models.py

"""
Data models for the QED Math Environment.

Defines action and observation types for mathematical proof submission
and grading.
"""

from openenv.core.env_server.types import Action, Observation
from pydantic import Field

RewardValue = bool | int | float | None


class QEDMathAction(Action):
    """Base action for the QED Math environment."""


class SubmitProof(QEDMathAction):
    """Submit a proof attempt for the current problem."""

    proof: str = Field(..., description="The proof text submitted by agent")
    attempt_number: int = Field(default=1, description="Attempt counter")


class GetProblem(QEDMathAction):
    """Request the current problem statement."""


class GetGradingGuidelines(QEDMathAction):
    """Request the grading guidelines/rubric for current problem."""


class QEDMathObservation(Observation):
    """Base observation for the QED Math environment."""


class ProblemObservation(QEDMathObservation):
    """Observation containing the problem statement."""

    problem: str = Field(default="", description="The mathematical problem")
    reference_solution: str = Field(default="", description="Ground truth solution")
    grading_guidelines: str = Field(default="", description="Rubric for grading (0-7 scale)")
    problem_id: str = Field(default="", description="Unique problem identifier")
    dataset_source: str = Field(default="", description="Source dataset name")
    problem_type: str = Field(
        default="proof",
        description="Problem type: proof, answer, or multi_step",
    )
    max_attempts: int = Field(
        default=1,
        description="Maximum number of allowed submission attempts",
    )


class ProofSubmissionObservation(QEDMathObservation):
    """Observation returned after submitting a proof."""

    proof: str = Field(default="", description="The submitted proof")
    score: int = Field(default=0, description="Grade from rubric (0-7)")
    feedback: str = Field(default="", description="Grader feedback")
    reward: RewardValue = Field(
        default=0.0,
        description="Normalized reward (score/7)",
    )
    done: bool = Field(default=True, description="Episode ends after proof submission")
    problem_type: str = Field(
        default="proof",
        description="Problem type used to evaluate this submission",
    )
    attempt_number: int = Field(default=1, description="1-based submission attempt index")
    attempts_remaining: int = Field(
        default=0,
        description="Remaining submission attempts in the current episode",
    )
    is_correct: bool = Field(
        default=False,
        description="Whether the submission is considered fully correct",
    )