File size: 15,450 Bytes
e368cec
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2b29775
 
e368cec
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
dc40458
 
 
 
e368cec
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
bf89481
 
 
 
 
 
 
 
e368cec
 
 
bf89481
 
 
 
 
 
 
 
2acb7a7
bf89481
 
 
e368cec
 
bf89481
 
 
 
 
 
 
 
 
 
e368cec
bf89481
 
 
 
 
 
 
 
db1f50e
bf89481
 
 
e368cec
 
bf89481
 
 
 
 
 
 
e368cec
bf89481
 
 
 
3b86414
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
e368cec
 
 
f6608c4
e368cec
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3b86414
e368cec
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
"""
Clean chatbot arena battle log.

Usage:
python3 clean_battle_data.py --mode conv_release
"""
import argparse
import datetime
import json
import os
import sys
from pytz import timezone
import time
import PIL
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True

from tqdm import tqdm

from .basic_stats import get_log_files, NUM_SERVERS, LOG_ROOT_DIR
from .utils import detect_language, get_time_stamp_from_date

VOTES = ["tievote", "leftvote", "rightvote", "bothbad_vote"]
IDENTITY_WORDS = [
    "vicuna",
    "lmsys",
    "koala",
    "uc berkeley",
    "open assistant",
    "laion",
    "chatglm",
    "chatgpt",
    "gpt-4",
    "openai",
    "anthropic",
    "claude",
    "bard",
    "palm",
    "lamda",
    "google",
    "llama",
    "qianwan",
    "alibaba",
    "mistral",
    "zhipu",
    "KEG lab",
    "01.AI",
    "AI2",
    "Tülu",
    "Tulu",
    "NETWORK ERROR DUE TO HIGH TRAFFIC. PLEASE REGENERATE OR REFRESH THIS PAGE.",
    "$MODERATION$ YOUR INPUT VIOLATES OUR CONTENT MODERATION GUIDELINES.",
    "API REQUEST ERROR. Please increase the number of max tokens.",
    "**API REQUEST ERROR** Reason: The response was blocked.",
    "**API REQUEST ERROR**",
]

for i in range(len(IDENTITY_WORDS)):
    IDENTITY_WORDS[i] = IDENTITY_WORDS[i].lower()


def remove_html(raw):
    if raw.startswith("<h3>"):
        return raw[raw.find(": ") + 2 : -len("</h3>\n")]
    if raw.startswith("### Model A: ") or raw.startswith("### Model B: "):
        return raw[13:]
    return raw


def to_openai_format(messages):
    roles = ["user", "assistant"]
    ret = []
    for i, x in enumerate(messages):
        ret.append({"role": roles[i % 2], "content": x[1]})
    return ret


def replace_model_name(old_name, tstamp):
    replace_dict = {
        "bard": "palm-2",
        "claude-v1": "claude-1",
        "claude-instant-v1": "claude-instant-1",
        "oasst-sft-1-pythia-12b": "oasst-pythia-12b",
        "claude-2": "claude-2.0",
        "PlayGroundV2": "PlayGround V2",
        "PlayGroundV2.5": "PlayGround V2.5",
    }
    if old_name in ["gpt-4", "gpt-3.5-turbo"]:
        if tstamp > 1687849200:
            return old_name + "-0613"
        else:
            return old_name + "-0314"
    if old_name in replace_dict:
        return replace_dict[old_name]
    return old_name


def read_file(filename):
    data = []
    for retry in range(5):
        try:
            # lines = open(filename).readlines()
            for l in open(filename):
                row = json.loads(l)
                if row["type"] in VOTES:
                    data.append(row)
            break
        except FileNotFoundError:
            time.sleep(2)
        except json.JSONDecodeError:
            print(f"Error in reading {filename}")
            print(row)
            exit(0)
    return data


def read_file_parallel(log_files, num_threads=16):
    data_all = []
    from multiprocessing import Pool

    with Pool(num_threads) as p:
        ret_all = list(tqdm(p.imap(read_file, log_files), total=len(log_files)))
        for ret in ret_all:
            data_all.extend(ret)
    return data_all

def load_image(image_path):
    try:
        return PIL.Image.open(image_path)
    except:
        return None

def clean_battle_data(
    log_files, exclude_model_names, ban_ip_list=None, sanitize_ip=False, mode="simple", task_name="image_editing"
):
    data = read_file_parallel(log_files, num_threads=16)

    convert_type = {
        "leftvote": "model_a",
        "rightvote": "model_b",
        "tievote": "tie",
        "bothbad_vote": "tie (bothbad)",
    }
    
    all_models = set()
    all_ips = dict()
    ct_anony = 0
    ct_invalid = 0
    ct_leaked_identity = 0
    ct_banned = 0
    battles = []
    for row in tqdm(data, desc="Cleaning"):
        if row["models"][0] is None or row["models"][1] is None:
            continue

