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import math
import warnings
from collections import Counter
from typing import Dict, Literal, Optional, Sequence, Text, Tuple, Union

import numpy as np
import torch
import torch.nn.functional
from matplotlib import pyplot as plt
from pyannote.core import Segment, SlidingWindowFeature
from pyannote.database.protocol import SpeakerDiarizationProtocol
from pyannote.database.protocol.protocol import Scope, Subset
from pytorch_lightning.loggers import MLFlowLogger, TensorBoardLogger
from rich.progress import track
from torch_audiomentations.core.transforms_interface import BaseWaveformTransform
from torchmetrics import Metric

from pyannote.audio.core.task import Problem, Resolution, Specifications
from pyannote.audio.tasks.segmentation.mixins import SegmentationTask
from pyannote.audio.torchmetrics import (
    DiarizationErrorRate,
    FalseAlarmRate,
    MissedDetectionRate,
    OptimalDiarizationErrorRate,
    OptimalDiarizationErrorRateThreshold,
    OptimalFalseAlarmRate,
    OptimalMissedDetectionRate,
    OptimalSpeakerConfusionRate,
    SpeakerConfusionRate,
)
from pyannote.audio.utils.loss import binary_cross_entropy, mse_loss, nll_loss
from pyannote.audio.utils.permutation import permutate
from pyannote.audio.utils.powerset import Powerset

Subsets = list(Subset.__args__)
Scopes = list(Scope.__args__)


class SpeakerDiarization(SegmentationTask):
    """Speaker diarization

    Parameters
    ----------
    protocol : SpeakerDiarizationProtocol
        pyannote.database protocol
    cache : str, optional
        As (meta-)data preparation might take a very long time for large datasets,
        it can be cached to disk for later (and faster!) re-use.
        When `cache` does not exist, `Task.prepare_data()` generates training
        and validation metadata from `protocol` and save them to disk.
        When `cache` exists, `Task.prepare_data()` is skipped and (meta)-data
        are loaded from disk. Defaults to a temporary path.
    duration : float, optional
        Chunks duration. Defaults to 2s.
    max_speakers_per_chunk : int, optional
        Maximum number of speakers per chunk (must be at least 2).
        Defaults to estimating it from the training set.
    max_speakers_per_frame : int, optional
        Maximum number of (overlapping) speakers per frame.
        Setting this value to 1 or more enables `powerset multi-class` training.
        Default behavior is to use `multi-label` training.
    weigh_by_cardinality: bool, optional
        Weigh each powerset classes by the size of the corresponding speaker set.
        In other words, {0, 1} powerset class weight is 2x bigger than that of {0}
        or {1} powerset classes. Note that empty (non-speech) powerset class is
        assigned the same weight as mono-speaker classes. Defaults to False (i.e. use
        same weight for every class). Has no effect with `multi-label` training.
    warm_up : float or (float, float), optional
        Use that many seconds on the left- and rightmost parts of each chunk
        to warm up the model. While the model does process those left- and right-most
        parts, only the remaining central part of each chunk is used for computing the
        loss during training, and for aggregating scores during inference.
        Defaults to 0. (i.e. no warm-up).
    balance: Sequence[Text], optional
        When provided, training samples are sampled uniformly with respect to these keys.
        For instance, setting `balance` to ["database","subset"] will make sure that each
        database & subset combination will be equally represented in the training samples.
    weight: str, optional
        When provided, use this key as frame-wise weight in loss function.
    batch_size : int, optional
        Number of training samples per batch. Defaults to 32.
    num_workers : int, optional
        Number of workers used for generating training samples.
        Defaults to multiprocessing.cpu_count() // 2.
    pin_memory : bool, optional
        If True, data loaders will copy tensors into CUDA pinned
        memory before returning them. See pytorch documentation
        for more details. Defaults to False.
    augmentation : BaseWaveformTransform, optional
        torch_audiomentations waveform transform, used by dataloader
        during training.
    vad_loss : {"bce", "mse"}, optional
        Add voice activity detection loss.
        Cannot be used in conjunction with `max_speakers_per_frame`.
    metric : optional
        Validation metric(s). Can be anything supported by torchmetrics.MetricCollection.
        Defaults to AUROC (area under the ROC curve).

