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

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

problem_solver = MaxSubarrayProblem()

class GenerateRequest(BaseModel):
    size: int = 100
    min_val: int = -100
    max_val: int = 100

class SolveRequest(BaseModel):
    array: List[int]
    algorithms: List[str] = ["kadane", "divide_conquer", "brute_force"]

class BenchmarkRequest(BaseModel):
    sizes: List[int] = [10, 50, 100, 500, 1000]
    algorithms: List[str] = ["kadane", "divide_conquer", "brute_force"]

@router.post("/generate")
async def generate_case(req: GenerateRequest):
    return problem_solver.generate_case(size=req.size, min_val=req.min_val, max_val=req.max_val)

@router.post("/solve")
async def solve_case(req: SolveRequest):
    input_data = {"array": req.array}
    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:
        # Generate a random case for this size
        # To reduce noise, we might want to run multiple times and average, 
        # but for now single run per size is fine for demonstration
        input_data = problem_solver.generate_case(size=size)
        
        size_result = {"size": size, "algorithms": []}
        for algo in req.algorithms:
            try:
                # Skip O(N^2) for very large inputs to avoid timeout
                if size > 5000 and algo == "brute_force":
                     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