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How to use execute_actions method in autotest
Best Python code snippet using autotest_python
muti_agent.py
Source:muti_agent.py 
...98 time = start99 while time <= end:100 self.EventQueue.put((time, house))101 time += interval102 def execute_actions(self, agent_host, action):103 # agent_host.sendCommand(action)104 # time.sleep(0)105 # world_state = agent_host.getWorldState()106 # for error in world_state.errors:107 # print("Error:", error.text)108 return109 def face_to(self,agent,face):110 # if self.agent_face[agent]>face:111 # for i in range(self.agent_face[agent]-face):112 # self.execute_actions(self.agent_host[agent],"turn -0.8")113 # time.sleep(0.8)114 # self.agent_face[agent] = face115 # elif self.agent_face[agent]<face:116 # for i in range(face-self.agent_face[agent]):117 # self.execute_actions(self.agent_host[agent],"turn 0.8")118 # time.sleep(0.8)119 # self.agent_face[agent] = face120 return121 def move_north(self,agent):122 # self.face_to(agent,1)123 # for i in range(7):124 # self.execute_actions(self.agent_host[agent],'movenorth 1')125 # time.sleep(0)126 if agent == 0:127 self.Agent_Location = [(self.Agent_Location[0][0],self.Agent_Location[0][1]-1),self.Agent_Location[1]]128 else:129 self.Agent_Location = [self.Agent_Location[0], (self.Agent_Location[1][0],self.Agent_Location[1][1]-1)]130 131 def move_south(self,agent):132 # self.face_to(agent,3)133 # for i in range(7):134 # self.execute_actions(self.agent_host[agent],'movesouth 1')135 # time.sleep(0)136 if agent == 0:137 self.Agent_Location = [(self.Agent_Location[0][0],self.Agent_Location[0][1]+1),self.Agent_Location[1]]138 else:139 self.Agent_Location = [self.Agent_Location[0], (self.Agent_Location[1][0],self.Agent_Location[1][1]+1)]140 def move_east(self, agent):141 # self.face_to(agent,2)142 # for i in range(7):143 # self.execute_actions(self.agent_host[agent],'moveeast 1')144 # time.sleep(0)145 if agent == 0:146 self.Agent_Location = [(self.Agent_Location[0][0]+1,self.Agent_Location[0][1]),self.Agent_Location[1]]147 else:148 self.Agent_Location = [self.Agent_Location[0], (self.Agent_Location[1][0]+1,self.Agent_Location[1][1])]149 150 def move_west(self, agent):151 # self.face_to(agent,4)152 # for i in range(7):153 # self.execute_actions(self.agent_host[agent],'movewest 1')154 # time.sleep(0)155 if agent == 0:156 self.Agent_Location = [(self.Agent_Location[0][0]-1,self.Agent_Location[0][1]),self.Agent_Location[1]]157 else:158 self.Agent_Location = [self.Agent_Location[0], (self.Agent_Location[1][0]-1,self.Agent_Location[1][1])]159 def deliever_food(self, agent):160 # if agent == 0:161 # self.execute_actions(self.agent_host[agent],'moveeast 1')162 # self.execute_actions(self.agent_host[agent],'moveeast 1')163 # self.execute_actions(self.agent_host[agent],'moveeast 1')164 # self.execute_actions(self.agent_host[agent],'movenorth 1')165 # time.sleep(0)166 # loc = self.Agent_Location[agent]167 # self.execute_actions(self.agent_host[0],"chat /fill "+str(loc[0]*7+2)+" 8 "+str(loc[1]*7+2)+" "+str(loc[0]*7+4)+" 8 "+str(loc[1]*7+3)+" minecraft:air")168 # self.execute_actions(self.agent_host[agent],'movesouth 1')169 # self.execute_actions(self.agent_host[agent],'movewest 1')170 # self.execute_actions(self.agent_host[agent],'movewest 1')171 # self.execute_actions(self.agent_host[agent],'movewest 1')172 # else:173 # self.execute_actions(self.agent_host[agent],'moveeast 1')174 # self.execute_actions(self.agent_host[agent],'moveeast 1')175 # time.sleep(0)176 # loc = self.Agent_Location[agent]177 # self.execute_actions(self.agent_host[0],"chat /fill "+str(loc[0]*7+2)+" 8 "+str(loc[1]*7+2)+" "+str(loc[0]*7+4)+" 8 "+str(loc[1]*7+3)+" minecraft:air")178 # self.execute_actions(self.agent_host[agent],'movewest 1')179 # self.execute_actions(self.agent_host[agent],'movewest 1')180 return 181 def get_path(self, source, dest):182 action = []183 x_axis = dest[0]-source[0]184 y_axis = dest[1]-source[1]185 if x_axis>0:186 for i in range(x_axis):187 action.append('E')188 elif x_axis<0:189 for i in range(-x_axis):190 action.append('W')191 if y_axis>0:192 for i in range(y_axis):193 action.append('S')194 elif y_axis<0:195 for i in range(-y_axis):196 action.append('N')197 return action198 199 def extract_action_list_from_path(self, path_list):200 201 action_trans = {'N':self.move_north,'S':self.move_south,'E':self.move_east,'W':self.move_west}202 alist = []203 for i in path_list:204 alist.append(action_trans[i])205 return alist206 207 def act(self, agent):208 if self.Agent_Action[agent][0] == None:209 self.Agent_Action[agent].pop(0)210 if Tracing:211 print("Stay")212 else:213 self.Agent_Action[agent][0](agent)214 self.Agent_Action[agent].pop(0)215 if Tracing:216 print("agent",agent, "now is on",self.Agent_Location[agent])217 for i in self.House:218 if i.location == self.Agent_Location[agent] and i.state == True:219 if Tracing:220 print("************Agent", agent, "Change the house", i.id)221 i.state = False222 self.deliever_food(agent)223 total = 0224 for order in i.orderTime:225 total += self.Clock - order226 i.orderTime = []227 self.total_waiting += (total)228 return 1000 - (total)229 return 0230 def set_toDoAction(self,dest,agent):231 if Tracing:232 print("\t\t\t\tplan to send",agent, "to", dest)233 if dest == self.Agent_Location[agent]:234 self.Agent_Action[agent] = [None]235 if Tracing:236 print("\t\t\t\tWait")237 return238 path = self.get_path(self.Agent_Location[agent], dest)239 extract_action_list = self.extract_action_list_from_path(path) 240 if Tracing:241 print("\t\t\t\t",path,sep="")242 print("\t\t\t\t^^^^^^^^^^^")243 self.Agent_Action[agent] = extract_action_list244 return 245 def get_possible_actions(self,agent):246 return [i.location for i in self.House]247 248 def get_curr_state(self):249 return self.Agent_Location.copy() + [i for i in self.House] 250 def choose_action(self, curr_state, possible_actions, eps,agent):251 if curr_state not in self.q_table[agent]:252 self.q_table[agent][curr_state] = {}253 for action in possible_actions:254 if action not in self.q_table[agent][curr_state]:255 self.q_table[agent][curr_state][action] = 0256 if len(possible_actions) == 0:257 return None258 actionProb = random.uniform(0.0, 1.0)259 # using the random move260 if actionProb <= eps:261 # actionIndex = random.randint(0, len(possible_actions) - 1)262 return possible_actions[random.randint(0, len(possible_actions) - 1)]263 # using greedy action264 else:265 maxQValue = min(self.q_table[agent][curr_state].values()) - 1266 actionList = []267 for (action, qValue) in self.q_table[agent][curr_state].items():268 if qValue > maxQValue:269 actionList.clear()270 maxQValue = qValue271 actionList.append(action)272 elif qValue == maxQValue:273 actionList.append(action)274 return actionList[random.randint(0, len(actionList) - 1)]275 def transferCurrState(self, state):276 # transfer the state into (num)...277 copy_state = []278 for i in state:279 if type(i) == House:280 if i.state == True:281 copy_state.append(i.location)282 else:283 copy_state.append(i)284 return tuple(copy_state)285 def best_policy(self):286 