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4433dc8 96b50a5 4433dc8 5b695bd 4433dc8 5b695bd 4433dc8 267d60a 4433dc8 5b695bd 4433dc8 267d60a 4433dc8 267d60a 4433dc8 5b695bd 4433dc8 267d60a 4433dc8 267d60a 5b695bd 4433dc8 5b695bd 267d60a 4433dc8 | 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 | from __future__ import annotations
from typing import Any
from pydantic import BaseModel, Field
try:
from openenv.core.env_server.types import Action, Observation, State
except ImportError:
class Action(BaseModel):
model_config = {"extra": "forbid"}
class Observation(BaseModel):
reward: float = Field(default=0.0, ge=0.0, le=1.0)
done: bool = False
class State(BaseModel):
episode_id: str = ""
step_count: int = 0
class AdaptAction(Action):
session_id: str = Field(
default="",
description="Environment session id for server-routed calls.",
)
code: str = Field(..., min_length=1, description="Python code to execute.")
class AdaptObservation(Observation):
session_id: str = Field(default="", description="Session id for the active environment instance.")
problem_id: str = Field(default="", description="Current problem identifier.")
problem_type: str = Field(default="", description="Current generated problem family.")
difficulty: str = Field(default="", description="Current curriculum difficulty tier.")
attempt_number: int = Field(default=0, ge=0, description="1-indexed attempt number within the episode.")
max_steps: int = Field(default=3, ge=1, description="Maximum attempts allowed for the episode.")
problem: str = Field(default="", description="Problem statement shown to the agent.")
input_format: str = Field(default="", description="Expected stdin format.")
constraints: str = Field(default="", description="Problem constraints.")
feedback: str = Field(default="", description="Human-readable execution feedback.")
pass_rate: float = Field(default=0.0, ge=0.0, le=1.0)
visible_pass_rate: float = Field(default=0.0, ge=0.0, le=1.0)
hidden_pass_rate: float = Field(default=0.0, ge=0.0, le=1.0)
syntax_valid: bool = Field(default=True)
execution_status: str = Field(default="not_run")
timeout_count: int = Field(default=0, ge=0)
runtime_error_count: int = Field(default=0, ge=0)
invalid_output_count: int = Field(default=0, ge=0)
wrong_answer_count: int = Field(default=0, ge=0)
format_compliance: float = Field(default=0.0, ge=0.0, le=1.0)
reward_components: dict[str, float] = Field(default_factory=dict)
generator_reward_signal: float = Field(default=0.0)
class AdaptState(State):
session_id: str = Field(default="")
problem_id: str = Field(default="")
problem_type: str = Field(default="")
difficulty: str = Field(default="")
generator_mode: str = Field(default="heuristic")
max_steps: int = Field(default=3, ge=1)
generated_problem: dict[str, Any] = Field(default_factory=dict)
last_reward: float = Field(default=0.0)
last_pass_rate: float = Field(default=0.0, ge=0.0, le=1.0)
last_feedback: str = Field(default="")
last_execution_status: str = Field(default="ready")
generator_reward_signal: float = Field(default=0.0)
history: dict[str, Any] = Field(default_factory=dict)
recent_metrics: dict[str, Any] = Field(default_factory=dict)
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