import json
import logging
import heapq

from flask import request, jsonify

from routes import app

logger = logging.getLogger(__name__)

@app.route('/bugfixer/p2', methods=['POST'])
def max_bugs_fixed():
    data = request.get_json()
    logging.info("Data received for evaluation: {}".format(data))
    results = []

    for test_case in data:
        bugseq = test_case.get("bugseq", [])
        # Sort the bugs by their escalation limits (deadlines)
        bugseq.sort(key=lambda x: x[1])

        total_time = 0
        max_heap = []

        for difficulty, limit in bugseq:
            total_time += difficulty
            # Use a max heap by inserting negative difficulty
            heapq.heappush(max_heap, -difficulty)

            if total_time > limit:
                # Remove the bug with the largest difficulty
                removed_difficulty = -heapq.heappop(max_heap)
                total_time -= removed_difficulty
                logging.debug(f"Removed bug with difficulty {removed_difficulty} to meet the limit.")

        num_bugs_fixed = len(max_heap)
        results.append(num_bugs_fixed)
        logging.info("Computed number of bugs fixed: {}".format(num_bugs_fixed))

    return jsonify(results)

@app.route('/bugfixer/p1', methods=['POST'])
def bugfixer_p1():
    data = request.get_json()
    logging.info("Data received for evaluation: {}".format(data))
    if data is None:
        logging.error("No data received")
        return json.dumps([])

    # Ensure data is in list form
    if isinstance(data, dict):
        items = [data]
    elif isinstance(data, list):
        items = data
    else:
        logging.error("Invalid data format")
        return json.dumps([])

    result = []
    for item in items:
        time_list = item.get('time') or []
        prerequisites_list = item.get('prerequisites') or []
        total_time = compute_min_time(time_list, prerequisites_list)
        result.append(total_time)
    logging.info("My result: {}".format(result))
    return jsonify(result)

def compute_min_time(time_list, prerequisites_list):
    n = len(time_list)
    time_list = [0] + time_list  # Adjust to 1-based indexing
    graph = [[] for _ in range(n + 1)]  # adjacency list
    in_degree = [0] * (n + 1)
    earliest_finish_time = [0] * (n + 1)
    
    for prereq in prerequisites_list:
        a, b = prereq
        graph[a].append(b)
        in_degree[b] += 1
    
    # Initialize queue with nodes with in-degree zero
    queue = []
    for i in range(1, n + 1):
        if in_degree[i] == 0:
            earliest_finish_time[i] = time_list[i]
            queue.append(i)
    
    # Process nodes
    while queue:
        u = queue.pop(0)
        for v in graph[u]:
            earliest_finish_time[v] = max(
                earliest_finish_time[v], earliest_finish_time[u] + time_list[v]
            )
            in_degree[v] -= 1
            if in_degree[v] == 0:
                queue.append(v)
    
    # Maximum earliest_finish_time among all projects
    total_time = max(earliest_finish_time)
    return total_time