import gradio as gr
import spaces
import os
import time
import json
import numpy as np
import av
from PIL import Image
import functools
from transformers import AutoProcessor, Idefics2ForConditionalGeneration
from models.conversation import conv_templates
from typing import List
processor = AutoProcessor.from_pretrained("Mantis-VL/mantis-8b-idefics2-video-eval-95k-mantis-2epoch_4096")
model = Idefics2ForConditionalGeneration.from_pretrained("Mantis-VL/mantis-8b-idefics2-video-eval-95k-mantis-2epoch_4096")
MAX_NUM_FRAMES = 24
conv_template = conv_templates["idefics_2"]

with open("./examples/all_subsets.json", 'r') as f:
    examples = json.load(f)

for item in examples:
    video_id = item['images'][0].split("_")[0]
    item['images'] = [os.path.join("./examples", video_id, x) for x in item['images']]
    item['video'] = os.path.join("./examples", item['video'])

VIDEO_EVAL_PROMPT = """
Suppose you are an expert in judging and evaluating the quality of AI-generated videos, 
please watch the following frames of a given video and see the text prompt for generating the video, 
then give scores from 7 different dimensions:
(1) visual quality: the quality of the video in terms of clearness, resolution, brightness, and color
(2) object consistency, the consistency of objects or humans in video
(3) dynamic degree, the degree of dynamic changes
(4) motion smoothness, the smoothness of motion or movements
(5) text-to-video alignment, the alignment between the text prompt and the video content
(6) factual consistency, the consistency of the video content with the common-sense and factual knowledge
(7) overall score, the overall quality of the video
for each dimension, output a number from [1,2,3], in which '1' is 'Bad', '2' is 'Average', '3' is 'Good'.
Here is an output example: 
visual quality: 3
object consistency: 2 
dynamic degree: 2
motion smoothness: 1
text-to-video alignment: 1
factual consistency: 2
overall score: 1

For this video, the text prompt is "{text_prompt}",
all the frames of video are as follows:

"""
@spaces.GPU
def generate(text:str, images:List[Image.Image], history: List[dict], **kwargs):
    global processor, model
    model = model.to("cuda") if model.device.type != "cuda" else model
    if not images:
        images = None
    
    user_role = conv_template.roles[0]
    assistant_role = conv_template.roles[1]
    
    idefics_2_message = []
    cur_img_idx = 0
    cur_vid_idx = 0
    all_videos = [x for x in images if isinstance(x, list)]
    flatten_images = []
    for x in images:
        if isinstance(x, list):
            flatten_images.extend(x)
        else:
            flatten_images.append(x)
    
    print(history)
    for i, message in enumerate(history):
        if message["role"] == user_role:
            idefics_2_message.append({
                "role": user_role,
                "content": []
            })
            message_text = message["text"]
            num_video_tokens_in_text = message_text.count("<video>")
            if num_video_tokens_in_text > 0:
                for _ in range(num_video_tokens_in_text):
                    message_text = message_text.replace("<video>", "<image> " * len(all_videos[cur_vid_idx]), 1)
                    cur_vid_idx += 1
            num_image_tokens_in_text = message_text.count("<image>")
            if num_image_tokens_in_text > 0:
                sub_texts = [x.strip() for x in message_text.split("<image>")]
                if sub_texts[0]:
                    idefics_2_message[-1]["content"].append({"type": "text", "text": sub_texts[0]})
                for sub_text in sub_texts[1:]:
                    idefics_2_message[-1]["content"].append({"type": "image"})
                    if sub_text:
                        idefics_2_message.append({
                            "role": user_role,
                            "content": [{"type": "text", "text": sub_text}]
                        })
            else:
                idefics_2_message[-1]["content"].append({"type": "text", "text": message_text})
        elif message["role"] == assistant_role:
            if i == len(history) - 1 and not message["text"]:
                break
            idefics_2_message.append({
                "role": assistant_role,
                "content": [{"type": "text", "text": message["text"]}]
            })
    if text:
        assert idefics_2_message[-1]["role"] == assistant_role and not idefics_2_message[-1]["content"], "Internal error"
        idefics_2_message.append({
            "role": user_role,
            "content": [{"type": "text", "text": text}]
        })
    
