Simplify annotations as we are now python>=3.9 (#426)

Starting from 3.9,
- using List, Type, Set, ... from typing is deprecated in favor of list,
  type, set, ... which can now be followed by []
- all classes existing in collections.abc like Callable should also
  directy be imported from there (from typing import Callable is now
  deprecated)

So we change the annotations accordingly, removing unnecessary imports from typing.

examples/full_multisolve.py

@@ -23,9 +23,10 @@
     to the proper certificate (https://stackoverflow.com/a/31060428).
 
 """
+from collections.abc import Callable
 from dataclasses import dataclass
 from math import sqrt
-from typing import Callable, Optional
+from typing import Optional
 
 import gymnasium as gym
 import numpy as np

examples/gym_jsbsim_greedy.py

@@ -2,7 +2,8 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
-from typing import Any, Callable
+from collections.abc import Callable
+from typing import Any
 
 import folium
 import gym_jsbsim

examples/gym_jsbsim_iw.py

@@ -2,7 +2,8 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
-from typing import Any, Callable, Optional
+from collections.abc import Callable
+from typing import Any, Optional
 
 import folium
 import gym_jsbsim

examples/gym_jsbsim_riw.py

@@ -5,7 +5,8 @@
 import bisect
 import json
 import math
-from typing import Any, Callable
+from collections.abc import Callable
+from typing import Any
 
 import folium
 import gym_jsbsim

examples/gym_line_control.py

@@ -2,8 +2,9 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
+from collections.abc import Callable
 from math import exp, fabs, sqrt
-from typing import Any, Callable, Optional
+from typing import Optional
 
 import gymnasium as gym
 import numpy as np

examples/nocycle_grid_goal_mdp.py

@@ -4,9 +4,10 @@
 
 import getopt
 import sys
+from collections.abc import Iterable
 from enum import IntEnum
 from math import sqrt
-from typing import Iterable, NamedTuple, Optional
+from typing import NamedTuple, Optional
 
 from skdecide import (
     DiscreteDistribution,

examples/optuna_gym.py

@@ -17,7 +17,7 @@
 from __future__ import annotations
 
 import logging
-from typing import Any, Dict, List, Tuple, Type, Union
+from typing import Any, Union
 
 import gymnasium as gym
 from discrete_optimization.generic_tools.hyperparameters.hyperparameter import (
@@ -95,18 +95,18 @@ def heuristic(domain, state):
 
 def bee1_features(
     domain: Union[GymDomainForWidthSolvers, GymDomain], state: "State"
-) -> Tuple[int, Any]:
+) -> tuple[int, Any]:
     return domain.bee1_features(state)
 
 
 def bee2_features(
     domain: Union[GymDomainForWidthSolvers, GymDomain], state: "State"
-) -> Tuple[int, Any]:
+) -> tuple[int, Any]:
     return domain.bee2_features(state)
 
 
 # Fixed kwargs per solver: either hyperparameters we do not want to search, or other parameters like time limits
-kwargs_fixed_by_solver: Dict[Type[Solver], Dict[str, Any]] = {
+kwargs_fixed_by_solver: dict[type[Solver], dict[str, Any]] = {
     Astar: dict(
         parallel=False,
         verbose=False,
@@ -123,7 +123,7 @@ def bee2_features(
 
 
 # Add new hyperparameters to some solvers
-additional_hyperparameters_by_solver: Dict[Type[Solver], List[Hyperparameter]] = {
+additional_hyperparameters_by_solver: dict[type[Solver], list[Hyperparameter]] = {
     StableBaseline: [
         # defined only if $algo_class \in [PPO]$
         FloatHyperparameter(
@@ -148,8 +148,8 @@ def bee2_features(
 
 
 # Restrict some hyperparameters choices, for some solvers (making use of `kwargs_by_name` of `suggest_with_optuna`)
-suggest_optuna_kwargs_by_name_by_solver: Dict[
-    Type[Solver], Dict[str, Dict[str, Any]]
+suggest_optuna_kwargs_by_name_by_solver: dict[
+    type[Solver], dict[str, dict[str, Any]]
 ] = {
     StableBaseline: {
         # restrict the choices of algo classes

examples/optuna_gym_utils.py

@@ -2,7 +2,8 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
-from typing import Callable, Optional
+from collections.abc import Callable
+from typing import Optional
 
 import gymnasium as gym
 

examples/optuna_maze.py

@@ -18,7 +18,7 @@
 
 import logging
 import math
-from typing import Any, Dict, List, Type
+from typing import Any
 
 from discrete_optimization.generic_tools.hyperparameters.hyperparameter import (
     CategoricalHyperparameter,
@@ -85,7 +85,7 @@ def state_features(domain: Maze, state: State):
 
