import os
import cv2
import gradio as gr
import numpy as np
import json
import pickle
from PIL import Image
import torch
from torch.nn.utils.rnn import pad_sequence
from transformers import BridgeTowerProcessor
from tqdm import tqdm

from bridgetower_custom import BridgeTowerTextFeatureExtractor, BridgeTowerForITC

import faiss
import webvtt

from pytube import YouTube
from youtube_transcript_api import YouTubeTranscriptApi
from youtube_transcript_api.formatters import WebVTTFormatter

if torch.cuda.is_available():
    device = 'cuda'
else:
    device = 'cpu'
model_name = 'BridgeTower/bridgetower-large-itm-mlm-itc'
model = BridgeTowerForITC.from_pretrained(model_name).to(device)
text_model = BridgeTowerTextFeatureExtractor.from_pretrained(model_name).to(device)

processor = BridgeTowerProcessor.from_pretrained(model_name)


def download_video(video_url, path='/tmp/'):
    
    yt = YouTube(video_url)
    yt = yt.streams.filter(progressive=True, file_extension='mp4').order_by('resolution').desc().first()
    if not os.path.exists(path):
        os.makedirs(path)
    filepath = os.path.join(path, yt.default_filename)
    if not os.path.exists(filepath):   
        print('Downloading video from YouTube...')
        yt.download(path)
    return filepath


# Get transcript in webvtt
def get_transcript_vtt(video_id, path='/tmp'):
    filepath = os.path.join(path,'test_vm.vtt')
    if os.path.exists(filepath):
        return filepath

    transcript = YouTubeTranscriptApi.get_transcript(video_id)
    formatter = WebVTTFormatter()
    webvtt_formatted = formatter.format_transcript(transcript)
    
    with open(filepath, 'w', encoding='utf-8') as webvtt_file:
        webvtt_file.write(webvtt_formatted)
    webvtt_file.close()

    return filepath

# https://stackoverflow.com/a/57781047
# Resizes a image and maintains aspect ratio
def maintain_aspect_ratio_resize(image, width=None, height=None, inter=cv2.INTER_AREA):
    # Grab the image size and initialize dimensions
    dim = None
    (h, w) = image.shape[:2]

    # Return original image if no need to resize
    if width is None and height is None:
        return image

    # We are resizing height if width is none
    if width is None:
        # Calculate the ratio of the height and construct the dimensions
        r = height / float(h)
        dim = (int(w * r), height)
    # We are resizing width if height is none
    else:
        # Calculate the ratio of the width and construct the dimensions
        r = width / float(w)
        dim = (width, int(h * r))

    # Return the resized image
    return cv2.resize(image, dim, interpolation=inter)

def time_to_frame(time, fps):
    '''
        convert time in seconds into frame number
    '''
    return int(time * fps - 1)

def str2time(strtime):
    strtime = strtime.strip('"')
    hrs, mins, seconds = [float(c) for c in strtime.split(':')]

    total_seconds = hrs * 60**2 + mins * 60 + seconds

    return total_seconds

def collate_fn(batch_list):
    batch = {}
    batch['input_ids']      = pad_sequence([encoding['input_ids'].squeeze(0)  for encoding in batch_list], batch_first=True)
    batch['attention_mask'] = pad_sequence([encoding['attention_mask'].squeeze(0) for encoding in batch_list], batch_first=True)
    batch['pixel_values']   = torch.cat([encoding['pixel_values'] for encoding in batch_list], dim=0)
    batch['pixel_mask']   = torch.cat([encoding['pixel_mask'] for encoding in batch_list], dim=0)
    return batch

def extract_images_and_embeds(video_id, video_path, subtitles, output, expanded=False, batch_size=2, progress=gr.Progress()):
    if os.path.exists(os.path.join(output, 'embeddings.pkl')):
        return

    os.makedirs(output, exist_ok=True)
    os.makedirs(os.path.join(output, 'frames'), exist_ok=True)
    os.makedirs(os.path.join(output, 'frames_thumb'), exist_ok=True)

    count = 0

    vidcap = cv2.VideoCapture(video_path)

