# Path: lcs/Perception.py
# class Perception(collections.abc.Sequence):
#     """
#     Represents current state of the environment at given time instance.
#     By default each environment attribute is represented as `str` type.
#     """
# 
#     __slots__ = ['_items', 'oktypes']
# 
#     def __init__(self, observation, oktypes=(str,)):
#         assert all(type(e) in oktypes for e in observation)
#         self._items = tuple(observation)
# 
#     @classmethod
#     def empty(cls):
#         return cls([], oktypes=(None,))
# 
#     def __hash__(self):
#         return hash(self._items)
# 
#     def __getitem__(self, i):
#         return self._items[i]
# 
#     def __len__(self) -> int:
#         return len(self._items)
# 
#     def __repr__(self):
#         return ' '.join(map(str, self))
# 
#     def __eq__(self, other):
#         return all(p0 == p1 for p0, p1 in zip(self, other))
# 
# Path: lcs/strategies/subsumption.py
# def find_subsumers(cl, population, theta_exp: int) -> List:
#     """
#     Looks for subsumers of `cl` inside `population`.
# 
#     Parameters
#     ----------
#     cl:
#         classifier
#     population:
#         population of classifiers
#     theta_exp: int
#         subsumption experience threshold
# 
#     Returns
#     -------
#     List
#         list of subsumers (classifiers) sorted by specificity (most general
#         are first)
#     """
#     subsumers = [sub for sub in population if does_subsume(sub, cl, theta_exp)]
#     return sorted(subsumers, key=lambda cl: cl.condition.specificity)


import random
import numpy as np
from typing import Callable, Dict
from lcs import Perception
from lcs.strategies.subsumption import find_subsumers
            cl.condition.generalize(idx)


def two_point_crossover(parent, donor) -> None:
    """
    Executes two-point crossover using condition parts of two classifiers.
    Condition in both classifiers are changed.

    Parameters
    ----------
    parent
        Classifier
    donor
        Classifier
    """
    left, right = sorted(np.random.choice(
        range(0, parent.cfg.classifier_length + 1), 2, replace=False))

    assert left < right

    # Extract chromosomes from condition parts
    chromosome1 = parent.condition[left:right]
    chromosome2 = donor.condition[left:right]

    # Flip them
    for idx, el in enumerate(range(left, right)):
        parent.condition[el] = chromosome2[idx]
        donor.condition[el] = chromosome1[idx]



# Path: lcs/Perception.py
# class Perception(collections.abc.Sequence):
#     """
#     Represents current state of the environment at given time instance.
#     By default each environment attribute is represented as `str` type.
#     """
# 
#     __slots__ = ['_items', 'oktypes']
# 
#     def __init__(self, observation, oktypes=(str,)):
#         assert all(type(e) in oktypes for e in observation)
#         self._items = tuple(observation)
# 
#     @classmethod
#     def empty(cls):
#         return cls([], oktypes=(None,))
# 
#     def __hash__(self):
#         return hash(self._items)
# 
#     def __getitem__(self, i):
#         return self._items[i]
# 
#     def __len__(self) -> int:
#         return len(self._items)
# 
#     def __repr__(self):
#         return ' '.join(map(str, self))
# 
#     def __eq__(self, other):
#         return all(p0 == p1 for p0, p1 in zip(self, other))
# 
# Path: lcs/strategies/subsumption.py
# def find_subsumers(cl, population, theta_exp: int) -> List:
#     """
#     Looks for subsumers of `cl` inside `population`.
# 
#     Parameters
#     ----------
#     cl:
#         classifier
#     population:
#         population of classifiers
#     theta_exp: int
#         subsumption experience threshold
# 
#     Returns
#     -------
#     List
#         list of subsumers (classifiers) sorted by specificity (most general
#         are first)
#     """
#     subsumers = [sub for sub in population if does_subsume(sub, cl, theta_exp)]
#     return sorted(subsumers, key=lambda cl: cl.condition.specificity)


# Path: lcs/strategies/genetic_algorithms.py
import random
import numpy as np
from typing import Callable, Dict
from lcs import Perception
from lcs.strategies.subsumption import find_subsumers

            cl.condition.generalize(idx)


def two_point_crossover(parent, donor) -> None:
    """
    Executes two-point crossover using condition parts of two classifiers.
    Condition in both classifiers are changed.

    Parameters
    ----------
    parent
        Classifier
    donor
        Classifier
    """
    left, right = sorted(np.random.choice(
        range(0, parent.cfg.classifier_length + 1), 2, replace=False))

    assert left < right

    # Extract chromosomes from condition parts
    chromosome1 = parent.condition[left:right]
    chromosome2 = donor.condition[left:right]

    # Flip them
    for idx, el in enumerate(range(left, right)):
        parent.condition[el] = chromosome2[idx]
        donor.condition[el] = chromosome1[idx]



def add_classifier(cl, p: Perception,