# 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
    assert f(low_value) == 0.0
    assert f(large_value) == 1.0

    full_gaussian = partial(gaussian, mu=mu, sig=sigma)
    max_gaussian_lambda = lambda x: 1.0 if x > mu else full_gaussian(x)
    max_gaussian = np.vectorize(max_gaussian_lambda)

    assert f(scalar_value) == max_gaussian(scalar_value)
    assert np.allclose(f(value_array), max_gaussian(value_array))


def test_tanimoto_threshold_function():
    threshold = 5.555
    f = ThresholdedLinearModifier(threshold=threshold)

    large_value = np.inf

    assert f(large_value) == 1.0
    assert f(threshold) == 1.0

    expected_array = np.minimum(value_array, threshold) / threshold
    assert np.array_equal(f(value_array), expected_array)


def test_clipped_function():
    min_x = 4.4
    max_x = 8.8
    min_score = -3.3
    max_score = 9.2


# 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

    assert f(low_value) == 0.0
    assert f(large_value) == 1.0

    full_gaussian = partial(gaussian, mu=mu, sig=sigma)
    max_gaussian_lambda = lambda x: 1.0 if x > mu else full_gaussian(x)
    max_gaussian = np.vectorize(max_gaussian_lambda)

    assert f(scalar_value) == max_gaussian(scalar_value)
    assert np.allclose(f(value_array), max_gaussian(value_array))


def test_tanimoto_threshold_function():
    threshold = 5.555
    f = ThresholdedLinearModifier(threshold=threshold)

    large_value = np.inf

    assert f(large_value) == 1.0
    assert f(threshold) == 1.0

    expected_array = np.minimum(value_array, threshold) / threshold
    assert np.array_equal(f(value_array), expected_array)


def test_clipped_function():
    min_x = 4.4
    max_x = 8.8
    min_score = -3.3
    max_score = 9.2


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