#!/usr/bin/python3
# -*- coding: utf-8 -*-
from typing import Optional

import torch
from torch_pesq import PesqLoss


class Pesq(object):
    def __init__(self):
        pass


class CategoricalAccuracy(object):
    def __init__(self, top_k: int = 1, tie_break: bool = False) -> None:
        if top_k > 1 and tie_break:
            raise AssertionError("Tie break in Categorical Accuracy "
                                 "can be done only for maximum (top_k = 1)")
        if top_k <= 0:
            raise AssertionError("top_k passed to Categorical Accuracy must be > 0")
        self._top_k = top_k
        self._tie_break = tie_break
        self.correct_count = 0.
        self.total_count = 0.

    def __call__(self,
                 predictions: torch.Tensor,
                 gold_labels: torch.Tensor,
                 mask: Optional[torch.Tensor] = None):

        # predictions, gold_labels, mask = self.unwrap_to_tensors(predictions, gold_labels, mask)

        # Some sanity checks.
        num_classes = predictions.size(-1)
        if gold_labels.dim() != predictions.dim() - 1:
            raise AssertionError("gold_labels must have dimension == predictions.size() - 1 but "
                                 "found tensor of shape: {}".format(predictions.size()))
        if (gold_labels >= num_classes).any():
            raise AssertionError("A gold label passed to Categorical Accuracy contains an id >= {}, "
                                 "the number of classes.".format(num_classes))

        predictions = predictions.view((-1, num_classes))
        gold_labels = gold_labels.view(-1).long()
        if not self._tie_break:
            # Top K indexes of the predictions (or fewer, if there aren't K of them).
            # Special case topk == 1, because it's common and .max() is much faster than .topk().
            if self._top_k == 1:
                top_k = predictions.max(-1)[1].unsqueeze(-1)
            else:
                top_k = predictions.topk(min(self._top_k, predictions.shape[-1]), -1)[1]

            # This is of shape (batch_size, ..., top_k).
            correct = top_k.eq(gold_labels.unsqueeze(-1)).float()
        else:
            # prediction is correct if gold label falls on any of the max scores. distribute score by tie_counts
            max_predictions = predictions.max(-1)[0]
            max_predictions_mask = predictions.eq(max_predictions.unsqueeze(-1))
            # max_predictions_mask is (rows X num_classes) and gold_labels is (batch_size)
            # ith entry in gold_labels points to index (0-num_classes) for ith row in max_predictions
            # For each row check if index pointed by gold_label is was 1 or not (among max scored classes)
            correct = max_predictions_mask[torch.arange(gold_labels.numel()).long(), gold_labels].float()
            tie_counts = max_predictions_mask.sum(-1)
            correct /= tie_counts.float()
            correct.unsqueeze_(-1)

        if mask is not None:
            correct *= mask.view(-1, 1).float()
            self.total_count += mask.sum()
        else:
            self.total_count += gold_labels.numel()
        self.correct_count += correct.sum()

    def get_metric(self, reset: bool = False):
        """
        Returns
        -------
        The accumulated accuracy.
        """
        if self.total_count > 1e-12:
            accuracy = float(self.correct_count) / float(self.total_count)
        else:
            accuracy = 0.0
        if reset:
            self.reset()
        return {'accuracy': accuracy}

    def reset(self):
        self.correct_count = 0.0
        self.total_count = 0.0


def main():
    pesq = PesqLoss(0.5,
                    sample_rate=8000,
                    )

    reference = torch.randn(1, 44100)
    degraded = torch.randn(1, 44100)

    mos = pesq.mos(reference, degraded)
    loss = pesq(reference, degraded)

    print(mos, loss)
    return


if __name__ == '__main__':
    main()