import torch
from torch import nn

from colossalai.constants import INPUT_GROUP_3D, WEIGHT_GROUP_3D
from colossalai.legacy.nn.layer.parallel_3d import reduce_by_batch_3d, split_tensor_3d
from colossalai.legacy.nn.layer.parallel_3d._utils import get_parallel_mode_from_env

from ._utils import calc_acc


class Accuracy3D(nn.Module):
    """Accuracy for 3D parallelism
    """

    def __init__(self):
        super().__init__()
        self.input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
        self.weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)

    def forward(self, logits, targets):
        """Calculate the accuracy of predicted labels.

        Args:
            logits (:class:`torch.tensor`): Predicted labels.
            targets (:class:`torch.tensor`): True labels from data.

        Returns:
            float: the accuracy of prediction.
         """
        with torch.no_grad():
            targets = split_tensor_3d(targets, 0, self.weight_parallel_mode)
            targets = split_tensor_3d(targets, 0, self.input_parallel_mode)
            correct = calc_acc(logits, targets)
            correct = reduce_by_batch_3d(correct, self.input_parallel_mode, self.weight_parallel_mode)
        return correct
