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19abe39 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 | from __future__ import annotations
from typing import Any, Optional
from openenv.core.env_server.interfaces import Environment
from env.environment import OrigamiEnvironment
from .models import OrigamiAction, OrigamiObservation, OrigamiState
class OpenEnvOrigamiEnvironment(Environment[OrigamiAction, OrigamiObservation, OrigamiState]):
"""OpenEnv adapter over the existing OrigamiEnvironment implementation."""
SUPPORTS_CONCURRENT_SESSIONS = True
def __init__(
self,
default_mode: str = "step",
max_steps: int = 8,
targets_dir: Optional[str] = None,
):
super().__init__()
self.default_mode = default_mode
self.max_steps = max_steps
self.targets_dir = targets_dir
self._env: Optional[OrigamiEnvironment] = None
self._episode_id: Optional[str] = None
def _new_env(self, mode: Optional[str] = None) -> OrigamiEnvironment:
return OrigamiEnvironment(
mode=mode or self.default_mode,
max_steps=self.max_steps,
targets_dir=self.targets_dir,
)
def reset(
self,
seed: Optional[int] = None,
episode_id: Optional[str] = None,
**kwargs: Any,
) -> OrigamiObservation:
del seed # deterministic seed plumbing can be added later
mode = kwargs.get("mode", self.default_mode)
target_name = kwargs.get("target_name")
self._env = self._new_env(mode=mode)
self._episode_id = episode_id
obs_dict = self._env.reset(target_name=target_name)
return OrigamiObservation(
done=False,
reward=None,
metadata={"available_targets": self._env.available_targets()},
prompt=obs_dict.get("prompt", ""),
target_name=obs_dict.get("target_name"),
step=obs_dict.get("step", 0),
paper_state=self._paper_state_snapshot(),
info=self._env._info(),
reward_components={},
)
def step(
self,
action: OrigamiAction,
timeout_s: Optional[float] = None,
**kwargs: Any,
) -> OrigamiObservation:
del timeout_s, kwargs
if self._env is None:
self.reset(target_name=action.target_name)
assert self._env is not None
if action.target_name and action.target_name != self._env.target_name:
self.reset(target_name=action.target_name, mode=self._env.mode)
try:
if action.mode == "sequence":
if not action.completion:
return self._error_observation("sequence mode requires completion")
seq_env = self._new_env(mode="code_as_policy")
seq_env.reset(target_name=self._env.target_name)
obs_dict, reward_dict, done, info = seq_env.step(action.completion)
self._env = seq_env
else:
if action.fold is not None:
fold_payload = {
"from": list(action.fold.from_point),
"to": list(action.fold.to_point),
"assignment": action.fold.assignment,
"instruction": action.fold.instruction,
}
env_action: Any = fold_payload
elif action.completion:
env_action = action.completion
else:
return self._error_observation("single mode requires fold or completion")
obs_dict, reward_dict, done, info = self._env.step(env_action)
total = reward_dict.get("total") if isinstance(reward_dict, dict) else None
return OrigamiObservation(
done=bool(done),
reward=float(total) if isinstance(total, (int, float)) else None,
metadata={"target_name": self._env.target_name},
prompt=obs_dict.get("prompt", ""),
target_name=obs_dict.get("target_name", self._env.target_name),
step=obs_dict.get("step", self._env.step_count),
paper_state=self._paper_state_snapshot(),
info=info or {},
reward_components=reward_dict or {},
)
except Exception as exc: # pragma: no cover - defensive path
return self._error_observation(str(exc))
@property
def state(self) -> OrigamiState:
if self._env is None:
tmp_env = self._new_env(mode=self.default_mode)
return OrigamiState(
episode_id=self._episode_id,
step_count=0,
mode=tmp_env.mode,
target_name=None,
paper={},
last_reward={},
available_targets=tmp_env.available_targets(),
)
env_state = self._env.state()
return OrigamiState(
episode_id=self._episode_id,
step_count=env_state.get("step", self._env.step_count),
mode=env_state.get("mode", self._env.mode),
target_name=env_state.get("target", self._env.target_name),
paper=env_state.get("paper", {}),
last_reward=self._env.last_reward or {},
available_targets=self._env.available_targets(),
)
def close(self) -> None:
if self._env is not None:
self._env.close()
self._env = None
def _paper_state_snapshot(self) -> dict[str, Any]:
if self._env is None or self._env.paper is None:
return {"vertices": {}, "edges": [], "anchor_points": []}
graph = self._env.paper.graph
return {
"vertices": {str(k): [float(v[0]), float(v[1])] for k, v in graph.vertices.items()},
"edges": [
{
"id": int(eid),
"v1": [float(graph.vertices[v1][0]), float(graph.vertices[v1][1])],
"v2": [float(graph.vertices[v2][0]), float(graph.vertices[v2][1])],
"assignment": assignment,
}
for eid, (v1, v2, assignment) in graph.edges.items()
],
"anchor_points": [
[float(x), float(y)] for (x, y) in self._env.paper.anchor_points()
],
}
def _error_observation(self, message: str) -> OrigamiObservation:
return OrigamiObservation(
done=False,
reward=-0.1,
metadata={"error": True},
prompt="",
target_name=self._env.target_name if self._env else None,
step=self._env.step_count if self._env else 0,
paper_state=self._paper_state_snapshot(),
info=self._env._info() if self._env else {},
reward_components={"format": 0.0, "total": -0.1, "error": message},
error=message,
)
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