ogrisel
diff --git a/‎sklearn/metrics/_dist_metrics.pxd.tp
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diff --git a/‎sklearn/metrics/_dist_metrics.pyx.tp
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@@ -101,23 +101,3 @@ cdef class DistanceMetric{{name_suffix}}:
     cdef DTYPE_t _dist_to_rdist(self, {{DTYPE_t}} dist) nogil except -1
 
 {{endfor}}
-
-######################################################################
-# DatasetsPair base class
-cdef class DatasetsPair:
-    cdef DistanceMetric distance_metric
-
-    cdef ITYPE_t n_samples_X(self) nogil
-
-    cdef ITYPE_t n_samples_Y(self) nogil
-
-    cdef DTYPE_t dist(self, ITYPE_t i, ITYPE_t j) nogil
-
-    cdef DTYPE_t surrogate_dist(self, ITYPE_t i, ITYPE_t j) nogil
-
-
-cdef class DenseDenseDatasetsPair(DatasetsPair):
-    cdef:
-        const DTYPE_t[:, ::1] X
-        const DTYPE_t[:, ::1] Y
-        ITYPE_t d
@@ -32,7 +32,6 @@ implementation_specific_values = [
 
 import numpy as np
 cimport numpy as cnp
-from cython cimport final
 
 cnp.import_array()  # required in order to use C-API
 
@@ -1171,163 +1170,3 @@ cdef class PyFuncDistance{{name_suffix}}(DistanceMetric{{name_suffix}}):
                             "vectors and return a float.")
 
 {{endfor}}
-
-######################################################################
-# Datasets Pair Classes
-cdef class DatasetsPair:
-    """Abstract class which wraps a pair of datasets (X, Y).
-
-    This class allows computing distances between a single pair of rows of
-    of X and Y at a time given the pair of their indices (i, j). This class is
-    specialized for each metric thanks to the :func:`get_for` factory classmethod.
-
-    The handling of parallelization over chunks to compute the distances
-    and aggregation for several rows at a time is done in dedicated
-    subclasses of PairwiseDistancesReduction that in-turn rely on
-    subclasses of DatasetsPair for each pair of rows in the data. The goal
-    is to make it possible to decouple the generic parallelization and
-    aggregation logic from metric-specific computation as much as
-    possible.
-
-    X and Y can be stored as C-contiguous np.ndarrays or CSR matrices
-    in subclasses.
-
-    This class avoids the overhead of dispatching distance computations
-    to :class:`sklearn.metrics.DistanceMetric` based on the physical
-    representation of the vectors (sparse vs. dense). It makes use of
-    cython.final to remove the overhead of dispatching method calls.
-
-    Parameters
-    ----------
-    distance_metric: DistanceMetric
-        The distance metric responsible for computing distances
-        between two vectors of (X, Y).
-    """
-
-    @classmethod
-    def get_for(
-        cls,
-        X,
-        Y,
-        str metric="euclidean",
-        dict metric_kwargs=None,
-    ) -> DatasetsPair:
-        """Return the DatasetsPair implementation for the given arguments.
-
-        Parameters
-        ----------
-        X : {ndarray, sparse matrix} of shape (n_samples_X, n_features)
-            Input data.
-            If provided as a ndarray, it must be C-contiguous.
-            If provided as a sparse matrix, it must be in CSR format.
-
-        Y : {ndarray, sparse matrix} of shape (n_samples_Y, n_features)
-            Input data.
-            If provided as a ndarray, it must be C-contiguous.
-            If provided as a sparse matrix, it must be in CSR format.
-
-        metric : str, default='euclidean'
-            The distance metric to compute between rows of X and Y.
-            The default metric is a fast implementation of the Euclidean
-            metric. For a list of available metrics, see the documentation
-            of :class:`~sklearn.metrics.DistanceMetric`.
-
-        metric_kwargs : dict, default=None
-            Keyword arguments to pass to specified metric function.
-
-        Returns
-        -------
-        datasets_pair: DatasetsPair
-            The suited DatasetsPair implementation.
-        """
-        cdef:
-            DistanceMetric distance_metric = DistanceMetric.get_metric(
-                metric,
-                **(metric_kwargs or {})
-            )
-
-        if not(X.dtype == Y.dtype == np.float64):
-            raise ValueError(
-                f"Only 64bit float datasets are supported at this time, "
-                f"got: X.dtype={X.dtype} and Y.dtype={Y.dtype}."
-            )
-
-        # Metric-specific checks that do not replace nor duplicate `check_array`.
-        distance_metric._validate_data(X)
-        distance_metric._validate_data(Y)
-
-        # TODO: dispatch to other dataset pairs for sparse support once available:
-        if issparse(X) or issparse(Y):
-            raise ValueError("Only dense datasets are supported for X and Y.")
-
-        return DenseDenseDatasetsPair(X, Y, distance_metric)
-
-    def __init__(self, DistanceMetric distance_metric):
-        self.distance_metric = distance_metric
-
-    cdef ITYPE_t n_samples_X(self) nogil:
-        """Number of samples in X."""
-        # This is a abstract method.
-        # This _must_ always be overwritten in subclasses.
-        # TODO: add "with gil: raise" here when supporting Cython 3.0
-        return -999
-
-    cdef ITYPE_t n_samples_Y(self) nogil:
-        """Number of samples in Y."""
-        # This is a abstract method.
-        # This _must_ always be overwritten in subclasses.
-        # TODO: add "with gil: raise" here when supporting Cython 3.0
-        return -999
-
-    cdef DTYPE_t surrogate_dist(self, ITYPE_t i, ITYPE_t j) nogil:
-        return self.dist(i, j)
-
-    cdef DTYPE_t dist(self, ITYPE_t i, ITYPE_t j) nogil:
-        # This is a abstract method.
-        # This _must_ always be overwritten in subclasses.
-        # TODO: add "with gil: raise" here when supporting Cython 3.0
-        return -1
-
-@final
-cdef class DenseDenseDatasetsPair(DatasetsPair):
-    """Compute distances between row vectors of two arrays.
-
-    Parameters
-    ----------
-    X: ndarray of shape (n_samples_X, n_features)
-        Rows represent vectors. Must be C-contiguous.
-
-    Y: ndarray of shape (n_samples_Y, n_features)
-        Rows represent vectors. Must be C-contiguous.
-
-    distance_metric: DistanceMetric
-        The distance metric responsible for computing distances
-        between two row vectors of (X, Y).
-    """
-
-    def __init__(self, X, Y, DistanceMetric distance_metric):
-        super().__init__(distance_metric)
-        # Arrays have already been checked
-        self.X = X
-        self.Y = Y
-        self.d = X.shape[1]
-
-    @final
-    cdef ITYPE_t n_samples_X(self) nogil:
-        return self.X.shape[0]
-
-    @final
-    cdef ITYPE_t n_samples_Y(self) nogil:
-        return self.Y.shape[0]
-
-    @final
-    cdef DTYPE_t surrogate_dist(self, ITYPE_t i, ITYPE_t j) nogil:
-        return self.distance_metric.rdist(&self.X[i, 0],
-                                          &self.Y[j, 0],
-                                          self.d)
-
-    @final
-    cdef DTYPE_t dist(self, ITYPE_t i, ITYPE_t j) nogil:
-        return self.distance_metric.dist(&self.X[i, 0],
-                                         &self.Y[j, 0],
-                                         self.d)