"""Functions to validate input and parameters within scikit-learn estimators."""

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

import numbers
import operator
import warnings
from collections.abc import Sequence
from functools import reduce, wraps
from inspect import Parameter, isclass, signature

import joblib
import narwhals.stable.v2 as nw
import numpy as np
import scipy.sparse as sp

from sklearn import get_config as _get_config
from sklearn.exceptions import (
    DataConversionWarning,
    NotFittedError,
    PositiveSpectrumWarning,
)
from sklearn.utils._array_api import (
    _asarray_with_order,
    _is_numpy_namespace,
    _max_precision_float_dtype,
    get_namespace,
    get_namespace_and_device,
    move_to,
)
from sklearn.utils._dataframe import is_pandas_df_or_series
from sklearn.utils._isfinite import FiniteStatus, cy_isfinite
from sklearn.utils._tags import get_tags
from sklearn.utils.fixes import (
    ComplexWarning,
    _object_dtype_isnan,
    _preserve_dia_indices_dtype,
)

FLOAT_DTYPES = (np.float64, np.float32, np.float16)


def _nw_into_df_or_series(x):
    return nw.dependencies.is_into_dataframe(x) or nw.dependencies.is_into_series(x)


# This function is not used anymore at this moment in the code base but we keep it in
# case that we merge a new public function without kwarg only by mistake, which would
# require a deprecation cycle to fix.
def _deprecate_positional_args(func=None, *, version="1.3"):
    """Decorator for methods that issues warnings for positional arguments.

    Using the keyword-only argument syntax in pep 3102, arguments after the
    * will issue a warning when passed as a positional argument.

    Parameters
    ----------
    func : callable, default=None
        Function to check arguments on.
    version : callable, default="1.3"
        The version when positional arguments will result in error.
    """

    def _inner_deprecate_positional_args(f):
        sig = signature(f)
        kwonly_args = []
        all_args = []

        for name, param in sig.parameters.items():
            if param.kind == Parameter.POSITIONAL_OR_KEYWORD:
                all_args.append(name)
            elif param.kind == Parameter.KEYWORD_ONLY:
                kwonly_args.append(name)

        @wraps(f)
        def inner_f(*args, **kwargs):
            extra_args = len(args) - len(all_args)
            if extra_args <= 0:
                return f(*args, **kwargs)

            # extra_args > 0
            args_msg = [
                f"{name}={arg}"
                for name, arg in zip(kwonly_args[:extra_args], args[-extra_args:])
            ]
            args_msg = ", ".join(args_msg)
            warnings.warn(
                (
                    f"Pass {args_msg} as keyword args. From version "
                    f"{version} passing these as positional arguments "
                    "will result in an error"
                ),
                FutureWarning,
            )
            kwargs.update(zip(sig.parameters, args))
            return f(**kwargs)

        return inner_f

    if func is not None:
        return _inner_deprecate_positional_args(func)

    return _inner_deprecate_positional_args


def _assert_all_finite(
    X, allow_nan=False, msg_dtype=None, estimator_name=None, input_name=""
):
    """Like assert_all_finite, but only for ndarray."""

    xp, is_array_api = get_namespace(X)

    if _get_config()["assume_finite"]:
        return

    X = xp.asarray(X)

    # for object dtype data, we only check for NaNs (GH-13254)
    if not is_array_api and X.dtype == np.dtype("object") and not allow_nan:
        if _object_dtype_isnan(X).any():
            raise ValueError("Input contains NaN")

    # We need only consider float arrays, hence can early return for all else.
    if not xp.isdtype(X.dtype, ("real floating", "complex floating")):
        return

    # First try an O(n) time, O(1) space solution for the common case that
    # everything is finite; fall back to O(n) space `np.isinf/isnan` or custom
    # Cython implementation to prevent false positives and provide a detailed
    # error message.
    with np.errstate(over="ignore"):
        first_pass_isfinite = xp.isfinite(xp.sum(X))
    if first_pass_isfinite:
        return

    _assert_all_finite_element_wise(
        X,
        xp=xp,
        allow_nan=allow_nan,
        msg_dtype=msg_dtype,
        estimator_name=estimator_name,
        input_name=input_name,
    )


def _assert_all_finite_element_wise(
    X, *, xp, allow_nan, msg_dtype=None, estimator_name=None, input_name=""
):
    # Cython implementation doesn't support FP16 or complex numbers
    use_cython = (
        xp is np and X.data.contiguous and X.dtype.type in {np.float32, np.float64}
    )
    if use_cython:
        out = cy_isfinite(X.reshape(-1), allow_nan=allow_nan)
        has_nan_error = False if allow_nan else out == FiniteStatus.has_nan
        has_inf = out == FiniteStatus.has_infinite
    else:
        has_inf = xp.any(xp.isinf(X))
        has_nan_error = False if allow_nan else xp.any(xp.isnan(X))
    if has_inf or has_nan_error:
        if has_nan_error:
            type_err = "NaN"
        else:
            msg_dtype = msg_dtype if msg_dtype is not None else X.dtype
            type_err = f"infinity or a value too large for {msg_dtype!r}"
        padded_input_name = input_name + " " if input_name else ""
        msg_err = f"Input {padded_input_name}contains {type_err}."
        if estimator_name and input_name == "X" and has_nan_error:
            # Improve the error message on how to handle missing values in
            # scikit-learn.
            msg_err += (
                f"\n{estimator_name} does not accept missing values"
                " encoded as NaN natively. For supervised learning, you might want"
                " to consider sklearn.ensemble.HistGradientBoostingClassifier and"
                " Regressor which accept missing values encoded as NaNs natively."
                " Alternatively, it is possible to preprocess the data, for"
                " instance by using an imputer transformer in a pipeline or drop"
                " samples with missing values. See"
                " https://scikit-learn.org/stable/modules/impute.html"
                " You can find a list of all estimators that handle NaN values"
                " at the following page:"
                " https://scikit-learn.org/stable/modules/impute.html"
                "#estimators-that-handle-nan-values"
            )
        raise ValueError(msg_err)


def assert_all_finite(
    X,
    *,
    allow_nan=False,
    estimator_name=None,
    input_name="",
):
    """Throw a ValueError if X contains NaN or infinity.

    Parameters
    ----------
    X : {ndarray, sparse matrix}
        The input data.

    allow_nan : bool, default=False
        If True, do not throw error when `X` contains NaN.

    estimator_name : str, default=None
        The estimator name, used to construct the error message.

    input_name : str, default=""
        The data name used to construct the error message. In particular
        if `input_name` is "X" and the data has NaN values and
        allow_nan is False, the error message will link to the imputer
        documentation.

    Examples
    --------
    >>> from sklearn.utils import assert_all_finite
    >>> import numpy as np
    >>> array = np.array([1, np.inf, np.nan, 4])
    >>> try:
    ...     assert_all_finite(array)
    ...     print("Test passed: Array contains only finite values.")
    ... except ValueError:
    ...     print("Test failed: Array contains non-finite values.")
    Test failed: Array contains non-finite values.
    """
    _assert_all_finite(
        X.data if sp.issparse(X) else X,
        allow_nan=allow_nan,
        estimator_name=estimator_name,
        input_name=input_name,
    )


def as_float_array(X, *, copy=True, ensure_all_finite=True):
    """Convert an array-like to an array of floats.

    The new dtype will be np.float32 or np.float64, depending on the original
    type. The function can create a copy or modify the argument depending
    on the argument copy.

    Parameters
    ----------
    X : {array-like, sparse matrix}
        The input data.

    copy : bool, default=True
        If True, a copy of X will be created. If False, a copy may still be
        returned if X's dtype is not a floating point type.

    ensure_all_finite : bool or 'allow-nan', default=True
        Whether to raise an error on np.inf, np.nan, pd.NA in X. The
        possibilities are:

        - True: Force all values of X to be finite.
        - False: accepts np.inf, np.nan, pd.NA in X.
        - 'allow-nan': accepts only np.nan and pd.NA values in X. Values cannot
          be infinite.

        .. versionadded:: 1.6
           `force_all_finite` was renamed to `ensure_all_finite`.

    Returns
    -------
    XT : {ndarray, sparse matrix}
        An array of type float.

    Examples
    --------
    >>> from sklearn.utils import as_float_array
    >>> import numpy as np
    >>> array = np.array([0, 0, 1, 2, 2], dtype=np.int64)
    >>> as_float_array(array)
    array([0., 0., 1., 2., 2.])
    """
    if isinstance(X, np.matrix) or (
        not isinstance(X, np.ndarray) and not sp.issparse(X)
    ):
        return check_array(
            X,
            accept_sparse=["csr", "csc", "coo"],
            dtype=np.float64,
            copy=copy,
            ensure_all_finite=ensure_all_finite,
            ensure_2d=False,
        )
    elif sp.issparse(X) and X.dtype in [np.float32, np.float64]:
        return X.copy() if copy else X
    elif X.dtype in [np.float32, np.float64]:  # is numpy array
        return X.copy("F" if X.flags["F_CONTIGUOUS"] else "C") if copy else X
    else:
        if X.dtype.kind in "uib" and X.dtype.itemsize <= 4:
            return_dtype = np.float32
        else:
            return_dtype = np.float64
        return X.astype(return_dtype)


def _is_arraylike(x):
    """Returns whether the input is array-like."""
    if sp.issparse(x):
        return False

    return hasattr(x, "__len__") or hasattr(x, "shape") or hasattr(x, "__array__")


def _is_arraylike_not_scalar(array):
    """Return True if array is array-like and not a scalar"""
    return _is_arraylike(array) and not np.isscalar(array)


def _num_features(X):
    """Return the number of features in an array-like X.

    This helper function tries hard to avoid to materialize an array version
    of X unless necessary. For instance, if X is a list of lists,
    this function will return the length of the first element, assuming
    that subsequent elements are all lists of the same length without
    checking.
    Parameters
    ----------
    X : array-like
        array-like to get the number of features.

    Returns
    -------
    features : int
        Number of features
    """
    type_ = type(X)
    if type_.__module__ == "builtins":
        type_name = type_.__qualname__
    else:
        type_name = f"{type_.__module__}.{type_.__qualname__}"
    message = f"Unable to find the number of features from X of type {type_name}"
    if not hasattr(X, "__len__") and not hasattr(X, "shape"):
        if not hasattr(X, "__array__"):
            raise TypeError(message)
        # Only convert X to a numpy array if there is no cheaper, heuristic
        # option.
        X = np.asarray(X)

    if hasattr(X, "shape"):
        if not hasattr(X.shape, "__len__") or len(X.shape) <= 1:
            message += f" with shape {X.shape}"
            raise TypeError(message)
        return X.shape[1]

    first_sample = X[0]

    # Do not consider an array-like of strings or dicts to be a 2D array
    if isinstance(first_sample, (str, bytes, dict)):
        message += f" where the samples are of type {type(first_sample).__qualname__}"
        raise TypeError(message)

    try:
        # If X is a list of lists, for instance, we assume that all nested
        # lists have the same length without checking or converting to
        # a numpy array to keep this function call as cheap as possible.
        return len(first_sample)
    except Exception as err:
        raise TypeError(message) from err


def _num_samples(x):
    """Return number of samples in array-like x."""
    message = "Expected sequence or array-like, got %s" % type(x)
    if hasattr(x, "fit") and callable(x.fit):
        # Don't get num_samples from an ensembles length!
        raise TypeError(message)

    if hasattr(x, "shape") and x.shape is not None:
        if len(x.shape) == 0:
            raise TypeError(
                "Input should have at least 1 dimension i.e. satisfy "
                f"`len(x.shape) > 0`, got scalar `{x!r}` instead."
            )
        # Check that shape is returning an integer or default to len
        # Dask dataframes may not return numeric shape[0] value
        if isinstance(x.shape[0], numbers.Integral):
            return x.shape[0]

    if _nw_into_df_or_series(x):
        return nw.from_native(x, allow_series=True).shape[0]

    if not hasattr(x, "__len__") and not hasattr(x, "shape"):
        if hasattr(x, "__array__"):
            xp, _ = get_namespace(x)
            x = xp.asarray(x)
        else:
            raise TypeError(message)

    try:
        return len(x)
    except TypeError as type_error:
        raise TypeError(message) from type_error


def check_memory(memory):
    """Check that ``memory`` is joblib.Memory-like.

    joblib.Memory-like means that ``memory`` can be converted into a
    joblib.Memory instance (typically a str denoting the ``location``)
    or has the same interface (has a ``cache`` method).

