from fastapi import APIRouter, HTTPException
from pydantic import BaseModel
from typing import List, Optional, Dict, Any
from problems.n_queens.problem import NQueensProblem

router = APIRouter(
    prefix="/n_queens",
    tags=["n_queens"],
    responses={404: {"description": "Not found"}},
)

problem_solver = NQueensProblem()

class GenerateRequest(BaseModel):
    n: int = 8

class SolveRequest(BaseModel):
    n: int
    algorithms: List[str] = ["backtracking"]

class BenchmarkRequest(BaseModel):
    sizes: List[int] = [4, 8, 10, 12]
    algorithms: List[str] = ["backtracking"]

@router.post("/generate")
async def generate_case(req: GenerateRequest):
    return problem_solver.generate_case(n=req.n)

@router.post("/solve")
async def solve_case(req: SolveRequest):
    input_data = {"n": req.n}
    results = []
    for algo in req.algorithms:
        try:
            res = problem_solver.solve(input_data, algo)
            results.append(res)
        except ValueError as e:
            results.append({"algorithm": algo, "error": str(e)})
    return results

@router.post("/benchmark")
async def benchmark(req: BenchmarkRequest):
    benchmark_results = []
    for size in req.sizes:
        input_data = {"n": size}
        
        size_result = {"size": size, "algorithms": []}
        for algo in req.algorithms:
            try:
                # Skip large N for backtracking if finding all solutions (though our solve defaults to one for large N)
                # But for benchmark we might want to be careful.
                if size > 14 and algo == "backtracking":
                     size_result["algorithms"].append({
                        "algorithm": algo,
                        "time_seconds": None,
                        "skipped": True
                    })
                     continue

                res = problem_solver.solve(input_data, algo)
                size_result["algorithms"].append(res)
            except Exception as e:
                size_result["algorithms"].append({"algorithm": algo, "error": str(e)})
        
        benchmark_results.append(size_result)
    return benchmark_results