from torch import nn
from torch.nn.modules.loss import *
from torch.nn.modules.loss import _Loss

from colossalai.global_variables import tensor_parallel_env as env
from colossalai.legacy.nn.layer.utils import get_tensor_parallel_mode

from .loss_1d import VocabParallelCrossEntropyLoss1D
from .loss_2d import CrossEntropyLoss2D, VocabParallelCrossEntropyLoss2D
from .loss_2p5d import CrossEntropyLoss2p5D, VocabParallelCrossEntropyLoss2p5D
from .loss_3d import CrossEntropyLoss3D, VocabParallelCrossEntropyLoss3D

_parallel_cross_entropy = {
    '2d': CrossEntropyLoss2D,
    '2.5d': CrossEntropyLoss2p5D,
    '3d': CrossEntropyLoss3D,
}

_vocab_parallel_cross_entropy = {
    '1d': VocabParallelCrossEntropyLoss1D,
    '2d': VocabParallelCrossEntropyLoss2D,
    '2.5d': VocabParallelCrossEntropyLoss2p5D,
    '3d': VocabParallelCrossEntropyLoss3D,
}


class CrossEntropyLoss(_Loss):

    def __init__(self, reduction: bool = True, *args, **kwargs):
        super().__init__()
        tensor_parallel = get_tensor_parallel_mode()
        if tensor_parallel is not None and env.vocab_parallel:
            self.loss = _vocab_parallel_cross_entropy[tensor_parallel](reduction=reduction, *args, **kwargs)
        elif tensor_parallel is None or tensor_parallel == '1d':
            reduction = 'mean' if reduction else 'none'
            self.loss = nn.CrossEntropyLoss(reduction=reduction, *args, **kwargs)
        else:
            self.loss = _parallel_cross_entropy[tensor_parallel](reduction=reduction, *args, **kwargs)

    def forward(self, *args):
        return self.loss(*args)
