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#### Chinese scope
#device = "cuda:0"
device = "cpu"
assert device.startswith("cpu") or device.startswith("cuda")
import sys
from predict import *
from transformers import (
T5ForConditionalGeneration,
MT5ForConditionalGeneration,
ByT5Tokenizer,
PreTrainedTokenizer,
T5TokenizerFast as T5Tokenizer,
MT5TokenizerFast as MT5Tokenizer,
AutoModelForSeq2SeqLM,
AutoTokenizer,
BertTokenizer,
GPT2LMHeadModel,
)
import pandas as pd
import numpy as np
import re
from rapidfuzz import fuzz
from tqdm import tqdm
import numpy as np
import os
import jieba
def repeat_to_one_f(x):
req = None
for token in jieba.lcut(x):
#print("req :", req)
if len(set(token)) == 1:
token = token[0]
if req is None:
req = token
else:
if (token in req and token not in [',', '，', '、', ' ']) or (req and token in [',', '，', '、', ' '] and req[-1] in [',', '，', '、', ' ']):
continue
else:
while req.endswith(token[0]):
token = token[1:]
req = req + token
if req is None:
return ""
return req.strip()
def shorten_exists(l, sim_threshold = 80, slice_size = 5):
req = []
for ele in l:
if not req:
req.append(ele)
else:
if max(map(lambda x: fuzz.ratio(x[:slice_size], ele[:slice_size]), req)) < sim_threshold:
req.append(ele)
return req
model_path = "svjack/summary-dialogue"
tokenizer0 = T5Tokenizer.from_pretrained(model_path)
model0 = T5ForConditionalGeneration.from_pretrained(model_path)
if device.startswith("cuda"):
model = Obj(model0, tokenizer0, device = "cuda:0")
else:
model = Obj(model0, tokenizer0, device = "cpu")
def loop_add(l, names = ["杰克", "安娜"]):
req = []
for i in range(len(l)):
ii = int(i % len(names))
req.append(
"{}:{}".format(names[ii], l[i])
)
return req
#### need some names drop in context(may not have ":")
#### '艾米-亚当斯在《沉睡的空洞》中，全身，双色大眼睛，咬牙切齿，恐怖，复杂的细节，电影，史诗，现实，解剖，汤姆-哈努卡，上光，艺术站，逼真，可怕'
def guess_name_candidates(context, cnt_threshold = 1):
from copy import deepcopy
assert type(context) == type("")
import re
l = re.findall(r"[\u4e00-\u9fa5a-zA-Z]+:", context)
l = list(filter(lambda x: x.strip(), l))
ori_l = deepcopy(l)
if not l:
return []
s = pd.Series(l).value_counts()
l = pd.Series(s[s > cnt_threshold].index.values.tolist()).map(lambda x: x[:-1]).values.tolist()
for ele in ori_l:
if len(ele[:-1]) not in l and (len(ele[:-1]) <= 3 or (
sum(map(len ,re.findall(r"[a-zA-Z]+:", ele))) == len(ele)
)):
l.append(ele[:-1])
l = list(set(l))
return l
def simple_pred(summary, candidates = ["杰克", "安娜"],
shorten_it = False, do_sample = True):
pred_text = model.predict(
"摘要：{} 候选集：{}".format(summary, " ".join(candidates)),
do_sample = do_sample
)[0]
candidates_ = guess_name_candidates(pred_text)
l = re.split("{}".format("|".join(map(lambda x: "{}:".format(x), candidates_))) ,pred_text)
l = list(filter(lambda x: x.strip(), l))
if shorten_it:
l = shorten_exists(l)
l = list(map(repeat_to_one_f, l))
l = loop_add(l, candidates)
return l
def percentile_sort(df, perc_num = 101):
score_tuple_s = df["score_tuple"]
score_array = np.asarray(score_tuple_s.values.tolist())
perc_list = np.linspace(0, 100, perc_num).tolist()
low_to_high_perc_array = np.stack(list(map(lambda p: np.percentile(score_array, p, axis = 0), perc_list)))
def get_rank(array_):
lookup_list = pd.DataFrame(array_ - low_to_high_perc_array[::-1]).apply(lambda s: min(s) >= 0, axis = 1).tolist()
if True not in lookup_list:
return len(lookup_list)
return lookup_list.index(True)
rank_list = []
for i in range(score_array.shape[0]):
rank_list.append(get_rank(score_array[i, :]))
rank_s = pd.Series(rank_list)
return df.iloc[np.argsort(rank_s.values)]