    # Get the frames per second
    fps = vidcap.get(cv2.CAP_PROP_FPS) 

    # Get the total numer of frames in the video.
    frame_count = vidcap.get(cv2.CAP_PROP_FRAME_COUNT)

    # print(fps, frame_count)

    frame_number = 0
    
    count = 0
    anno = []

    embeddings = []
    batch_list = []
    vtt = webvtt.read(subtitles)
   
    for idx, caption in enumerate(tqdm(vtt, total=vtt.total_length, desc="Generating embeddings")):
        st_time = str2time(caption.start)
        ed_time = str2time(caption.end)

        mid_time = (ed_time + st_time) / 2
        text = caption.text.replace('\n', ' ')

        if expanded :
            raise NotImplementedError
        
        frame_no =  time_to_frame(mid_time, fps)
        mid_time_ms = mid_time * 1000
        # vidcap.set(1, frame_no)    # added this line 
        vidcap.set(cv2.CAP_PROP_POS_MSEC, mid_time_ms)
        print('Read a new frame: ', idx, mid_time, frame_no, text)
        success, frame = vidcap.read()
        if success:
            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            frame = Image.fromarray(frame)
            img_fname = f'{video_id}_{idx:06d}'
            img_fpath = os.path.join(output, 'frames', img_fname + '.jpg')
            # image = maintain_aspect_ratio_resize(image, height=350)     # save frame as JPEG file
            # cv2.imwrite( img_fpath, image)     # save frame as JPEG file
		    
            count += 1
            anno.append({
                'image_id': idx,
                'img_fname': img_fname,
                'caption': text,
                'time': mid_time_ms,
                'frame_no': frame_no
            })

            encoding = processor(frame, text, return_tensors="pt").to(device)
            encoding['text'] = text
            encoding['image_filepath'] = img_fpath
            encoding['start_time'] = caption.start
            encoding['time'] = mid_time_ms 
            
            batch_list.append(encoding)

        else:
            break
        
        if len(batch_list) == batch_size:
            batch = collate_fn(batch_list)
            with torch.no_grad():
                outputs = model(**batch, output_hidden_states=True)
            
            for i in range(batch_size):
                embeddings.append({
                    'embeddings':outputs.logits[i,2,:].detach().cpu().numpy(),
                    'text': batch_list[i]['text'],
                    'image_filepath': batch_list[i]['image_filepath'],
                    'start_time': batch_list[i]['start_time'],
                    'time': batch_list[i]['time'],
                })
            batch_list = []

    if batch_list:
        batch = collate_fn(batch_list)
        with torch.no_grad():
            outputs = model(**batch, output_hidden_states=True)

        for i in range(len(batch_list)):
            embeddings.append({
                'embeddings':outputs.logits[i,2,:].detach().cpu().numpy(),
                'text': batch_list[i]['text'],
                'image_filepath': batch_list[i]['image_filepath'],
                'start_time': batch_list[i]['start_time'],
                'time': batch_list[i]['time'],
            })

        batch_list = []

    with open(os.path.join(output, 'annotations.json'), 'w') as fh:
        json.dump(anno, fh)

    with open(os.path.join(output, 'embeddings.pkl'), 'wb') as fh:
        pickle.dump(embeddings, fh)

def run_query(video_path, text_query, path='/tmp'):
    
    vidcap = cv2.VideoCapture(video_path)
    
    embeddings_filepath = os.path.join(path, 'embeddings.pkl')
    faiss_filepath = os.path.join(path, 'faiss_index.pkl')

    embeddings = pickle.load(open(embeddings_filepath, 'rb'))

    if os.path.exists(faiss_filepath):
        faiss_index = pickle.load(open(faiss_filepath, 'rb'))
    else :
        embs = [emb['embeddings'] for emb in embeddings]
        vectors = np.stack(embs, axis=0)
        num_vectors, vector_dim  = vectors.shape
        faiss_index = faiss.IndexFlatIP(vector_dim)
        faiss_index.add(vectors)
        pickle.dump(faiss_index, open(faiss_filepath, 'wb'))