    References
    ----------
    Hervé Bredin and Antoine Laurent
    "End-To-End Speaker Segmentation for Overlap-Aware Resegmentation."
    Proc. Interspeech 2021

    Zhihao Du, Shiliang Zhang, Siqi Zheng, and Zhijie Yan
    "Speaker Embedding-aware Neural Diarization: an Efficient Framework for Overlapping
    Speech Diarization in Meeting Scenarios"
    https://arxiv.org/abs/2203.09767

    """

    def __init__(
        self,
        protocol: SpeakerDiarizationProtocol,
        cache: Optional[Union[str, None]] = None,
        duration: float = 2.0,
        max_speakers_per_chunk: Optional[int] = None,
        max_speakers_per_frame: Optional[int] = None,
        weigh_by_cardinality: bool = False,
        warm_up: Union[float, Tuple[float, float]] = 0.0,
        balance: Optional[Sequence[Text]] = None,
        weight: Optional[Text] = None,
        batch_size: int = 32,
        num_workers: Optional[int] = None,
        pin_memory: bool = False,
        augmentation: Optional[BaseWaveformTransform] = None,
        vad_loss: Literal["bce", "mse"] = None,
        metric: Union[Metric, Sequence[Metric], Dict[str, Metric]] = None,
        max_num_speakers: Optional[
            int
        ] = None,  # deprecated in favor of `max_speakers_per_chunk``
        loss: Literal["bce", "mse"] = None,  # deprecated
    ):
        super().__init__(
            protocol,
            duration=duration,
            warm_up=warm_up,
            batch_size=batch_size,
            num_workers=num_workers,
            pin_memory=pin_memory,
            augmentation=augmentation,
            metric=metric,
            cache=cache,
        )

        if not isinstance(protocol, SpeakerDiarizationProtocol):
            raise ValueError(
                "SpeakerDiarization task requires a SpeakerDiarizationProtocol."
            )

        # deprecation warnings
        if max_speakers_per_chunk is None and max_num_speakers is not None:
            max_speakers_per_chunk = max_num_speakers
            warnings.warn(
                "`max_num_speakers` has been deprecated in favor of `max_speakers_per_chunk`."
            )
        if loss is not None:
            warnings.warn("`loss` has been deprecated and has no effect.")

        # parameter validation
        if max_speakers_per_frame is not None:
            if max_speakers_per_frame < 1:
                raise ValueError(
                    f"`max_speakers_per_frame` must be 1 or more (you used {max_speakers_per_frame})."
                )
            if vad_loss is not None:
                raise ValueError(
                    "`vad_loss` cannot be used jointly with `max_speakers_per_frame`"
                )

        self.max_speakers_per_chunk = max_speakers_per_chunk
        self.max_speakers_per_frame = max_speakers_per_frame
        self.weigh_by_cardinality = weigh_by_cardinality
        self.balance = balance
        self.weight = weight
        self.vad_loss = vad_loss

    def setup(self, stage=None):
        super().setup(stage)

        # estimate maximum number of speakers per chunk when not provided
        if self.max_speakers_per_chunk is None:
            training = self.prepared_data["audio-metadata"]["subset"] == Subsets.index(
                "train"
            )

            num_unique_speakers = []
            progress_description = f"Estimating maximum number of speakers per {self.duration:g}s chunk in the training set"
            for file_id in track(
                np.where(training)[0], description=progress_description
            ):
                annotations = self.prepared_data["annotations-segments"][
                    np.where(
                        self.prepared_data["annotations-segments"]["file_id"] == file_id
                    )[0]
                ]
                annotated_regions = self.prepared_data["annotations-regions"][
                    np.where(
                        self.prepared_data["annotations-regions"]["file_id"] == file_id
                    )[0]
                ]
                for region in annotated_regions:
                    # find annotations within current region
                    region_start = region["start"]
                    region_end = region["start"] + region["duration"]
                    region_annotations = annotations[
                        np.where(
                            (annotations["start"] >= region_start)
                            * (annotations["end"] <= region_end)
                        )[0]
                    ]

                    for window_start in np.arange(
                        region_start, region_end - self.duration, 0.25 * self.duration
                    ):
                        window_end = window_start + self.duration
                        window_annotations = region_annotations[
                            np.where(
                                (region_annotations["start"] <= window_end)
                                * (region_annotations["end"] >= window_start)
                            )[0]
                        ]
                        num_unique_speakers.append(
                            len(np.unique(window_annotations["file_label_idx"]))
                        )

