from collections.abc import Iterable
from typing import Callable, Literal
from typing_extensions import Self

from matplotlib.artist import Artist
from matplotlib.axes import Axes
from matplotlib.figure import Figure
from numpy.random import RandomState

from .._typing import ArrayLike, Float, Int, MatrixLike
from . import BaseCrossValidator

class LearningCurveDisplay:
    fill_between_: Artist | None = ...
    lines_: Artist | None = ...
    errorbar_: Artist | None = ...
    figure_: Figure = ...
    ax_: Axes = ...

    def __init__(
        self,
        *,
        train_sizes: ArrayLike,
        train_scores: MatrixLike,
        test_scores: MatrixLike,
        score_name: None | str = None,
    ) -> None: ...
    def plot(
        self,
        ax: None | Axes = None,
        *,
        negate_score: bool = False,
        score_name: None | str = None,
        score_type: Literal["test", "train", "both"] = "test",
        log_scale: bool = False,
        std_display_style: None | Literal["errorbar", "fill_between"] = "fill_between",
        line_kw: None | dict = None,
        fill_between_kw: None | dict = None,
        errorbar_kw: None | dict = None,
    ) -> LearningCurveDisplay: ...
    @classmethod
    def from_estimator(
        cls,
        estimator,
        X: MatrixLike,
        y: None | MatrixLike | ArrayLike,
        *,
        groups: None | ArrayLike = None,
        train_sizes: ArrayLike = ...,
        cv: int | BaseCrossValidator | Iterable | None = None,
        scoring: None | str | Callable = None,
        exploit_incremental_learning: bool = False,
        n_jobs: None | Int = None,
        pre_dispatch: str | Int = "all",
        verbose: Int = 0,
        shuffle: bool = False,
        random_state: RandomState | None | Int = None,
        error_score: str | Float = ...,
        fit_params: None | dict = None,
        ax: None | Axes = None,
        negate_score: bool = False,
        score_name: None | str = None,
        score_type: Literal["test", "train", "both"] = "test",
        log_scale: bool = False,
        std_display_style: None | Literal["errorbar", "fill_between"] = "fill_between",
        line_kw: None | dict = None,
        fill_between_kw: None | dict = None,
        errorbar_kw: None | dict = None,
    ) -> LearningCurveDisplay: ...

class ValidationCurveDisplay:
    def __init__(self, *, param_name, param_range, train_scores, test_scores, score_name=None) -> None: ...
    def plot(
        self,
        ax=None,
        *,
        negate_score=False,
        score_name=None,
        score_type="both",
        std_display_style="fill_between",
        line_kw=None,
        fill_between_kw=None,
        errorbar_kw=None,
    ) -> Self: ...
    @classmethod
    def from_estimator(
        cls,
        estimator,
        X,
        y,
        *,
        param_name,
        param_range,
        groups=None,
        cv=None,
        scoring=None,
        n_jobs=None,
        pre_dispatch="all",
        verbose=0,
        error_score=...,
        fit_params=None,
        ax=None,
        negate_score=False,
        score_name=None,
        score_type="both",
        std_display_style="fill_between",
        line_kw=None,
        fill_between_kw=None,
        errorbar_kw=None,
    ) -> Self: ...