#!/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 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 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") 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 #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 = sentence_prob_mean(sentence_man) LM_woman = sentence_prob_mean(sentence_woman) #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} return {"彼 (man)": float(score_man * 1), "彼女 (woman)": float(score_woman)* 1} #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()