            # because there might a few outliers, estimate the upper bound for the
            # number of speakers as the 97th percentile

            num_speakers, counts = zip(*list(Counter(num_unique_speakers).items()))
            num_speakers, counts = np.array(num_speakers), np.array(counts)

            sorting_indices = np.argsort(num_speakers)
            num_speakers = num_speakers[sorting_indices]
            counts = counts[sorting_indices]

            ratios = np.cumsum(counts) / np.sum(counts)

            for k, ratio in zip(num_speakers, ratios):
                if k == 0:
                    print(f"   - {ratio:7.2%} of all chunks contain no speech at all.")
                elif k == 1:
                    print(f"   - {ratio:7.2%} contain 1 speaker or less")
                else:
                    print(f"   - {ratio:7.2%} contain {k} speakers or less")

            self.max_speakers_per_chunk = max(
                2,
                num_speakers[np.where(ratios > 0.97)[0][0]],
            )

            print(
                f"Setting `max_speakers_per_chunk` to {self.max_speakers_per_chunk}. "
                f"You can override this value (or avoid this estimation step) by passing `max_speakers_per_chunk={self.max_speakers_per_chunk}` to the task constructor."
            )

        if (
            self.max_speakers_per_frame is not None
            and self.max_speakers_per_frame > self.max_speakers_per_chunk
        ):
            raise ValueError(
                f"`max_speakers_per_frame` ({self.max_speakers_per_frame}) must be smaller "
                f"than `max_speakers_per_chunk` ({self.max_speakers_per_chunk})"
            )

        # now that we know about the number of speakers upper bound
        # we can set task specifications
        self.specifications = Specifications(
            problem=Problem.MULTI_LABEL_CLASSIFICATION
            if self.max_speakers_per_frame is None
            else Problem.MONO_LABEL_CLASSIFICATION,
            resolution=Resolution.FRAME,
            duration=self.duration,
            min_duration=self.min_duration,
            warm_up=self.warm_up,
            classes=[f"speaker#{i+1}" for i in range(self.max_speakers_per_chunk)],
            powerset_max_classes=self.max_speakers_per_frame,
            permutation_invariant=True,
        )

    def setup_loss_func(self):
        if self.specifications.powerset:
            self.model.powerset = Powerset(
                len(self.specifications.classes),
                self.specifications.powerset_max_classes,
            )

    def prepare_chunk(self, file_id: int, start_time: float, duration: float):
        """Prepare chunk

        Parameters
        ----------
        file_id : int
            File index
        start_time : float
            Chunk start time
        duration : float
            Chunk duration.

        Returns
        -------
        sample : dict
            Dictionary containing the chunk data with the following keys:
            - `X`: waveform
            - `y`: target as a SlidingWindowFeature instance where y.labels is
                   in meta.scope space.
            - `meta`:
                - `scope`: target scope (0: file, 1: database, 2: global)
                - `database`: database index
                - `file`: file index
        """

        file = self.get_file(file_id)

        # get label scope
        label_scope = Scopes[self.prepared_data["audio-metadata"][file_id]["scope"]]
        label_scope_key = f"{label_scope}_label_idx"

        #
        chunk = Segment(start_time, start_time + duration)

        sample = dict()
        sample["X"], _ = self.model.audio.crop(file, chunk, duration=duration)

        # gather all annotations of current file
        annotations = self.prepared_data["annotations-segments"][
            self.prepared_data["annotations-segments"]["file_id"] == file_id
        ]

        # gather all annotations with non-empty intersection with current chunk
        chunk_annotations = annotations[
            (annotations["start"] < chunk.end) & (annotations["end"] > chunk.start)
        ]

