),3,num_first=True)
        tensor([[[0., 0.], [1., 0.]],
                [[0., 1.], [0., 0.]],
                [[1., 0.], [0., 1.]]])
    """
    assert isinstance(val, torch.Tensor), type(val)
    assert val.dtype == torch.long
    assert len(val.shape) >= 1
    old_shape = val.shape
    val_reshape = val.reshape(-1, 1)
    ret = torch.zeros(val_reshape.shape[0], num, device=val.device)
    index_neg_one = torch.eq(val_reshape, -1).long()
    if index_neg_one.sum() != 0:
        val_reshape = torch.where(val_reshape != -1, val_reshape, torch.
            zeros(val_reshape.shape, device=val.device).long())
    try:
        ret.scatter_(1, val_reshape, 1)
        if index_neg_one.sum() != 0:
            ret = ret * (1 - index_neg_one)
    except RuntimeError:
        raise RuntimeError('value: {}\nnum: {}\t:val_shape: {}\n'.format(
            val_reshape, num, val_reshape.shape))
    if num_first:
        return ret.permute(1, 0).reshape(num, *old_shape)
    else:
        return ret.reshape(*old_shape, num)


class LabelSmoothCELossNew(nn.Module):
    """
    Overview:
        Label smooth cross entropy loss.
    Interfaces:
        forward
    """

    def __init__(self, ratio: 'float') ->None:
        super().__init__()
        self.ratio = ratio

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