    Parameters
    ----------
    memory : None, str or object with the joblib.Memory interface
        - If string, the location where to create the `joblib.Memory` interface.
        - If None, no caching is done and the Memory object is completely transparent.

    Returns
    -------
    memory : object with the joblib.Memory interface
        A correct joblib.Memory object.

    Raises
    ------
    ValueError
        If ``memory`` is not joblib.Memory-like.

    Examples
    --------
    >>> from sklearn.utils.validation import check_memory
    >>> check_memory("caching_dir")
    Memory(location=caching_dir/joblib)
    """
    if memory is None or isinstance(memory, str):
        memory = joblib.Memory(location=memory, verbose=0)
    elif not hasattr(memory, "cache"):
        raise ValueError(
            "'memory' should be None, a string or have the same"
            " interface as joblib.Memory."
            f" Got memory='{memory}' instead."
        )
    return memory


def check_consistent_length(*arrays):
    """Check that all arrays have consistent first dimensions.

    Checks whether all objects in arrays have the same shape or length.

    Parameters
    ----------
    *arrays : list or tuple of input objects.
        Objects that will be checked for consistent length.

    Examples
    --------
    >>> from sklearn.utils.validation import check_consistent_length
    >>> a = [1, 2, 3]
    >>> b = [2, 3, 4]
    >>> check_consistent_length(a, b)
    """
    lengths = [_num_samples(X) for X in arrays if X is not None]
    if len(set(lengths)) > 1:
        raise ValueError(
            "Found input variables with inconsistent numbers of samples: %r"
            % [int(l) for l in lengths]
        )


def _make_indexable(iterable):
    """Ensure iterable supports indexing or convert to an indexable variant.

    Convert sparse matrices to csr and other non-indexable iterable to arrays.
    Let `None` and indexable objects (e.g. pandas dataframes) pass unchanged.

    Parameters
    ----------
    iterable : {list, dataframe, ndarray, sparse matrix} or None
        Object to be converted to an indexable iterable.
    """
    if sp.issparse(iterable):
        return iterable.tocsr()
    elif hasattr(iterable, "__getitem__") or hasattr(iterable, "iloc"):
        return iterable
    elif iterable is None:
        return iterable
    return np.array(iterable)


def indexable(*iterables):
    """Make arrays indexable for cross-validation.

    Checks consistent length, passes through None, and ensures that everything
    can be indexed by converting sparse matrices to csr and converting
    non-iterable objects to arrays.

    Parameters
    ----------
    *iterables : {lists, dataframes, ndarrays, sparse matrices}
        List of objects to ensure sliceability.

    Returns
    -------
    result : list of {ndarray, sparse matrix, dataframe} or None
        Returns a list containing indexable arrays (i.e. NumPy array,
        sparse matrix, or dataframe) or `None`.

    Examples
    --------
    >>> from sklearn.utils import indexable
    >>> from scipy.sparse import csr_array
    >>> import numpy as np
    >>> iterables = [
    ...     [1, 2, 3], np.array([2, 3, 4]), None, csr_array([[5], [6], [7]])
    ... ]
    >>> indexable(*iterables)
    [[1, 2, 3], array([2, 3, 4]), None, <...Sparse...dtype 'int64'...shape (3, 1)>]
    """

    result = [_make_indexable(X) for X in iterables]
    check_consistent_length(*result)
    return result


def _ensure_sparse_format(
    sparse_container,
    accept_sparse,
    dtype,
    copy,
    ensure_all_finite,
    accept_large_sparse,
    estimator_name=None,
    input_name="",
):
    """Convert a sparse container to a given format.

    Checks the sparse format of `sparse_container` and converts if necessary.

    Parameters
    ----------
    sparse_container : sparse matrix or array
        Input to validate and convert.

    accept_sparse : str, bool or list/tuple of str
        String[s] representing allowed sparse matrix formats ('csc',
        'csr', 'coo', 'dok', 'bsr', 'lil', 'dia'). If the input is sparse but
        not in the allowed format, it will be converted to the first listed
        format. True allows the input to be any format. False means
        that a sparse matrix input will raise an error.

    dtype : str, type or None
        Data type of result. If None, the dtype of the input is preserved.

    copy : bool
        Whether a forced copy will be triggered. If copy=False, a copy might
        be triggered by a conversion.

    ensure_all_finite : bool or 'allow-nan'
        Whether to raise an error on np.inf, np.nan, pd.NA in X. The
        possibilities are:

        - True: Force all values of X to be finite.
        - False: accepts np.inf, np.nan, pd.NA in X.
        - 'allow-nan': accepts only np.nan and pd.NA values in X. Values cannot
          be infinite.

        .. versionadded:: 0.20
           ``ensure_all_finite`` accepts the string ``'allow-nan'``.

        .. versionchanged:: 0.23
           Accepts `pd.NA` and converts it into `np.nan`

    accept_large_sparse : bool
        If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by
        accept_sparse, accept_large_sparse will cause it to be accepted only
        if its indices are stored with a 32-bit dtype.

    estimator_name : str, default=None
        The estimator name, used to construct the error message.

    input_name : str, default=""
        The data name used to construct the error message. In particular
        if `input_name` is "X" and the data has NaN values and
        allow_nan is False, the error message will link to the imputer
        documentation.

    Returns
    -------
    sparse_container_converted : sparse matrix or array
        Sparse container (matrix/array) that is ensured to have an allowed type.
    """
    if dtype is None:
        dtype = sparse_container.dtype

    changed_format = False
    sparse_container_type_name = type(sparse_container).__name__

    if isinstance(accept_sparse, str):
        accept_sparse = [accept_sparse]

    # Indices dtype validation
    _check_large_sparse(sparse_container, accept_large_sparse)

    if accept_sparse is False:
        padded_input = " for " + input_name if input_name else ""
        raise TypeError(
            f"Sparse data was passed{padded_input}, but dense data is required. "
            "Use '.toarray()' to convert to a dense numpy array."
        )
    elif isinstance(accept_sparse, (list, tuple)):
        if len(accept_sparse) == 0:
            raise ValueError(
                "When providing 'accept_sparse' as a tuple or list, it must contain at "
                "least one string value."
            )
        # ensure correct sparse format
        if sparse_container.format not in accept_sparse:
            # create new with correct sparse
            sparse_container = sparse_container.asformat(accept_sparse[0])
            changed_format = True
    elif accept_sparse is not True:
        # any other type
        raise ValueError(
            "Parameter 'accept_sparse' should be a string, boolean or list of strings."
            f" You provided 'accept_sparse={accept_sparse}'."
        )

    if dtype != sparse_container.dtype:
        # convert dtype
        sparse_container = sparse_container.astype(dtype)
    elif copy and not changed_format:
        # force copy
        sparse_container = sparse_container.copy()

    if ensure_all_finite:
        if not hasattr(sparse_container, "data"):
            warnings.warn(
                f"Can't check {sparse_container.format} sparse matrix for nan or inf.",
                stacklevel=2,
            )
        else:
            _assert_all_finite(
                sparse_container.data,
                allow_nan=ensure_all_finite == "allow-nan",
                estimator_name=estimator_name,
                input_name=input_name,
            )

    # TODO: Remove when the minimum version of SciPy supported is 1.12
    # With SciPy sparse arrays, conversion from DIA format to COO, CSR, or BSR
    # triggers the use of `np.int64` indices even if the data is such that it could
    # be more efficiently represented with `np.int32` indices.
    # https://github.com/scipy/scipy/issues/19245 Since not all scikit-learn
    # algorithms support large indices, the following code downcasts to `np.int32`
    # indices when it's safe to do so.
    if changed_format:
        # accept_sparse is specified to a specific format and a conversion occurred
        requested_sparse_format = accept_sparse[0]
        _preserve_dia_indices_dtype(
            sparse_container, sparse_container_type_name, requested_sparse_format
        )

    return sparse_container


def _ensure_no_complex_data(array):
    if (
        hasattr(array, "dtype")
        and array.dtype is not None
        and hasattr(array.dtype, "kind")
        and array.dtype.kind == "c"
    ):
        raise ValueError("Complex data not supported\n{}\n".format(array))


def _check_estimator_name(estimator):
    if estimator is not None:
        if isinstance(estimator, str):
            return estimator
        else:
            return estimator.__class__.__name__
    return None


def _pandas_dtype_needs_early_conversion(pd_dtype):
    """Return True if pandas extension pd_dtype need to be converted early."""
    # Check these early for pandas versions without extension dtypes
    from pandas import SparseDtype
    from pandas.api.types import (
        is_bool_dtype,
        is_float_dtype,
        is_integer_dtype,
    )

    if is_bool_dtype(pd_dtype):
        # bool and extension booleans need early conversion because __array__
        # converts mixed dtype dataframes into object dtypes
        return True

    if isinstance(pd_dtype, SparseDtype):
        # Sparse arrays will be converted later in `check_array`
        return False

    try:
        from pandas.api.types import is_extension_array_dtype
    except ImportError:
        return False

    if isinstance(pd_dtype, SparseDtype) or not is_extension_array_dtype(pd_dtype):
        # Sparse arrays will be converted later in `check_array`
        # Only handle extension arrays for integer and floats
        return False
    elif is_float_dtype(pd_dtype):
        # Float ndarrays can normally support nans. They need to be converted
        # first to map pd.NA to np.nan
        return True
    elif is_integer_dtype(pd_dtype):
        # XXX: Warn when converting from a high integer to a float
        return True

    return False


def _is_pandas_string_dtype(dtype):
    """Return True if dtype is a pandas StringDtype."""
    try:
        from pandas import StringDtype

        return isinstance(dtype, StringDtype)
    except ImportError:
        return False


def _is_extension_array_dtype(array):
    # Pandas extension arrays have a dtype with an na_value
    return hasattr(array, "dtype") and hasattr(array.dtype, "na_value")


def check_array(
    array,
    accept_sparse=False,
    *,
    accept_large_sparse=True,
    dtype="numeric",
    order=None,
    copy=False,
    force_writeable=False,
    ensure_all_finite=True,
    ensure_non_negative=False,
    ensure_2d=True,
    allow_nd=False,
    ensure_min_samples=1,
    ensure_min_features=1,
    estimator=None,
    input_name="",
):
    """Input validation on an array, list, sparse matrix or similar.

    By default, the input is checked to be a non-empty 2D array containing
    only finite values. If the dtype of the array is object, attempt
    converting to float, raising on failure.

    Parameters
    ----------
    array : object
        Input object to check / convert.

    accept_sparse : str, bool or list/tuple of str, default=False
        String[s] representing allowed sparse matrix formats, such as 'csc',
        'csr', etc. If the input is sparse but not in the allowed format,
        it will be converted to the first listed format. True allows the input
        to be any format. False means that a sparse matrix input will
        raise an error.

    accept_large_sparse : bool, default=True
        If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by
        accept_sparse, accept_large_sparse=False will cause it to be accepted
        only if its indices are stored with a 32-bit dtype.

        .. versionadded:: 0.20

    dtype : 'numeric', type, list of type or None, default='numeric'
        Data type of result. If None, the dtype of the input is preserved.
        If "numeric", dtype is preserved unless array.dtype is object.
        If dtype is a list of types, conversion on the first type is only
        performed if the dtype of the input is not in the list.

    order : {'F', 'C'} or None, default=None
        Whether an array will be forced to be fortran or c-style.
        When order is None (default), then if copy=False, nothing is ensured
        about the memory layout of the output array; otherwise (copy=True)
        the memory layout of the returned array is kept as close as possible
        to the original array.

    copy : bool, default=False
        Whether a forced copy will be triggered. If copy=False, a copy might
        be triggered by a conversion.

    force_writeable : bool, default=False
        Whether to force the output array to be writeable. If True, the returned array
        is guaranteed to be writeable, which may require a copy. Otherwise the
        writeability of the input array is preserved.

