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from __future__ import annotations
import uuid
from typing import Any
from openenv.core.env_server.interfaces import Environment
from openenv.core.env_server.types import State
try:
from ..models import (
ModerationDecision, ModerationObservation, ModerationReward, ModerationState, ContentItem
)
except ImportError:
from models import (
ModerationDecision, ModerationObservation, ModerationReward, ModerationState, ContentItem
)
from server.dataset import (
get_posts, get_image_descriptions, get_ad_copies, get_whatsapp_threads,
get_community_standards, get_ad_policies,
)
from server.graders import (
grade_single_label, grade_multi_label, grade_ad_policy, grade_thread_hard, get_ground_truth,
)
from server.tasks.task_single_label import build_episode as build_single_label_episode, build_observation as build_single_label_obs, MAX_STEPS as SINGLE_MAX, TASK_NAME as SINGLE_TASK
from server.tasks.task_multi_label import build_episode as build_multi_label_episode, build_observation as build_multi_label_obs, MAX_STEPS as MULTI_MAX, TASK_NAME as MULTI_TASK
from server.tasks.task_ad_policy import build_episode as build_ad_episode, build_observation as build_ad_obs, MAX_STEPS as AD_MAX, TASK_NAME as AD_TASK
from server.tasks.task_thread_hard import build_episode as build_thread_episode, build_observation as build_thread_obs, MAX_STEPS as THREAD_MAX, TASK_NAME as THREAD_TASK
VALID_TASKS = {SINGLE_TASK, MULTI_TASK, AD_TASK, THREAD_TASK}
class MetaContentModerationEnv(Environment[ModerationDecision, ModerationObservation, ModerationState]):
SUPPORTS_CONCURRENT_SESSIONS: bool = True
_instance = None
def __new__(cls, *args, **kwargs):
if cls._instance is None:
cls._instance = super().__new__(cls)
cls._instance._initialized = False
return cls._instance
def __init__(self, task: str = "single-label-classify", seed: int = 42) -> None:
if getattr(self, "_initialized", False):
return
self._initialized = True
if task not in VALID_TASKS:
raise ValueError(f"Unknown task '{task}'. Valid: {VALID_TASKS}")
self.task = task
self.seed = seed
self._episode_id: str = ""
self._step: int = 0
self._max_steps: int = 0
self._done: bool = False
self._cumulative_reward: float = 0.0
self._decisions_log: list[dict[str, Any]] = []
self._items: list[ContentItem] = []
self._ground_truth_all: list[dict] = []
self._thread_steps: list[Any] = []
def reset(self, task: str = None, seed: int = None) -> ModerationObservation:
if task is not None:
if task not in VALID_TASKS:
raise ValueError(f"Unknown task '{task}'. Valid: {VALID_TASKS}")
self.task = task
if seed is not None:
self.seed = seed
self._episode_id = str(uuid.uuid4())
self._step = 0
self._done = False
self._cumulative_reward = 0.0
self._decisions_log = []
self._load_episode_data()
obs = self._make_observation()
obs.reward = 0.0
obs.done = False
obs.metadata = {
"episode_id": self._episode_id,
"step": self._step,
"cumulative_reward": 0.0
}
return obs
def step(self, action: ModerationDecision) -> ModerationObservation:
if not self._episode_id:
raise RuntimeError("Call reset() before step()")
if self._done:
raise RuntimeError("Episode is done. Call reset() to start a new episode.")
