<p>You are given two <strong>0-indexed</strong> strings <code>source</code> and <code>target</code>, both of length <code>n</code> and consisting of <strong>lowercase</strong> English characters. You are also given two <strong>0-indexed</strong> string arrays <code>original</code> and <code>changed</code>, and an integer array <code>cost</code>, where <code>cost[i]</code> represents the cost of converting the string <code>original[i]</code> to the string <code>changed[i]</code>.</p>

<p>You start with the string <code>source</code>. In one operation, you can pick a <strong>substring</strong> <code>x</code> from the string, and change it to <code>y</code> at a cost of <code>z</code> <strong>if</strong> there exists <strong>any</strong> index <code>j</code> such that <code>cost[j] == z</code>, <code>original[j] == x</code>, and <code>changed[j] == y</code>. You are allowed to do <strong>any</strong> number of operations, but any pair of operations must satisfy <strong>either</strong> of these two conditions:</p>

<ul>
	<li>The substrings picked in the operations are <code>source[a..b]</code> and <code>source[c..d]</code> with either <code>b &lt; c</code> <strong>or</strong> <code>d &lt; a</code>. In other words, the indices picked in both operations are <strong>disjoint</strong>.</li>
	<li>The substrings picked in the operations are <code>source[a..b]</code> and <code>source[c..d]</code> with <code>a == c</code> <strong>and</strong> <code>b == d</code>. In other words, the indices picked in both operations are <strong>identical</strong>.</li>
</ul>

<p>Return <em>the <strong>minimum</strong> cost to convert the string </em><code>source</code><em> to the string </em><code>target</code><em> using <strong>any</strong> number of operations</em>. <em>If it is impossible to convert</em> <code>source</code> <em>to</em> <code>target</code>,<em> return</em> <code>-1</code>.</p>

<p><strong>Note</strong> that there may exist indices <code>i</code>, <code>j</code> such that <code>original[j] == original[i]</code> and <code>changed[j] == changed[i]</code>.</p>

<p>&nbsp;</p>
<p><strong class="example">Example 1:</strong></p>

<pre>
<strong>Input:</strong> source = &quot;abcd&quot;, target = &quot;acbe&quot;, original = [&quot;a&quot;,&quot;b&quot;,&quot;c&quot;,&quot;c&quot;,&quot;e&quot;,&quot;d&quot;], changed = [&quot;b&quot;,&quot;c&quot;,&quot;b&quot;,&quot;e&quot;,&quot;b&quot;,&quot;e&quot;], cost = [2,5,5,1,2,20]
<strong>Output:</strong> 28
<strong>Explanation:</strong> To convert &quot;abcd&quot; to &quot;acbe&quot;, do the following operations:
- Change substring source[1..1] from &quot;b&quot; to &quot;c&quot; at a cost of 5.
- Change substring source[2..2] from &quot;c&quot; to &quot;e&quot; at a cost of 1.
- Change substring source[2..2] from &quot;e&quot; to &quot;b&quot; at a cost of 2.
- Change substring source[3..3] from &quot;d&quot; to &quot;e&quot; at a cost of 20.
The total cost incurred is 5 + 1 + 2 + 20 = 28. 
It can be shown that this is the minimum possible cost.
</pre>

<p><strong class="example">Example 2:</strong></p>

<pre>
<strong>Input:</strong> source = &quot;abcdefgh&quot;, target = &quot;acdeeghh&quot;, original = [&quot;bcd&quot;,&quot;fgh&quot;,&quot;thh&quot;], changed = [&quot;cde&quot;,&quot;thh&quot;,&quot;ghh&quot;], cost = [1,3,5]
<strong>Output:</strong> 9
<strong>Explanation:</strong> To convert &quot;abcdefgh&quot; to &quot;acdeeghh&quot;, do the following operations:
- Change substring source[1..3] from &quot;bcd&quot; to &quot;cde&quot; at a cost of 1.
- Change substring source[5..7] from &quot;fgh&quot; to &quot;thh&quot; at a cost of 3. We can do this operation because indices [5,7] are disjoint with indices picked in the first operation.
- Change substring source[5..7] from &quot;thh&quot; to &quot;ghh&quot; at a cost of 5. We can do this operation because indices [5,7] are disjoint with indices picked in the first operation, and identical with indices picked in the second operation.
The total cost incurred is 1 + 3 + 5 = 9.
It can be shown that this is the minimum possible cost.
</pre>