        .. versionadded:: 1.6

    ensure_all_finite : bool or 'allow-nan', default=True
        Whether to raise an error on np.inf, np.nan, pd.NA in array. The
        possibilities are:

        - True: Force all values of array to be finite.
        - False: accepts np.inf, np.nan, pd.NA in array.
        - 'allow-nan': accepts only np.nan and pd.NA values in array. Values
          cannot be infinite.

        .. versionadded:: 1.6
           `force_all_finite` was renamed to `ensure_all_finite`.

    ensure_non_negative : bool, default=False
        Make sure the array has only non-negative values. If True, an array that
        contains negative values will raise a ValueError.

        .. versionadded:: 1.6

    ensure_2d : bool, default=True
        Whether to raise a value error if array is not 2D.

    allow_nd : bool, default=False
        Whether to allow array.ndim > 2.

    ensure_min_samples : int, default=1
        Make sure that the array has a minimum number of samples in its first
        axis (rows for a 2D array). Setting to 0 disables this check.

    ensure_min_features : int, default=1
        Make sure that the 2D array has some minimum number of features
        (columns). The default value of 1 rejects empty datasets.
        This check is only enforced when the input data has effectively 2
        dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0
        disables this check.

    estimator : str or estimator instance, default=None
        If passed, include the name of the estimator in warning messages.

    input_name : str, default=""
        The data name used to construct the error message. In particular
        if `input_name` is "X" and the data has NaN values and
        allow_nan is False, the error message will link to the imputer
        documentation.

        .. versionadded:: 1.1.0

    Returns
    -------
    array_converted : object
        The converted and validated array.

    Examples
    --------
    >>> from sklearn.utils.validation import check_array
    >>> X = [[1, 2, 3], [4, 5, 6]]
    >>> X_checked = check_array(X)
    >>> X_checked
    array([[1, 2, 3], [4, 5, 6]])
    """
    if isinstance(array, np.matrix):
        raise TypeError(
            "np.matrix is not supported. Please convert to a numpy array with "
            "np.asarray. For more information see: "
            "https://numpy.org/doc/stable/reference/generated/numpy.matrix.html"
        )

    xp, is_array_api_compliant = get_namespace(array)

    # store reference to original array to check if copy is needed when
    # function returns
    array_orig = array

    # store whether originally we wanted numeric dtype
    dtype_numeric = isinstance(dtype, str) and dtype == "numeric"

    dtype_orig = getattr(array, "dtype", None)
    if not is_array_api_compliant and not hasattr(dtype_orig, "kind"):
        # not a data type (e.g. a column named dtype in a pandas DataFrame)
        dtype_orig = None

    # check if the object contains several dtypes (typically a pandas
    # DataFrame), and store them. If not, store None.
    dtypes_orig = None
    pandas_requires_conversion = False
    # track if we have a Series-like object to raise a better error message
    type_if_series = None
    # For dataframes, use narwhals
    if _nw_into_df_or_series(array):
        array_df = nw.from_native(array, allow_series=True)
    else:
        array_df = None

    # pandas.DataFrame may have sparse columns
    is_pandas_fully_sparse_df = False
    if (
        array_df is not None
        and array_df.implementation.is_pandas()
        and len(array_df.shape) >= 2
    ):
        # Throw warning if some columns are sparse. If all columns are sparse, then
        # array.sparse exists and sparsity will be preserved (later).
        from pandas import SparseDtype

        def is_pd_sparse(dtype):
            return isinstance(dtype, SparseDtype)

        if hasattr(array, "sparse") and array.dtypes.apply(is_pd_sparse).all():
            # All columns of the pandas.DataFrame are sparse. Note that the `sparse`
            # attribute is not a guaranteed detection for all sparse columns.
            is_pandas_fully_sparse_df = True
        elif array.dtypes.apply(is_pd_sparse).any():
            warnings.warn(
                "pandas.DataFrame with sparse columns found."
                "It will be converted to a dense numpy array."
            )

        dtypes_orig = list(array.dtypes)
        pandas_requires_conversion = any(
            _pandas_dtype_needs_early_conversion(i) for i in dtypes_orig
        )
        has_pandas_string = any(_is_pandas_string_dtype(d) for d in dtypes_orig)
        if all(isinstance(dtype_iter, np.dtype) for dtype_iter in dtypes_orig):
            dtype_orig = np.result_type(*dtypes_orig)
        elif has_pandas_string:
            # Force object if any of the dtypes is a StringDtype.
            dtype_orig = object
        elif pandas_requires_conversion and any(d == object for d in dtypes_orig):
            # Force object if any of the dtypes is an object
            dtype_orig = object

    elif (_is_extension_array_dtype(array) or hasattr(array, "iloc")) and hasattr(
        array, "dtype"
    ):
        # array is a pandas series or a pandas array.
        type_if_series = type(array)
        pandas_requires_conversion = _pandas_dtype_needs_early_conversion(array.dtype)
        if isinstance(array.dtype, np.dtype):
            dtype_orig = array.dtype
        elif _is_pandas_string_dtype(array.dtype):
            # pandas 3 uses StringDtype for string columns instead of object.
            # Treat as object so that dtype_numeric detection works correctly.
            dtype_orig = object
        else:
            # Set to None to let array.astype work out the best dtype
            dtype_orig = None

    if dtype_numeric:
        if dtype_orig is not None and (
            (hasattr(dtype_orig, "kind") and dtype_orig.kind == "O")
            or dtype_orig == object
        ):
            # if input is object, convert to float.
            dtype = xp.float64
        else:
            dtype = None

    if isinstance(dtype, (list, tuple)):
        if dtype_orig is not None and dtype_orig in dtype:
            # no dtype conversion required
            dtype = None
        else:
            # dtype conversion required. Let's select the first element of the
            # list of accepted types.
            dtype = dtype[0]

    if pandas_requires_conversion:
        # pandas dataframe requires conversion earlier to handle extension dtypes with
        # nans
        # Use the original dtype for conversion if dtype is None
        new_dtype = dtype_orig if dtype is None else dtype
        array = array.astype(new_dtype)
        # Since we converted here, we do not need to convert again later
        dtype = None

    if ensure_all_finite not in (True, False, "allow-nan"):
        raise ValueError(
            "ensure_all_finite should be a bool or 'allow-nan'. Got "
            f"{ensure_all_finite!r} instead."
        )

    if dtype is not None and _is_numpy_namespace(xp):
        # convert to dtype object to conform to Array API to be use `xp.isdtype` later
        dtype = np.dtype(dtype)

    estimator_name = _check_estimator_name(estimator)
    context = " by %s" % estimator_name if estimator is not None else ""

    # When all dataframe columns are sparse, convert to a sparse array
    if is_pandas_fully_sparse_df and array.ndim > 1:
        # DataFrame.sparse only supports `to_coo`
        array = array.sparse.to_coo()

    if sp.issparse(array):
        _ensure_no_complex_data(array)
        array = _ensure_sparse_format(
            array,
            accept_sparse=accept_sparse,
            dtype=dtype,
            copy=copy,
            ensure_all_finite=ensure_all_finite,
            accept_large_sparse=accept_large_sparse,
            estimator_name=estimator_name,
            input_name=input_name,
        )
        if ensure_2d and array.ndim < 2:
            raise ValueError(
                f"Expected 2D input, got input with shape {array.shape}.\n"
                "Reshape your data either using array.reshape(-1, 1) if "
                "your data has a single feature or array.reshape(1, -1) "
                "if it contains a single sample."
            )
    else:
        # If np.array(..) gives ComplexWarning, then we convert the warning
        # to an error. This is needed because specifying a non complex
        # dtype to the function converts complex to real dtype,
        # thereby passing the test made in the lines following the scope
        # of warnings context manager.
        with warnings.catch_warnings():
            try:
                warnings.simplefilter("error", ComplexWarning)
                if dtype is not None and xp.isdtype(dtype, "integral"):
                    # Conversion float -> int should not contain NaN or
                    # inf (numpy#14412). We cannot use casting='safe' because
                    # then conversion float -> int would be disallowed.
                    array = _asarray_with_order(array, order=order, xp=xp)
                    if xp.isdtype(array.dtype, ("real floating", "complex floating")):
                        _assert_all_finite(
                            array,
                            allow_nan=False,
                            msg_dtype=dtype,
                            estimator_name=estimator_name,
                            input_name=input_name,
                        )
                    array = xp.astype(array, dtype, copy=False)
                else:
                    array = _asarray_with_order(array, order=order, dtype=dtype, xp=xp)
            except ComplexWarning as complex_warning:
                raise ValueError(
                    "Complex data not supported\n{}\n".format(array)
                ) from complex_warning

        # It is possible that the np.array(..) gave no warning. This happens
        # when no dtype conversion happened, for example dtype = None. The
        # result is that np.array(..) produces an array of complex dtype
        # and we need to catch and raise exception for such cases.
        _ensure_no_complex_data(array)

        if ensure_2d:
            # If input is scalar raise error
            if array.ndim == 0:
                raise ValueError(
                    "Expected 2D array, got scalar array instead:\narray={}.\n"
                    "Reshape your data either using array.reshape(-1, 1) if "
                    "your data has a single feature or array.reshape(1, -1) "
                    "if it contains a single sample.".format(array)
                )
            # If input is 1D raise error
            if array.ndim == 1:
                # If input is a Series-like object (eg. pandas Series or polars Series)
                if type_if_series is not None:
                    msg = (
                        f"Expected a 2-dimensional container but got {type_if_series} "
                        "instead. Pass a DataFrame containing a single row (i.e. "
                        "single sample) or a single column (i.e. single feature) "
                        "instead."
                    )
                else:
                    msg = (
                        f"Expected 2D array, got 1D array instead:\narray={array}.\n"
                        "Reshape your data either using array.reshape(-1, 1) if "
                        "your data has a single feature or array.reshape(1, -1) "
                        "if it contains a single sample."
                    )
                raise ValueError(msg)

        if dtype_numeric and hasattr(array.dtype, "kind") and array.dtype.kind in "USV":
            raise ValueError(
                "dtype='numeric' is not compatible with arrays of bytes/strings."
                "Convert your data to numeric values explicitly instead."
            )
        if not allow_nd and array.ndim >= 3:
            raise ValueError(
                f"Found array with dim {array.ndim},"
                f" while dim <= 2 is required{context}."
            )

        if ensure_all_finite:
            _assert_all_finite(
                array,
                input_name=input_name,
                estimator_name=estimator_name,
                allow_nan=ensure_all_finite == "allow-nan",
            )

        if copy:
            if _is_numpy_namespace(xp):
                # only make a copy if `array` and `array_orig` may share memory`
                if np.may_share_memory(array, array_orig):
                    array = _asarray_with_order(
                        array, dtype=dtype, order=order, copy=True, xp=xp
                    )
            else:
                # always make a copy for non-numpy arrays
                array = _asarray_with_order(
                    array, dtype=dtype, order=order, copy=True, xp=xp
                )

    if ensure_min_samples > 0:
        n_samples = _num_samples(array)
        if n_samples < ensure_min_samples:
            raise ValueError(
                "Found array with %d sample(s) (shape=%s) while a"
                " minimum of %d is required%s."
                % (n_samples, array.shape, ensure_min_samples, context)
            )

    if ensure_min_features > 0 and array.ndim == 2:
        n_features = array.shape[1]
        if n_features < ensure_min_features:
            raise ValueError(
                "Found array with %d feature(s) (shape=%s) while"
                " a minimum of %d is required%s."
                % (n_features, array.shape, ensure_min_features, context)
            )

    if ensure_non_negative:
        whom = input_name
        if estimator_name:
            whom += f" in {estimator_name}"
        check_non_negative(array, whom)

    if force_writeable:
        # By default, array.copy() creates a C-ordered copy. We set order=K to
        # preserve the order of the array.
        copy_params = {"order": "K"} if not sp.issparse(array) else {}

        array_data = array.data if sp.issparse(array) else array
        flags = getattr(array_data, "flags", None)
        if not getattr(flags, "writeable", True):
            # This situation can only happen when copy=False, the array is read-only and
            # a writeable output is requested. This is an ambiguous setting so we chose
            # to always (except for one specific setting, see below) make a copy to
            # ensure that the output is writeable, even if avoidable, to not overwrite
            # the user's data by surprise.

