import gradio as gr
import os, gc
from datetime import datetime
from transformers import CLIPImageProcessor
from huggingface_hub import hf_hub_download
from typing import List, Dict
from dataclasses import dataclass
DEFAULT_IMAGE_TOKEN = "<image>"


ctx_limit = 3500
num_image_embeddings = 4096
title = "rwkv1b5-vitl336p14-577token_mix665k_rwkv"
vision_tower_name = 'openai/clip-vit-large-patch14-336'

os.environ["RWKV_JIT_ON"] = '1'
os.environ["RWKV_CUDA_ON"] = '0' # if '1' then use CUDA kernel for seq mode (much faster)

from rwkv.model import RWKV
model_path = hf_hub_download(repo_id="howard-hou/visualrwkv-5", filename=f"{title}.pth")
model = RWKV(model=model_path, strategy='cpu fp32')
from rwkv.utils import PIPELINE, PIPELINE_ARGS
pipeline = PIPELINE(model, "rwkv_vocab_v20230424")

##########################################################################
from modeling import VisualEncoder, EmbeddingMixer, VisualEncoderConfig
emb_mixer = EmbeddingMixer(model.w["emb.weight"], 
                           num_image_embeddings=num_image_embeddings)
config = VisualEncoderConfig(n_embd=model.args.n_embd, 
                             vision_tower_name=vision_tower_name, 
                             grid_size=-1)
visual_encoder = VisualEncoder(config)
image_processor = CLIPImageProcessor.from_pretrained(vision_tower_name)
##########################################################################
def generate_prompt(instruction):
    instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n')
    input = input.strip().replace('\r\n','\n').replace('\n\n','\n')
    return f"\n{instruction}\n\nAssistant:"

def generate(
    ctx,
    image_ids,
    token_count=200,
    temperature=1.0,
    top_p=0.7,
    presencePenalty = 0.1,
    countPenalty = 0.1,
):
    args = PIPELINE_ARGS(temperature = max(0.2, float(temperature)), top_p = float(top_p),
                     alpha_frequency = countPenalty,
                     alpha_presence = presencePenalty,
                     token_ban = [], # ban the generation of some tokens
                     token_stop = [0]) # stop generation whenever you see any token here
    ctx = ctx.strip()
    all_tokens = []
    out_last = 0
    out_str = ''
    occurrence = {}
    state = None
    for i in range(int(token_count)):
        if i == 0:
            input_ids = (image_ids + pipeline.encode(ctx))[-ctx_limit:]
        else:
            input_ids = [token]
        out, state = model.forward(input_ids, state)
        for n in occurrence:
            out[n] -= (args.alpha_presence + occurrence[n] * args.alpha_frequency)

        token = pipeline.sample_logits(out, temperature=args.temperature, top_p=args.top_p)
        if token in args.token_stop:
            break
        all_tokens += [token]
        for xxx in occurrence:
            occurrence[xxx] *= 0.996        
        if token not in occurrence:
            occurrence[token] = 1
        else:
            occurrence[token] += 1
        
        tmp = pipeline.decode(all_tokens[out_last:])
        if '\ufffd' not in tmp:
            out_str += tmp
            yield out_str.strip()
            out_last = i + 1

    del out
    del state
    gc.collect()
    yield out_str.strip()


##########################################################################
cur_dir = os.path.dirname(os.path.abspath(__file__))
examples = [
    [
        f"{cur_dir}/examples_extreme_ironing.jpg",
        "What is unusual about this image?",
    ],
    [
        f"{cur_dir}/examples_waterview.jpg",
        "What are the things I should be cautious about when I visit here?",
    ]
]
def test(image, question):
    image = image_processor(images=image.convert('RGB'), return_tensors='pt')['pixel_values']
    image_features = visual_encoder.encode_images(image.unsqueeze(0))
    emb_mixer.set_image_embeddings(image_features)
    model.w["emb.weight"] = emb_mixer.get_input_embeddings()
    image_ids = [i for i in range(emb_mixer.image_start_index, emb_mixer.image_start_index + len(image_features))]
    input_text = generate_prompt(question)
    for output in generate(input_text, image_ids):
        yield output

with gr.Blocks(title=title) as demo:
    with gr.Row():
        with gr.Column():
            image = gr.Image(type='pil', label="Image")
        with gr.Column():
            prompt = gr.Textbox(lines=2, label="Prompt", 
                value="Assistant: Please upload an image and ask a question.")
        with gr.Column():
            with gr.Row():
                submit = gr.Button("Submit", variant="primary")
                clear = gr.Button("Clear", variant="secondary")
            output = gr.Textbox(label="Output", lines=5)
    data = gr.Dataset(components=[image, prompt], samples=examples, label="Examples", headers=["Image", "Prompt"])
    submit.click(test, [image, prompt], [output])
    clear.click(lambda: None, [], [output])
    data.click(lambda x: x, [data], [image, prompt])

demo.queue(max_size=10)
demo.launch(share=False)