import numpy as np
import pandas as pd
import pytest

import ray
from ray.data.preprocessors import Normalizer


def test_normalizer():
    """Tests basic Normalizer functionality."""

    col_a = [10, 10, 10]
    col_b = [1, 3, 3]
    col_c = [2, 4, -4]
    in_df = pd.DataFrame.from_dict({"A": col_a, "B": col_b, "C": col_c})
    ds = ray.data.from_pandas(in_df)

    # l2 norm
    normalizer = Normalizer(["B", "C"])
    transformed = normalizer.transform(ds)
    out_df = transformed.to_pandas()

    processed_col_a = col_a
    processed_col_b = [1 / np.sqrt(5), 0.6, 0.6]
    processed_col_c = [2 / np.sqrt(5), 0.8, -0.8]
    expected_df = pd.DataFrame.from_dict(
        {"A": processed_col_a, "B": processed_col_b, "C": processed_col_c}
    ).astype(out_df.dtypes.to_dict())

    pd.testing.assert_frame_equal(out_df, expected_df, check_like=True)

    # l1 norm
    normalizer = Normalizer(["B", "C"], norm="l1")

    transformed = normalizer.transform(ds)
    out_df = transformed.to_pandas()

    processed_col_a = col_a
    processed_col_b = [1 / 3, 3 / 7, 3 / 7]
    processed_col_c = [2 / 3, 4 / 7, -4 / 7]
    expected_df = pd.DataFrame.from_dict(
        {"A": processed_col_a, "B": processed_col_b, "C": processed_col_c}
    ).astype(out_df.dtypes.to_dict())

    pd.testing.assert_frame_equal(out_df, expected_df, check_like=True)

    # max norm
    normalizer = Normalizer(["B", "C"], norm="max")

    transformed = normalizer.transform(ds)
    out_df = transformed.to_pandas()

    processed_col_a = col_a
    processed_col_b = [0.5, 0.75, 0.75]
    processed_col_c = [1.0, 1.0, -1.0]
    expected_df = pd.DataFrame.from_dict(
        {"A": processed_col_a, "B": processed_col_b, "C": processed_col_c}
    ).astype(out_df.dtypes.to_dict())

    pd.testing.assert_frame_equal(out_df, expected_df, check_like=True)

    # append mode
    with pytest.raises(ValueError):
        Normalizer(columns=["B", "C"], output_columns=["B_encoded"])

    normalizer = Normalizer(["B", "C"], output_columns=["B_normalized", "C_normalized"])
    transformed = normalizer.transform(ds)
    out_df = transformed.to_pandas()

    processed_col_a = col_a
    processed_col_b = [1 / np.sqrt(5), 0.6, 0.6]
    processed_col_c = [2 / np.sqrt(5), 0.8, -0.8]
    expected_df = pd.DataFrame.from_dict(
        {
            "A": col_a,
            "B": col_b,
            "C": col_c,
            "B_normalized": processed_col_b,
            "C_normalized": processed_col_c,
        }
    ).astype(out_df.dtypes.to_dict())

    pd.testing.assert_frame_equal(out_df, expected_df, check_like=True)


def test_normalizer_serialization():
    """Test Normalizer serialization and deserialization functionality."""
    from ray.data.preprocessor import SerializablePreprocessorBase

    # Create normalizer with test data
    normalizer = Normalizer(columns=["A", "B"], norm="l1")

    # Serialize using CloudPickle
    serialized = normalizer.serialize()

    # Verify it's binary CloudPickle format
    assert isinstance(serialized, bytes)
    assert serialized.startswith(SerializablePreprocessorBase.MAGIC_CLOUDPICKLE)

    # Deserialize
    deserialized = Normalizer.deserialize(serialized)

    # Verify type and field values
    assert isinstance(deserialized, Normalizer)
    assert deserialized.columns == ["A", "B"]
    assert deserialized.norm == "l1"
    assert deserialized.output_columns == ["A", "B"]

    # Verify it works correctly
    df = pd.DataFrame({"A": [3.0, 4.0], "B": [4.0, 3.0]})
    result = deserialized.transform_batch(df)

    # For l1 norm, values should sum to 1 for each row
    assert abs(result["A"][0] + result["B"][0] - 1.0) < 1e-10
    assert abs(result["A"][1] + result["B"][1] - 1.0) < 1e-10


if __name__ == "__main__":
    import sys

    sys.exit(pytest.main(["-sv", __file__]))