"""
Task definitions for the SQL agent benchmark.

Three difficulty tiers, each with 5 questions and a grader function.

Grader contract: grader(sql, rows, error, attempts) -> float strictly in (0, 1)
  - rows: list[dict] from the executed SQL (may be empty)
  - error: str | None
  - attempts: int (1-indexed count of attempts taken)

All graders return values strictly in (_EPS, 1 - _EPS) so no path can
emit exact 0.0 or 1.0.
"""

from __future__ import annotations

import re
from dataclasses import dataclass, field
from typing import Callable, Optional

from env.database import execute_query


# ─── Score clamping (strictly in (0, 1)) ──────────────────────────

_EPS = 0.05  # margin so :.2f/:.3f formatting never rounds to 0.00 or 1.00


def _clamp(x: float) -> float:
    """Clamp to strictly (0, 1). NaN/None → 0.5."""
    if x is None or x != x:  # None or NaN
        return 0.5
    return max(_EPS, min(1.0 - _EPS, float(x)))


# ─── Task Definitions ─────────────────────────────────────────────

@dataclass
class TaskQuestion:
    id: str
    question: str
    expected_columns: list[str]     # at least these columns should appear
    min_rows: int                   # minimum expected rows
    max_rows: Optional[int] = None  # None = no upper bound
    hint_tables: list[str] = field(default_factory=list)  # tables that must be touched


@dataclass
class Task:
    id: str
    name: str
    difficulty: str  # "easy" | "medium" | "hard"
    description: str
    questions: list[TaskQuestion]
    grader: Callable  # grader(question, sql, rows, error, attempts) -> float


# ─── Grader Helpers ───────────────────────────────────────────────

def _has_required_columns(rows: list[dict], required: list[str]) -> bool:
    if not rows:
        return False
    row_keys = {k.lower() for k in rows[0].keys()}
    return all(col.lower() in row_keys for col in required)


def _row_count_score(rows: list[dict], min_rows: int, max_rows: Optional[int]) -> float:
    """Returns a raw score in [0, 1]; graders must clamp before returning."""
    n = len(rows)
    if n == 0:
        return 0.0
    if n >= min_rows:
        if max_rows is None or n <= max_rows:
            return 1.0
        return 0.5
    return 0.5 * (n / min_rows)


# ─── Task 1: Simple Queries (Easy) ────────────────────────────────

_SIMPLE_QUESTIONS = [
    TaskQuestion(
        id="sq-01",
        question="List all users from the USA.",
        expected_columns=["name", "email", "country"],
        min_rows=10,
        max_rows=25,
        hint_tables=["users"],
    ),
    TaskQuestion(
        id="sq-02",
        question="Show all products in the 'Electronics' category with their prices.",
        expected_columns=["name", "price"],
        min_rows=8,
        max_rows=20,
        hint_tables=["products"],
    ),
    TaskQuestion(
        id="sq-03",
        question="Find all orders with status 'delivered'.",
        expected_columns=["id", "status"],
        min_rows=30,
        max_rows=50,
        hint_tables=["orders"],
    ),
    TaskQuestion(
        id="sq-04",
        question="List all sellers and their countries.",
        expected_columns=["name", "country"],
        min_rows=10,
        max_rows=10,
        hint_tables=["sellers"],
    ),
    TaskQuestion(
        id="sq-05",
        question="Show all reviews with a rating of 5 stars.",
        expected_columns=["rating"],
        min_rows=15,
        max_rows=35,
        hint_tables=["reviews"],
    ),
]


def _grade_simple(
    question: TaskQuestion,
    sql: str,
    rows: list[dict],
    error: Optional[str],
    attempts: int,
) -> float:
    if error:
        return _clamp(0.0)

    col_ok = _has_required_columns(rows, question.expected_columns)
    row_score = _row_count_score(rows, question.min_rows, question.max_rows)

    if col_ok and row_score == 1.0:
        return _clamp(1.0)
    if col_ok or row_score >= 0.5:
        return _clamp(0.5)
    return _clamp(0.0)


_TASK_SIMPLE = Task(
    id="simple_queries",
    name="Simple Queries",
    difficulty="easy",
    description="Single-table SELECT queries with basic filters.",
    questions=_SIMPLE_QUESTIONS,
    grader=_grade_simple,
)


# ─── Task 2: Join Queries (Medium) ────────────────────────────────

_JOIN_QUESTIONS = [
    TaskQuestion(
        id="jq-01",
        question="Show the total number of orders per user, including the user's name.",
        expected_columns=["name"],
        min_rows=10,
        hint_tables=["users", "orders"],
    ),
    TaskQuestion(
        id="jq-02",
        question="List products along with the name of their seller.",
        expected_columns=["name", "name"],  # product name + seller name both called 'name'
        min_rows=20,
        hint_tables=["products", "sellers"],
    ),
    TaskQuestion(
        id="jq-03",
        question="Find the average rating for each product category.",
        expected_columns=["category"],
        min_rows=5,
        max_rows=10,
        hint_tables=["products", "reviews"],
    ),
    TaskQuestion(
        id="jq-04",
        question="Show the total revenue (sum of total_price) per seller.",
        expected_columns=["name"],
        min_rows=5,
        hint_tables=["sellers", "products", "orders"],
    ),
    TaskQuestion(
        id="jq-05",
        question="List the top 5 most reviewed products with their review counts.",
        expected_columns=["name"],
        min_rows=5,
        max_rows=5,
        hint_tables=["products", "reviews"],
    ),
]


def _grade_join(
    question: TaskQuestion,
    sql: str,
    rows: list[dict],
    error: Optional[str],
    attempts: int,
) -> float:
    if error:
        return _clamp(0.0)

    col_ok = _has_required_columns(rows, [question.expected_columns[0]])
    row_score = _row_count_score(rows, question.min_rows, question.max_rows)

    base = 0.0
    if col_ok and row_score == 1.0:
        base = 1.0
    elif col_ok or row_score >= 0.5:
        base = 0.5

    attempt_penalty = max(0.0, 0.1 * (attempts - 1))
    return _clamp(base - attempt_penalty)


_TASK_JOIN = Task(
    id="join_queries",
    name="Join Queries",
    difficulty="medium",
    description="Multi-table JOINs with GROUP BY and aggregation.",
    questions=_JOIN_QUESTIONS,
    grader=_grade_join,
)


# ─── Task 3: Complex Queries (Hard) ───────────────────────────────

_COMPLEX_QUESTIONS = [
    TaskQuestion(
        id="cq-01",
        question=(
            "Find users who have placed more than 1 order, showing their name "
            "and total number of orders, ordered by order count descending."
        ),
        expected_columns=["name"],
        min_rows=1,
        hint_tables=["users", "orders"],
    ),
    TaskQuestion(
        id="cq-02",
        question=(
            "For each product category, show the category name, number of products, "
            "average price, and total stock. Use a CTE."
        ),
        expected_columns=["category"],
        min_rows=5,
        max_rows=10,
        hint_tables=["products"],
    ),
    TaskQuestion(
        id="cq-03",
        question=(
            "Show each seller's name, their total sales revenue, and rank them "
            "by revenue using a window function (RANK() or ROW_NUMBER())."
        ),
        expected_columns=["name"],
        min_rows=5,
        hint_tables=["sellers", "products", "orders"],
    ),
    TaskQuestion(
        id="cq-04",
        question=(
            "Find the top-rated product in each category (highest average review rating). "
            "Show category, product name, and average rating."
        ),
        expected_columns=["category", "name"],
        min_rows=5,
        max_rows=10,
        hint_tables=["products", "reviews"],
    ),
    TaskQuestion(
        id="cq-05",
        question=(
            "Calculate the month-over-month order count for 2024, showing year, "
            "month, order_count, and a running total."
        ),
        expected_columns=["month"],
        min_rows=6,
        max_rows=12,
        hint_tables=["orders"],
    ),
]


def _grade_complex(
    question: TaskQuestion,
    sql: str,
    rows: list[dict],
    error: Optional[str],
    attempts: int,
) -> float:
    if error:
        return _clamp(0.0)

    col_ok = _has_required_columns(rows, question.expected_columns)
    row_score = _row_count_score(rows, question.min_rows, question.max_rows)

    if not col_ok or row_score == 0.0:
        return _clamp(0.0)

    if row_score == 1.0 and col_ok:
        base = 0.8 + (0.2 if attempts == 1 else 0.0)
    else:
        base = 0.4

    attempt_penalty = 0.1 * (attempts - 1)
    return _clamp(base - attempt_penalty)


_TASK_COMPLEX = Task(
    id="complex_queries",
    name="Complex Queries",
    difficulty="hard",
    description="CTEs, window functions, and nested aggregations.",
    questions=_COMPLEX_QUESTIONS,
    grader=_grade_complex,
)


# ─── Registry ─────────────────────────────────────────────────────

TASKS: dict[str, Task] = {
    "simple_queries": _TASK_SIMPLE,
    "join_queries":   _TASK_JOIN,
    "complex_queries": _TASK_COMPLEX,
}


def get_task(task_id: str) -> Task:
    if task_id not in TASKS:
        raise ValueError(f"Unknown task_id: {task_id!r}. Valid: {list(TASKS)}")
    return TASKS[task_id]


def get_all_tasks() -> list[Task]:
    return list(TASKS.values())


def grade_response(
    task_id: str,
    question_id: str,
    sql: str,
    rows: list[dict],
    error: Optional[str],
    attempts: int,
) -> float:
    task = get_task(task_id)
    question = next((q for q in task.questions if q.id == question_id), None)
    if question is None:
        raise ValueError(f"Unknown question_id {question_id!r} in task {task_id!r}")
    # Graders already clamp internally; this is a final safety net.
    return _clamp(task.grader(question, sql, rows, error, attempts))