    print('Processing query')
    encoding = processor.tokenizer(text_query, return_tensors="pt").to(device)
    with torch.no_grad():
        outputs = text_model(**encoding)
    emb_query = outputs.cpu().numpy()
    print('Running FAISS search')
    _, I = faiss_index.search(emb_query, 6) 

    clip_images = []
    transcripts = []
    for idx in I[0]:
        # frame_no = embeddings[idx]['frame_no']
        # vidcap.set(1, frame_no)    # added this line 
        frame_timestamp = embeddings[idx]['time']
        vidcap.set(cv2.CAP_PROP_POS_MSEC, frame_timestamp)

        success, frame = vidcap.read()
        if success:
            frame = maintain_aspect_ratio_resize(frame, height=400)
            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            frame = Image.fromarray(frame)
            clip_images.append(frame)
            transcripts.append(f"({embeddings[idx]['start_time']}) {embeddings[idx]['text']}")

    return clip_images, transcripts


#https://stackoverflow.com/a/7936523
def get_video_id_from_url(video_url):
    """
    Examples:
    - http://youtu.be/SA2iWivDJiE
    - http://www.youtube.com/watch?v=_oPAwA_Udwc&feature=feedu
    - http://www.youtube.com/embed/SA2iWivDJiE
    - http://www.youtube.com/v/SA2iWivDJiE?version=3&hl=en_US
    """
    import urllib.parse
    url = urllib.parse.urlparse(video_url)
    if url.hostname == 'youtu.be':
        return url.path[1:]
    if url.hostname in ('www.youtube.com', 'youtube.com'):
        if url.path == '/watch':
            p = urllib.parse.parse_qs(url.query)
            return p['v'][0]
        if url.path[:7] == '/embed/':
            return url.path.split('/')[2]
        if url.path[:3] == '/v/':
            return url.path.split('/')[2]

    return None


def process(video_url, text_query, progress=gr.Progress(track_tqdm=True)):
    tmp_dir = os.environ.get('TMPDIR', '/tmp')
    video_id = get_video_id_from_url(video_url)
    output_dir = os.path.join(tmp_dir, video_id)
    video_file = download_video(video_url, path=output_dir)
    subtitles = get_transcript_vtt(video_id, path=output_dir)
    extract_images_and_embeds(video_id=video_id, 
        video_path=video_file, 
        subtitles=subtitles, 
        output=output_dir, 
        expanded=False,
        batch_size=8,
        progress=progress,
    )
    frame_paths, transcripts = run_query(video_file, text_query, path=output_dir)
    return video_file, [(image, caption) for image, caption in zip(frame_paths, transcripts)]


description = "This Space lets you run semantic search on a video."

with gr.Blocks() as demo:
    gr.Markdown(description)
    with gr.Row():
        with gr.Column():
            video_url = gr.Text(label="Youtube url")
            text_query = gr.Text(label="Text query")
            btn = gr.Button("Run query")
        video_player = gr.Video(label="Video")
    
    with gr.Row():
        gallery = gr.Gallery(label="Images")
        
    gr.Examples(
        examples=[
            ['https://www.youtube.com/watch?v=CvjoXdC-WkM','wedding'],
            ['https://www.youtube.com/watch?v=fWs2dWcNGu0', 'cheesecake'],
            ['https://www.youtube.com/watch?v=rmPpNsx4yAk', 'bunny'],
            ['https://www.youtube.com/watch?v=KCFYf4TJdN0' ,'sandwich'],
        ],
        inputs=[video_url, text_query],
    )

    btn.click(fn=process, 
        inputs=[video_url, text_query],
        outputs=[video_player, gallery],
    )

try:
    demo.queue(concurrency_count=3)
    demo.launch(share=True)
except:
    demo.launch()