# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

import numpy as np

from . import check_consistent_length
from ._optional_dependencies import check_matplotlib_support
from ._response import _get_response_values_binary
from .multiclass import type_of_target
from .validation import _check_pos_label_consistency


class _BinaryClassifierCurveDisplayMixin:
    """Mixin class to be used in Displays requiring a binary classifier.

    The aim of this class is to centralize some validations regarding the estimator and
    the target and gather the response of the estimator.
    """

    def _validate_plot_params(self, *, ax=None, name=None):
        check_matplotlib_support(f"{self.__class__.__name__}.plot")
        import matplotlib.pyplot as plt

        if ax is None:
            _, ax = plt.subplots()

        name = self.estimator_name if name is None else name
        return ax, ax.figure, name

    @classmethod
    def _validate_and_get_response_values(
        cls, estimator, X, y, *, response_method="auto", pos_label=None, name=None
    ):
        check_matplotlib_support(f"{cls.__name__}.from_estimator")

        name = estimator.__class__.__name__ if name is None else name

        y_pred, pos_label = _get_response_values_binary(
            estimator,
            X,
            response_method=response_method,
            pos_label=pos_label,
        )

        return y_pred, pos_label, name

    @classmethod
    def _validate_from_predictions_params(
        cls, y_true, y_pred, *, sample_weight=None, pos_label=None, name=None
    ):
        check_matplotlib_support(f"{cls.__name__}.from_predictions")

        if type_of_target(y_true) != "binary":
            raise ValueError(
                f"The target y is not binary. Got {type_of_target(y_true)} type of"
                " target."
            )

        check_consistent_length(y_true, y_pred, sample_weight)
        pos_label = _check_pos_label_consistency(pos_label, y_true)

        name = name if name is not None else "Classifier"

        return pos_label, name


def _validate_score_name(score_name, scoring, negate_score):
    """Validate the `score_name` parameter.

    If `score_name` is provided, we just return it as-is.
    If `score_name` is `None`, we use `Score` if `negate_score` is `False` and
    `Negative score` otherwise.
    If `score_name` is a string or a callable, we infer the name. We replace `_` by
    spaces and capitalize the first letter. We remove `neg_` and replace it by
    `"Negative"` if `negate_score` is `False` or just remove it otherwise.
    """
    if score_name is not None:
        return score_name
    elif scoring is None:
        return "Negative score" if negate_score else "Score"
    else:
        score_name = scoring.__name__ if callable(scoring) else scoring
        if negate_score:
            if score_name.startswith("neg_"):
                score_name = score_name[4:]
            else:
                score_name = f"Negative {score_name}"
        elif score_name.startswith("neg_"):
            score_name = f"Negative {score_name[4:]}"
        score_name = score_name.replace("_", " ")
        return score_name.capitalize()


def _interval_max_min_ratio(data):
    """Compute the ratio between the largest and smallest inter-point distances.

    A value larger than 5 typically indicates that the parameter range would
    better be displayed with a log scale while a linear scale would be more
    suitable otherwise.
    """
    diff = np.diff(np.sort(data))
    return diff.max() / diff.min()


def _validate_style_kwargs(default_style_kwargs, user_style_kwargs):
    """Create valid style kwargs by avoiding Matplotlib alias errors.

    Matplotlib raises an error when, for example, 'color' and 'c', or 'linestyle' and
    'ls', are specified together. To avoid this, we automatically keep only the one
    specified by the user and raise an error if the user specifies both.

    Parameters
    ----------
    default_style_kwargs : dict
        The Matplotlib style kwargs used by default in the scikit-learn display.
    user_style_kwargs : dict
        The user-defined Matplotlib style kwargs.

    Returns
    -------
    valid_style_kwargs : dict
        The validated style kwargs taking into account both default and user-defined
        Matplotlib style kwargs.
    """

    invalid_to_valid_kw = {
        "ls": "linestyle",
        "c": "color",
        "ec": "edgecolor",
        "fc": "facecolor",
        "lw": "linewidth",
        "mec": "markeredgecolor",
        "mfcalt": "markerfacecoloralt",
        "ms": "markersize",
        "mew": "markeredgewidth",
        "mfc": "markerfacecolor",
        "aa": "antialiased",
        "ds": "drawstyle",
        "font": "fontproperties",
        "family": "fontfamily",
        "name": "fontname",
        "size": "fontsize",
        "stretch": "fontstretch",
        "style": "fontstyle",
        "variant": "fontvariant",
        "weight": "fontweight",
        "ha": "horizontalalignment",
        "va": "verticalalignment",
        "ma": "multialignment",
    }
    for invalid_key, valid_key in invalid_to_valid_kw.items():
        if invalid_key in user_style_kwargs and valid_key in user_style_kwargs:
            raise TypeError(
                f"Got both {invalid_key} and {valid_key}, which are aliases of one "
                "another"
            )
    valid_style_kwargs = default_style_kwargs.copy()

    for key in user_style_kwargs.keys():
        if key in invalid_to_valid_kw:
            valid_style_kwargs[invalid_to_valid_kw[key]] = user_style_kwargs[key]
        else:
            valid_style_kwargs[key] = user_style_kwargs[key]

    return valid_style_kwargs


def _despine(ax):
    """Remove the top and right spines of the plot.

    Parameters
    ----------
    ax : matplotlib.axes.Axes
        The axes of the plot to despine.
    """
    for s in ["top", "right"]:
        ax.spines[s].set_visible(False)
    for s in ["bottom", "left"]:
        ax.spines[s].set_bounds(0, 1)