 
 # Fixed kwargs per solver: either hyperparameters we do not want to search, or other parameters like time limits
-kwargs_fixed_by_solver: Dict[Type[Solver], Dict[str, Any]] = {
+kwargs_fixed_by_solver: dict[type[Solver], dict[str, Any]] = {
     Astar: dict(
         parallel=False,
         verbose=False,
@@ -120,7 +120,7 @@ def state_features(domain: Maze, state: State):
 
 
 # Add new hyperparameters to some solvers
-additional_hyperparameters_by_solver: Dict[Type[Solver], List[Hyperparameter]] = {
+additional_hyperparameters_by_solver: dict[type[Solver], list[Hyperparameter]] = {
     # ex 1: ent_coef for StableBaselines for PPO algo only
     StableBaseline: [
         # defined only if $algo_class \in [PPO]$
@@ -156,8 +156,8 @@ def state_features(domain: Maze, state: State):
 
 
 # Restrict some hyperparameters choices, for some solvers (making use of `kwargs_by_name` of `suggest_with_optuna`)
-suggest_optuna_kwargs_by_name_by_solver: Dict[
-    Type[Solver], Dict[str, Dict[str, Any]]
+suggest_optuna_kwargs_by_name_by_solver: dict[
+    type[Solver], dict[str, dict[str, Any]]
 ] = {
     StableBaseline: {
         # restrict the choices of algo classes

examples/optuna_maze_explored_nodes.py

@@ -15,7 +15,7 @@
 
 import logging
 import math
-from typing import Any, Dict, List, Tuple, Type
+from typing import Any
 
 from discrete_optimization.generic_tools.hyperparameters.hyperparameter import (
     CategoricalHyperparameter,
@@ -81,7 +81,7 @@ def state_features(domain: Maze, state: State):
 
 
 # Fixed kwargs per solver: either hyperparameters we do not want to search, or other parameters like time limits
-kwargs_fixed_by_solver: Dict[Type[Solver], Dict[str, Any]] = {
+kwargs_fixed_by_solver: dict[type[Solver], dict[str, Any]] = {
     Astar: dict(
         parallel=False,
         verbose=False,
@@ -111,7 +111,7 @@ def state_features(domain: Maze, state: State):
 }
 
 # Add new hyperparameters to some solvers
-additional_hyperparameters_by_solver: Dict[Type[Solver], List[Hyperparameter]] = {
+additional_hyperparameters_by_solver: dict[type[Solver], list[Hyperparameter]] = {
     # ex: heuristic for A* and co
     Astar: [
         CategoricalHyperparameter(
@@ -135,8 +135,8 @@ def state_features(domain: Maze, state: State):
 
 
 # Restrict some hyperparameters choices, for some solvers (making use of `kwargs_by_name` of `suggest_with_optuna`)
-suggest_optuna_kwargs_by_name_by_solver: Dict[
-    Type[Solver], Dict[str, Dict[str, Any]]
+suggest_optuna_kwargs_by_name_by_solver: dict[
+    type[Solver], dict[str, dict[str, Any]]
 ] = {
     StableBaseline: {
         # restrict the choices of algo classes
@@ -152,11 +152,11 @@ def state_features(domain: Maze, state: State):
 
 def objective(
     solver: Solver,
-    episodes: List[
-        Tuple[
-            List[D.T_agent[D.T_observation]],
-            List[D.T_agent[D.T_concurrency[D.T_event]]],
-            List[D.T_agent[Value[D.T_value]]],
+    episodes: list[
+        tuple[
+            list[D.T_agent[D.T_observation]],
+            list[D.T_agent[D.T_concurrency[D.T_event]]],
+            list[D.T_agent[Value[D.T_value]]],
         ]
     ],
 ) -> float: