"""OpenEnv Pydantic models for TemporalBenchEnv."""

from __future__ import annotations

from typing import Any

from pydantic import BaseModel, ConfigDict, Field

try:
    from openenv.core.env_server.types import Action as _ActionBase
    from openenv.core.env_server.types import Observation as _ObservationBase
    from openenv.core.env_server.types import State as _StateBase
except ImportError:
    _ActionBase = BaseModel
    _ObservationBase = BaseModel
    _StateBase = BaseModel


class TemporalBenchAction(_ActionBase):
    """Agent submits an MCQ answer (optional confidence / reasoning)."""

    if _ActionBase is BaseModel:
        model_config = ConfigDict(extra="forbid")
        metadata: dict[str, Any] = Field(default_factory=dict)

    answer: str = Field(..., description="MCQ answer label matching an option")
    confidence: float | None = Field(default=None, ge=0.0, le=1.0)
    reasoning: str | None = Field(default=None, description="Optional chain-of-thought")


class TemporalBenchObservation(_ObservationBase):
    """Current question and progress."""

    if _ObservationBase is BaseModel:
        model_config = ConfigDict(extra="forbid")
        done: bool = Field(default=False)
        reward: float | None = Field(default=None)
        metadata: dict[str, Any] = Field(default_factory=dict)

    step_idx: int = Field(..., ge=0)
    steps_remaining: int = Field(..., ge=0)
    max_steps: int = Field(default=9, ge=1)
    question: str = Field(..., description="Current MCQ prompt")
    options: list[str] = Field(..., description="Answer choices")
    task_type: str = Field(..., description="T1U | T3 | T2_MCQ")
    dataset: str = Field(..., description="Source dataset")
    history: list[dict[str, Any]] = Field(default_factory=list)
    accuracy_so_far: float = Field(default=0.0, ge=0.0, le=1.0)


class TemporalBenchState(_StateBase):
    """Serializable environment state."""

    if _StateBase is BaseModel:
        model_config = ConfigDict(extra="allow")
        episode_id: str | None = Field(default=None)
        step_count: int = Field(default=0, ge=0)

    total_correct: int = Field(default=0, ge=0)
    total_questions: int = Field(default=9, ge=0)
    current_accuracy: float = Field(default=0.0, ge=0.0, le=1.0)
    primary_domain: str = Field(default="PSML")
    per_task_type_accuracy: dict[str, float] = Field(default_factory=dict)
    total_reward: float = Field(default=0.0)