#!/usr/bin/env python
# encoding: utf-8

# The MIT License (MIT)

# Copyright (c) 2012-2019 CNRS

# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.

# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.

# AUTHORS
# Hervé BREDIN - http://herve.niderb.fr

"""Metrics for diarization"""
from typing import Optional, Dict, TYPE_CHECKING

from pyannote_audio_utils.core import Annotation, Timeline
from pyannote_audio_utils.core.utils.types import Label

from .identification import IdentificationErrorRate
from .matcher import HungarianMapper
from .types import Details, MetricComponents

if TYPE_CHECKING:
    pass

# TODO: can't we put these as class attributes?
DER_NAME = 'diarization error rate'


class DiarizationErrorRate(IdentificationErrorRate):
    """Diarization error rate

    First, the optimal mapping between reference and hypothesis labels
    is obtained using the Hungarian algorithm. Then, the actual diarization
    error rate is computed as the identification error rate with each hypothesis
    label translated into the corresponding reference label.

    Parameters
    ----------
    collar : float, optional
        Duration (in seconds) of collars removed from evaluation around
        boundaries of reference segments.
    skip_overlap : bool, optional
        Set to True to not evaluate overlap regions.
        Defaults to False (i.e. keep overlap regions).

    Usage
    -----

    * Diarization error rate between `reference` and `hypothesis` annotations

        >>> metric = DiarizationErrorRate()
        >>> reference = Annotation(...)           # doctest: +SKIP
        >>> hypothesis = Annotation(...)          # doctest: +SKIP
        >>> value = metric(reference, hypothesis) # doctest: +SKIP

    * Compute global diarization error rate and confidence interval
      over multiple documents

        >>> for reference, hypothesis in ...      # doctest: +SKIP
        ...    metric(reference, hypothesis)      # doctest: +SKIP
        >>> global_value = abs(metric)            # doctest: +SKIP
        >>> mean, (lower, upper) = metric.confidence_interval() # doctest: +SKIP

    * Get diarization error rate detailed components

        >>> components = metric(reference, hypothesis, detailed=True) #doctest +SKIP

    * Get accumulated components

        >>> components = metric[:]                # doctest: +SKIP
        >>> metric['confusion']                   # doctest: +SKIP

    See Also
    --------
    :class:`pyannote_audio_utils.metric.base.BaseMetric`: details on accumulation
    :class:`pyannote_audio_utils.metric.identification.IdentificationErrorRate`: identification error rate

    """

    @classmethod
    def metric_name(cls) -> str:
        return DER_NAME

    def __init__(self, collar: float = 0.0, skip_overlap: bool = False,
                 **kwargs):
        super().__init__(collar=collar, skip_overlap=skip_overlap, **kwargs)
        self.mapper_ = HungarianMapper()

    def optimal_mapping(self,
                        reference: Annotation,
                        hypothesis: Annotation,
                        uem: Optional[Timeline] = None) -> Dict[Label, Label]:
        """Optimal label mapping

        Parameters
        ----------
        reference : Annotation
        hypothesis : Annotation
            Reference and hypothesis diarization
        uem : Timeline
            Evaluation map

        Returns
        -------
        mapping : dict
            Mapping between hypothesis (key) and reference (value) labels
        """

        # NOTE that this 'uemification' will not be called when
        # 'optimal_mapping' is called from 'compute_components' as it
        # has already been done in 'compute_components'
        if uem:
            reference, hypothesis = self.uemify(reference, hypothesis, uem=uem)

        # call hungarian mapper
        return self.mapper_(hypothesis, reference)

    def compute_components(self,
                           reference: Annotation,
                           hypothesis: Annotation,
                           uem: Optional[Timeline] = None,
                           **kwargs) -> Details:
        # crop reference and hypothesis to evaluated regions (uem)
        # remove collars around reference segment boundaries
        # remove overlap regions (if requested)
        reference, hypothesis, uem = self.uemify(
            reference, hypothesis, uem=uem,
            collar=self.collar, skip_overlap=self.skip_overlap,
            returns_uem=True)
        # NOTE that this 'uemification' must be done here because it
        # might have an impact on the search for the optimal mapping.

        # make sure reference only contains string labels ('A', 'B', ...)
        reference = reference.rename_labels(generator='string')

        # make sure hypothesis only contains integer labels (1, 2, ...)
        hypothesis = hypothesis.rename_labels(generator='int')

        # optimal (int --> str) mapping
        mapping = self.optimal_mapping(reference, hypothesis)

        # compute identification error rate based on mapped hypothesis
        # NOTE that collar is set to 0.0 because 'uemify' has already
        # been applied (same reason for setting skip_overlap to False)
        mapped = hypothesis.rename_labels(mapping=mapping)
        return super(DiarizationErrorRate, self) \
            .compute_components(reference, mapped, uem=uem,
                                collar=0.0, skip_overlap=False,
                                **kwargs)