        # Resolve model names
        models_public = [remove_html(row["models"][0]), remove_html(row["models"][1])]
        if "model_name" in row["states"][0]:
            models_hidden = [
                row["states"][0]["model_name"],
                row["states"][1]["model_name"],
            ]
            if models_hidden[0] is None:
                models_hidden = models_public
        else:
            models_hidden = models_public

        if (models_public[0] == "" and models_public[1] != "") or (
            models_public[1] == "" and models_public[0] != ""
        ):
            ct_invalid += 1
            continue

        if models_public[0] == "" or models_public[0] == "Model A":
            anony = True
            models = models_hidden
            ct_anony += 1
        else:
            anony = False
            models = models_public
            if not models_public == models_hidden:
                ct_invalid += 1
                continue

        # # Detect langauge
        # state = row["states"][0]
        # if state["offset"] >= len(state["messages"]):
        #     ct_invalid += 1
        #     continue
        # lang_code = detect_language(state["messages"][state["offset"]][1])

        # # Drop conversations if the model names are leaked
        # leaked_identity = False
        # messages = ""
        # for i in range(2):
        #     state = row["states"][i]
        #     for turn_idx, (role, msg) in enumerate(
        #         state["messages"][state["offset"] :]
        #     ):
        #         if msg:
        #             messages += msg.lower()
        # for word in IDENTITY_WORDS:
        #     if word in messages:
        #         leaked_identity = True
        #         break

        # if leaked_identity:
        #     ct_leaked_identity += 1
        #     continue
        
        def preprocess_model_name(m):
            if m == "Playground v2":
                return 'playground_PlayGroundV2_generation'
            if m == "Playground v2.5":
                return 'playground_PlayGroundV2.5_generation'
            return m
        models = [preprocess_model_name(m) for m in models]

        # Replace bard with palm
        if task_name == "image_editing":
            valid = True
            for _model in models:
                try:
                    platform, model_name, task = _model.split("_")
                except ValueError:
                    print(f"Invalid model names: {_model}")
                    valid = False
                    break
                if not (platform in ["playground", "imagenhub"] and task == "edition"):
                    valid = False
                    break
            if not valid:
                ct_invalid += 1
                continue
            for i, _model in enumerate(models):
                platform, model_name, task = _model.split("_")
                models[i] = model_name
                
            # if not all(x.startswith("imagenhub_") and x.endswith("_edition") for x in models):
            #     # print(f"Invalid model names: {models}")
            #     ct_invalid += 1
            #     continue
        
            # models = [x[len("imagenhub_"):-len("_edition")] for x in models]
        elif task_name == "t2i_generation":
            valid = True
            for _model in models:
                try:
                    platform, model_name, task = _model.split("_")
                except ValueError:
                    print(f"Invalid model names: {_model}")
                    valid = False
                    break
                if not (platform.lower() in ["playground", "imagenhub", 'fal'] and (task == "generation" or task == "text2image")):
                    valid = False
                    break
            if not valid:
                ct_invalid += 1
                continue
            for i, _model in enumerate(models):
                platform, model_name, task = _model.split("_")
                models[i] = model_name
            # if not all("playground" in x.lower() or (x.startswith("imagenhub_") and x.endswith("_generation")) for x in models):
            #     print(f"Invalid model names: {models}")
            #     ct_invalid += 1
            #     continue
            # models = [x[len("imagenhub_"):-len("_generation")] for x in models]
            # for i, model_name in enumerate(models):
            #     mode
            #     if model_name.startswith("imagenhub_"):
            #         models[i] = model_name[len("imagenhub_"):-len("_generation")]
            
        elif task_name == "video_generation":
            valid = True
            for _model in models:
                try:
                    platform, model_name, task = _model.split("_")
                except ValueError:
                    print(f"Invalid model names: {_model}")
                    valid = False
                    break
                if not (platform in ["videogenhub", "fal"] and task == "generation" or task == "text2video"):
                    valid = False
                    break
            if not valid:
                ct_invalid += 1
                continue
            for i, _model in enumerate(models):
                platform, model_name, task = _model.split("_")
                models[i] = model_name

        else:
            raise ValueError(f"Invalid task_name: {task_name}")
        models = [replace_model_name(m, row["tstamp"]) for m in models]
        
        # Exclude certain models
        if exclude_model_names and any(x in exclude_model_names for x in models):
            ct_invalid += 1
            continue
        