            if is_pandas_df_or_series(array_orig):
                try:
                    # In pandas >= 3, np.asarray(df), called earlier in check_array,
                    # returns a read-only intermediate array. It can be made writeable
                    # safely without copy because if the original DataFrame was backed
                    # by a read-only array, trying to change the flag would raise an
                    # error, in which case we make a copy.
                    array_data.flags.writeable = True
                except ValueError:
                    array = array.copy(**copy_params)
            else:
                array = array.copy(**copy_params)

    return array


def _check_large_sparse(X, accept_large_sparse=False):
    """Raise a ValueError if X has 64bit indices and accept_large_sparse=False"""
    if not accept_large_sparse:
        supported_indices = ["int32"]
        if X.format == "coo":
            index_keys = ["col", "row"]
        elif X.format in ["csr", "csc", "bsr"]:
            index_keys = ["indices", "indptr"]
        else:
            return
        for key in index_keys:
            indices_datatype = getattr(X, key).dtype
            if indices_datatype not in supported_indices:
                raise ValueError(
                    "Only sparse matrices with 32-bit integer indices are accepted."
                    f" Got {indices_datatype} indices. Please do report a minimal"
                    " reproducer on scikit-learn issue tracker so that support for"
                    " your use-case can be studied by maintainers. See:"
                    " https://scikit-learn.org/dev/developers/minimal_reproducer.html"
                )


def check_X_y(
    X,
    y,
    accept_sparse=False,
    *,
    accept_large_sparse=True,
    dtype="numeric",
    order=None,
    copy=False,
    force_writeable=False,
    ensure_all_finite=True,
    ensure_2d=True,
    allow_nd=False,
    multi_output=False,
    ensure_min_samples=1,
    ensure_min_features=1,
    y_numeric=False,
    estimator=None,
):
    """Input validation for standard estimators.

    Checks X and y for consistent length, enforces X to be 2D and y 1D. By
    default, X is checked to be non-empty and containing only finite values.
    Standard input checks are also applied to y, such as checking that y
    does not have np.nan or np.inf targets. For multi-label y, set
    multi_output=True to allow 2D and sparse y. If the dtype of X is
    object, attempt converting to float, raising on failure.

    Parameters
    ----------
    X : {ndarray, list, sparse matrix}
        Input data.

    y : {ndarray, list, sparse matrix}
        Labels.

    accept_sparse : str, bool or list of str, default=False
        String[s] representing allowed sparse matrix formats, such as 'csc',
        'csr', etc. If the input is sparse but not in the allowed format,
        it will be converted to the first listed format. True allows the input
        to be any format. False means that a sparse matrix input will
        raise an error.

    accept_large_sparse : bool, default=True
        If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by
        accept_sparse, accept_large_sparse will cause it to be accepted only
        if its indices are stored with a 32-bit dtype.

        .. versionadded:: 0.20

    dtype : 'numeric', type, list of type or None, default='numeric'
        Data type of result. If None, the dtype of the input is preserved.
        If "numeric", dtype is preserved unless array.dtype is object.
        If dtype is a list of types, conversion on the first type is only
        performed if the dtype of the input is not in the list.

    order : {'F', 'C'}, default=None
        Whether an array will be forced to be fortran or c-style. If
        `None`, then the input data's order is preserved when possible.

    copy : bool, default=False
        Whether a forced copy will be triggered. If copy=False, a copy might
        be triggered by a conversion.

    force_writeable : bool, default=False
        Whether to force the output array to be writeable. If True, the returned array
        is guaranteed to be writeable, which may require a copy. Otherwise the
        writeability of the input array is preserved.

        .. versionadded:: 1.6

    ensure_all_finite : bool or 'allow-nan', default=True
        Whether to raise an error on np.inf, np.nan, pd.NA in array. This parameter
        does not influence whether y can have np.inf, np.nan, pd.NA values.
        The possibilities are:

        - True: Force all values of X to be finite.
        - False: accepts np.inf, np.nan, pd.NA in X.
        - 'allow-nan': accepts only np.nan or pd.NA values in X. Values cannot
          be infinite.

        .. versionadded:: 1.6
           `force_all_finite` was renamed to `ensure_all_finite`.

    ensure_2d : bool, default=True
        Whether to raise a value error if X is not 2D.

    allow_nd : bool, default=False
        Whether to allow X.ndim > 2.

    multi_output : bool, default=False
        Whether to allow 2D y (array or sparse matrix). If false, y will be
        validated as a vector. y cannot have np.nan or np.inf values if
        multi_output=True.

    ensure_min_samples : int, default=1
        Make sure that X has a minimum number of samples in its first
        axis (rows for a 2D array).

    ensure_min_features : int, default=1
        Make sure that the 2D array has some minimum number of features
        (columns). The default value of 1 rejects empty datasets.
        This check is only enforced when X has effectively 2 dimensions or
        is originally 1D and ``ensure_2d`` is True. Setting to 0 disables
        this check.

    y_numeric : bool, default=False
        Whether to ensure that y has a numeric type. If dtype of y is object,
        it is converted to float64. Should only be used for regression
        algorithms.

    estimator : str or estimator instance, default=None
        If passed, include the name of the estimator in warning messages.

    Returns
    -------
    X_converted : object
        The converted and validated X.

    y_converted : object
        The converted and validated y.

    Examples
    --------
    >>> from sklearn.utils.validation import check_X_y
    >>> X = [[1, 2], [3, 4], [5, 6]]
    >>> y = [1, 2, 3]
    >>> X, y = check_X_y(X, y)
    >>> X
    array([[1, 2],
          [3, 4],
          [5, 6]])
    >>> y
    array([1, 2, 3])
    """
    if y is None:
        if estimator is None:
            estimator_name = "estimator"
        else:
            estimator_name = _check_estimator_name(estimator)
        raise ValueError(
            f"{estimator_name} requires y to be passed, but the target y is None"
        )

    X = check_array(
        X,
        accept_sparse=accept_sparse,
        accept_large_sparse=accept_large_sparse,
        dtype=dtype,
        order=order,
        copy=copy,
        force_writeable=force_writeable,
        ensure_all_finite=ensure_all_finite,
        ensure_2d=ensure_2d,
        allow_nd=allow_nd,
        ensure_min_samples=ensure_min_samples,
        ensure_min_features=ensure_min_features,
        estimator=estimator,
        input_name="X",
    )

    y = _check_y(y, multi_output=multi_output, y_numeric=y_numeric, estimator=estimator)

    check_consistent_length(X, y)

    return X, y


def _check_y(y, multi_output=False, y_numeric=False, estimator=None):
    """Isolated part of check_X_y dedicated to y validation"""
    if multi_output:
        y = check_array(
            y,
            accept_sparse="csr",
            ensure_all_finite=True,
            ensure_2d=False,
            dtype=None,
            input_name="y",
            estimator=estimator,
        )
    else:
        estimator_name = _check_estimator_name(estimator)
        y = column_or_1d(y, warn=True)
        _assert_all_finite(y, input_name="y", estimator_name=estimator_name)
        _ensure_no_complex_data(y)
    if y_numeric and hasattr(y.dtype, "kind") and y.dtype.kind == "O":
        y = y.astype(np.float64)

    return y


def column_or_1d(y, *, dtype=None, input_name="y", warn=False, device=None):
    """Ravel column or 1d numpy array, else raises an error.

    Parameters
    ----------
    y : array-like
       Input data.

    dtype : data-type, default=None
        Data type for `y`.

        .. versionadded:: 1.2

    input_name : str, default="y"
        The data name used to construct the error message.

        .. versionadded:: 1.8

    warn : bool, default=False
       To control display of warnings.

    device : device, default=None
        `device` object.
        See the :ref:`Array API User Guide <array_api>` for more details.

        .. versionadded:: 1.6

    Returns
    -------
    y : ndarray
       Output data.

    Raises
    ------
    ValueError
        If `y` is not a 1D array or a 2D array with a single row or column.

    Examples
    --------
    >>> from sklearn.utils.validation import column_or_1d
    >>> column_or_1d([1, 1])
    array([1, 1])
    """
    xp, _ = get_namespace(y)
    y = check_array(
        y,
        ensure_2d=False,
        dtype=dtype,
        input_name=input_name,
        ensure_all_finite=False,
        ensure_min_samples=0,
    )

    shape = y.shape
    if len(shape) == 1:
        return _asarray_with_order(
            xp.reshape(y, (-1,)), order="C", xp=xp, device=device
        )
    if len(shape) == 2 and shape[1] == 1:
        if warn:
            warnings.warn(
                (
                    "A column-vector y was passed when a 1d array was"
                    " expected. Please change the shape of y to "
                    "(n_samples, ), for example using ravel()."
                ),
                DataConversionWarning,
                stacklevel=2,
            )
        return _asarray_with_order(
            xp.reshape(y, (-1,)), order="C", xp=xp, device=device
        )

    raise ValueError(
        "y should be a 1d array, got an array of shape {} instead.".format(shape)
    )


def check_random_state(seed):
    """Turn seed into an np.random.RandomState instance.

    Parameters
    ----------
    seed : None, int or instance of RandomState
        If seed is None, return the RandomState singleton used by np.random.
        If seed is an int, return a new RandomState instance seeded with seed.
        If seed is already a RandomState instance, return it.
        Otherwise raise ValueError.

    Returns
    -------
    :class:`numpy:numpy.random.RandomState`
        The random state object based on `seed` parameter.

    Examples
    --------
    >>> from sklearn.utils.validation import check_random_state
    >>> check_random_state(42)
    RandomState(MT19937) at 0x...
    """
    if seed is None or seed is np.random:
        return np.random.mtrand._rand
    if isinstance(seed, numbers.Integral):
        return np.random.RandomState(seed)
    if isinstance(seed, np.random.RandomState):
        return seed
    raise ValueError(
        f"{seed!r} cannot be used to seed a numpy.random.RandomState instance"
    )


def has_fit_parameter(estimator, parameter):
    """Check whether the estimator's fit method supports the given parameter.

    Parameters
    ----------
    estimator : object
        An estimator to inspect.

    parameter : str
        The searched parameter.

    Returns
    -------
    is_parameter : bool
        Whether the parameter was found to be a named parameter of the
        estimator's fit method.

    Examples
    --------
    >>> from sklearn.svm import SVC
    >>> from sklearn.utils.validation import has_fit_parameter
    >>> has_fit_parameter(SVC(), "sample_weight")
    True
    """
    return (
        # This is used during test collection in common tests. The
        # hasattr(estimator, "fit") makes it so that we don't fail for an estimator
        # that does not have a `fit` method during collection of checks. The right
        # checks will fail later.
        hasattr(estimator, "fit") and parameter in signature(estimator.fit).parameters
    )


def check_symmetric(array, *, tol=1e-10, raise_warning=True, raise_exception=False):
    """Make sure that array is 2D, square and symmetric.

    If the array is not symmetric, then a symmetrized version is returned.
    Optionally, a warning or exception is raised if the matrix is not
    symmetric.

    Parameters
    ----------
    array : {ndarray, sparse matrix}
        Input object to check / convert. Must be two-dimensional and square,
        otherwise a ValueError will be raised.

    tol : float, default=1e-10
        Absolute tolerance for equivalence of arrays. Default = 1E-10.

    raise_warning : bool, default=True
        If True then raise a warning if conversion is required.

    raise_exception : bool, default=False
        If True then raise an exception if array is not symmetric.

    Returns
    -------
    array_sym : {ndarray, sparse matrix}
        Symmetrized version of the input array, i.e. the average of array
        and array.transpose(). If sparse, then duplicate entries are first
        summed and zeros are eliminated.