        # discretize chunk annotations at model output resolution
        step = self.model.receptive_field.step
        half = 0.5 * self.model.receptive_field.duration

        start = np.maximum(chunk_annotations["start"], chunk.start) - chunk.start - half
        start_idx = np.maximum(0, np.round(start / step)).astype(int)

        end = np.minimum(chunk_annotations["end"], chunk.end) - chunk.start - half
        end_idx = np.round(end / step).astype(int)

        # get list and number of labels for current scope
        labels = list(np.unique(chunk_annotations[label_scope_key]))
        num_labels = len(labels)

        if num_labels > self.max_speakers_per_chunk:
            pass

        # initial frame-level targets
        num_frames = self.model.num_frames(
            round(duration * self.model.hparams.sample_rate)
        )
        y = np.zeros((num_frames, num_labels), dtype=np.uint8)

        # map labels to indices
        mapping = {label: idx for idx, label in enumerate(labels)}

        for start, end, label in zip(
            start_idx, end_idx, chunk_annotations[label_scope_key]
        ):
            mapped_label = mapping[label]
            y[start : end + 1, mapped_label] = 1

        sample["y"] = SlidingWindowFeature(y, self.model.receptive_field, labels=labels)

        metadata = self.prepared_data["audio-metadata"][file_id]
        sample["meta"] = {key: metadata[key] for key in metadata.dtype.names}
        sample["meta"]["file"] = file_id

        return sample

    def collate_y(self, batch) -> torch.Tensor:
        """

        Parameters
        ----------
        batch : list
            List of samples to collate.
            "y" field is expected to be a SlidingWindowFeature.

        Returns
        -------
        y : torch.Tensor
            Collated target tensor of shape (num_frames, self.max_speakers_per_chunk)
            If one chunk has more than `self.max_speakers_per_chunk` speakers, we keep
            the max_speakers_per_chunk most talkative ones. If it has less, we pad with
            zeros (artificial inactive speakers).
        """

        collated_y = []
        for b in batch:
            y = b["y"].data
            num_speakers = len(b["y"].labels)
            if num_speakers > self.max_speakers_per_chunk:
                # sort speakers in descending talkativeness order
                indices = np.argsort(-np.sum(y, axis=0), axis=0)
                # keep only the most talkative speakers
                y = y[:, indices[: self.max_speakers_per_chunk]]

                # TODO: we should also sort the speaker labels in the same way

            elif num_speakers < self.max_speakers_per_chunk:
                # create inactive speakers by zero padding
                y = np.pad(
                    y,
                    ((0, 0), (0, self.max_speakers_per_chunk - num_speakers)),
                    mode="constant",
                )

            else:
                # we have exactly the right number of speakers
                pass

            collated_y.append(y)

        return torch.from_numpy(np.stack(collated_y))

    def segmentation_loss(
        self,
        permutated_prediction: torch.Tensor,
        target: torch.Tensor,
        weight: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        """Permutation-invariant segmentation loss

        Parameters
        ----------
        permutated_prediction : (batch_size, num_frames, num_classes) torch.Tensor
            Permutated speaker activity predictions.
        target : (batch_size, num_frames, num_speakers) torch.Tensor
            Speaker activity.
        weight : (batch_size, num_frames, 1) torch.Tensor, optional
            Frames weight.

        Returns
        -------
        seg_loss : torch.Tensor
            Permutation-invariant segmentation loss
        """

        if self.specifications.powerset:
            # `clamp_min` is needed to set non-speech weight to 1.
            class_weight = (
                torch.clamp_min(self.model.powerset.cardinality, 1.0)
                if self.weigh_by_cardinality
                else None
            )
            seg_loss = nll_loss(
                permutated_prediction,
                torch.argmax(target, dim=-1),
                class_weight=class_weight,
                weight=weight,
            )
        else:
            seg_loss = binary_cross_entropy(
                permutated_prediction, target.float(), weight=weight
            )

        return seg_loss

    def voice_activity_detection_loss(
        self,
        permutated_prediction: torch.Tensor,
        target: torch.Tensor,
        weight: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        """Voice activity detection loss

        Parameters
        ----------
        permutated_prediction : (batch_size, num_frames, num_classes) torch.Tensor
            Speaker activity predictions.
        target : (batch_size, num_frames, num_speakers) torch.Tensor
            Speaker activity.
        weight : (batch_size, num_frames, 1) torch.Tensor, optional
            Frames weight.