returnAction = {"0":[],"1":[]}287 s0 = self.transferCurrState(self.get_curr_state())288 possible_actions1= self.get_possible_actions(0)289 possible_actions2= self.get_possible_actions(1)290 agent1_a0 = self.choose_action(s0, possible_actions1, -1, 0)291 agent2_a0 = self.choose_action(s0, possible_actions2, -1, 1)292 self.set_toDoAction(agent1_a0,0)293 self.set_toDoAction(agent2_a0,1)294 returnAction["0"].append(agent1_a0)295 returnAction["1"].append(agent2_a0)296 done_update = False297 while not done_update:298 count = 0299 if self.EventQueue.qsize() != 0:300 for i in self.EventQueue.queue:301 if i[0] == self.Clock:302 count += 1303 for i in range(count):304 event_time, house = self.EventQueue.get()305 house.state = True 306 house.orderTime.append(event_time)307 loc = house.location 308 self.execute_actions(self.agent_host[0],"chat /fill "+str(loc[0]*7+2)+" 8 "+str(loc[1]*7+2)+" "+str(loc[0]*7+4)+" 8 "+str(loc[1]*7+3)+" minecraft:gold_block")309 if Tracing:310 print("----------------------------------------------------")311 print("Now",self.Clock)312 print("Houses:")313 for i in self.House:314 print("|",i.location,i.state)315 for agent in [0,1]:316 current_r = self.act(agent)317 if self.Agent_Action[agent] == []:318 if (self.EventQueue.qsize() == 0 and [i.state for i in self.House] == [False for i in self.House]):319 if agent == 0:320 returnAction["1"].pop(-1)321 return returnAction322 else:323 s = self.transferCurrState(self.get_curr_state())324 possible_actions = self.get_possible_actions(agent)325 next_a = self.choose_action(s, possible_actions, -1,agent)326 self.set_toDoAction(next_a,agent)327 returnAction[str(agent)].append(next_a)328 self.Clock += 1329 330 return returnAction331 def update_q_table(self, tau, S, A, R, T,agent):332 333 # curr_s = S.popleft()334 # curr_a = A.popleft()335 # curr_r = R.popleft()336 # G = sum([self.gamma ** i * R[i] for i in range(len(S))])337 # if tau + self.n < T:338 # G += self.gamma ** self.n * self.q_table[agent][S[-1]][A[-1]]339 # old_q = self.q_table[agent][curr_s][curr_a]340 # self.q_table[agent][curr_s][curr_a] = old_q + self.alpha * (G - old_q)341 342 curr_s = S.popleft()343 curr_a = A.popleft()344 R.popleft()345 if len(S) != 0:346 nextMaxReward = max(self.q_table[agent][S[0]].values())347 else:348 nextMaxReward = 0349 if len(S) != 0:350 self.q_table[agent][curr_s][curr_a] = self.q_table[agent][curr_s][curr_a] + self.alpha * (R[0] + self.gamma * nextMaxReward) 351 else:352 self.q_table[agent][curr_s][curr_a] = self.q_table[agent][curr_s][curr_a] + self.alpha * (self.gamma * nextMaxReward) 353 # def update_q_table(self, tau, S, A, R, T,agent):354 355 # curr_s = S.popleft()356 # curr_a = A.popleft()357 # R.popleft()358 # nextMaxReward = max(self.q_table[agent][S[0]].values())359 # self.q_table[agent][curr_s][curr_a] = self.q_table[agent][curr_s][curr_a] + self.alpha * (R[0] + self.gamma * nextMaxReward - self.q_table[agent][curr_s][curr_a]) 360 def run(self):361 S = [deque(), deque()]362 A = [deque(), deque()]363 R = [deque(), deque()]364 present_reward = 0365 done_update = False366 while not done_update:367 s0 = self.transferCurrState(self.get_curr_state())368 possible_actions1= self.get_possible_actions(0)369 possible_actions2= self.get_possible_actions(1)370 agent1_a0 = self.choose_action(s0, possible_actions1, self.epsilon, 0)371 agent2_a0 = self.choose_action(s0, possible_actions2, self.epsilon, 1)372 self.set_toDoAction(agent1_a0,0)373 self.set_toDoAction(agent2_a0,1)374 S[0].append(s0)375 S[1].append(s0)376 A[0].append(agent1_a0)377 