reward_obj = self._grade(action)
reward = reward_obj.total
self._cumulative_reward += reward
self._decisions_log.append({
"step": self._step,
"content_id": action.content_id,
"labels": [l.value for l in action.labels],
"action": action.action.value,
"reward": reward,
"breakdown": reward_obj.breakdown,
})
self._step += 1
self._done = self._step >= self._max_steps
if self._done:
next_obs = self._make_terminal_observation()
else:
next_obs = self._make_observation()
next_obs.reward = reward
next_obs.done = self._done
next_obs.metadata = {
"episode_id": self._episode_id,
"cumulative_reward": self._cumulative_reward,
"step": self._step,
"reward_breakdown": reward_obj.model_dump(),
}
return next_obs
@property
def state(self) -> ModerationState:
score = self._compute_score()
return ModerationState(
task_name=self.task,
episode_id=self._episode_id,
current_step=self._step,
max_steps=self._max_steps,
done=self._done,
cumulative_reward=self._cumulative_reward,
items_seen=self._step,
items_remaining=max(0, self._max_steps - self._step),
decisions_log=self._decisions_log,
score=score,
ground_truth_data=self._ground_truth_all,
has_policy_conflict=bool(self._thread_steps[self._step][2]) if self.task == THREAD_TASK and self._step < len(self._thread_steps) else False,
is_final_message=(self._step == self._max_steps - 1) if self.task == THREAD_TASK else False,
)
# ─── Private Helpers ──────────────────────────────────────────────────────
def _load_episode_data(self) -> None:
if self.task == SINGLE_TASK:
self._items = build_single_label_episode(self.seed)
self._max_steps = min(SINGLE_MAX, len(self._items))
raw_all = get_posts(self.seed) + get_image_descriptions(self.seed)
self._ground_truth_all = raw_all
elif self.task == MULTI_TASK:
self._items = build_multi_label_episode(self.seed)
self._max_steps = min(MULTI_MAX, len(self._items))
self._ground_truth_all = get_posts(self.seed) + get_ad_copies(self.seed)
elif self.task == AD_TASK:
self._items = build_ad_episode(self.seed)
self._max_steps = min(AD_MAX, len(self._items))
self._ground_truth_all = get_ad_copies(self.seed)
elif self.task == THREAD_TASK:
self._thread_steps = build_thread_episode(self.seed)
self._max_steps = min(THREAD_MAX, len(self._thread_steps))
threads = get_whatsapp_threads(self.seed)
self._ground_truth_all = [
msg for t in threads for msg in t["messages"]
]
self._items = [step[0] for step in self._thread_steps]
def _make_observation(self) -> ModerationObservation:
if self.task == THREAD_TASK:
item, history, conflicts = self._thread_steps[self._step]
return build_thread_obs(self._step, item, history, conflicts)
item = self._items[self._step]
if self.task == SINGLE_TASK:
return build_single_label_obs(self._step, item)
elif self.task == MULTI_TASK:
return build_multi_label_obs(self._step, item)
elif self.task == AD_TASK:
return build_ad_obs(self._step, item)
raise ValueError(f"Unknown task: {self.task}")
def _make_terminal_observation(self) -> ModerationObservation:
try:
from ..models import ContentItem, ContentType
except ImportError:
from models import ContentItem, ContentType
dummy = ContentItem(
content_id="__terminal__",
content_type=ContentType.TEXT_POST,
text="Episode complete.",
)
return ModerationObservation(
step=self._step,
content_item=dummy,
task_name=self.task,
instructions="Episode complete. No more items.",
)
def _grade(self, action: ModerationDecision) -> ModerationReward:
gt = get_ground_truth(action.content_id, self._ground_truth_all)
if self.task == SINGLE_TASK:
return grade_single_label(action, gt["labels"], gt["action"])
elif self.task == MULTI_TASK:
return grade_multi_label(action, gt["labels"], gt["action"])
elif self.task == AD_TASK:
return grade_ad_policy(action, gt["labels"], gt["action"], gt["policy_ids"])
elif self.task == THREAD_TASK:
_, _, conflicts = self._thread_steps[self._step]
is_final = (self._step == self._max_steps - 1)
return grade_thread_hard(
action, gt["labels"], gt["action"],
has_policy_conflict=bool(conflicts),
is_final_message=is_final,
)
raise ValueError(f"Unknown task: {self.task}")
def _compute_score(self) -> float:
if not self._decisions_log:
return 0.01
max_possible = self._max_steps * 1.0
if max_possible <= 0:
return 0.01
avg_reward = self._cumulative_reward / max_possible
# Map avg_reward from [-1.0, 1.0] to [0.0, 1.0]
normalized = (avg_reward + 1.0) / 2.0
# Clamp strictly between 0.01 and 0.99 for OpenEnv
score = min(max(normalized, 0.01), 0.99)
return round(score, 4)
|