<p>You are given an array of strings <code>products</code> and a string <code>searchWord</code>.</p>

<p>Design a system that suggests at most three product names from <code>products</code> after each character of <code>searchWord</code> is typed. Suggested products should have common prefix with <code>searchWord</code>. If there are more than three products with a common prefix return the three lexicographically minimums products.</p>

<p>Return <em>a list of lists of the suggested products after each character of </em><code>searchWord</code><em> is typed</em>.</p>

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

<pre>
<strong>Input:</strong> products = [&quot;mobile&quot;,&quot;mouse&quot;,&quot;moneypot&quot;,&quot;monitor&quot;,&quot;mousepad&quot;], searchWord = &quot;mouse&quot;
<strong>Output:</strong> [[&quot;mobile&quot;,&quot;moneypot&quot;,&quot;monitor&quot;],[&quot;mobile&quot;,&quot;moneypot&quot;,&quot;monitor&quot;],[&quot;mouse&quot;,&quot;mousepad&quot;],[&quot;mouse&quot;,&quot;mousepad&quot;],[&quot;mouse&quot;,&quot;mousepad&quot;]]
<strong>Explanation:</strong> products sorted lexicographically = [&quot;mobile&quot;,&quot;moneypot&quot;,&quot;monitor&quot;,&quot;mouse&quot;,&quot;mousepad&quot;].
After typing m and mo all products match and we show user [&quot;mobile&quot;,&quot;moneypot&quot;,&quot;monitor&quot;].
After typing mou, mous and mouse the system suggests [&quot;mouse&quot;,&quot;mousepad&quot;].
</pre>

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

<pre>
<strong>Input:</strong> products = [&quot;havana&quot;], searchWord = &quot;havana&quot;
<strong>Output:</strong> [[&quot;havana&quot;],[&quot;havana&quot;],[&quot;havana&quot;],[&quot;havana&quot;],[&quot;havana&quot;],[&quot;havana&quot;]]
<strong>Explanation:</strong> The only word &quot;havana&quot; will be always suggested while typing the search word.
</pre>

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

<ul>
	<li><code>1 &lt;= products.length &lt;= 1000</code></li>
	<li><code>1 &lt;= products[i].length &lt;= 3000</code></li>
	<li><code>1 &lt;= sum(products[i].length) &lt;= 2 * 10<sup>4</sup></code></li>
	<li>All the strings of <code>products</code> are <strong>unique</strong>.</li>
	<li><code>products[i]</code> consists of lowercase English letters.</li>
	<li><code>1 &lt;= searchWord.length &lt;= 1000</code></li>
	<li><code>searchWord</code> consists of lowercase English letters.</li>
</ul>

class TrieNode:
    def __init__(self):
        self.words = [] 
        self.child = [None]*26

class Trie:
    def __init__(self):
        self.root = TrieNode()
    
    def getIndex(self, ch):
        return ord(ch) - ord('a')
    
    def add(self, word):
        cur = self.root
        for ch in word:
            i = self.getIndex(ch)
            if not cur.child[i]:
                cur.child[i] = TrieNode()
            cur = cur.child[i]
            cur.words.append(word)
        return
    
    def search(self, word):
        cur = self.root
        for ch in word:
            i = self.getIndex(ch)
            if not cur.child[i]:
                return []
            cur = cur.child[i]
        cur.words.sort()
        return cur.words[:3]

class Solution:
    def suggestedProducts(self, products: List[str], searchWord: str) -> List[List[str]]:
        newTrie = Trie()
        for p in products:
            newTrie.add(p)
        
        ans = []
        w = ''
        for s in searchWord:
            w += s
            ans.append(newTrie.search(w))
        return ans




# class Solution:
#     def suggestedProducts(self, products: List[str], searchWord: str) -> List[List[str]]:
#         # Binary search approach
#         products.sort()
#         res = []
#         cur = ''
#         for ch in searchWord:
#             cur += ch
#             startIndx = bisect_left(products, cur)
#             words = []
#             for word in products[startIndx:startIndx+3]:
#                 if word[:len(cur)] == cur:
#                     words.append(word)
#             res.append(words)
#         return res

# Trie Approach
# https://leetcode.com/problems/search-suggestions-system/solutions/2169179/python-trie-solution-industry-level-code

# class TreeNode:
#     def __init__(self):
#         self.child = [None] * 26
#         self.words = []

# class Trie:
#     def __init__(self):
#         self.root = TreeNode()

#     def _getIndx(self, ch):
#         return ord(ch) - ord('a')
    
#     def insert(self, word):
#         cur = self.root
#         for ch in word:
#             i = self._getIndx(ch)
#             if not cur.child[i]:
#                 cur.child[i] = TreeNode()
#             cur = cur.child[i]
#             cur.words.append(word)
#         return
    
#     def search(self, prefix):
#         cur = self.root
#         for ch in prefix:
#             i = self._getIndx(ch)
#             if not cur.child[i]:
#                 return []
#             cur = cur.child[i]
#         return sorted(cur.words)[:3]

# class Solution:
#     def suggestedProducts(self, products: List[str], searchWord: str) -> List[List[str]]:
#         trie = Trie()
#         for product in products:
#             trie.insert(product)
        
#         res = []
#         cur = ''
#         for ch in searchWord:
#             cur += ch
#             res.append(trie.search(cur))
#         return res

# https://leetcode.com/problems/search-suggestions-system/solutions/2169179/python-trie-solution-industry-level-code
24500