PACKAGE

using Microsoft.ML.Tokenizers;
using System.IO;
using System.Net.Http;

//
// Using Tiktoken Tokenizer
//

// Initialize the tokenizer for the `gpt-4o` model. This instance should be cached for all subsequent use.
Tokenizer tokenizer = TiktokenTokenizer.CreateForModel("gpt-4o");

string source = "Text tokenization is the process of splitting a string into a list of tokens.";

Console.WriteLine($"Tokens: {tokenizer.CountTokens(source)}");
// prints: Tokens: 16

var trimIndex = tokenizer.GetIndexByTokenCountFromEnd(source, 5, out string normalizedText, out _);
Console.WriteLine($"5 tokens from end: {(normalizedText ?? source).Substring(trimIndex)}");
// prints: 5 tokens from end:  a list of tokens.

trimIndex = tokenizer.GetIndexByTokenCount(source, 5, out normalizedText, out _);
Console.WriteLine($"5 tokens from start: {(normalizedText ?? source).Substring(0, trimIndex)}");
// prints: 5 tokens from start: Text tokenization is the

IReadOnlyList<int> ids = tokenizer.EncodeToIds(source);
Console.WriteLine(string.Join(", ", ids));
// prints: 1199, 4037, 2065, 374, 279, 1920, 315, 45473, 264, 925, 1139, 264, 1160, 315, 11460, 13

//
// Using Llama Tokenizer
//

// Open a stream to the remote Llama tokenizer model data file.
using HttpClient httpClient = new();
const string modelUrl = @"https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model";
using Stream remoteStream = await httpClient.GetStreamAsync(modelUrl);

// Create the Llama tokenizer using the remote stream. This should be cached for all subsequent use.
Tokenizer llamaTokenizer = LlamaTokenizer.Create(remoteStream);

string input = "Hello, world!";
ids = llamaTokenizer.EncodeToIds(input);
Console.WriteLine(string.Join(", ", ids));
// prints: 1, 15043, 29892, 3186, 29991

Console.WriteLine($"Tokens: {llamaTokenizer.CountTokens(input)}");
// prints: Tokens: 5