import token
import tokenize
import gradio as gr
from datasets import load_dataset
from transformers import AutoTokenizer

def ReturnTokens(dataset_name, tokenizer_name="openai-community/gpt2", split="train"):
    # Initialize tokenizer
    tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
    
    # Load dataset
    dataset = load_dataset(dataset_name, split=split)
    
    # Function to count tokens in a single example
    def count_tokens_in_example(example):
        total_tokens = 0
        tokenized = tokenizer.batch_encode_plus(example)
        for i in tokenized:
            total_tokens+=len(i)
        return total_tokens
    
    tokens_=0

    for field in dataset[0].keys():
        tokens_+=count_tokens_in_example(dataset[field])
    
    return tokens_

with gr.Blocks(title="Dataset token counter") as app:
    gr.Markdown("# Token Counter")

    with gr.Row():
        prompt = gr.Textbox(label="Dataset", elem_id="dataset", info="", placeholder="")
        tokenizer = gr.Textbox(label="Tokenizer", elem_id="tokenizer", info="", placeholder="openai-community/gpt2", value="openai-community/gpt2")
        split = gr.Textbox(label="Split (default: train)", elem_id="split", info="", placeholder="train", value="train")
        tokens = gr.Label(label="Tokens", elem_id="tokens")
        prompt.submit().success(
            ReturnTokens,
            inputs=[prompt,tokenizer,split],
            outputs=[tokens]
        )

        gr.on(
            triggers=[
                prompt.submit,
                tokenizer.submit,
                split.submit,
            ],
            fn=ReturnTokens,
            inputs=[
                prompt,
                tokenizer,
                split
            ],
            outputs=[tokens],
            api_name="run",
        )

app.launch()