import numpy as np
import sklearn
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_classification

sklearn.set_config(enable_metadata_routing=True, metadata_request_policy="auto")

class DefaultRoutingClassifier(ClassifierMixin, BaseEstimator):
    # Class-level default request for fit method
    __metadata_request__fit = {"sample_weight": True}

    def get_metadata_routing(self, **auto_requests):
        # Each instance can configure metadata which should be requested by default if
        # `set_config(metadata_request_policy="auto")` is set. These request values are
        # passed to the parent's `get_metadata_routing` method.
        requests = super().get_metadata_routing(predict="groups")
        return requests

    def fit(self, X, y, sample_weight=None):
        self.classes_ = np.array([0, 1])
        print(sample_weight)
        return self

    def predict(self, X, groups=None):
        print(groups)
        return np.ones(len(X))

X, y = make_classification()

pipeline = Pipeline([
    ("scaler", StandardScaler().set_fit_request(sample_weight=False)),
    ("classifier", DefaultRoutingClassifier().set_predict_request(groups="my_groups")),
])
pipeline.fit(X, y, sample_weight=np.ones(len(X)))
pipeline.predict(X, my_groups=np.ones(len(X)) + 1)


pipeline = Pipeline([
    ("scaler", StandardScaler().set_fit_request(sample_weight=False)),
    ("classifier", DefaultRoutingClassifier()),
])
pipeline.fit(X, y, sample_weight=np.ones(len(X)))
pipeline.predict(X, groups=np.ones(len(X)) + 1)

pipeline = Pipeline([
    ("scaler", StandardScaler().set_fit_request(sample_weight=False)),
    ("classifier", DefaultRoutingClassifier().set_predict_request(groups=False)),
])
pipeline.fit(X, y, sample_weight=np.ones(len(X)))
pipeline.predict(X, groups=np.ones(len(X)) + 1)