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# Copyright (c) 2023- CNRS
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from typing import Optional

import torch
from pyannote.metrics.binary_classification import det_curve
from torchmetrics import Metric
from torchmetrics.utilities.data import dim_zero_cat


class EqualErrorRate(Metric):

    is_differentiable: Optional[bool] = False
    higher_is_better: Optional[bool] = False
    full_state_update: bool = True

    def __init__(self, distances: bool = True, compute_on_cpu: bool = True, **kwargs):
        super().__init__(compute_on_cpu=compute_on_cpu, **kwargs)
        self.distances = distances
        self.add_state("scores", default=[], dist_reduce_fx="cat")
        self.add_state("y_true", default=[], dist_reduce_fx="cat")

    def update(self, scores: torch.Tensor, y_true: torch.Tensor) -> None:
        self.scores.append(scores)
        self.y_true.append(y_true)

    def compute(self) -> torch.Tensor:
        scores = dim_zero_cat(self.scores)
        y_true = dim_zero_cat(self.y_true)
        _, _, _, eer = det_curve(y_true.cpu(), scores.cpu(), distances=self.distances)
        return torch.tensor(eer)