A[1].append(agent2_a0)378 R[0].append(0)379 R[1].append(0)380 T = sys.maxsize381 for t in range(sys.maxsize):382 time.sleep(0.1)383 # eventQueue update384 count = 0385 if self.EventQueue.qsize() != 0:386 for i in self.EventQueue.queue:387 if i[0] == self.Clock:388 count += 1389 for i in range(count):390 event_time, house = self.EventQueue.get()391 house.state = True 392 house.orderTime.append(event_time)393 loc = house.location 394 self.execute_actions(self.agent_host[0],"chat /fill "+str(loc[0]*7+2)+" 8 "+str(loc[1]*7+2)+" "+str(loc[0]*7+4)+" 8 "+str(loc[1]*7+3)+" minecraft:gold_block")395 #-----------------------396 if Tracing:397 print("----------------------------------------------------")398 print("Now",self.Clock)399 print("Houses:")400 for i in self.House:401 print("|",i.location,i.state)402 #-----------------------403 if t < T:404 #åä¹åæ¯ä¸æ ·çé»è¾ç»æï¼åªä¸è¿ç°å¨æ两个agent405 for agent in [0,1]:406 current_r = self.act(agent)407 if self.Agent_Action[agent] == []:#åªæå½å°è¾¾äºæä¸ä¸ªæ¿åæéè¦è®¡ç® rewardï¼ update q-tableï¼ choose action408 R[agent].append(current_r)...
evaluate_dagger.py
Source:evaluate_dagger.py
1import copy2import numpy as np3import torch4import os5import sys6sys.path.insert(0, os.environ['ALFWORLD_ROOT'])7from agents.utils.misc import extract_admissible_commands8def evaluate_dagger(env, agent, num_games, debug=False):9 env.seed(42)10 agent.eval()11 episode_no = 012 res_points, res_steps, res_gcs = [], [], []13 res_info = []14 final_dynamics = {}15 with torch.no_grad():16 while(True):17 if episode_no >= num_games:18 break19 obs, infos = env.reset()20 game_names = infos["extra.gamefile"]21 batch_size = len(obs)22 if agent.unstick_by_beam_search:23 smart = [{"not working": [], "to try": []} for _ in range(batch_size)]24 agent.init(batch_size)25 previous_dynamics = None26 execute_actions = []27 prev_step_dones, prev_rewards = [], []28 for _ in range(batch_size):29 execute_actions.append("restart")30 prev_step_dones.append(0.0)31 prev_rewards.append(0.0)32 observation_strings = list(obs)33 task_desc_strings, observation_strings = agent.get_task_and_obs(observation_strings)34 task_desc_strings = agent.preprocess_task(task_desc_strings)35 observation_strings = agent.preprocess_observation(observation_strings)36 first_sight_strings = copy.deepcopy(observation_strings)37 agent.observation_pool.push_first_sight(first_sight_strings)38 if agent.action_space == "exhaustive":39 action_candidate_list = [extract_admissible_commands(intro, obs) for intro, obs in zip(first_sight_strings, observation_strings)]40 else:41 action_candidate_list = list(infos["admissible_commands"])42 action_candidate_list = agent.preprocess_action_candidates(action_candidate_list)43 observation_strings = [item + " [SEP] " + a for item, a in zip(observation_strings, execute_actions)] # appending the chosen action at previous step into the observation44 still_running_mask = []45 sequence_game_points = []46 goal_condition_points = []47 print_actions = []48 report = agent.report_frequency > 0 and (episode_no % agent.report_frequency <= (episode_no - batch_size) % agent.report_frequency)49 if debug:50 print(first_sight_strings[0])51 print(task_desc_strings[0])52 for step_no in range(agent.max_nb_steps_per_episode):53 # push obs into observation pool54 agent.observation_pool.push_batch(observation_strings)55 # get most recent k observations56 most_recent_observation_strings = agent.observation_pool.get()57 # predict actions58 if agent.action_space == "generation":59 # heuristically unstick the agent from generating