# Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, Sequence, Union

import numpy as np
import torch
from mmengine.structures import BaseDataElement, InstanceData
from mmengine.utils import is_str

LABEL_TYPE = Union[torch.Tensor, np.ndarray, Sequence, int]
SCORE_TYPE = Union[torch.Tensor, np.ndarray, Sequence, Dict]


def format_label(value: LABEL_TYPE) -> torch.Tensor:
    """Convert various python types to label-format tensor.

    Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`,
    :class:`Sequence`, :class:`int`.

    Args:
        value (torch.Tensor | numpy.ndarray | Sequence | int): Label value.

    Returns:
        :obj:`torch.Tensor`: The formatted label tensor.
    """

    # Handle single number
    if isinstance(value, (torch.Tensor, np.ndarray)) and value.ndim == 0:
        value = int(value.item())

    if isinstance(value, np.ndarray):
        value = torch.from_numpy(value).to(torch.long)
    elif isinstance(value, Sequence) and not is_str(value):
        value = torch.tensor(value).to(torch.long)
    elif isinstance(value, int):
        value = torch.LongTensor([value])
    elif not isinstance(value, torch.Tensor):
        raise TypeError(f'Type {type(value)} is not an available label type.')

    return value


def format_score(value: SCORE_TYPE) -> Union[torch.Tensor, Dict]:
    """Convert various python types to score-format tensor.

    Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`,
    :class:`Sequence`.

    Args:
        value (torch.Tensor | numpy.ndarray | Sequence | dict):
            Score values or dict of scores values.

    Returns:
        :obj:`torch.Tensor` | dict: The formatted scores.
    """

    if isinstance(value, np.ndarray):
        value = torch.from_numpy(value).float()
    elif isinstance(value, Sequence) and not is_str(value):
        value = torch.tensor(value).float()
    elif isinstance(value, dict):
        for k, v in value.items():
            value[k] = format_score(v)
    elif not isinstance(value, torch.Tensor):
        raise TypeError(f'Type {type(value)} is not an available label type.')

    return value


class ActionDataSample(BaseDataElement):

    def set_gt_label(self, value: LABEL_TYPE) -> 'ActionDataSample':
        """Set `gt_label``."""
        self.set_field(format_label(value), 'gt_label', dtype=torch.Tensor)
        return self

    def set_pred_label(self, value: LABEL_TYPE) -> 'ActionDataSample':
        """Set ``pred_label``."""
        self.set_field(format_label(value), 'pred_label', dtype=torch.Tensor)
        return self

    def set_pred_score(self, value: SCORE_TYPE) -> 'ActionDataSample':
        """Set score of ``pred_label``."""
        score = format_score(value)
        self.set_field(score, 'pred_score')
        if hasattr(self, 'num_classes'):
            assert len(score) == self.num_classes, \
                f'The length of score {len(score)} should be '\
                f'equal to the num_classes {self.num_classes}.'
        else:
            self.set_field(
                name='num_classes', value=len(score), field_type='metainfo')
        return self

    @property
    def proposals(self):
        """Property of `proposals`"""
        return self._proposals

    @proposals.setter
    def proposals(self, value):
        """Setter of `proposals`"""
        self.set_field(value, '_proposals', dtype=InstanceData)

    @proposals.deleter
    def proposals(self):
        """Deleter of `proposals`"""
        del self._proposals

    @property
    def gt_instances(self):
        """Property of `gt_instances`"""
        return self._gt_instances

    @gt_instances.setter
    def gt_instances(self, value):
        """Setter of `gt_instances`"""
        self.set_field(value, '_gt_instances', dtype=InstanceData)

    @gt_instances.deleter
    def gt_instances(self):
        """Deleter of `gt_instances`"""
        del self._gt_instances

    @property
    def features(self):
        """Setter of `features`"""
        return self._features

    @features.setter
    def features(self, value):
        """Setter of `features`"""
        self.set_field(value, '_features', dtype=InstanceData)

    @features.deleter
    def features(self):
        """Deleter of `features`"""
        del self._features