__all__ = [
    'glue_similarity',
    'HRRASA',
]
import crowdkit.aggregation.base
import pandas
import typing


def glue_similarity(hyp, ref): ...


class HRRASA(crowdkit.aggregation.base.BaseClassificationAggregator):
    """Hybrid Reliability and Representation Aware Sequence Aggregation.


    At the first step, the HRRASA estimates *local* workers reliabilities that represent how good is a
    worker's answer to *one particular task*. The local reliability of the worker $k$ on the task $i$ is
    denoted by $\gamma_i^k$ and is calculated as a sum of two terms:
    $$
    \gamma_i^k = \lambda_{emb}\gamma_{i,emb}^k + \lambda_{out}\gamma_{i,out}^k, \; \lambda_{emb} + \lambda_{out} = 1.
    $$
    The $\gamma_{i,emb}^k$ is a reliability calculated on `embedding` and the $\gamma_{i,seq}^k$ is a
    reliability calculated on `output`.

    The $\gamma_{i,emb}^k$ is calculated by the following equation:
    $$
    \gamma_{i,emb}^k = \frac{1}{|\mathcal{U}_i| - 1}\sum_{a_i^{k'} \in \mathcal{U}_i, k \neq k'}
    \exp\left(\frac{\|e_i^k-e_i^{k'}\|^2}{\|e_i^k\|^2\|e_i^{k'}\|^2}\right),
    $$
    where $\mathcal{U_i}$ is a set of workers' responses on task $i$. The $\gamma_{i,out}^k$ makes use
    of some similarity measure $sim$ on the `output` data, e.g. GLUE similarity on texts:
    $$
    \gamma_{i,out}^k = \frac{1}{|\mathcal{U}_i| - 1}\sum_{a_i^{k'} \in \mathcal{U}_i, k \neq k'}sim(a_i^k, a_i^{k'}).
    $$

    The HRRASA also estimates *global* workers' reliabilities $\beta$ that are initialized by ones.

    Next, the algorithm iteratively performs two steps:
    1. For each task, estimate the aggregated embedding: $\hat{e}_i = \frac{\sum_k \gamma_i^k
    \beta_k e_i^k}{\sum_k \gamma_i^k \beta_k}$
    2. For each worker, estimate the global reliability: $\beta_k = \frac{\chi^2_{(\alpha/2,
    |\mathcal{V}_k|)}}{\sum_i\left(\|e_i^k - \hat{e}_i\|^2/\gamma_i^k\right)}$, where $\mathcal{V}_k$
    is a set of tasks completed by the worker $k$

    Finally, the aggregated result is the output which embedding is
    the closest one to the $\hat{e}_i$. If `calculate_ranks` is true, the method also calculates ranks for
    each workers' response as
    $$
    s_i^k = \beta_k \exp\left(-\frac{\|e_i^k - \hat{e}_i\|^2}{\|e_i^k\|^2\|\hat{e}_i\|^2}\right) + \gamma_i^k.
    $$

    Jiyi Li. Crowdsourced Text Sequence Aggregation based on Hybrid Reliability and Representation.
    *Proceedings of the 43rd International ACM SIGIR Conference on Research and Development
    in Information Retrieval (SIGIR ’20)*, July 25–30, 2020, Virtual Event, China. ACM, New York, NY, USA,

    <https://doi.org/10.1145/3397271.3401239>

    Args:
        n_iter: A number of iterations.
        lambda_emb: A weight of reliability calculated on embeddigs.
        lambda_out: A weight of reliability calculated on outputs.
        alpha: Confidence level of chi-squared distribution quantiles in beta parameter formula.
        calculate_ranks: If true, calculate additional attribute `ranks_`.

    Examples:
        >>> import numpy as np
        >>> import pandas as pd
        >>> from crowdkit.aggregation import HRRASA
        >>> df = pd.DataFrame(
        >>>     [
        >>>         ['t1', 'p1', 'a', np.array([1.0, 0.0])],
        >>>         ['t1', 'p2', 'a', np.array([1.0, 0.0])],
        >>>         ['t1', 'p3', 'b', np.array([0.0, 1.0])]
        >>>     ],
        >>>     columns=['task', 'worker', 'output', 'embedding']
        >>> )
        >>> result = HRRASA().fit_predict(df)
    """

    def fit(
        self,
        data: pandas.DataFrame,
        true_embeddings: pandas.Series = None
    ) -> 'HRRASA':
        """Fit the model.
        Args:
            data (DataFrame): Workers' outputs with their embeddings.
                A pandas.DataFrame containing `task`, `worker`, `output` and `embedding` columns.
            true_embeddings (Series): Tasks' embeddings.
                A pandas.Series indexed by `task` and holding corresponding embeddings.
        Returns:
            HRRASA: self.
        """
        ...

    def fit_predict_scores(
        self,
        data: pandas.DataFrame,
        true_embeddings: pandas.Series = None
    ) -> pandas.DataFrame:
        """Fit the model and return scores.
        Args:
            data (DataFrame): Workers' outputs with their embeddings.
                A pandas.DataFrame containing `task`, `worker`, `output` and `embedding` columns.
            true_embeddings (Series): Tasks' embeddings.
                A pandas.Series indexed by `task` and holding corresponding embeddings.
        Returns:
            DataFrame: Tasks' label scores.
                A pandas.DataFrame indexed by `task` such that `result.loc[task, label]`
                is the score of `label` for `task`.
        """
        ...

    def fit_predict(
        self,
        data: pandas.DataFrame,
        true_embeddings: pandas.Series = None
    ) -> pandas.DataFrame:
        """Fit the model and return aggregated outputs.
        Args:
            data (DataFrame): Workers' outputs with their embeddings.
                A pandas.DataFrame containing `task`, `worker`, `output` and `embedding` columns.
            true_embeddings (Series): Tasks' embeddings.
                A pandas.Series indexed by `task` and holding corresponding embeddings.
        Returns:
            DataFrame: Tasks' embeddings and outputs.
                A pandas.DataFrame indexed by `task` with `embedding` and `output` columns.
        """
        ...

    def __init__(
        self,
        n_iter: int = 100,
        tol: float = 1e-09,
        lambda_emb: float = 0.5,
        lambda_out: float = 0.5,
        alpha: float = 0.05,
        calculate_ranks: bool = False,
        output_similarity=glue_similarity
    ) -> None:
        """Method generated by attrs for class HRRASA.
        """
        ...

    labels_: typing.Optional[pandas.Series]
    n_iter: int
    tol: float
    lambda_emb: float
    lambda_out: float
    alpha: float
    calculate_ranks: bool
    loss_history_: typing.List[float]
