from collections import Sequence

import mmcv
import numpy as np
import torch


def remove_dontcare(image_anno):
    img_filtered_annotations = {}
    relevant_annotation_indices = [
        i for i, x in enumerate(image_anno['name']) if x != 'DontCare'
    ]
    for key in image_anno.keys():
        img_filtered_annotations[key] = (
            image_anno[key][relevant_annotation_indices])
    return img_filtered_annotations


def to_tensor(data):
    # TODO: remove this duplicated method in the future
    """Convert objects of various python types to :obj:`torch.Tensor`.
    Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`,
    :class:`Sequence`, :class:`int` and :class:`float`.
    """
    if isinstance(data, torch.Tensor):
        return data
    elif isinstance(data, np.ndarray):
        return torch.from_numpy(data)
    elif isinstance(data, Sequence) and not mmcv.is_str(data):
        return torch.tensor(data)
    elif isinstance(data, int):
        return torch.LongTensor([data])
    elif isinstance(data, float):
        return torch.FloatTensor([data])
    else:
        raise TypeError('type {} cannot be converted to tensor.'.format(
            type(data)))