        Returns
        -------
        vad_loss : torch.Tensor
            Voice activity detection loss.
        """

        vad_prediction, _ = torch.max(permutated_prediction, dim=2, keepdim=True)
        # (batch_size, num_frames, 1)

        vad_target, _ = torch.max(target.float(), dim=2, keepdim=False)
        # (batch_size, num_frames)

        if self.vad_loss == "bce":
            loss = binary_cross_entropy(vad_prediction, vad_target, weight=weight)

        elif self.vad_loss == "mse":
            loss = mse_loss(vad_prediction, vad_target, weight=weight)

        return loss

    def training_step(self, batch, batch_idx: int):
        """Compute permutation-invariant segmentation loss

        Parameters
        ----------
        batch : (usually) dict of torch.Tensor
            Current batch.
        batch_idx: int
            Batch index.

        Returns
        -------
        loss : {str: torch.tensor}
            {"loss": loss}
        """

        # target
        target = batch["y"]
        # (batch_size, num_frames, num_speakers)

        waveform = batch["X"]
        # (batch_size, num_channels, num_samples)

        # drop samples that contain too many speakers
        num_speakers: torch.Tensor = torch.sum(torch.any(target, dim=1), dim=1)
        keep: torch.Tensor = num_speakers <= self.max_speakers_per_chunk
        target = target[keep]
        waveform = waveform[keep]

        # corner case
        if not keep.any():
            return None

        # forward pass
        prediction = self.model(waveform)
        batch_size, num_frames, _ = prediction.shape
        # (batch_size, num_frames, num_classes)

        # frames weight
        weight_key = getattr(self, "weight", None)
        weight = batch.get(
            weight_key,
            torch.ones(batch_size, num_frames, 1, device=self.model.device),
        )
        # (batch_size, num_frames, 1)

        # warm-up
        warm_up_left = round(self.warm_up[0] / self.duration * num_frames)
        weight[:, :warm_up_left] = 0.0
        warm_up_right = round(self.warm_up[1] / self.duration * num_frames)
        weight[:, num_frames - warm_up_right :] = 0.0

        if self.specifications.powerset:
            multilabel = self.model.powerset.to_multilabel(prediction)
            permutated_target, _ = permutate(multilabel, target)
            permutated_target_powerset = self.model.powerset.to_powerset(
                permutated_target.float()
            )
            seg_loss = self.segmentation_loss(
                prediction, permutated_target_powerset, weight=weight
            )

        else:
            permutated_prediction, _ = permutate(target, prediction)
            seg_loss = self.segmentation_loss(
                permutated_prediction, target, weight=weight
            )

        self.model.log(
            "loss/train/segmentation",
            seg_loss,
            on_step=False,
            on_epoch=True,
            prog_bar=False,
            logger=True,
        )

        if self.vad_loss is None:
            vad_loss = 0.0

        else:
            # TODO: vad_loss probably does not make sense in powerset mode
            # because first class (empty set of labels) does exactly this...
            if self.specifications.powerset:
                vad_loss = self.voice_activity_detection_loss(
                    prediction, permutated_target_powerset, weight=weight
                )

            else:
                vad_loss = self.voice_activity_detection_loss(
                    permutated_prediction, target, weight=weight
                )

            self.model.log(
                "loss/train/vad",
                vad_loss,
                on_step=False,
                on_epoch=True,
                prog_bar=False,
                logger=True,
            )

        loss = seg_loss + vad_loss

        # skip batch if something went wrong for some reason
        if torch.isnan(loss):
            return None

        self.model.log(
            "loss/train",
            loss,
            on_step=False,
            on_epoch=True,
            prog_bar=False,
            logger=True,
        )

        return {"loss": loss}

    def default_metric(
        self,
    ) -> Union[Metric, Sequence[Metric], Dict[str, Metric]]:
        """Returns diarization error rate and its components"""

