We have n jobs, where every job is scheduled to be done from startTime[i] to endTime[i], obtaining a profit of profit[i].
You're given the startTime , endTime and profit arrays, you need to output the maximum profit you can take such that there are no 2 jobs in the subset with overlapping time range.
If you choose a job that ends at time X you will be able to start another job that starts at time X.
 
Example 1:

Input: startTime = [1,2,3,3], endTime = [3,4,5,6], profit = [50,10,40,70]
Output: 120
Explanation: The subset chosen is the first and fourth job. 
Time range [1-3]+[3-6] , we get profit of 120 = 50 + 70.

Example 2:
 

Input: startTime = [1,2,3,4,6], endTime = [3,5,10,6,9], profit = [20,20,100,70,60]
Output: 150
Explanation: The subset chosen is the first, fourth and fifth job. 
Profit obtained 150 = 20 + 70 + 60.

Example 3:

Input: startTime = [1,1,1], endTime = [2,3,4], profit = [5,6,4]
Output: 6

 
Constraints:

1 <= startTime.length == endTime.length == profit.length <= 5 * 10^4
1 <= startTime[i] < endTime[i] <= 10^9
1 <= profit[i] <= 10^4

```python

USER_FILENAME = "<user_code>"
import sys, json, builtins
import math, heapq, bisect, itertools, functools, collections, statistics, fractions, decimal, operator, array, random, re, string, os, pathlib
try:
    import resource as _res
except Exception:
    _res = None
from collections import Counter, defaultdict, deque, OrderedDict
from functools import lru_cache, reduce
from itertools import accumulate, permutations, combinations, product
from math import gcd, ceil, floor, sqrt
from decimal import Decimal
try:
    from typing import *
except Exception:
    pass
try:
    sys.setrecursionlimit(1_000_000)
except Exception:
    pass
try:
    import site as _site
    try:
        _sys_sites = _site.getsitepackages() if hasattr(_site, "getsitepackages") else []
        for _p in _sys_sites:
            if _p and _p not in sys.path:
                sys.path.append(_p)
    except Exception:
        pass
    try:
        _usp = _site.getusersitepackages()
        if isinstance(_usp, str):
            _usp = [_usp]
        for _p in _usp:
            if _p and _p not in sys.path:
                sys.path.append(_p)
    except Exception:
        pass
except Exception:
    pass

def _disabled_input(*args, **kwargs):
    raise RuntimeError("input() is disabled in callable mode")
builtins.input = _disabled_input

ns = {"__name__": "__judge__"}  # prevent if __name__ == "__main__" blocks
for _k, _v in list(globals().items()):
    if not _k.startswith("_"):
        ns[_k] = _v
_CODE = '# This class represents a job = [start-time, end-time, weight]\nclass Job:\n    def __init__(self, start_time, end_time, weight):\n        self.start_time = start_time\n        self.end_time = end_time\n        self.weight = weight\n\n    #overriding hashcode and equals to use the Job object as a key in dict\n    def __hash__(self):\n        return hash((self.start_time, self.end_time, self.weight))\n\n    def __eq__(self, other):\n        return self.start_time == other.start_time and self.end_time == other.end_time and self.weight == other.weight\n\n\nclass WeightedIntervalSchedule:\n\n    def __init__(self, sch):\n        self.jobs = list()\n        for job in sch:\n            self.jobs.append(Job(job[0], job[1], job[2]))\n\n        self.jobs_end_first = []\n        self.jobs_start_first = []\n        self.X = len(self.jobs) * [0]\n        self.previous_job_mapping = dict()\n        # Memoization will be done using this memory dict\n        self.memory = dict()\n        #computing the previous mappings\n        self.compute_latest_job_scheduled_before()\n        \n\n    def getResult(self):\n        return self.dp(self.jobs_end_first, len(self.jobs) - 1, self.previous_job_mapping)\n\n    def compute_latest_job_scheduled_before(self):\n        \n        # Sorting the jobs in non decreasing order of start_time - O(nlogn)\n        self.jobs_start_first = sorted(self.jobs, key=lambda x: x.start_time)\n        # Sorting the jobs in non decreasing order of end_time - O(nlogn)\n        self.jobs_end_first = sorted(self.jobs, key=lambda x: x.end_time)\n        # This list X will store the index of the previous job in jobs_end_first\n        self.X[0] = -1\n\n        # The time complexity of this method is O(n) as the statement inside the while loop\n        # is only called once for each index\n        # We use the intution that the latest job for the current job will be greater than or \n        # equal to the latest job for the preious job\n        for i in range(1, len(self.jobs_start_first)):\n            j = self.X[i - 1]\n            while (self.jobs_start_first[i].start_time >= self.jobs_end_first[j + 1].end_time):\n                j = j + 1\n\n            self.X[i] = j\n\n        # We now map the respective job to its index in jobs_end_first.\n        # O(n)        \n        for i in range(0, len(self.jobs_start_first)):\n            self.previous_job_mapping[self.jobs_start_first[i]] = self.X[i]\n            \n\n    def dp(self, jobs, index, mapping):\n\n        current_job = jobs[index]\n\n        profit_including_current_job = current_job.weight\n        profit_excluding_current_job = 0\n\n        if index == 0:\n            return profit_including_current_job\n\n        # Dynamic programming - checking if the subproblem is already solved\n        if index in self.memory:\n            return self.memory[index]\n\n        if mapping[jobs[index]] != -1:\n            profit_including_current_job += self.dp(\n                jobs, mapping[jobs[index]], mapping)\n\n        profit_excluding_current_job = self.dp(\n            jobs, index - 1, mapping)\n\n        # Storing the result in the memory\n        result = max(profit_including_current_job, profit_excluding_current_job)\n        \n        self.memory[index] = result\n\n        return result\n\n\nclass Solution:\n    def jobScheduling(self, startTime: List[int], endTime: List[int], profit: List[int]) -> int:\n        \n        temp = list()\n        for i in range(len(startTime)):\n            temp.append([startTime[i], endTime[i], profit[i]])\n            \n        return WeightedIntervalSchedule(temp).getResult()\n        \n'
_co = compile(_CODE, USER_FILENAME, "exec")
exec(_co, ns, ns)

_target = None
_fn_name = 'jobScheduling'
if _fn_name:
    if "Solution" in ns:
        try:
            _obj = ns["Solution"]()
            if hasattr(_obj, _fn_name):
                _target = getattr(_obj, _fn_name)
        except Exception:
            _target = None
    if _target is None and _fn_name in ns and callable(ns[_fn_name]):
        _target = ns[_fn_name]
# Fallback: if there's exactly one user-defined function, use it
if _target is None:
    _cands = [v for k, v in ns.items() if callable(v) and getattr(v, "__module__", "") in ("__judge__", "__main__")]
    _user_funcs = [c for c in _cands if getattr(c, "__name__", "").startswith("_") is False]
    if len(_user_funcs) == 1:
        _target = _user_funcs[0]
if _target is None:
    raise RuntimeError(f"Could not resolve callable '{_fn_name}'")

_raw = sys.stdin.read().strip()
if _raw:
    _parsed = json.loads(_raw)
    if isinstance(_parsed, dict):
        _kwargs = _parsed.get("kwargs", {})
        _args = _parsed.get("args", [])
    else:
        _args, _kwargs = _parsed, {}
else:
    _args, _kwargs = [], {}
if not isinstance(_args, (list, tuple)):
    _args = [_args]
_res = _target(*_args, **_kwargs)
try:
    sys.stdout.write(repr(_res) + "\n")
except Exception:
    pass

```

Measured memory_bytes: 10434