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.utils.validation import check_symmetric
    >>> symmetric_array = np.array([[0, 1, 2], [1, 0, 1], [2, 1, 0]])
    >>> check_symmetric(symmetric_array)
    array([[0, 1, 2],
           [1, 0, 1],
           [2, 1, 0]])
    >>> from scipy.sparse import csr_array
    >>> sparse_symmetric_array = csr_array(symmetric_array)
    >>> check_symmetric(sparse_symmetric_array)
    <Compressed Sparse Row sparse array of dtype 'int64'
        with 6 stored elements and shape (3, 3)>
    """
    if (array.ndim != 2) or (array.shape[0] != array.shape[1]):
        raise ValueError(
            "array must be 2-dimensional and square. shape = {0}".format(array.shape)
        )

    if sp.issparse(array):
        diff = array - array.T
        # only csr, csc, and coo have `data` attribute
        if diff.format not in ["csr", "csc", "coo"]:
            diff = diff.tocsr()
        symmetric = np.all(abs(diff.data) < tol)
    else:
        symmetric = np.allclose(array, array.T, atol=tol)

    if not symmetric:
        if raise_exception:
            raise ValueError("Array must be symmetric")
        if raise_warning:
            warnings.warn(
                (
                    "Array is not symmetric, and will be converted "
                    "to symmetric by average with its transpose."
                ),
                stacklevel=2,
            )
        if sp.issparse(array):
            conversion = "to" + array.format
            array = getattr(0.5 * (array + array.T), conversion)()
        else:
            array = 0.5 * (array + array.T)

    return array


def _is_fitted(estimator, attributes=None, all_or_any=all):
    """Determine if an estimator is fitted

    Parameters
    ----------
    estimator : estimator instance
        Estimator instance for which the check is performed.

    attributes : str, list or tuple of str, default=None
        Attribute name(s) given as string or a list/tuple of strings
        Eg.: ``["coef_", "estimator_", ...], "coef_"``

        If `None`, `estimator` is considered fitted if there exist an
        attribute that ends with a underscore and does not start with double
        underscore.

    all_or_any : callable, {all, any}, default=all
        Specify whether all or any of the given attributes must exist.

    Returns
    -------
    fitted : bool
        Whether the estimator is fitted.
    """
    if attributes is not None:
        if not isinstance(attributes, (list, tuple)):
            attributes = [attributes]
        return all_or_any([hasattr(estimator, attr) for attr in attributes])

    if hasattr(estimator, "__sklearn_is_fitted__"):
        return estimator.__sklearn_is_fitted__()

    fitted_attrs = [
        v for v in vars(estimator) if v.endswith("_") and not v.startswith("__")
    ]
    return len(fitted_attrs) > 0


def check_is_fitted(estimator, attributes=None, *, msg=None, all_or_any=all):
    """Perform is_fitted validation for estimator.

    Checks if the estimator is fitted by verifying the presence of
    fitted attributes (ending with a trailing underscore) and otherwise
    raises a :class:`~sklearn.exceptions.NotFittedError` with the given message.

    If an estimator does not set any attributes with a trailing underscore, it
    can define a ``__sklearn_is_fitted__`` method returning a boolean to
    specify if the estimator is fitted or not. See
    :ref:`sphx_glr_auto_examples_developing_estimators_sklearn_is_fitted.py`
    for an example on how to use the API.

    If no `attributes` are passed, this function will pass if an estimator is stateless.
    An estimator can indicate it's stateless by setting the `requires_fit` tag. See
    :ref:`estimator_tags` for more information. Note that the `requires_fit` tag
    is ignored if `attributes` are passed.

    Parameters
    ----------
    estimator : estimator instance
        Estimator instance for which the check is performed.

    attributes : str, list or tuple of str, default=None
        Attribute name(s) given as string or a list/tuple of strings
        Eg.: ``["coef_", "estimator_", ...], "coef_"``

        If `None`, `estimator` is considered fitted if there exist an
        attribute that ends with a underscore and does not start with double
        underscore.

    msg : str, default=None
        The default error message is, "This %(name)s instance is not fitted
        yet. Call 'fit' with appropriate arguments before using this
        estimator."

        For custom messages if "%(name)s" is present in the message string,
        it is substituted for the estimator name.

        Eg. : "Estimator, %(name)s, must be fitted before sparsifying".

    all_or_any : callable, {all, any}, default=all
        Specify whether all or any of the given attributes must exist.

    Raises
    ------
    TypeError
        If the estimator is a class or not an estimator instance

    NotFittedError
        If the attributes are not found.

    Examples
    --------
    >>> from sklearn.linear_model import LogisticRegression
    >>> from sklearn.utils.validation import check_is_fitted
    >>> from sklearn.exceptions import NotFittedError
    >>> lr = LogisticRegression()
    >>> try:
    ...     check_is_fitted(lr)
    ... except NotFittedError as exc:
    ...     print(f"Model is not fitted yet.")
    Model is not fitted yet.
    >>> lr.fit([[1, 2], [1, 3]], [1, 0])
    LogisticRegression()
    >>> check_is_fitted(lr)
    """
    if isclass(estimator):
        raise TypeError(f"{estimator} is a class, not an instance.")
    if msg is None:
        msg = (
            "This %(name)s instance is not fitted yet. Call 'fit' with "
            "appropriate arguments before using this estimator."
        )

    if not hasattr(estimator, "fit"):
        raise TypeError(f"{estimator} is not an estimator instance.")

    tags = get_tags(estimator)

    if not tags.requires_fit and attributes is None:
        return

    if not _is_fitted(estimator, attributes, all_or_any):
        raise NotFittedError(msg % {"name": type(estimator).__name__})


def _estimator_has(attr, *, delegates=("estimator_", "estimator")):
    """Check if we can delegate a method to the underlying estimator.

    We check the `delegates` in the order they are passed. By default, we first check
    the fitted estimator if available, otherwise we check the unfitted estimator.

    Parameters
    ----------
    attr : str
        Name of the attribute the delegate might or might not have.

    delegates: tuple of str, default=("estimator_", "estimator")
        A tuple of sub-estimator(s) to check if we can delegate the `attr` method.

    Returns
    -------
    check : function
        Function to check if the delegate has the attribute.

    Raises
    ------
    ValueError
        Raised when none of the delegates are present in the object.
    """

    def check(self):
        for delegate in delegates:
            # In meta estimators with multiple sub estimators,
            # only the attribute of the first sub estimator is checked,
            # assuming uniformity across all sub estimators.
            if hasattr(self, delegate):
                delegator = getattr(self, delegate)
                if isinstance(delegator, Sequence):
                    return getattr(delegator[0], attr)
                else:
                    return getattr(delegator, attr)

        raise ValueError(f"None of the delegates {delegates} are present in the class.")

    return check


def check_non_negative(X, whom):
    """
    Check if there is any negative value in an array.

    Parameters
    ----------
    X : {array-like, sparse matrix}
        Input data.

    whom : str
        Who passed X to this function.
    """
    xp, _ = get_namespace(X)
    # avoid X.min() on sparse matrix since it also sorts the indices
    if sp.issparse(X):
        if X.format in ["lil", "dok"]:
            X = X.tocsr()
        if X.data.size == 0:
            X_min = 0
        else:
            X_min = X.data.min()
    else:
        X_min = xp.min(X)

    if X_min < 0:
        raise ValueError(f"Negative values in data passed to {whom}.")


def check_scalar(
    x,
    name,
    target_type,
    *,
    min_val=None,
    max_val=None,
    include_boundaries="both",
):
    """Validate scalar parameters type and value.

    Parameters
    ----------
    x : object
        The scalar parameter to validate.

    name : str
        The name of the parameter to be printed in error messages.

    target_type : type or tuple
        Acceptable data types for the parameter.

    min_val : float or int, default=None
        The minimum valid value the parameter can take. If None (default) it
        is implied that the parameter does not have a lower bound.

    max_val : float or int, default=None
        The maximum valid value the parameter can take. If None (default) it
        is implied that the parameter does not have an upper bound.

    include_boundaries : {"left", "right", "both", "neither"}, default="both"
        Whether the interval defined by `min_val` and `max_val` should include
        the boundaries. Possible choices are:

        - `"left"`: only `min_val` is included in the valid interval.
          It is equivalent to the interval `[ min_val, max_val )`.
        - `"right"`: only `max_val` is included in the valid interval.
          It is equivalent to the interval `( min_val, max_val ]`.
        - `"both"`: `min_val` and `max_val` are included in the valid interval.
          It is equivalent to the interval `[ min_val, max_val ]`.
        - `"neither"`: neither `min_val` nor `max_val` are included in the
          valid interval. It is equivalent to the interval `( min_val, max_val )`.

    Returns
    -------
    x : numbers.Number
        The validated number.

    Raises
    ------
    TypeError
        If the parameter's type does not match the desired type.

    ValueError
        If the parameter's value violates the given bounds.
        If `min_val`, `max_val` and `include_boundaries` are inconsistent.

    Examples
    --------
    >>> from sklearn.utils.validation import check_scalar
    >>> check_scalar(10, "x", int, min_val=1, max_val=20)
    10
    """

    def type_name(t):
        """Convert type into humman readable string."""
        module = t.__module__
        qualname = t.__qualname__
        if module == "builtins":
            return qualname
        elif t == numbers.Real:
            return "float"
        elif t == numbers.Integral:
            return "int"
        return f"{module}.{qualname}"

    if not isinstance(x, target_type):
        if isinstance(target_type, tuple):
            types_str = ", ".join(type_name(t) for t in target_type)
            target_type_str = f"{{{types_str}}}"
        else:
            target_type_str = type_name(target_type)

        raise TypeError(
            f"{name} must be an instance of {target_type_str}, not"
            f" {type(x).__qualname__}."
        )

    expected_include_boundaries = ("left", "right", "both", "neither")
    if include_boundaries not in expected_include_boundaries:
        raise ValueError(
            f"Unknown value for `include_boundaries`: {include_boundaries!r}. "
            f"Possible values are: {expected_include_boundaries}."
        )

    if max_val is None and include_boundaries == "right":
        raise ValueError(
            "`include_boundaries`='right' without specifying explicitly `max_val` "
            "is inconsistent."
        )

    if min_val is None and include_boundaries == "left":
        raise ValueError(
            "`include_boundaries`='left' without specifying explicitly `min_val` "
            "is inconsistent."
        )

    comparison_operator = (
        operator.lt if include_boundaries in ("left", "both") else operator.le
    )
    if min_val is not None and comparison_operator(x, min_val):
        raise ValueError(
            f"{name} == {x}, must be"
            f" {'>=' if include_boundaries in ('left', 'both') else '>'} {min_val}."
        )

    comparison_operator = (
        operator.gt if include_boundaries in ("right", "both") else operator.ge
    )
    if max_val is not None and comparison_operator(x, max_val):
        raise ValueError(
            f"{name} == {x}, must be"
            f" {'<=' if include_boundaries in ('right', 'both') else '<'} {max_val}."
        )

    return x


def _check_psd_eigenvalues(lambdas, enable_warnings=False):
    """Check the eigenvalues of a positive semidefinite (PSD) matrix.

    Checks the provided array of PSD matrix eigenvalues for numerical or
    conditioning issues and returns a fixed validated version. This method
    should typically be used if the PSD matrix is user-provided (e.g. a
    Gram matrix) or computed using a user-provided dissimilarity metric
    (e.g. kernel function), or if the decomposition process uses approximation
    methods (randomized SVD, etc.).

    It checks for three things:

    - that there are no significant imaginary parts in eigenvalues (more than
      1e-5 times the maximum real part). If this check fails, it raises a
      ``ValueError``. Otherwise all non-significant imaginary parts that may
      remain are set to zero. This operation is traced with a
      ``PositiveSpectrumWarning`` when ``enable_warnings=True``.

    - that eigenvalues are not all negative. If this check fails, it raises a
      ``ValueError``

    - that there are no significant negative eigenvalues with absolute value
      more than 1e-10 (1e-6) and more than 1e-5 (5e-3) times the largest
      positive eigenvalue in double (simple) precision. If this check fails,
      it raises a ``ValueError``. Otherwise all negative eigenvalues that may
      remain are set to zero. This operation is traced with a
      ``PositiveSpectrumWarning`` when ``enable_warnings=True``.

    Finally, all the positive eigenvalues that are too small (with a value
    smaller than the maximum eigenvalue multiplied by 1e-12 (2e-7)) are set to
    zero. This operation is traced with a ``PositiveSpectrumWarning`` when
    ``enable_warnings=True``.