    print(idefics_2_message)
    prompt = processor.apply_chat_template(idefics_2_message, add_generation_prompt=True) 
    
    images = [Image.open(x) if isinstance(x, str) else x for x in flatten_images]
    inputs = processor(text=prompt, images=images, return_tensors="pt")
    inputs = {k: v.to(model.device) for k, v in inputs.items()}
    outputs = model.generate(**inputs, max_new_tokens=1024)
    generated_text = processor.decode(outputs[0, inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
    return generated_text

    
def read_video_pyav(container, indices):
    '''
    Decode the video with PyAV decoder.

    Args:
        container (av.container.input.InputContainer): PyAV container.
        indices (List[int]): List of frame indices to decode.

    Returns:
        np.ndarray: np array of decoded frames of shape (num_frames, height, width, 3).
    '''
    frames = []
    container.seek(0)
    start_index = indices[0]
    end_index = indices[-1]
    for i, frame in enumerate(container.decode(video=0)):
        if i > end_index:
            break
        if i >= start_index and i in indices:
            frames.append(frame)
    return np.stack([x.to_ndarray(format="rgb24") for x in frames])

def eval_video(prompt, video:str):
    container = av.open(video)

    # sample uniformly 8 frames from the video
    total_frames = container.streams.video[0].frames
    if total_frames > MAX_NUM_FRAMES:
        indices = np.arange(0, total_frames, total_frames / MAX_NUM_FRAMES).astype(int)
    else:
        indices = np.arange(total_frames)
    video_frames = read_video_pyav(container, indices)

    frames = [Image.fromarray(x) for x in video_frames]
    
    eval_prompt = VIDEO_EVAL_PROMPT.format(text_prompt=prompt)
    eval_prompt += "<video>"
    user_role = conv_template.roles[0]
    assistant_role = conv_template.roles[1]
    chat_messages = [
        {
            "role": user_role,
            "text": eval_prompt
        },
        {
            "role": assistant_role,
            "text": ""
        }
    ]
    response = generate(None, [frames], chat_messages)
    return response

def build_demo():
    with gr.Blocks() as demo:
        gr.Markdown("""
            ## Video Evaluation
            upload a video along with a text prompt when generating the video, this model will evaluate the video's quality from 7 different dimensions.
        """)
        with gr.Row():
            video = gr.Video(width=500, label="Video")
            with gr.Column():
                video_prompt = gr.Textbox(label="Text Prompt", interactive=False, lines=1)
                with gr.Row():
                    eval_button = gr.Button("Evaluate Video")
                    clear_button = gr.ClearButton([video])
                eval_result = gr.Textbox(label="Evaluation result", interactive=False, lines=7)
        
        eval_button.click(
            eval_video, [video_prompt, video], [eval_result]
        )
        
        dummy_id = gr.Textbox("id", label="id", visible=False, min_width=50)
        dummy_output = gr.Textbox("reference score", label="reference scores", visible=False, lines=7)
        
        gr.Examples(
            examples=
            [
                [
                    item['id'], 
                    item['prompt'],
                    item['video'],
                    item['conversations'][1]['value']
                ] for item in examples
            ],
            inputs=[dummy_id, video_prompt, video, dummy_output],
        )        
        
#         gr.Markdown("""
# ## Citation
# ```
# @article{jiang2024mantis,
#   title={MANTIS: Interleaved Multi-Image Instruction Tuning},
#   author={Jiang, Dongfu and He, Xuan and Zeng, Huaye and Wei, Con and Ku, Max and Liu, Qian and Chen, Wenhu},
#   journal={arXiv preprint arXiv:2405.01483},
#   year={2024}
# }
# ```""")
    return demo    
    

if __name__ == "__main__":
    demo = build_demo()
    demo.launch()