the same thing over and over again60 prev_actions = copy.copy(execute_actions)61 if agent.unstick_by_beam_search:62 for i in range(batch_size):63 if "Nothing happens" in observation_strings[i]:64 smart[i]["not working"].append(execute_actions[i])65 execute_actions, current_dynamics = agent.command_generation_greedy_generation(most_recent_observation_strings, task_desc_strings, previous_dynamics)66 # heuristically unstick the agent from generating the same thing over and over again67 if agent.unstick_by_beam_search:68 for i in range(batch_size):69 if "Nothing happens" in observation_strings[i] and execute_actions[i] in smart[i]["not working"]:70 if len(smart[i]["to try"]) == 0:71 bs_actions, _ = agent.command_generation_beam_search_generation(most_recent_observation_strings[i: i + 1], task_desc_strings[i: i + 1], None if previous_dynamics is None else previous_dynamics[i: i + 1])72 bs_actions = bs_actions[0]73 smart[i]["to try"] += bs_actions74 smart[i]["to try"] = [item for item in smart[i]["to try"] if item != prev_actions[i]]75 if len(smart[i]["to try"]) > 0:76 execute_actions[i] = smart[i]["to try"][0]77 else:78 smart[i] = {"not working": [], "to try": []} # reset79 elif agent.action_space in ["admissible", "exhaustive"]:80 execute_actions, _, current_dynamics = agent.admissible_commands_greedy_generation(most_recent_observation_strings, task_desc_strings, action_candidate_list, previous_dynamics)81 else:82 raise NotImplementedError()83 obs, _, dones, infos = env.step(execute_actions)84 scores = [float(item) for item in infos["won"]]85 gcs =[float(item) for item in infos["goal_condition_success_rate"]] if "goal_condition_success_rate" in infos else [0.0]*batch_size86 dones = [float(item) for item in dones]87 if debug:88 print(execute_actions[0])89 print(obs[0])90 observation_strings = list(obs)91 observation_strings = agent.preprocess_observation(observation_strings)92 if agent.action_space == "exhaustive":93 action_candidate_list = [extract_admissible_commands(intro, obs) for intro, obs in zip(first_sight_strings, observation_strings)]94 else:95 action_candidate_list = list(infos["admissible_commands"])96 action_candidate_list = agent.preprocess_action_candidates(action_candidate_list)97 observation_strings = [item + " [SEP] " + a for item, a in zip(observation_strings, execute_actions)] # appending the chosen action at previous step into the observation98 previous_dynamics = current_dynamics99 if step_no == agent.max_nb_steps_per_episode - 1:100 # terminate the game because DQN requires one extra step101 dones = [1.0 for _ in dones]102 still_running = [1.0 - float(item) for item in prev_step_dones] # list of float103 prev_step_dones = dones104 step_rewards = [float(curr) - float(prev) for curr, prev in zip(scores, prev_rewards)] # list of float105 prev_rewards = scores106 sequence_game_points.append(step_rewards)107 goal_condition_points.append(gcs)108 still_running_mask.append(still_running)109 print_actions.append(execute_actions[0] if still_running[0] else "--")110 # if all ended, break111 if np.sum(still_running) == 0:112 break113 try:114 for n in range(batch_size):115 Thor = env.envs[n]116 task_name = Thor.env.save_frames_path117 final_dynamics[task_name] = {}118 final_dynamics[task_name]["final_dynamics"] = current_dynamics[n].to('cpu').numpy()119 final_dynamics[task_name]["label"] = Thor.traj_data['task_type']120 except Exception as e:121 pass122 game_steps = np.sum(np.array(still_running_mask), 0).tolist() # batch123 game_points = np.max(np.array(sequence_game_points), 