    Parameters
    ----------
    lambdas : array-like of shape (n_eigenvalues,)
        Array of eigenvalues to check / fix.

    enable_warnings : bool, default=False
        When this is set to ``True``, a ``PositiveSpectrumWarning`` will be
        raised when there are imaginary parts, negative eigenvalues, or
        extremely small non-zero eigenvalues. Otherwise no warning will be
        raised. In both cases, imaginary parts, negative eigenvalues, and
        extremely small non-zero eigenvalues will be set to zero.

    Returns
    -------
    lambdas_fixed : ndarray of shape (n_eigenvalues,)
        A fixed validated copy of the array of eigenvalues.

    Examples
    --------
    >>> from sklearn.utils.validation import _check_psd_eigenvalues
    >>> _check_psd_eigenvalues([1, 2])      # nominal case
    array([1, 2])
    >>> _check_psd_eigenvalues([5, 5j])     # significant imag part
    Traceback (most recent call last):
        ...
    ValueError: There are significant imaginary parts in eigenvalues (1
        of the maximum real part). Either the matrix is not PSD, or there was
        an issue while computing the eigendecomposition of the matrix.
    >>> _check_psd_eigenvalues([5, 5e-5j])  # insignificant imag part
    array([5., 0.])
    >>> _check_psd_eigenvalues([-5, -1])    # all negative
    Traceback (most recent call last):
        ...
    ValueError: All eigenvalues are negative (maximum is -1). Either the
        matrix is not PSD, or there was an issue while computing the
        eigendecomposition of the matrix.
    >>> _check_psd_eigenvalues([5, -1])     # significant negative
    Traceback (most recent call last):
        ...
    ValueError: There are significant negative eigenvalues (0.2 of the
        maximum positive). Either the matrix is not PSD, or there was an issue
        while computing the eigendecomposition of the matrix.
    >>> _check_psd_eigenvalues([5, -5e-5])  # insignificant negative
    array([5., 0.])
    >>> _check_psd_eigenvalues([5, 4e-12])  # bad conditioning (too small)
    array([5., 0.])

    """

    lambdas = np.array(lambdas)
    is_double_precision = lambdas.dtype == np.float64

    # note: the minimum value available is
    #  - single-precision: np.finfo('float32').eps = 1.2e-07
    #  - double-precision: np.finfo('float64').eps = 2.2e-16

    # the various thresholds used for validation
    # we may wish to change the value according to precision.
    significant_imag_ratio = 1e-5
    significant_neg_ratio = 1e-5 if is_double_precision else 5e-3
    significant_neg_value = 1e-10 if is_double_precision else 1e-6
    small_pos_ratio = 1e-12 if is_double_precision else 2e-7

    # Check that there are no significant imaginary parts
    if not np.isreal(lambdas).all():
        max_imag_abs = np.abs(np.imag(lambdas)).max()
        max_real_abs = np.abs(np.real(lambdas)).max()
        if max_imag_abs > significant_imag_ratio * max_real_abs:
            raise ValueError(
                "There are significant imaginary parts in eigenvalues (%g "
                "of the maximum real part). Either the matrix is not PSD, or "
                "there was an issue while computing the eigendecomposition "
                "of the matrix." % (max_imag_abs / max_real_abs)
            )

        # warn about imaginary parts being removed
        if enable_warnings:
            warnings.warn(
                "There are imaginary parts in eigenvalues (%g "
                "of the maximum real part). Either the matrix is not"
                " PSD, or there was an issue while computing the "
                "eigendecomposition of the matrix. Only the real "
                "parts will be kept." % (max_imag_abs / max_real_abs),
                PositiveSpectrumWarning,
            )

    # Remove all imaginary parts (even if zero)
    lambdas = np.real(lambdas)

    # Check that there are no significant negative eigenvalues
    max_eig = lambdas.max()
    if max_eig < 0:
        raise ValueError(
            "All eigenvalues are negative (maximum is %g). "
            "Either the matrix is not PSD, or there was an "
            "issue while computing the eigendecomposition of "
            "the matrix." % max_eig
        )

    else:
        min_eig = lambdas.min()
        if (
            min_eig < -significant_neg_ratio * max_eig
            and min_eig < -significant_neg_value
        ):
            raise ValueError(
                "There are significant negative eigenvalues (%g"
                " of the maximum positive). Either the matrix is "
                "not PSD, or there was an issue while computing "
                "the eigendecomposition of the matrix." % (-min_eig / max_eig)
            )
        elif min_eig < 0:
            # Remove all negative values and warn about it
            if enable_warnings:
                warnings.warn(
                    "There are negative eigenvalues (%g of the "
                    "maximum positive). Either the matrix is not "
                    "PSD, or there was an issue while computing the"
                    " eigendecomposition of the matrix. Negative "
                    "eigenvalues will be replaced with 0." % (-min_eig / max_eig),
                    PositiveSpectrumWarning,
                )
            lambdas[lambdas < 0] = 0

    # Check for conditioning (small positive non-zeros)
    too_small_lambdas = (0 < lambdas) & (lambdas < small_pos_ratio * max_eig)
    if too_small_lambdas.any():
        if enable_warnings:
            warnings.warn(
                "Badly conditioned PSD matrix spectrum: the largest "
                "eigenvalue is more than %g times the smallest. "
                "Small eigenvalues will be replaced with 0."
                "" % (1 / small_pos_ratio),
                PositiveSpectrumWarning,
            )
        lambdas[too_small_lambdas] = 0

    return lambdas


def _check_sample_weight(
    sample_weight,
    X,
    *,
    dtype=None,
    force_float_dtype=True,
    ensure_non_negative=False,
    ensure_same_device=True,
    copy=False,
    allow_all_zero_weights=False,
):
    """Validate sample weights.

    Note that passing sample_weight=None will output an array of ones.
    Therefore, in some cases, you may want to protect the call with:
    if sample_weight is not None:
        sample_weight = _check_sample_weight(...)

    Parameters
    ----------
    sample_weight : {ndarray, Number or None}, shape (n_samples,)
        Input sample weights.

    X : {ndarray, list, sparse matrix}
        Input data.

    dtype : dtype, default=None
        dtype of the validated `sample_weight`.
        If None, and `sample_weight` is an array:

            - If `sample_weight.dtype` is one of `{np.float64, np.float32}`,
              then the dtype is preserved.
            - Else the output has NumPy's default dtype: `np.float64`.

        If `dtype` is not `{np.float32, np.float64, None}`, then output will
        be `np.float64`.

    force_float_dtype : bool, default=True
        Whether `X` should be forced to be float dtype, when `dtype` is a non-float
        dtype or None.

    ensure_non_negative : bool, default=False,
        Whether or not the weights are expected to be non-negative.

        .. versionadded:: 1.0

    ensure_same_device : bool, default=True
        Whether `sample_weight` should be forced to be on the same device as `X`.

    copy : bool, default=False
        If True, a copy of sample_weight will be created.

    allow_all_zero_weights : bool, default=False,
        Whether or not to raise an error when sample weights are all zero.

    Returns
    -------
    sample_weight : ndarray of shape (n_samples,)
        Validated sample weight. It is guaranteed to be "C" contiguous.
    """
    xp, is_array_api, device = get_namespace_and_device(
        X, remove_types=(list, int, float)
    )

    n_samples = _num_samples(X)

    max_float_type = _max_precision_float_dtype(xp, device)
    float_dtypes = (
        [xp.float32] if max_float_type == xp.float32 else [xp.float64, xp.float32]
    )
    if force_float_dtype and dtype is not None and dtype not in float_dtypes:
        dtype = max_float_type

    if sample_weight is None:
        sample_weight = xp.ones(n_samples, dtype=dtype, device=device)
    elif isinstance(sample_weight, numbers.Number):
        sample_weight = xp.full(n_samples, sample_weight, dtype=dtype, device=device)
    else:
        if force_float_dtype and dtype is None:
            dtype = float_dtypes
        if is_array_api and ensure_same_device:
            sample_weight = move_to(sample_weight, xp=xp, device=device)
        sample_weight = check_array(
            sample_weight,
            accept_sparse=False,
            ensure_2d=False,
            dtype=dtype,
            order="C",
            copy=copy,
            input_name="sample_weight",
        )
        if sample_weight.ndim != 1:
            raise ValueError(
                f"Sample weights must be 1D array or scalar, got "
                f"{sample_weight.ndim}D array. Expected either a scalar value "
                f"or a 1D array of length {n_samples}."
            )

        if sample_weight.shape != (n_samples,):
            raise ValueError(
                "sample_weight.shape == {}, expected {}!".format(
                    sample_weight.shape, (n_samples,)
                )
            )

    if not allow_all_zero_weights:
        if xp.all(sample_weight == 0):
            raise ValueError(
                "Sample weights must contain at least one non-zero number."
            )

    if ensure_non_negative:
        check_non_negative(sample_weight, "`sample_weight`")

    return sample_weight


def _allclose_dense_sparse(x, y, rtol=1e-7, atol=1e-9):
    """Check allclose for sparse and dense data.

    Both x and y need to be either sparse or dense, they
    can't be mixed.

    Parameters
    ----------
    x : {array-like, sparse matrix}
        First array to compare.

    y : {array-like, sparse matrix}
        Second array to compare.

    rtol : float, default=1e-7
        Relative tolerance; see numpy.allclose.

    atol : float, default=1e-9
        absolute tolerance; see numpy.allclose. Note that the default here is
        more tolerant than the default for numpy.testing.assert_allclose, where
        atol=0.
    """
    if sp.issparse(x) and sp.issparse(y):
        x = x.tocsr()
        y = y.tocsr()
        x.sum_duplicates()
        y.sum_duplicates()
        return (
            np.array_equal(x.indices, y.indices)
            and np.array_equal(x.indptr, y.indptr)
            and np.allclose(x.data, y.data, rtol=rtol, atol=atol)
        )
    elif not sp.issparse(x) and not sp.issparse(y):
        return np.allclose(x, y, rtol=rtol, atol=atol)
    raise ValueError(
        "Can only compare two sparse matrices, not a sparse matrix and an array"
    )


def _check_response_method(estimator, response_method):
    """Check if `response_method` is available in estimator and return it.

    .. versionadded:: 1.3

    Parameters
    ----------
    estimator : estimator instance
        Classifier or regressor to check.

    response_method : {"predict_proba", "predict_log_proba", "decision_function",
            "predict"} or list of such str
        Specifies the response method to use get prediction from an estimator
        (i.e. :term:`predict_proba`, :term:`predict_log_proba`,
        :term:`decision_function` or :term:`predict`). Possible choices are:
        - if `str`, it corresponds to the name to the method to return;
        - if a list of `str`, it provides the method names in order of
          preference. The method returned corresponds to the first method in
          the list and which is implemented by `estimator`.

    Returns
    -------
    prediction_method : callable
        Prediction method of estimator.

    Raises
    ------
    AttributeError
        If `response_method` is not available in `estimator`.
    """
    if isinstance(response_method, str):
        list_methods = [response_method]
    else:
        list_methods = response_method

    prediction_method = [getattr(estimator, method, None) for method in list_methods]
    prediction_method = reduce(lambda x, y: x or y, prediction_method)
    if prediction_method is None:
        raise AttributeError(
            f"{estimator.__class__.__name__} has none of the following attributes: "
            f"{', '.join(list_methods)}."
        )

    return prediction_method


def _check_method_params(X, params, indices=None):
    """Check and validate the parameters passed to a specific
    method like `fit`.

    Parameters
    ----------
    X : array-like of shape (n_samples, n_features)
        Data array.

    params : dict
        Dictionary containing the parameters passed to the method.

    indices : array-like of shape (n_samples,), default=None
        Indices to be selected if the parameter has the same size as `X`.

