import os

import rwkv_rs
import numpy as np
import huggingface_hub
import tokenizers

import gradio as gr

model_path = "./rnn.safetensors"
if not os.path.exists(model_path):
    model_path = huggingface_hub.hf_hub_download(repo_id="mrsteyk/RWKV-LM-safetensors", filename="RWKV-4-Pile-7B-Instruct-test1-20230124.rnn.safetensors")
assert model_path is not None

model = rwkv_rs.Rwkv(model_path)
tokenizer = tokenizers.Tokenizer.from_pretrained("EleutherAI/gpt-neox-20b")

GT = [
    gr.Button.update(visible=False),
    gr.Button.update(visible=True),
]
GF = [
    gr.Button.update(visible=True),
    gr.Button.update(visible=False),
]

def complete_fn(inpt, max_tokens, min_tokens, alpha_f, alpha_p):
    try:
        state = rwkv_rs.State(model)
        text = inpt
        counts = [0]*tokenizer.get_vocab_size()
        tokens = tokenizer.encode(inpt).ids
        # yield ("Preproc...", gr.Text.update(visible=False))
        # logits = model.forward(tokens, state)
        for i in range(len(tokens) - 1):
            model.forward_token_preproc(tokens[i], state)
            yield (tokenizer.decode(tokens[:i + 1]), None)
        logits = model.forward_token(tokens, state)
        yield (text, None)
        max_tokens = int(max_tokens)
        for i in range(max_tokens):
            if i < min_tokens:
                logits[0] = -100
            for i in range(len(counts)):
                logits[i] -= (counts[i]* alpha_f) + (float(counts[i] > 0) * alpha_p)
            token = np.argmax(logits)
            counts[token] += 1
            if token == 0:
                break
            if i == max_tokens - 1:
                break
            tokens += [token]
            text = tokenizer.decode(tokens)
            yield (text, None)
            logits = model.forward_token(token, state)
        yield (text, None)
    except Exception as e:
        print(e)
        yield ("Error...", gr.Text.update(value=str(e), visible=True))
    # finally:
    #     return (None, None)

def generator_wrap(l, fn):
    def wrap(*args):
        last_i = list([None] * l)
        try:
            for i in fn(*args):
                last_i = list(i)
                yield last_i + GT
        finally:
            yield last_i + GF
    return wrap


with gr.Blocks() as app:
    gr.Markdown(f"Running on `{model_path}`")
    error_box = gr.Text(label="Error", visible=False)

    with gr.Tab("Complete"):
        with gr.Row():
            inpt = gr.TextArea(label="Input")
            out = gr.TextArea(label="Output")
        complete = gr.Button("Complete", variant="primary")
        c_stop = gr.Button("Stop", variant="stop", visible=False)
    with gr.Tab("Insert (WIP)"):
        gr.Markdown("WIP, use `<|INSERT|>` to indicate a place to replace")
        with gr.Row():
            inpt_i = gr.TextArea(label="Input")
            out_i = gr.TextArea(label="Output")
        insert = gr.Button("Insert")

    with gr.Column():
        max_tokens = gr.Slider(label="Max Tokens", minimum=1, maximum=4096, step=1, value=767)
        min_tokens = gr.Slider(label="Min Tokens", minimum=0, maximum=4096, step=1)
        alpha_f = gr.Slider(label="Alpha Frequency", minimum=0, maximum=100, step=0.01)
        alpha_p = gr.Slider(label="Alpha Presence", minimum=0, maximum=100, step=0.01)

    G = [complete, c_stop]

    c = complete.click(generator_wrap(2, complete_fn), [inpt, max_tokens, min_tokens, alpha_f, alpha_p], [out, error_box] + G)
    c_stop.click(lambda: (complete.update(visible=True), c_stop.update(visible=False)), inputs=None, outputs=[complete, c_stop], cancels=[c], queue=False)

app.queue()
app.launch()