        if self.specifications.powerset:
            return {
                "DiarizationErrorRate": DiarizationErrorRate(0.5),
                "DiarizationErrorRate/Confusion": SpeakerConfusionRate(0.5),
                "DiarizationErrorRate/Miss": MissedDetectionRate(0.5),
                "DiarizationErrorRate/FalseAlarm": FalseAlarmRate(0.5),
            }

        return {
            "DiarizationErrorRate": OptimalDiarizationErrorRate(),
            "DiarizationErrorRate/Threshold": OptimalDiarizationErrorRateThreshold(),
            "DiarizationErrorRate/Confusion": OptimalSpeakerConfusionRate(),
            "DiarizationErrorRate/Miss": OptimalMissedDetectionRate(),
            "DiarizationErrorRate/FalseAlarm": OptimalFalseAlarmRate(),
        }

    # TODO: no need to compute gradient in this method
    def validation_step(self, batch, batch_idx: int):
        """Compute validation loss and metric

        Parameters
        ----------
        batch : dict of torch.Tensor
            Current batch.
        batch_idx: int
            Batch index.
        """

        # target
        target = batch["y"]
        # (batch_size, num_frames, num_speakers)

        waveform = batch["X"]
        # (batch_size, num_channels, num_samples)

        # TODO: should we handle validation samples with too many speakers
        # waveform = waveform[keep]
        # target = target[keep]

        # forward pass
        prediction = self.model(waveform)
        batch_size, num_frames, _ = prediction.shape

        # frames weight
        weight_key = getattr(self, "weight", None)
        weight = batch.get(
            weight_key,
            torch.ones(batch_size, num_frames, 1, device=self.model.device),
        )
        # (batch_size, num_frames, 1)

        # warm-up
        warm_up_left = round(self.warm_up[0] / self.duration * num_frames)
        weight[:, :warm_up_left] = 0.0
        warm_up_right = round(self.warm_up[1] / self.duration * num_frames)
        weight[:, num_frames - warm_up_right :] = 0.0

        if self.specifications.powerset:
            multilabel = self.model.powerset.to_multilabel(prediction)
            permutated_target, _ = permutate(multilabel, target)

            # FIXME: handle case where target have too many speakers?
            # since we don't need
            permutated_target_powerset = self.model.powerset.to_powerset(
                permutated_target.float()
            )
            seg_loss = self.segmentation_loss(
                prediction, permutated_target_powerset, weight=weight
            )

        else:
            permutated_prediction, _ = permutate(target, prediction)
            seg_loss = self.segmentation_loss(
                permutated_prediction, target, weight=weight
            )

        self.model.log(
            "loss/val/segmentation",
            seg_loss,
            on_step=False,
            on_epoch=True,
            prog_bar=False,
            logger=True,
        )

        if self.vad_loss is None:
            vad_loss = 0.0

        else:
            # TODO: vad_loss probably does not make sense in powerset mode
            # because first class (empty set of labels) does exactly this...
            if self.specifications.powerset:
                vad_loss = self.voice_activity_detection_loss(
                    prediction, permutated_target_powerset, weight=weight
                )

            else:
                vad_loss = self.voice_activity_detection_loss(
                    permutated_prediction, target, weight=weight
                )

            self.model.log(
                "loss/val/vad",
                vad_loss,
                on_step=False,
                on_epoch=True,
                prog_bar=False,
                logger=True,
            )

        loss = seg_loss + vad_loss

        self.model.log(
            "loss/val",
            loss,
            on_step=False,
            on_epoch=True,
            prog_bar=False,
            logger=True,
        )

        if self.specifications.powerset:
            self.model.validation_metric(
                torch.transpose(
                    multilabel[:, warm_up_left : num_frames - warm_up_right], 1, 2
                ),
                torch.transpose(
                    target[:, warm_up_left : num_frames - warm_up_right], 1, 2
                ),
            )
        else:
            self.model.validation_metric(
                torch.transpose(
                    prediction[:, warm_up_left : num_frames - warm_up_right], 1, 2
                ),
                torch.transpose(
                    target[:, warm_up_left : num_frames - warm_up_right], 1, 2
                ),
            )

        self.model.log_dict(
            self.model.validation_metric,
            on_step=False,
            on_epoch=True,
            prog_bar=True,
            logger=True,
        )

