from typing import Union, Optional, Callable, Sequence

import torch

from ignite.metrics.metric import Metric
from ignite.exceptions import NotComputableError
from ignite.metrics.metric import sync_all_reduce, reinit__is_reduced

__all__ = [
    'TopKCategoricalAccuracy'
]


class TopKCategoricalAccuracy(Metric):
    """
    Calculates the top-k categorical accuracy.

    - `update` must receive output of the form `(y_pred, y)` or `{'y_pred': y_pred, 'y': y}`.
    """

    def __init__(self, k=5, output_transform: Callable = lambda x: x,
                 device: Optional[Union[str, torch.device]] = None):
        super(TopKCategoricalAccuracy, self).__init__(output_transform, device=device)
        self._k = k

    @reinit__is_reduced
    def reset(self) -> None:
        self._num_correct = 0
        self._num_examples = 0

    @reinit__is_reduced
    def update(self, output: Sequence) -> None:
        y_pred, y = output
        sorted_indices = torch.topk(y_pred, self._k, dim=1)[1]
        expanded_y = y.view(-1, 1).expand(-1, self._k)
        correct = torch.sum(torch.eq(sorted_indices, expanded_y), dim=1)
        self._num_correct += torch.sum(correct).item()
        self._num_examples += correct.shape[0]

    @sync_all_reduce("_num_correct", "_num_examples")
    def compute(self) -> Union[float, torch.Tensor]:
        if self._num_examples == 0:
            raise NotComputableError("TopKCategoricalAccuracy must have at"
                                     "least one example before it can be computed.")
        return self._num_correct / self._num_examples