<p><strong class="example">Example 3:</strong></p>

<pre>
<strong>Input:</strong> source = &quot;abcdefgh&quot;, target = &quot;addddddd&quot;, original = [&quot;bcd&quot;,&quot;defgh&quot;], changed = [&quot;ddd&quot;,&quot;ddddd&quot;], cost = [100,1578]
<strong>Output:</strong> -1
<strong>Explanation:</strong> It is impossible to convert &quot;abcdefgh&quot; to &quot;addddddd&quot;.
If you select substring source[1..3] as the first operation to change &quot;abcdefgh&quot; to &quot;adddefgh&quot;, you cannot select substring source[3..7] as the second operation because it has a common index, 3, with the first operation.
If you select substring source[3..7] as the first operation to change &quot;abcdefgh&quot; to &quot;abcddddd&quot;, you cannot select substring source[1..3] as the second operation because it has a common index, 3, with the first operation.
</pre>

<p>&nbsp;</p>
<p><strong>Constraints:</strong></p>

<ul>
	<li><code>1 &lt;= source.length == target.length &lt;= 1000</code></li>
	<li><code>source</code>, <code>target</code> consist only of lowercase English characters.</li>
	<li><code>1 &lt;= cost.length == original.length == changed.length &lt;= 100</code></li>
	<li><code>1 &lt;= original[i].length == changed[i].length &lt;= source.length</code></li>
	<li><code>original[i]</code>, <code>changed[i]</code> consist only of lowercase English characters.</li>
	<li><code>original[i] != changed[i]</code></li>
	<li><code>1 &lt;= cost[i] &lt;= 10<sup>6</sup></code></li>
</ul>

class Solution:
    def minimumCost(self, source: str, target: str, original: List[str], changed: List[str], cost: List[int]) -> int:
        def djikstra(node):
            minhpq = [(0, node)]         # min heap storing (cost, node)
            min_cost_map = dict()        # stores the {node: cost}

            while minhpq:
                cost, curr = heappop(minhpq)

                # pass
                if curr in min_cost_map:
                    continue

                # mark as seen
                min_cost_map[curr] = cost

                # search for next candidates
                for neigh, neigh_cost in adj[curr]:
                    new_cost = cost + neigh_cost
                    heappush(minhpq, (new_cost, neigh))

            return min_cost_map

        def dfs(i):
            nonlocal n, lengths

            # base
            if i >= n:
                return 0

            # seen before
            if i in memo:
                return memo[i]

            # 
            res = float('inf')

            # recur to index i + 1 if same value for ith index in source and target
            if source[i] == target[i]:
                res = dfs(i + 1)

            # recur on subset of source and target based on valid size length
            for size in lengths:
                # slice source and target string
                sub_s = source[i: i + size]
                sub_t = target[i: i + size]

                # run bfs for sub_s if substring sub_s 
                # does not exist in min_cost_maps
                if sub_s not in min_cost_maps:
                    min_cost_maps[sub_s] = djikstra(sub_s)    # append results to min_cost_maps

                # recur after sub_s if sub_t exist in min_cost_maps[sub_s]
                if sub_t in min_cost_maps[sub_s]:
                    cost = min_cost_maps[sub_s][sub_t]        # pull out cost from sub_s -> sub_t
                    new_i = i + size                          # new index after slicing
                    solution = cost + dfs(new_i)   

                    # compare  
                    res = min(res, solution)
                
            # allocate final res
            memo[i] = res

            return memo[i]

        # get the lenth of source strings
        lengths = set()

        # 1. build the directed graph
        adj = defaultdict(list)
        for src, des, c in zip(original, changed, cost):
            adj[src] += [(des, c)]
            lengths.add(len(src))

        # 2. stores min cost for each pair of (src, des)
        min_cost_maps = {ch: djikstra(ch) for ch in set(source)}

        n = len(source)

        # memo[i] := minimum cost to convert string/substring source to string/substring target starting at index i
        memo = dict()

        # 3. run dp starting at index 0 of source
        res = dfs(0)

        # 4. return -1 if dp result is inf
        return -1 if res == float('inf') else res
1198