app.py

#!/usr/bin/env python3
from doctest import OutputChecker
import sys
import argparse
#import torch
import re
import os
import gradio as gr
import requests
from sentence_transformers import SentenceTransformer, util
import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel
from transformers import T5Tokenizer, AutoModelForCausalLM
import torch 

from transformers import BertJapaneseTokenizer, BertModel
import torch


class SentenceBertJapanese:
    def __init__(self, model_name_or_path, device=None):
        self.tokenizer = BertJapaneseTokenizer.from_pretrained(model_name_or_path)
        self.model = BertModel.from_pretrained(model_name_or_path)
        self.model.eval()

        if device is None:
            device = "cuda" if torch.cuda.is_available() else "cpu"
        self.device = torch.device(device)
        self.model.to(device)

    def _mean_pooling(self, model_output, attention_mask):
        token_embeddings = model_output[0] #First element of model_output contains all token embeddings
        input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
        return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)


    def encode(self, sentences, batch_size=8):
        all_embeddings = []
        iterator = range(0, len(sentences), batch_size)
        for batch_idx in iterator:
            batch = sentences[batch_idx:batch_idx + batch_size]

            encoded_input = self.tokenizer.batch_encode_plus(batch, padding="longest", 
                                           truncation=True, return_tensors="pt").to(self.device)
            model_output = self.model(**encoded_input)
            sentence_embeddings = self._mean_pooling(model_output, encoded_input["attention_mask"]).to('cpu')

            all_embeddings.extend(sentence_embeddings)

        # return torch.stack(all_embeddings).numpy()
        return torch.stack(all_embeddings)


#model_sbert = SentenceTransformer('stsb-distilbert-base')
    
model_sbert = SentenceTransformer("colorfulscoop/sbert-base-ja")
#MODEL_NAME = "sonoisa/sentence-bert-base-ja-mean-tokens-v2"



#model_sbert = SentenceBertJapanese(MODEL_NAME)


#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))
	
# Load pre-trained model 

#model = GPT2LMHeadModel.from_pretrained('distilgpt2', output_hidden_states = True, output_attentions = True)
#model = GPT2LMHeadModel.from_pretrained('colorfulscoop/gpt2-small-ja',output_hidden_states= True, output_attentions=True)

tokenizer = T5Tokenizer.from_pretrained("rinna/japanese-gpt-1b")
model = AutoModelForCausalLM.from_pretrained("rinna/japanese-gpt-1b")

#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('distilgpt2')
#tokenizer = T5Tokenizer.from_pretrained('colorfulscoop/gpt2-small-ja')
#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))





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

def get_sim(x):
    x =  str(x)[1:-1]
    x =  str(x)[1:-1]
    return x



  
#def Visual_re_ranker(caption, visual_context_label, visual_context_prob):
def Visual_re_ranker(sentence_man, sentence_woman, context_label, context_prob):
    sentence_man = sentence_man  
    sentence_woman = sentence_woman
    context_label= context_label
    context_prob = context_prob
    sentence_emb_man = model_sbert.encode(sentence_man, convert_to_tensor=True)
    sentence_emb_woman = model_sbert.encode(sentence_woman, convert_to_tensor=True)
    context_label_emb = model_sbert.encode(context_label, convert_to_tensor=True)

    sim_m =  cosine_scores = util.pytorch_cos_sim(sentence_emb_man, context_label_emb)
    sim_m = sim_m.cpu().numpy()
    sim_m = get_sim(sim_m)

    sim_w = cosine_scores = util.pytorch_cos_sim(sentence_emb_woman, context_label_emb) 
    sim_w = sim_w.cpu().numpy()
    sim_w = get_sim(sim_w)


    LM_man = cloze_prob(sentence_man)
    LM_woman = cloze_prob(sentence_woman)
    score_man     = pow(float(LM_man),pow((1-float(sim_m))/(1+ float(sim_m)),1-float(context_prob)))
    score_woman   = pow(float(LM_woman),pow((1-float(sim_w))/(1+ float(sim_w)),1-float(context_prob)))





    return {"彼 (man)": float(score_man * 100000000), "彼女 (woman)": float(score_woman)* 1000000000}



#print(Visual_re_ranker("ハイデルベルク大学は彼の出身大学である。", "大学", "0.7458009"))


demo = gr.Interface(
    fn=Visual_re_ranker,
    description="Demo for Women Wearing Lipstick: Measuring the Bias Between Object and Its Related Gender",
    inputs=[gr.Textbox(value="ハイデルベルク大学は彼の出身大学である。") , gr.Textbox(value="ハイデルベルク大学は彼女の出身大学である。"), gr.Textbox(value="大学"),  gr.Textbox(value="0.7458009")],
    # inputs=[gr.Textbox(value="これこれ！！なっちょのインスタ開設はこれがあるから尚幸せなのよ！") , gr.Textbox(value="インスタ開設"), gr.Textbox(value="大学"),  gr.Textbox(value="0.5239")],
    
    
    #inputs=[gr.Textbox(value="a man is blow drying his hair in the bathroom") , gr.Textbox(value="a woman is blow drying her hair in the bathroom"), gr.Textbox(value="hair spray"),  gr.Textbox(value="0.7385")],
    #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()