#!/usr/bin/env python3
from doctest import OutputChecker
import sys
import torch
import re
import os
import gradio as gr
import requests
from doctest import OutputChecker
import sys
import torch
import re
import os
import gradio as gr
import requests
import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel
from torch.nn.functional import softmax
import numpy as np



#url = "https://github.com/simonepri/lm-scorer/tree/master/lm_scorer/models"
#resp = requests.get(url)

from sentence_transformers import SentenceTransformer, util
#from sentence_transformers import SentenceTransformer, util
#from sklearn.metrics.pairwise import cosine_similarity
#from lm_scorer.models.auto import AutoLMScorer as LMScorer
#from sentence_transformers import SentenceTransformer, util
#from sklearn.metrics.pairwise import cosine_similarity

#device = "cuda:0" if torch.cuda.is_available() else "cpu"
#model_sts = gr.Interface.load('huggingface/sentence-transformers/stsb-distilbert-base') 

#model_sts = SentenceTransformer('stsb-distilbert-base')
model_sts = SentenceTransformer('roberta-large-nli-stsb-mean-tokens')
#batch_size = 1
#scorer = LMScorer.from_pretrained('gpt2' , device=device, batch_size=batch_size)

#import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel
import numpy as np
import re

# def Sort_Tuple(tup):  
  
# 	# (Sorts in descending order)  
# 	tup.sort(key = lambda x: x[1])  
# 	return tup[::-1]


# def softmax(x):
# 	exps = np.exp(x)
# 	return np.divide(exps, np.sum(exps))
	

def get_sim(x):
    x =  str(x)[1:-1]
    x =  str(x)[1:-1]
    return x
     
# Load pre-trained model 

# model = GPT2LMHeadModel.from_pretrained('gpt2', output_hidden_states = True, output_attentions = True)

# #model  =  gr.Interface.load('huggingface/distilgpt2', output_hidden_states = True, output_attentions = True)

# #model.eval()
# #tokenizer =  gr.Interface.load('huggingface/distilgpt2')

# tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
# #tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')


# def cloze_prob(text):

# 	whole_text_encoding = tokenizer.encode(text)
# 	# Parse out the stem of the whole sentence (i.e., the part leading up to but not including the critical word)
# 	text_list = text.split()
# 	stem = ' '.join(text_list[:-1])
# 	stem_encoding = tokenizer.encode(stem)
# 	# cw_encoding is just the difference between whole_text_encoding and stem_encoding
# 	# note: this might not correspond exactly to the word itself
# 	cw_encoding = whole_text_encoding[len(stem_encoding):]
# 	# Run the entire sentence through the model. Then go "back in time" to look at what the model predicted for each token, starting at the stem.
# 	# Put the whole text encoding into a tensor, and get the model's comprehensive output
# 	tokens_tensor = torch.tensor([whole_text_encoding])
	
# 	with torch.no_grad():
# 		outputs = model(tokens_tensor)
# 		predictions = outputs[0]   

# 	logprobs = []
# 	# start at the stem and get downstream probabilities incrementally from the model(see above)
# 	start = -1-len(cw_encoding)
# 	for j in range(start,-1,1):
# 			raw_output = []
# 			for i in predictions[-1][j]:
# 					raw_output.append(i.item())
	
# 			logprobs.append(np.log(softmax(raw_output)))
			
# 	# if the critical word is three tokens long, the raw_probabilities should look something like this:
# 	# [ [0.412, 0.001, ... ] ,[0.213, 0.004, ...], [0.002,0.001, 0.93 ...]]
# 	# Then for the i'th token we want to find its associated probability
# 	# this is just: raw_probabilities[i][token_index]
# 	conditional_probs = []
# 	for cw,prob in zip(cw_encoding,logprobs):
# 			conditional_probs.append(prob[cw])
# 	# now that you have all the relevant probabilities, return their product.
# 	# This is the probability of the critical word given the context before it.

