import gradio as gr
import pyarrow.parquet as pq
import pyarrow.compute as pc
from transformers import AutoTokenizer
from datasets import load_dataset
import os
import numpy as np


token_table = pq.read_table("weights/tokens.parquet")
cache_path = "weights/caches"
parquets = os.listdir(cache_path)
TOKENIZER = "microsoft/Phi-3-mini-4k-instruct"

dataset = load_dataset("kisate-team/feature-explanations", split="train")

layers = dataset.unique("layer")

features = {layer:{item["feature"]:item for item in dataset if item["layer"] == layer} for layer in layers}

nearby = 8
stride = 0.25
n_bins = 10

def make_cache_name(layer):
    return f"{cache_path}/phi-l{layer}-r4-st0.25x128-activations.parquet"

with gr.Blocks() as demo:
    feature_table = gr.State(None)

    tokenizer_name = gr.Textbox(TOKENIZER)
    layer_dropdown = gr.Dropdown(layers)
    feature_dropdown = gr.Dropdown()

    def update_features(layer):
        feature_dropdown = gr.Dropdown(features[layer].keys())
        return feature_dropdown

    layer_dropdown.input(update_features, layer_dropdown, feature_dropdown)


    frequency = gr.Number(0, label="Total frequency (%)")
    # histogram = gr.LinePlot(x="activation", y="freq")

    autoi_expl = gr.Textbox()
    selfe_expl = gr.Textbox()

    cm = gr.HighlightedText()
    frame = gr.Highlightedtext()

    def update(layer, feature, tokenizer_name):
        tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
        table = pq.read_table(make_cache_name(layer))
        table_feat = table.filter(pc.field("feature") == feature).to_pandas()

        # freq_t = table_feat[["activation", "freq"]]
        total_freq = float(table_feat["freq"].sum()) * 100
        
        table_feat = table_feat[table_feat["activation"] > 0]
        table_feat = table_feat[table_feat["freq"] > 0]

        table_feat = table_feat.sort_values("activation", ascending=False)

        texts = table_feat["token"].apply(
            lambda x: tokenizer.decode(token_table[max(0, x - nearby - 1):x + nearby + 1]["tokens"].to_numpy())
        )

        texts = [tokenizer.tokenize(text) for text in texts]
        activations = table_feat["nearby"].to_numpy()
        if len(activations) > 0:
            activations = np.stack(activations) * stride
            max_act = table_feat["activation"].max()
            activations = activations / max_act

            highlight_data = [
                [(token, activation) for token, activation in zip(text, activation)] + [("\n", 0)]
                for text, activation in zip(texts, activations)
            ]

            flat_data = [item for sublist in highlight_data for item in sublist]
            
            color_map_data = [i / n_bins for i in range(n_bins + 1)]
            color_map_data = [(f"{i*max_act:.2f}", i) for i in color_map_data]
        else:
            flat_data = []
            color_map_data = []

        autoi_expl = features[layer][feature]["explanation"]
        selfe_expl = features[layer][feature]["gen_explanations"]

        if selfe_expl is not None:
            selfe_expl = "\n".join(
                f"{i+1}. \"{x}\"" for i, x in enumerate(selfe_expl)
            )

        return flat_data, color_map_data, total_freq, autoi_expl, selfe_expl
        

    feature_dropdown.change(update, [layer_dropdown, feature_dropdown, tokenizer_name], [frame, cm, frequency, autoi_expl, selfe_expl])
    # feature_input.change(update, [dropdown, feature_input, tokenizer_name, token_range], [frame, cm, histogram, frequency])


if __name__ == "__main__":
    demo.launch(share=True)