You are going to implement a data compression program in Python using Huffman codes and the Burrows$-$Wheeler transform, which, together, form the basis of the bzip$2$ compression algorithm. Bzip$2$ consistently compresses at better ratios than ubiquitous compression algorithms like gzip and PKZIP. The basic idea behind the compression algorithm is: $1.$ Transform an input text file into a text file where the same letter appears more frequently together. We accomplish this with the Burrows$-$Wheeler transform $($BWT$).2.$ Convert the BWT into a text file where small integers appear more frequently than large ones, or$,$ move$-$to$-$front encoding. $3.$ Given a BWT and move$-$to$-$front encoded text, compress it by encoding common letters with short codewords and rare letters with long codewords, or$,$ Huffman encoding. Your task will be to implement an efficient inverse BWT and Huffman encoding and decoding. We provide a framework; you will fill in several functions. Your choice of internal representations is up to you. A stub file, bwt huffman.py$,$ is available for download on HuskyCT.