0).tolist() # batch124 game_gcs = np.max(np.array(goal_condition_points), 0).tolist() # batch125 for i in range(batch_size):126 if len(res_points) >= num_games:127 break128 res_points.append(game_points[i])129 res_gcs.append(game_gcs[i])130 res_steps.append(game_steps[i])131 res_info.append("/".join(game_names[i].split("/")[-3:-1]) + ", score: " + str(game_points[i]) + ", step: " + str(game_steps[i]))132 # finish game133 agent.finish_of_episode(episode_no, batch_size)134 episode_no += batch_size135 if not report:136 continue137 print("Model: {:s} | Episode: {:3d} | {:s} | game points: {:2.3f} | game goal-condition points: {:2.3f} | game steps: {:2.3f}".format(agent.experiment_tag, episode_no, game_names[0], np.mean(res_points), np.mean(res_gcs), np.mean(res_steps)))138 # print(game_id + ": " + " | ".join(print_actions))139 print(" | ".join(print_actions))140 average_points, average_gc_points, average_steps = np.mean(res_points), np.mean(res_gcs), np.mean(res_steps)141 print("================================================")142 print("eval game points: " + str(average_points) + ", eval game goal-condition points : " + str(average_gc_points) + ", eval game steps: " + str(average_steps))143 for item in res_info:144 print(item)145 return {146 'average_points': average_points,147 'average_goal_condition_points': average_gc_points,148 'average_steps': average_steps,149 'res_points': res_points,150 'res_gcs': res_gcs,151 'res_steps': res_steps,152 'res_info': res_info...
navigator.py
Source:navigator.py
1from __future__ import annotations2from typing import Optional, Union3from dataclasses import dataclass4from src.svc import common5from src.svc.common import error6from src.svc.common.states import State, SPACE_LITERAL, tree7@dataclass8class Navigator:9 """10 ## Traces user's path between states11 So he can use `â Back` button12 """13 trace: list[State]14 """15 # Example:16 ```17 [ Settings.I_MAIN, Settings.II_GROUP, Settings.I_BROADCAST ]18 ^ ^19 where started current state20 ```notpython21 """22 back_trace: list[State]23 """24 # Current state moves here when you press `Back` button25 - so you can use "Next" button26 """27 ignored: set[State]28 """29 # States that user is not supposed to get to30 """31 everything: Optional[common.CommonEverything]32 """33 # Last recieved event34 ## Used35 - to pass it to `on_enter`, `on_exit` methods of states36 """37 @classmethod38 def default(cls: type[Navigator]):39 return cls(40 trace = [tree.Init.I_MAIN],41 back_trace = [],42 ignored = set(),43 everything = None44 )45 @property46 def current(self) -> Optional[State]:47 """48 ## Last state from `trace`49 """50 if len(self.trace) < 1:51 return None52 return self.trace[-1]53 @property54 def first(self) -> Optional[State]:55 if len(self.trace) < 1:56 return None57 58 return self.trace[0]59 @property60 def current_back_trace(self) -> Optional[State]:61 """62 ## Last state from `back_trace`63 """64 if len(self.back_trace) < 1:65 return None66 67 return self.back_trace[-1]68 @property69 def space(self) -> SPACE_LITERAL:70 """71 ## Get space of current state72 """73 return self.current.space74 @property75 def previous_space(self) -> SPACE_LITERAL:76 for state in reversed(self.trace):77 if state.space != self.space:78 return state.space79 80 return self.space81 def append(self, state: State):82 """ ## Add state to trace """83 if state in self.ignored:84 raise error.GoingToIgnoredState(85 f"appending {state.anchor} that is ignored: {self.ignored}"86 )87 88 if self.current is state:89 raise error.DuplicateState(90 f"appending {state.anchor} that was the current one"91 )92 93 if self.current_back_trace is state:94 return self.next()95 self.append_no_checks(state)96 def append_no_checks(self, state: State):97 # we exit current state, 98 # but it will remain99 # in trace, we can go back100 # to it, so we call `on_traced_exit`101 if self.current:102 self.current.on_traced_exit(self.everything)103 104 self.trace.append(state)105 # we entered a state106 # for the first time,107 # it wasn't in trace before,108 # so we call `on_enter`109 self.current.on_enter(self.everything)110 def back(111 self, 112 trace_it: bool = True,113 execute_actions: bool = True114 ):115 """116 # Remove last state from current space117 ## Params118 - `trace_it` - if current state119 should be appended to `back_trace`120 - `execute_actions` - we can assign121 actions to do when we enter/exit122 specific state. This parameter123 tells if we should execute them124 """125 if len(self.trace) < 2:126 return None127 if trace_it and self.trace[-1].back_trace:128 self.back_trace.append(self.current)129 if execute_actions:130 # this state won't be in trace131 # anymore, so we call `on_exit`132 self.current.on_exit(self.everything)133 del self.trace[-1]134 if execute_actions:135 # state we just got to was136 # in trace, so we call `on_traced_enter`137 self.current.on_traced_enter(self.everything)138 def next(self):139 if len(self.back_trace) > 0:140 self.append_no_checks(self.current_back_trace)141 del self.back_trace[-1]142 143 def delete(self, state: State) -> bool:144 for (i, traced_state) in enumerate(self.trace):145 if traced_state == state:146 # MOTHERFUCKER GETS EJECTED147 traced_state.on_delete(self.everything)148 del self.trace[i]149 return True150 151 return False152 153 def delete_back_trace(self, state: State) -> bool:154 for (i, traced_state) in enumerate(self.back_trace):155 if traced_state == state:156 # MOTHERFUCKER GETS EJECTED157 traced_state.on_delete(self.everything)158 159 del self.back_trace[i]160 return True161 162 return False163 @property164 def spaces(self) -> set[SPACE_LITERAL]:165 unique_spaces: set[SPACE_LITERAL] = set()166 for state in self.trace:167 unique_spaces.add(state.space)168 169 return unique_spaces170 def is_space_mixed(self) -> bool: 171 return len(self.spaces) > 1172 def jump_back_to(173 self, 174 state: State, 175 trace_it: bool = False, 176 execute_actions: bool = True177 ):178 if state not in self.trace:179 raise error.ThisStateNotInTrace(180 "you tried to jump back to state "181 "that is not in trace"182 )183 184 while self.current != state:185 self.back(186 trace_it = trace_it, 187 execute_actions = execute_actions188 )189 190 def space_jump_back(self, trace_it: bool = False):191 if not self.is_space_mixed():192 raise error.NotSpaceMixed(193 "jumping back from current space to other isn't possible, "194 "'cause there's only one type of space in trace"195 )196 initial_space = self.space197 while initial_space == self.space:198 self.back(trace_it = trace_it)199 200 def jump_back_to_or_append(self, state: State, trace_it: bool = False):201 try:202 self.jump_back_to(state, trace_it = trace_it)203 except error.ThisStateNotInTrace:204 self.append(state)205 206 def clear(self) -> None:207 self.trace = []208 self.back_trace = []209 self.ignored = set()210 211 def auto_ignored(self):212 if tree.Init.I_MAIN in self.spaces:213 self.ignored.add(tree.Settings.I_MAIN)214 215 if tree.Settings.I_MAIN in self.spaces:216 ...217 218 if not self.everything.is_group_chat:...
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