import gradio
import os
import numpy as np
import pandas as pd
from IPython import display
import faiss
import torch
from transformers import AutoTokenizer, CLIPTextModelWithProjection


DATA_PATH = '/data'

ft_visual_features_file = DATA_PATH + '/features/features_Cl4Cl_ckpt_webvid_retrieval_looseType_bs26_gpus2_lr7_150k_finalsample/dataset_v1_visual_features_database.npy'
binary_visual_features_file = DATA_PATH + '/features/features_Cl4Cl_ckpt_webvid_retrieval_looseType_bs26_gpus2_lr7_150k_finalsample_binary20/dataset_v1_visual_features_database_packed.npy'
ft_visual_features_database = np.load(ft_visual_features_file)
binary_visual_features = np.load(binary_visual_features_file)

database_csv_path = os.path.join(DATA_PATH, 'dataset_v1.csv')
database_df = pd.read_csv(database_csv_path)

#Gradio can display URL
def display_videos(display_df):
    display_path_list = display_df['contentUrl'].to_list()

    display_text_list = display_df['name'].to_list()
    html = ''
    for path, text in zip(display_path_list, display_text_list):
        html_line = '<video autoplay loop {}> <source src="{}" type="video/mp4"> </video> <div class="caption">{}</div><br/>'.format("muted", path, text)
        html += html_line
    return display.HTML(html)


class NearestNeighbors:
    """
    Class for NearestNeighbors.   
    """
    def __init__(self, n_neighbors=10, metric='cosine', rerank_from=-1):
        """
         metric = 'cosine' / 'binary' 
         if metric ~= 'cosine' and rerank_from > n_neighbors then a cosine rerank will be performed
        """
        self.n_neighbors = n_neighbors
        self.metric = metric        
        self.rerank_from = rerank_from                
        
    def normalize(self, a):
        return a / np.sum(a**2, axis=1, keepdims=True)
    
    def fit(self, data, o_data=None):
        if self.metric == 'cosine':
            data = self.normalize(data)
            self.index = faiss.IndexFlatIP(data.shape[1])        
        elif self.metric == 'binary':
            self.o_data = data if o_data is None else o_data
            #assuming data already packed
            self.index = faiss.IndexBinaryFlat(data.shape[1]*8)            
        self.index.add(np.ascontiguousarray(data))
        
    def kneighbors(self, q_data):                
        if self.metric == 'cosine':
            print('cosine search')
            q_data = self.normalize(q_data)            
            sim, idx = self.index.search(q_data, self.n_neighbors)        
        else:            
            if self.metric == 'binary':
                print('binary search')
                bq_data = np.packbits((q_data > 0.0).astype(bool), axis=1)            
            print(bq_data.shape, self.index.d)
            sim, idx = self.index.search(bq_data, max(self.rerank_from, self.n_neighbors))
            
            if self.rerank_from > self.n_neighbors:
                sim_float = np.zeros([len(q_data), self.rerank_from], dtype=float)
                for i, q in enumerate(q_data):
                    candidates = np.take_along_axis(self.o_data, idx[i:i+1,:].T, axis=0)
                    sim_float[i,:] = q @ candidates.T
                    sort_idx = np.argsort(sim_float[i,:])[::-1]
                    sim_float[i,:] = sim_float[i,:][sort_idx]
                    idx[i,:] = idx[i,:][sort_idx]
                sim = sim_float[:,:self.n_neighbors]
                idx = idx[:,:self.n_neighbors]
        
        return sim, idx
    

def search(search_sentence):
    my_model = CLIPTextModelWithProjection.from_pretrained("Diangle/clip4clip-webvid")
    tokenizer = AutoTokenizer.from_pretrained("Diangle/clip4clip-webvid")


    inputs = tokenizer(text=search_sentence , return_tensors="pt", padding=True)

    outputs = my_model(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], return_dict=False)
        
    text_projection = my_model.state_dict()['text_projection.weight']
    text_embeds = outputs[1] @ text_projection
    final_output = text_embeds[torch.arange(text_embeds.shape[0]), inputs["input_ids"].argmax(dim=-1)]


    final_output = final_output / final_output.norm(dim=-1, keepdim=True)
    final_output = final_output.cpu().detach().numpy()
    sequence_output = final_output / np.sum(final_output**2, axis=1, keepdims=True)
    
    nn_search = NearestNeighbors(n_neighbors=5, metric='binary', rerank_from=100)
    nn_search.fit(np.packbits((ft_visual_features_database > 0.0).astype(bool), axis=1), o_data=ft_visual_features_database)
    sims, idxs = nn_search.kneighbors(sequence_output)  
    return database_df.iloc[idxs[0]]['contentUrl'].to_list()

    
gradio.close_all()
    
interface = gradio.Interface(search, 
                             inputs=[gradio.Textbox()],
                             outputs=[gradio.Video(format='mp4') for _ in range(5)],
                             title = 'Video Search Demo',
                             description = 'Type some text to search by content within a video database!',
                            ).launch()