# Path: guacamol/score_modifier.py
# class ScoreModifier:
# class ChainedModifier(ScoreModifier):
# class LinearModifier(ScoreModifier):
# class SquaredModifier(ScoreModifier):
# class AbsoluteScoreModifier(ScoreModifier):
# class GaussianModifier(ScoreModifier):
# class MinMaxGaussianModifier(ScoreModifier):
# class ClippedScoreModifier(ScoreModifier):
# class SmoothClippedScoreModifier(ScoreModifier):
# class ThresholdedLinearModifier(ScoreModifier):
#     def __call__(self, x):
#     def __init__(self, modifiers: List[ScoreModifier]) -> None:
#     def __call__(self, x):
#     def __init__(self, slope=1.0):
#     def __call__(self, x):
#     def __init__(self, target_value: float, coefficient=1.0) -> None:
#     def __call__(self, x):
#     def __init__(self, target_value: float) -> None:
#     def __call__(self, x):
#     def __init__(self, mu: float, sigma: float) -> None:
#     def __call__(self, x):
#     def __init__(self, mu: float, sigma: float, minimize=False) -> None:
#     def __call__(self, x):
#     def __init__(self, upper_x: float, lower_x=0.0, high_score=1.0, low_score=0.0) -> None:
#     def __call__(self, x):
#     def __init__(self, upper_x: float, lower_x=0.0, high_score=1.0, low_score=0.0) -> None:
#     def __call__(self, x):
#     def __init__(self, threshold: float) -> None:
#     def __call__(self, x):


from functools import partial
from guacamol.score_modifier import LinearModifier, SquaredModifier, AbsoluteScoreModifier, GaussianModifier, \
    MinGaussianModifier, MaxGaussianModifier, ThresholdedLinearModifier, ClippedScoreModifier, \
    SmoothClippedScoreModifier, ChainedModifier
import numpy as np
import pytest
    max_x = 4.4
    min_x = 8.8
    min_score = -3.3
    max_score = 9.2

    modifier = SmoothClippedScoreModifier(upper_x=max_x, lower_x=min_x, high_score=max_score, low_score=min_score)

    # assert that the slope in the middle is correct

    middle_x = (min_x + max_x) / 2
    delta = 1e-5
    vp = modifier(middle_x + delta)
    vm = modifier(middle_x - delta)

    slope = (vp - vm) / (2 * delta)
    expected_slope = (max_score - min_score) / (max_x - min_x)

    assert slope == pytest.approx(expected_slope)

    # assert behavior at +- infinity

    assert modifier(1e5) == pytest.approx(min_score)
    assert modifier(-1e5) == pytest.approx(max_score)


def test_chained_modifier():
    linear = LinearModifier(slope=2)
    squared = SquaredModifier(10.0)

    chained_1 = ChainedModifier([linear, squared])

# Path: guacamol/score_modifier.py
# class ScoreModifier:
# class ChainedModifier(ScoreModifier):
# class LinearModifier(ScoreModifier):
# class SquaredModifier(ScoreModifier):
# class AbsoluteScoreModifier(ScoreModifier):
# class GaussianModifier(ScoreModifier):
# class MinMaxGaussianModifier(ScoreModifier):
# class ClippedScoreModifier(ScoreModifier):
# class SmoothClippedScoreModifier(ScoreModifier):
# class ThresholdedLinearModifier(ScoreModifier):
#     def __call__(self, x):
#     def __init__(self, modifiers: List[ScoreModifier]) -> None:
#     def __call__(self, x):
#     def __init__(self, slope=1.0):
#     def __call__(self, x):
#     def __init__(self, target_value: float, coefficient=1.0) -> None:
#     def __call__(self, x):
#     def __init__(self, target_value: float) -> None:
#     def __call__(self, x):
#     def __init__(self, mu: float, sigma: float) -> None:
#     def __call__(self, x):
#     def __init__(self, mu: float, sigma: float, minimize=False) -> None:
#     def __call__(self, x):
#     def __init__(self, upper_x: float, lower_x=0.0, high_score=1.0, low_score=0.0) -> None:
#     def __call__(self, x):
#     def __init__(self, upper_x: float, lower_x=0.0, high_score=1.0, low_score=0.0) -> None:
#     def __call__(self, x):
#     def __init__(self, threshold: float) -> None:
#     def __call__(self, x):


# Path: tests/test_score_modifier.py
from functools import partial
from guacamol.score_modifier import LinearModifier, SquaredModifier, AbsoluteScoreModifier, GaussianModifier, \
    MinGaussianModifier, MaxGaussianModifier, ThresholdedLinearModifier, ClippedScoreModifier, \
    SmoothClippedScoreModifier, ChainedModifier
import numpy as np
import pytest

    max_x = 4.4
    min_x = 8.8
    min_score = -3.3
    max_score = 9.2

    modifier = SmoothClippedScoreModifier(upper_x=max_x, lower_x=min_x, high_score=max_score, low_score=min_score)

    # assert that the slope in the middle is correct

    middle_x = (min_x + max_x) / 2
    delta = 1e-5
    vp = modifier(middle_x + delta)
    vm = modifier(middle_x - delta)

    slope = (vp - vm) / (2 * delta)
    expected_slope = (max_score - min_score) / (max_x - min_x)

    assert slope == pytest.approx(expected_slope)

    # assert behavior at +- infinity

    assert modifier(1e5) == pytest.approx(min_score)
    assert modifier(-1e5) == pytest.approx(max_score)


def test_chained_modifier():
    linear = LinearModifier(slope=2)
    squared = SquaredModifier(10.0)

    chained_1 = ChainedModifier([linear, squared])

    chained_2 = ChainedModifier([squared, linear])