# 	return np.exp(np.sum(conditional_probs))


tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained('gpt2')

def sentence_prob_mean(text):
    # Tokenize the input text and add special tokens
    input_ids = tokenizer.encode(text, return_tensors='pt')

    # Obtain model outputs
    with torch.no_grad():
        outputs = model(input_ids, labels=input_ids)
        logits = outputs.logits  # logits are the model outputs before applying softmax

    # Shift logits and labels so that tokens are aligned:
    shift_logits = logits[..., :-1, :].contiguous()
    shift_labels = input_ids[..., 1:].contiguous()

    # Calculate the softmax probabilities
    probs = softmax(shift_logits, dim=-1)

    # Gather the probabilities of the actual token IDs
    gathered_probs = torch.gather(probs, 2, shift_labels.unsqueeze(-1)).squeeze(-1)

    # Compute the mean probability across the tokens
    mean_prob = torch.mean(gathered_probs).item()

    return mean_prob





def cos_sim(a, b):
    return np.inner(a, b) / (np.linalg.norm(a) * (np.linalg.norm(b)))


  
def Visual_re_ranker(caption_G, caption_B, caption_VR, visual_context_label, visual_context_prob):
    caption_G = caption_G
    caption_B = caption_B
    caption_VR = caption_VR
    visual_context_label= visual_context_label
    visual_context_prob = visual_context_prob
    caption_emb_G = model_sts.encode(caption_G, convert_to_tensor=True)
    caption_emb_B = model_sts.encode(caption_B, convert_to_tensor=True)
    caption_emb_VR = model_sts.encode(caption_VR, convert_to_tensor=True)

    visual_context_label_emb = model_sts.encode(visual_context_label, convert_to_tensor=True)


    sim_1 =  cosine_scores = util.pytorch_cos_sim(caption_emb_G, visual_context_label_emb)
    sim_1 = sim_1.cpu().numpy()
    sim_1 = get_sim(sim_1)

    sim_2 = cosine_scores = util.pytorch_cos_sim(caption_emb_B, visual_context_label_emb)
    sim_2 = sim_2.cpu().numpy()
    sim_2 = get_sim(sim_2)

    sim_3 = cosine_scores = util.pytorch_cos_sim(caption_emb_VR, visual_context_label_emb)
    sim_3 = sim_3.cpu().numpy()
    sim_3 = get_sim(sim_3)
 

    LM_1 = sentence_prob_mean(caption_G)
    LM_2 = sentence_prob_mean(caption_B)
    LM_3 = sentence_prob_mean(caption_VR)

    #LM  = scorer.sentence_score(caption, reduce="mean")
    score_1 = pow(float(LM_1),pow((1-float(sim_1))/(1+ float(sim_1)),1-float(visual_context_prob)))
    score_2 = pow(float(LM_2),pow((1-float(sim_2))/(1+ float(sim_2)),1-float(visual_context_prob)))
    score_3 = pow(float(LM_3),pow((1-float(sim_3))/(1+ float(sim_3)),1-float(visual_context_prob)))

    #return {"LM": float(LM)/1, "sim": float(sim)/1, "score": float(score)/1 }
    return {"Greedy": float(score_1)/1, "Best-Beam-5": float(score_2)/1, "Visual_re-Ranker": float(score_3)/1  }
    #return LM, sim, score 


 
     

demo = gr.Interface(
    fn=Visual_re_ranker,
    #description="Demo for Belief Revision based Caption Re-ranker with Visual Semantic Information",
    description="Demo for Caption Re-ranker with Visual Semantic Information",
    #inputs=[gr.Textbox(value="a city street filled with traffic at night") , gr.Textbox(value="traffic"),  gr.Textbox(value="0.7458009")],
    # a baby is eating in front of a birthday cake /a baby sitting in front of a giant cake 
    inputs=[gr.Textbox(value="baby is eating in front of a birthday cake") , gr.Textbox(value="a baby sitting in front of a cake"), gr.Textbox(value="a baby sitting in front of a birthday cake"), gr.Textbox(value="candle wax light"),  gr.Textbox(value="0.958")],
    #outputs=[gr.Textbox(value="Language Model Score") , gr.Textbox(value="Semantic Similarity Score"),  gr.Textbox(value="Belief revision score via visual context")],
    outputs="label",
)

demo.launch()