        # log first batch visualization every 2^n epochs.
        if (
            self.model.current_epoch == 0
            or math.log2(self.model.current_epoch) % 1 > 0
            or batch_idx > 0
        ):
            return

        # visualize first 9 validation samples of first batch in Tensorboard/MLflow

        if self.specifications.powerset:
            y = permutated_target.float().cpu().numpy()
            y_pred = multilabel.cpu().numpy()
        else:
            y = target.float().cpu().numpy()
            y_pred = permutated_prediction.cpu().numpy()

        # prepare 3 x 3 grid (or smaller if batch size is smaller)
        num_samples = min(self.batch_size, 9)
        nrows = math.ceil(math.sqrt(num_samples))
        ncols = math.ceil(num_samples / nrows)
        fig, axes = plt.subplots(
            nrows=2 * nrows, ncols=ncols, figsize=(8, 5), squeeze=False
        )

        # reshape target so that there is one line per class when plotting it
        y[y == 0] = np.nan
        if len(y.shape) == 2:
            y = y[:, :, np.newaxis]
        y *= np.arange(y.shape[2])

        # plot each sample
        for sample_idx in range(num_samples):
            # find where in the grid it should be plotted
            row_idx = sample_idx // nrows
            col_idx = sample_idx % ncols

            # plot target
            ax_ref = axes[row_idx * 2 + 0, col_idx]
            sample_y = y[sample_idx]
            ax_ref.plot(sample_y)
            ax_ref.set_xlim(0, len(sample_y))
            ax_ref.set_ylim(-1, sample_y.shape[1])
            ax_ref.get_xaxis().set_visible(False)
            ax_ref.get_yaxis().set_visible(False)

            # plot predictions
            ax_hyp = axes[row_idx * 2 + 1, col_idx]
            sample_y_pred = y_pred[sample_idx]
            ax_hyp.axvspan(0, warm_up_left, color="k", alpha=0.5, lw=0)
            ax_hyp.axvspan(
                num_frames - warm_up_right, num_frames, color="k", alpha=0.5, lw=0
            )
            ax_hyp.plot(sample_y_pred)
            ax_hyp.set_ylim(-0.1, 1.1)
            ax_hyp.set_xlim(0, len(sample_y))
            ax_hyp.get_xaxis().set_visible(False)

        plt.tight_layout()

        for logger in self.model.loggers:
            if isinstance(logger, TensorBoardLogger):
                logger.experiment.add_figure("samples", fig, self.model.current_epoch)
            elif isinstance(logger, MLFlowLogger):
                logger.experiment.log_figure(
                    run_id=logger.run_id,
                    figure=fig,
                    artifact_file=f"samples_epoch{self.model.current_epoch}.png",
                )

        plt.close(fig)


def main(protocol: str, subset: str = "test", model: str = "pyannote/segmentation"):
    """Evaluate a segmentation model"""

    from pyannote.database import FileFinder, get_protocol
    from rich.progress import Progress

    from pyannote.audio import Inference
    from pyannote.audio.pipelines.utils import get_devices
    from pyannote.audio.utils.metric import DiscreteDiarizationErrorRate
    from pyannote.audio.utils.signal import binarize

    (device,) = get_devices(needs=1)
    metric = DiscreteDiarizationErrorRate()
    protocol = get_protocol(protocol, preprocessors={"audio": FileFinder()})
    files = list(getattr(protocol, subset)())

    with Progress() as progress:
        main_task = progress.add_task(protocol.name, total=len(files))
        file_task = progress.add_task("Processing", total=1.0)

        def progress_hook(completed: Optional[int] = None, total: Optional[int] = None):
            progress.update(file_task, completed=completed / total)

        inference = Inference(model, device=device)

        for file in files:
            progress.update(file_task, description=file["uri"])
            reference = file["annotation"]
            hypothesis = binarize(inference(file, hook=progress_hook))
            uem = file["annotated"]
            _ = metric(reference, hypothesis, uem=uem)
            progress.advance(main_task)

    _ = metric.report(display=True)


if __name__ == "__main__":
    import typer

    typer.run(main)