    Returns
    -------
    method_params_validated : dict
        Validated parameters. We ensure that the values support indexing.
    """
    from sklearn.utils import _safe_indexing

    method_params_validated = {}
    for param_key, param_value in params.items():
        if (
            not _is_arraylike(param_value) and not sp.issparse(param_value)
        ) or _num_samples(param_value) != _num_samples(X):
            # Non-indexable pass-through (for now for backward-compatibility).
            # https://github.com/scikit-learn/scikit-learn/issues/15805
            method_params_validated[param_key] = param_value
        else:
            # Any other method_params should support indexing
            # (e.g. for cross-validation).
            method_params_validated[param_key] = _make_indexable(param_value)
            method_params_validated[param_key] = _safe_indexing(
                method_params_validated[param_key], indices
            )

    return method_params_validated


def _get_feature_names(X):
    """Get feature names from X.

    Support for other (2d) data containers should place its implementation here.

    Parameters
    ----------
    X : {ndarray, dataframe} of shape (n_samples, n_features)
        Array container to extract feature names.

        - narwhals compliant dataframe : The columns will be considered to be feature
          names.
        - All other array containers will return `None`.

    Returns
    -------
    names: ndarray or None
        Feature names of `X`. Unrecognized data containers will return `None`.
    """
    feature_names = None

    # Extract feature names for supported data containers.
    if nw.dependencies.is_into_dataframe(X):
        # Note: Narwhals API says that the .columns property ist a list of strings, but
        # this does not hold. If pandas has integer column names, .columns returns a
        # list of integers, see https://github.com/narwhals-dev/narwhals/issues/3571.
        feature_names = np.asarray(nw.from_native(X).columns, dtype=object)

    if feature_names is None or len(feature_names) == 0:
        return

    types = sorted(t.__qualname__ for t in set(type(v) for v in feature_names))

    # mixed type of string and non-string is not supported
    if len(types) > 1 and "str" in types:
        raise TypeError(
            "Feature names are only supported if all input features have string names, "
            f"but your input has {types} as feature name / column name types. "
            "If you want feature names to be stored and validated, you must convert "
            "them all to strings, by using X.columns = X.columns.astype(str) for "
            "example. Otherwise you can remove feature / column names from your input "
            "data, or convert them all to a non-string data type."
        )

    # Only feature names of all strings are supported
    if len(types) == 1 and types[0] == "str":
        return feature_names


def _check_feature_names_in(estimator, input_features=None, *, generate_names=True):
    """Check `input_features` and generate names if needed.

    Commonly used in :term:`get_feature_names_out`.

    Parameters
    ----------
    input_features : array-like of str or None, default=None
        Input features.

        - If `input_features` is `None`, then `feature_names_in_` is
          used as feature names in. If `feature_names_in_` is not defined,
          then the following input feature names are generated:
          `["x0", "x1", ..., "x(n_features_in_ - 1)"]`.
        - If `input_features` is an array-like, then `input_features` must
          match `feature_names_in_` if `feature_names_in_` is defined.

    generate_names : bool, default=True
        Whether to generate names when `input_features` is `None` and
        `estimator.feature_names_in_` is not defined. This is useful for transformers
        that validates `input_features` but do not require them in
        :term:`get_feature_names_out` e.g. `PCA`.

    Returns
    -------
    feature_names_in : ndarray of str or `None`
        Feature names in.
    """

    feature_names_in_ = getattr(estimator, "feature_names_in_", None)
    n_features_in_ = getattr(estimator, "n_features_in_", None)

    if input_features is not None:
        input_features = np.asarray(input_features, dtype=object)
        if feature_names_in_ is not None and not np.array_equal(
            feature_names_in_, input_features
        ):
            raise ValueError("input_features is not equal to feature_names_in_")

        if n_features_in_ is not None and len(input_features) != n_features_in_:
            raise ValueError(
                "input_features should have length equal to number of "
                f"features ({n_features_in_}), got {len(input_features)}"
            )
        return input_features

    if feature_names_in_ is not None:
        return feature_names_in_

    if not generate_names:
        return

    # Generates feature names if `n_features_in_` is defined
    if n_features_in_ is None:
        raise ValueError("Unable to generate feature names without n_features_in_")

    return np.asarray([f"x{i}" for i in range(n_features_in_)], dtype=object)


def _generate_get_feature_names_out(estimator, n_features_out, input_features=None):
    """Generate feature names out for estimator using the estimator name as the prefix.

    The input_feature names are validated but not used. This function is useful
    for estimators that generate their own names based on `n_features_out`, i.e. PCA.

    Parameters
    ----------
    estimator : estimator instance
        Estimator producing output feature names.

    n_feature_out : int
        Number of feature names out.

    input_features : array-like of str or None, default=None
        Only used to validate feature names with `estimator.feature_names_in_`.

    Returns
    -------
    feature_names_in : ndarray of str or `None`
        Feature names in.
    """
    _check_feature_names_in(estimator, input_features, generate_names=False)
    estimator_name = estimator.__class__.__name__.lower()
    return np.asarray(
        [f"{estimator_name}{i}" for i in range(n_features_out)], dtype=object
    )


def _check_categorical_features(X, categorical_features):
    """Check and validate categorical features in X

    Parameters
    ----------
    X : {array-like, pandas DataFrame} of shape (n_samples, n_features)
        Input data.

    categorical_features : array-like of {bool, int, str} of shape (n_features) \
            or shape (n_categorical_features,), default='from_dtype'
        Indicates the categorical features in `X`.

        - None : no feature will be considered categorical.
        - boolean array-like : boolean mask indicating categorical features.
        - integer array-like : integer indices indicating categorical
          features.
        - str array-like: names of categorical features (assuming the training
          data has feature names).
        - `"from_dtype"`: dataframe columns with dtype "Categorical" and "Enum" are
          considered to be categorical features. The input must be a dataframe that
          is supported by narwhals (or supports it): :func:`narwhals.from_native` must
          work. This is the case, for instance, for pandas and polars DataFrames.

    Return
    ------
    is_categorical : ndarray of shape (n_features,) or None, dtype=bool
        Indicates whether a feature is categorical. If no feature is
        categorical, this is None.
    """
    if nw.dependencies.is_into_dataframe(X):
        X = nw.from_native(X)
        dtypes = X.schema.dtypes()
        X_is_dataframe = True
        categorical_columns_mask = np.asarray(
            [d in (nw.Categorical, nw.Enum) for d in dtypes]
        )
    else:
        X_is_dataframe = False
        categorical_columns_mask = None

    categorical_by_dtype = (
        isinstance(categorical_features, str) and categorical_features == "from_dtype"
    )
    no_categorical_dtype = categorical_features is None or (
        categorical_by_dtype and not X_is_dataframe
    )

    if no_categorical_dtype:
        return None

    if categorical_by_dtype and X_is_dataframe:
        categorical_features = categorical_columns_mask
    else:
        categorical_features = np.asarray(categorical_features)

    if categorical_features.size == 0:
        return None

    if categorical_features.dtype.kind not in ("i", "b", "U", "O"):
        raise ValueError(
            "categorical_features must be an array-like of bool, int or "
            f"str, got: {categorical_features.dtype.name}."
        )

    if categorical_features.dtype.kind == "O":
        types = set(type(f) for f in categorical_features)
        if types != {str}:
            raise ValueError(
                "categorical_features must be an array-like of bool, int or "
                f"str, got: {', '.join(sorted(t.__name__ for t in types))}."
            )

    n_features = X.shape[1]
    # At this point `validate_data` was not called yet because we use the original
    # dtypes to discover the categorical features. Thus `feature_names_in_`
    # is not defined yet.
    feature_names_in_ = getattr(X, "columns", None)

    if categorical_features.dtype.kind in ("U", "O"):
        # check for feature names
        if feature_names_in_ is None:
            raise ValueError(
                "categorical_features should be passed as an array of "
                "integers or as a boolean mask when the model is fitted "
                "on data without feature names."
            )
        is_categorical = np.zeros(n_features, dtype=bool)
        feature_names = list(feature_names_in_)
        for feature_name in categorical_features:
            try:
                is_categorical[feature_names.index(feature_name)] = True
            except ValueError as e:
                raise ValueError(
                    f"categorical_features has an item value '{feature_name}' "
                    "which is not a valid feature name of the training "
                    f"data. Observed feature names: {feature_names}"
                ) from e
    elif categorical_features.dtype.kind == "i":
        # check for categorical features as indices
        if (
            np.max(categorical_features) >= n_features
            or np.min(categorical_features) < 0
        ):
            raise ValueError(
                "categorical_features set as integer "
                "indices must be in [0, n_features - 1]"
            )
        is_categorical = np.zeros(n_features, dtype=bool)
        is_categorical[categorical_features] = True
    else:
        if categorical_features.shape[0] != n_features:
            raise ValueError(
                "categorical_features set as a boolean mask "
                "must have shape (n_features,), got: "
                f"{categorical_features.shape}"
            )
        is_categorical = categorical_features

    if not np.any(is_categorical):
        return None
    return is_categorical


def _check_monotonic_cst(estimator, monotonic_cst=None):
    """Check the monotonic constraints and return the corresponding array.

    This helper function should be used in the `fit` method of an estimator
    that supports monotonic constraints and called after the estimator has
    introspected input data to set the `n_features_in_` and optionally the
    `feature_names_in_` attributes.

    .. versionadded:: 1.2

    Parameters
    ----------
    estimator : estimator instance

    monotonic_cst : array-like of int, dict of str or None, default=None
        Monotonic constraints for the features.

        - If array-like, then it should contain only -1, 0 or 1. Each value
            will be checked to be in [-1, 0, 1]. If a value is -1, then the
            corresponding feature is required to be monotonically decreasing.
        - If dict, then it the keys should be the feature names occurring in
            `estimator.feature_names_in_` and the values should be -1, 0 or 1.
        - If None, then an array of 0s will be allocated.

    Returns
    -------
    monotonic_cst : ndarray of int
        Monotonic constraints for each feature.
    """
    original_monotonic_cst = monotonic_cst
    if monotonic_cst is None or isinstance(monotonic_cst, dict):
        monotonic_cst = np.full(
            shape=estimator.n_features_in_,
            fill_value=0,
            dtype=np.int8,
        )
        if isinstance(original_monotonic_cst, dict):
            if not hasattr(estimator, "feature_names_in_"):
                raise ValueError(
                    f"{estimator.__class__.__name__} was not fitted on data "
                    "with feature names. Pass monotonic_cst as an integer "
                    "array instead."
                )
            unexpected_feature_names = list(
                set(original_monotonic_cst) - set(estimator.feature_names_in_)
            )
            unexpected_feature_names.sort()  # deterministic error message
            n_unexpected = len(unexpected_feature_names)
            if unexpected_feature_names:
                if len(unexpected_feature_names) > 5:
                    unexpected_feature_names = unexpected_feature_names[:5]
                    unexpected_feature_names.append("...")
                raise ValueError(
                    f"monotonic_cst contains {n_unexpected} unexpected feature "
                    f"names: {unexpected_feature_names}."
                )
            for feature_idx, feature_name in enumerate(estimator.feature_names_in_):
                if feature_name in original_monotonic_cst:
                    cst = original_monotonic_cst[feature_name]
                    if cst not in [-1, 0, 1]:
                        raise ValueError(
                            f"monotonic_cst['{feature_name}'] must be either "
                            f"-1, 0 or 1. Got {cst!r}."
                        )
                    monotonic_cst[feature_idx] = cst
    else:
        unexpected_cst = np.setdiff1d(monotonic_cst, [-1, 0, 1])
        if unexpected_cst.shape[0]:
            raise ValueError(
                "monotonic_cst must be an array-like of -1, 0 or 1. Observed "
                f"values: {unexpected_cst.tolist()}."
            )

        monotonic_cst = np.asarray(monotonic_cst, dtype=np.int8)
        if monotonic_cst.shape[0] != estimator.n_features_in_:
            raise ValueError(
                f"monotonic_cst has shape {monotonic_cst.shape} but the input data "
                f"X has {estimator.n_features_in_} features."
            )
    return monotonic_cst


def _check_pos_label_consistency(pos_label, y_true):
    """Check if `pos_label` need to be specified or not.

    In binary classification, we fix `pos_label=1` if the labels are in the set
    {-1, 1} or {0, 1}. Otherwise, we raise an error asking to specify the
    `pos_label` parameters.

    Parameters
    ----------
    pos_label : int, float, bool, str or None
        The positive label.
    y_true : ndarray of shape (n_samples,)
        The target vector.

