"""
Core OpenEnv environment: SQLOptimizerEnv

Implements the three required methods:
  reset(task_id)  → Observation
  step(action)    → (Observation, Reward, done, info)
  state()         → dict (current internal snapshot)
"""
from __future__ import annotations

from typing import Any, Dict, Optional, Tuple

from .models import Action, Observation, Reward, RewardBreakdown
from .tasks import TASKS, TaskDef, get_task
from .reward import compute_step_reward


_MIN_SCORE_EPS = 0.001
_MAX_SCORE_EPS = 0.999


def _strict_score(value: float) -> float:
    return round(min(max(float(value), _MIN_SCORE_EPS), _MAX_SCORE_EPS), 4)


class SQLOptimizerEnv:
    """SQL Query Optimizer OpenEnv environment."""

    def __init__(self) -> None:
        self._task: Optional[TaskDef] = None
        self._step_number: int = 0
        self._done: bool = False
        self._cumulative_score: float = 0.0
        self._prev_grader_score: float = 0.0
        self._history: list[Dict[str, Any]] = []
        self._last_grader_score: float = 0.0

    # ──────────────────────────────────────────────────────────────────────────
    # reset
    # ──────────────────────────────────────────────────────────────────────────

    def reset(self, task_id: int = 1) -> Observation:
        """Start a fresh episode for the given task."""
        self._task = get_task(task_id)
        self._step_number = 0
        self._done = False
        self._cumulative_score = 0.0
        self._prev_grader_score = 0.0
        self._last_grader_score = 0.0
        self._history = []

        return self._make_observation()

    # ──────────────────────────────────────────────────────────────────────────
    # step
    # ──────────────────────────────────────────────────────────────────────────

    def step(self, action: Action) -> Tuple[Observation, Reward, bool, Dict[str, Any]]:
        """
        Advance the environment by one step.

        Returns:
            observation: next Observation
            reward:      Reward for this step
            done:        whether the episode has ended
            info:        auxiliary dict
        """
        if self._task is None:
            raise RuntimeError("Call reset() before step().")
        if self._done:
            raise RuntimeError("Episode is done. Call reset() to start a new episode.")

        # Validate action
        is_invalid = not action.rewritten_query or not action.rewritten_query.strip()

        # Run grader
        if is_invalid:
            grader_result_score = self._prev_grader_score
            breakdown = RewardBreakdown()
            feedback = "Empty or invalid query submitted."
        else:
            gr = self._task.grader(action.rewritten_query)
            grader_result_score = gr.score
            breakdown = RewardBreakdown(
                correctness=gr.correctness,
                performance=gr.performance,
                style=gr.style,
                step_penalty=0.0,
            )
            feedback = gr.feedback

        grader_result_score = _strict_score(grader_result_score)

        # Compute shaped reward
        step_reward = compute_step_reward(
            grader_score=grader_result_score,
            prev_grader_score=self._prev_grader_score,
            step_number=self._step_number,
            max_steps=self._task.max_steps,
            is_done=action.is_done,
            is_invalid=is_invalid,
        )

        # Apply step penalty to breakdown
        import math
        halfway = math.ceil(self._task.max_steps / 2)
        if self._step_number > halfway and not action.is_done:
            breakdown.step_penalty = -0.02

        self._cumulative_score = _strict_score(self._cumulative_score + step_reward)
        self._prev_grader_score = grader_result_score
        self._last_grader_score = grader_result_score
        self._step_number += 1

        # Episode ends if agent signals done OR max steps reached
        self._done = action.is_done or self._step_number >= self._task.max_steps

        # Record history
        self._history.append(
            {
                "step": self._step_number,
                "rewritten_query": action.rewritten_query,
                "grader_score": grader_result_score,
                "step_reward": step_reward,
                "is_done": action.is_done,
            }
        )

        reward = Reward(
            score=_strict_score(step_reward),
            grader_score=grader_result_score,
            breakdown=breakdown,
            feedback=feedback,
            cumulative_score=self._cumulative_score,
        )

        info = {
            "step_number": self._step_number,
            "grader_score": grader_result_score,
            "cumulative_score": self._cumulative_score,
            "is_invalid": is_invalid,
        }

        return self._make_observation(), reward, self._done, info

    # ──────────────────────────────────────────────────────────────────────────
    # state
    # ──────────────────────────────────────────────────────────────────────────

    def state(self) -> Dict[str, Any]:
        """Return the current internal state snapshot."""
        if self._task is None:
            return {"status": "not_started"}
        return {
            "task_id": self._task.id,
            "task_name": self._task.name,
            "difficulty": self._task.difficulty,
            "step_number": self._step_number,
            "max_steps": self._task.max_steps,
            "done": self._done,
            "cumulative_score": self._cumulative_score,
            "last_grader_score": self._last_grader_score,
            "history": self._history,
        }

    # ──────────────────────────────────────────────────────────────────────────
    # Internal helpers
    # ──────────────────────────────────────────────────────────────────────────

    def _make_observation(self) -> Observation:
        assert self._task is not None
        return Observation(
            task_id=self._task.id,
            task_name=self._task.name,
            task_description=self._task.description,
            query=self._task.query,
            schema_context=self._task.schema_context,
            hint=self._task.hint,
            step_number=self._step_number,
            max_steps=self._task.max_steps,
            done=self._done,
        )