The time complexity of an algorithm depends on the model of computation. Algorithms are usually analyzed in the RAM machine, in which basic operations on machine words (such as assignment, arithmetic and comparison) cost $O(1)$. A machine word has length $O(log n)$, where $n$ is the size of the input.

In your case, the size of the input is at least $n$ (defined to be the length of `array`

), and so `count`

fits in a single machine word. Each of the basic operations in the algorithm cost $O(1)$, and so the overall time complexity is $Theta(n^2)$, since the algorithm executes this many basic operations.