    Returns
    -------
    pos_label : int, float, bool or str
        If `pos_label` can be inferred, it will be returned.

    Raises
    ------
    ValueError
        In the case that `y_true` does not have label in {-1, 1} or {0, 1},
        it will raise a `ValueError`.
    """
    # ensure binary classification if pos_label is not specified
    # classes.dtype.kind in ('O', 'U', 'S') is required to avoid
    # triggering a FutureWarning by calling np.array_equal(a, b)
    # when elements in the two arrays are not comparable.
    if pos_label is None:
        # Compute classes only if pos_label is not specified:
        xp, _, device = get_namespace_and_device(y_true)
        classes = xp.unique_values(y_true)
        if (
            (_is_numpy_namespace(xp) and classes.dtype.kind in "OUS")
            or classes.shape[0] > 2
            or not (
                xp.all(classes == xp.asarray([0, 1], device=device))
                or xp.all(classes == xp.asarray([-1, 1], device=device))
                or xp.all(classes == xp.asarray([0], device=device))
                or xp.all(classes == xp.asarray([-1], device=device))
                or xp.all(classes == xp.asarray([1], device=device))
            )
        ):
            classes = move_to(classes, xp=np, device="cpu")
            classes_repr = ", ".join([repr(c) for c in classes.tolist()])
            raise ValueError(
                f"y_true takes value in {{{classes_repr}}} and pos_label is not "
                "specified: either make y_true take value in {0, 1} or "
                "{-1, 1} or pass pos_label explicitly."
            )
        pos_label = 1

    return pos_label


def _to_object_array(sequence):
    """Convert sequence to a 1-D NumPy array of object dtype.

    numpy.array constructor has a similar use but it's output
    is ambiguous. It can be 1-D NumPy array of object dtype if
    the input is a ragged array, but if the input is a list of
    equal length arrays, then the output is a 2D numpy.array.
    _to_object_array solves this ambiguity by guarantying that
    the output is a 1-D NumPy array of objects for any input.

    Parameters
    ----------
    sequence : array-like of shape (n_elements,)
        The sequence to be converted.

    Returns
    -------
    out : ndarray of shape (n_elements,), dtype=object
        The converted sequence into a 1-D NumPy array of object dtype.

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.utils.validation import _to_object_array
    >>> _to_object_array([np.array([0]), np.array([1])])
    array([array([0]), array([1])], dtype=object)
    >>> _to_object_array([np.array([0]), np.array([1, 2])])
    array([array([0]), array([1, 2])], dtype=object)
    >>> _to_object_array([np.array([0]), np.array([1, 2])])
    array([array([0]), array([1, 2])], dtype=object)
    """
    out = np.empty(len(sequence), dtype=object)
    out[:] = sequence
    return out


def _check_feature_names(estimator, X, *, reset):
    """Set or check the `feature_names_in_` attribute of an estimator.

    .. versionadded:: 1.0

    .. versionchanged:: 1.6
        Moved from :class:`~sklearn.base.BaseEstimator` to
        :mod:`sklearn.utils.validation`.

    .. note::
        To only check feature names without conducting a full data validation, prefer
        using `validate_data(..., skip_check_array=True)` if possible.

    Parameters
    ----------
    estimator : estimator instance
        The estimator to validate the input for.

    X : {ndarray, dataframe} of shape (n_samples, n_features)
        The input samples.

    reset : bool
        Whether to reset the `feature_names_in_` attribute.
        If True, resets the `feature_names_in_` attribute as inferred from `X`.
        If False, the input will be checked for consistency with
        feature names of data provided when reset was last True.

        .. note::
           It is recommended to call `reset=True` in `fit` and in the first
           call to `partial_fit`. All other methods that validate `X`
           should set `reset=False`.
    """

    if reset:
        feature_names_in = _get_feature_names(X)
        if feature_names_in is not None:
            estimator.feature_names_in_ = feature_names_in
        elif hasattr(estimator, "feature_names_in_"):
            # Delete the attribute when the estimator is fitted on a new dataset
            # that has no feature names.
            delattr(estimator, "feature_names_in_")
        return

    fitted_feature_names = getattr(estimator, "feature_names_in_", None)
    X_feature_names = _get_feature_names(X)

    if fitted_feature_names is None and X_feature_names is None:
        # no feature names seen in fit and in X
        return

    if X_feature_names is not None and fitted_feature_names is None:
        warnings.warn(
            f"X has feature names, but {estimator.__class__.__name__} was fitted "
            "without feature names"
        )
        return

    if X_feature_names is None and fitted_feature_names is not None:
        warnings.warn(
            "X does not have valid feature names, but"
            f" {estimator.__class__.__name__} was fitted with feature names"
        )
        return

    # validate the feature names against the `feature_names_in_` attribute
    if len(fitted_feature_names) != len(X_feature_names) or np.any(
        fitted_feature_names != X_feature_names
    ):
        message = "The feature names should match those that were passed during fit.\n"
        fitted_feature_names_set = set(fitted_feature_names)
        X_feature_names_set = set(X_feature_names)

        unexpected_names = sorted(X_feature_names_set - fitted_feature_names_set)
        missing_names = sorted(fitted_feature_names_set - X_feature_names_set)

        def add_names(names):
            output = ""
            max_n_names = 5
            for i, name in enumerate(names):
                if i >= max_n_names:
                    output += "- ...\n"
                    break
                output += f"- {name}\n"
            return output

        if unexpected_names:
            message += "Feature names unseen at fit time:\n"
            message += add_names(unexpected_names)

        if missing_names:
            message += "Feature names seen at fit time, yet now missing:\n"
            message += add_names(missing_names)

        if not missing_names and not unexpected_names:
            message += "Feature names must be in the same order as they were in fit.\n"

        raise ValueError(message)


def _check_n_features(estimator, X, reset):
    """Set the `n_features_in_` attribute, or check against it on an estimator.

    .. note::
        To only check n_features without conducting a full data validation, prefer
        using `validate_data(..., skip_check_array=True)` if possible.

    .. versionchanged:: 1.6
        Moved from :class:`~sklearn.base.BaseEstimator` to
        :mod:`~sklearn.utils.validation`.

    Parameters
    ----------
    estimator : estimator instance
        The estimator to validate the input for.

    X : {ndarray, sparse matrix} of shape (n_samples, n_features)
        The input samples.

    reset : bool
        Whether to reset the `n_features_in_` attribute.
        If True, the `n_features_in_` attribute is set to `X.shape[1]`.
        If False and the attribute exists, then check that it is equal to
        `X.shape[1]`. If False and the attribute does *not* exist, then
        the check is skipped.

        .. note::
           It is recommended to call `reset=True` in `fit` and in the first
           call to `partial_fit`. All other methods that validate `X`
           should set `reset=False`.
    """
    try:
        n_features = _num_features(X)
    except TypeError as e:
        if not reset and hasattr(estimator, "n_features_in_"):
            raise ValueError(
                "X does not contain any features, but "
                f"{estimator.__class__.__name__} is expecting "
                f"{estimator.n_features_in_} features"
            ) from e
        # If the number of features is not defined and reset=True,
        # then we skip this check
        return

    if reset:
        estimator.n_features_in_ = n_features
        return

    if not hasattr(estimator, "n_features_in_"):
        # Skip this check if the expected number of expected input features
        # was not recorded by calling fit first. This is typically the case
        # for stateless transformers.
        return

    if n_features != estimator.n_features_in_:
        raise ValueError(
            f"X has {n_features} features, but {estimator.__class__.__name__} "
            f"is expecting {estimator.n_features_in_} features as input."
        )


def validate_data(
    _estimator,
    /,
    X="no_validation",
    y="no_validation",
    reset=True,
    validate_separately=False,
    skip_check_array=False,
    **check_params,
):
    """Validate input data and set or check feature names and counts of the input.

    This helper function should be used in an estimator that requires input
    validation. This mutates the estimator and sets the `n_features_in_` and
    `feature_names_in_` attributes if `reset=True`.

    .. versionadded:: 1.6

    Parameters
    ----------
    _estimator : estimator instance
        The estimator to validate the input for.

    X : {array-like, sparse matrix, dataframe} of shape \
            (n_samples, n_features), default='no validation'
        The input samples.
        If `'no_validation'`, no validation is performed on `X`. This is
        useful for meta-estimator which can delegate input validation to
        their underlying estimator(s). In that case `y` must be passed and
        the only accepted `check_params` are `multi_output` and
        `y_numeric`.

    y : array-like of shape (n_samples,), default='no_validation'
        The targets.

        - If `None`, :func:`~sklearn.utils.check_array` is called on `X`. If
          the estimator's `requires_y` tag is True, then an error will be raised.
        - If `'no_validation'`, :func:`~sklearn.utils.check_array` is called
          on `X` and the estimator's `requires_y` tag is ignored. This is a default
          placeholder and is never meant to be explicitly set. In that case `X` must be
          passed.
        - Otherwise, only `y` with `_check_y` or both `X` and `y` are checked with
          either :func:`~sklearn.utils.check_array` or
          :func:`~sklearn.utils.check_X_y` depending on `validate_separately`.

    reset : bool, default=True
        Whether to reset the `n_features_in_` attribute.
        If False, the input will be checked for consistency with data
        provided when reset was last True.

        .. note::

           It is recommended to call `reset=True` in `fit` and in the first
           call to `partial_fit`. All other methods that validate `X`
           should set `reset=False`.

    validate_separately : False or tuple of dicts, default=False
        Only used if `y` is not `None`.
        If `False`, call :func:`~sklearn.utils.check_X_y`. Else, it must be a tuple of
        kwargs to be used for calling :func:`~sklearn.utils.check_array` on `X` and `y`
        respectively.

        `estimator=self` is automatically added to these dicts to generate
        more informative error message in case of invalid input data.

    skip_check_array : bool, default=False
        If `True`, `X` and `y` are unchanged and only `feature_names_in_` and
        `n_features_in_` are checked. Otherwise, :func:`~sklearn.utils.check_array`
        is called on `X` and `y`.

    **check_params : kwargs
        Parameters passed to :func:`~sklearn.utils.check_array` or
        :func:`~sklearn.utils.check_X_y`. Ignored if validate_separately
        is not False.

        `estimator=self` is automatically added to these params to generate
        more informative error message in case of invalid input data.

    Returns
    -------
    out : {ndarray, sparse matrix} or tuple of these
        The validated input. A tuple is returned if both `X` and `y` are
        validated.
    """
    _check_feature_names(_estimator, X, reset=reset)
    tags = get_tags(_estimator)
    if y is None and tags.target_tags.required:
        raise ValueError(
            f"This {_estimator.__class__.__name__} estimator "
            "requires y to be passed, but the target y is None."
        )

    no_val_X = isinstance(X, str) and X == "no_validation"
    no_val_y = y is None or (isinstance(y, str) and y == "no_validation")

    if no_val_X and no_val_y:
        raise ValueError("Validation should be done on X, y or both.")

    default_check_params = {"estimator": _estimator}
    check_params = {**default_check_params, **check_params}

    if skip_check_array:
        if not no_val_X and no_val_y:
            out = X
        elif no_val_X and not no_val_y:
            out = y
        else:
            out = X, y
    elif not no_val_X and no_val_y:
        out = check_array(X, input_name="X", **check_params)
    elif no_val_X and not no_val_y:
        out = _check_y(y, **check_params)
    else:
        if validate_separately:
            # We need this because some estimators validate X and y
            # separately, and in general, separately calling check_array()
            # on X and y isn't equivalent to just calling check_X_y()
            # :(
            check_X_params, check_y_params = validate_separately
            if "estimator" not in check_X_params:
                check_X_params = {**default_check_params, **check_X_params}
            X = check_array(X, input_name="X", **check_X_params)
            if "estimator" not in check_y_params:
                check_y_params = {**default_check_params, **check_y_params}
            y = check_array(y, input_name="y", **check_y_params)
        else:
            X, y = check_X_y(X, y, **check_params)
        out = X, y

    if not no_val_X and check_params.get("ensure_2d", True):
        _check_n_features(_estimator, X, reset=reset)

    return out
