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Certainly! Here are the main efficiency aspects for this code.
- The only real computation is converting a sequence of numbers (from 0 to n-1) into a space-separated string.
- `lru_cache` is already helping for repeated calls with the same argument, so further internal caching won't help much.
- However, `str.join(map(str, range(n)))` can still be optimized for Python (especially for big n), since `" ".join(map(str, ...))` builds many intermediate strings.
#### Optimization Approach
The main optimization possible.
- Use a direct string concatenation method that's faster for large inputs, avoiding generator overhead.
- Pre-allocate and fill a list of all number strings, then join once.
However, `" ".join(...list of strings...)` is already quite fast and optimal in CPython, so the improvement is limited and mostly negligible unless used at very large n.
But we can do a bit better with.
- Use a faster conversion for small n (shortcut for n == 0, n == 1).
- Avoid map completely; list comprehension is just as fast or faster in Py3.
#### Additional Micro-optimization
- Avoid `min(number, 1000)` by using an early return if input number >= 1000 (helps with call overhead if called with huge numbers frequently).
Here's the rewritten code with faster runtime.
#### Changes.
- Early exits for n == 0 and n == 1 (eliminates unnecessary work for tiny inputs).
- Slightly faster list comprehension for string conversion.
- Reduced overhead in the conditional min.
- Preserved all original cache semantics and function signatures.
- No unnecessary `map` generator creation.
#### Result.
- Slightly lower runtime overhead, especially for small n and at scale.
- Lower memory overhead by not keeping an intermediate generator alive.
- The code remains simple and clean.
If you use `funcA` repeatedly with numbers 0–1000, this is about as fast as Python can go for this problem!
Let me know if you have a larger scale or further constraints.
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