def repeat_score(l, slice_size = 200 ,sim_threshold = 70):
from copy import deepcopy
assert type(l) == type([])
l = deepcopy(l)
l = sorted(l)
cnt_num = 0
set0 = set([])
for ele in l:
if ":" in ele:
ele = "".join(ele.split(":")[1:])
if set0 and max(map(lambda x: fuzz.ratio(x[:slice_size], ele[:slice_size]), set0)) > sim_threshold:
#if ele in set0:
cnt_num += 1
set0.add(ele)
return cnt_num
#### "svjack/prompt-extend-chinese-gpt"
#model_path = "/home/featurize/zh_p_extend_outputs/simplet5-epoch-3-train-loss-1.2628-val-loss-1.6293"
model_path = "svjack/prompt-extend-chinese-gpt"
tokenizer1 = BertTokenizer.from_pretrained(model_path)
model1 = GPT2LMHeadModel.from_pretrained(model_path)
if device.startswith("cuda"):
zh_pe_model = Obj(model1, tokenizer1, device = "cuda:0")
else:
zh_pe_model = Obj(model1, tokenizer1, device = "cpu")
def one_ele_trans(x):
x = x.strip()
x = x[1:] if x.startswith("'") else x
x = x[:-1] if x.endswith("'") else x
x = x[1:] if x.startswith('"') else x
x = x[:-1] if x.endswith('"') else x
return x
def stdf_prompt_expander(x):
assert type(x) == type("")
return zh_pe_model.predict(
one_ele_trans(x.strip()).strip(),
max_length = 128
)[0].replace(" ", "").strip()
def sample_pred(context, times = 5, stdf_prompt_expander = lambda _: _):
df_req = []
for i in tqdm(range(times)):
ele = stdf_prompt_expander(context)
#ele = context
l = simple_pred(ele, do_sample = True)
df_req.append(
[ele, l]
)
df = pd.DataFrame(df_req)
df.columns = ["context", "dialogue"]
df["fuzz"] = df["dialogue"].map(
lambda x: fuzz.ratio(context, " ".join(x))
)
df["max_fuzz"] = df["dialogue"].map(
lambda x: max(map(lambda y: fuzz.ratio(y, context), x))
)
df["length"] = df["dialogue"].map(len)
df["rpt_score"] = df["dialogue"].map(repeat_score)
df["score_tuple"] = df.apply(
lambda x: (x["fuzz"], -1 * x["max_fuzz"], x["length"], -1 * x["rpt_score"]), axis = 1
)
df = percentile_sort(df)
return df
def sample_pred_wrapper(context, i2c_obj, times = 5, extend_by_diffusion = False):
assert type(context) == type("")
if any(map(lambda x: context.endswith(x), [".jpg", ".png", ".jpeg"])):
img_path = context
i2c_df = i2c_obj.predict_to_df([img_path])
assert i2c_df.size > 0
context = i2c_df["caption"].iloc[0]
else:
pass
assert type(context) == type("")
if extend_by_diffusion:
req_df = sample_pred(context, times = times, stdf_prompt_expander = stdf_prompt_expander)
else:
req_df = sample_pred(context, times = times, stdf_prompt_expander = lambda _:_)
return req_df
from ofa import *
ofa_obj = OFA()
if __name__ == "__main__":
'''
from image2caption import *
i2c_tiny_zh_obj = Image2Caption("svjack/vit-gpt-diffusion-zh",
overwrite_encoder_checkpoint_path = "google/vit-base-patch16-224",
overwrite_token_model_path = "IDEA-CCNL/Wenzhong-GPT2-110M",
device = device
)
'''
from ofa import *
ofa_obj = OFA()
img_path = "../pic/bug.jpg"
img_path = "../pic/baobao.jpeg"
img_path = "../pic/cat0.jpg"
img_path = "../pic/cat.jpg"
os.path.exists(img_path)
df = sample_pred_wrapper(img_path, i2c_obj = ofa_obj)
df["dialogue"].values.tolist()
img_url = "https://datasets-server.huggingface.co/assets/metashift/--/metashift/train/2/image/image.jpg"
img_url = "https://datasets-server.huggingface.co/assets/metashift/--/metashift/train/6/image/image.jpg"
#### diffusion model, general model
df = sample_pred_wrapper(img_url, i2c_obj = ofa_obj)
df["dialogue"].values.tolist()
ds_en_zh_df = pd.read_csv("../ds_en_zh_df.csv")
idx = 3
ds_en_zh_df.iloc[:, -1].iloc[idx]
df = sample_pred(ds_en_zh_df.iloc[:, -1].iloc[idx])
df["dialogue"].values.tolist()
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