        # if models[0] not in model_infos or models[1] not in model_infos:
        #     continue

        # # Exclude votes before the starting date
        # if model_infos and (model_infos[models[0]]["starting_from"] > row["tstamp"] or model_infos[models[1]]["starting_from"] > row["tstamp"]):
        #     print(f"Invalid vote before the valid starting date for {models[0]} and {models[1]}")
        #     ct_invalid += 1
        #     continue
        
        

        if mode == "conv_release":
            # assert the two images are the same
            date = datetime.datetime.fromtimestamp(row["tstamp"], tz=timezone("US/Pacific")).strftime("%Y-%m-%d") # 2024-02-29
            image_path_format = f"{LOG_ROOT_DIR}/{date}-convinput_images/input_image_"
            image_path_0 = image_path_format + str(row["states"][0]["conv_id"]) + ".png"
            image_path_1 = image_path_format + str(row["states"][1]["conv_id"]) + ".png"
            if not os.path.exists(image_path_0) or not os.path.exists(image_path_1):
                print(f"Image not found for {image_path_0} or {image_path_1}")
                ct_invalid += 1
                continue
            
            image_0 = load_image(image_path_0)
            image_1 = load_image(image_path_1)
            if image_0 is None or image_1 is None:
                print(f"Image not found for {image_path_0} or {image_path_1}")
                ct_invalid += 1
                continue
            if image_0.tobytes() != image_1.tobytes():
                print(f"Image not the same for {image_path_0} and {image_path_1}")
                ct_invalid += 1
                continue
        
        
        question_id = row["states"][0]["conv_id"]
        # conversation_a = to_openai_format(
        #     row["states"][0]["messages"][row["states"][0]["offset"] :]
        # )
        # conversation_b = to_openai_format(
        #     row["states"][1]["messages"][row["states"][1]["offset"] :]
        # )

        ip = row["ip"]
        if ip not in all_ips:
            all_ips[ip] = {"ip": ip, "count": 0, "sanitized_id": len(all_ips)}
        all_ips[ip]["count"] += 1
        if sanitize_ip:
            user_id = f"arena_user_{all_ips[ip]['sanitized_id']}"
        else:
            user_id = f"{all_ips[ip]['ip']}"

        if ban_ip_list is not None and ip in ban_ip_list:
            ct_banned += 1
            continue

        # Save the results
        battles.append(
            dict(
                question_id=question_id,
                model_a=models[0],
                model_b=models[1],
                winner=convert_type[row["type"]],
                judge=f"arena_user_{user_id}",
                # conversation_a=conversation_a,
                # conversation_b=conversation_b,
                # turn=len(conversation_a) // 2,
                anony=anony,
                # language=lang_code,
                tstamp=row["tstamp"],
            )
        )

        all_models.update(models_hidden)
    battles.sort(key=lambda x: x["tstamp"])
    last_updated_tstamp = battles[-1]["tstamp"]

    last_updated_datetime = datetime.datetime.fromtimestamp(
        last_updated_tstamp, tz=timezone("US/Pacific")
    ).strftime("%Y-%m-%d %H:%M:%S %Z")

    print(
        f"#votes: {len(data)}, #invalid votes: {ct_invalid}, "
        f"#leaked_identity: {ct_leaked_identity} "
        f"#banned: {ct_banned} "
    )
    print(f"#battles: {len(battles)}, #anony: {ct_anony}")
    print(f"#models: {len(all_models)}, {all_models}")
    print(f"last-updated: {last_updated_datetime}")

    if ban_ip_list is not None:
        for ban_ip in ban_ip_list:
            if ban_ip in all_ips:
                del all_ips[ban_ip]
    print("Top 30 IPs:")
    print(sorted(all_ips.values(), key=lambda x: x["count"], reverse=True)[:30])
    return battles


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--max-num-files", type=int)
    parser.add_argument(
        "--mode", type=str, choices=["simple", "conv_release"], default="simple"
    )
    parser.add_argument("--task_name", type=str, default="image_editing", choices=["image_editing", "t2i_generation", "video_generation"])
    parser.add_argument("--exclude-model-names", type=str, nargs="+")
    parser.add_argument("--ban-ip-file", type=str)
    parser.add_argument("--sanitize-ip", action="store_true", default=False)
    args = parser.parse_args()

    log_files = get_log_files(args.max_num_files)
    ban_ip_list = json.load(open(args.ban_ip_file)) if args.ban_ip_file else None

    battles = clean_battle_data(
        log_files, args.exclude_model_names or [], ban_ip_list, args.sanitize_ip, args.mode, args.task_name
    )
    last_updated_tstamp = battles[-1]["tstamp"]
    cutoff_date = datetime.datetime.fromtimestamp(
        last_updated_tstamp, tz=timezone("US/Pacific")
    ).strftime("%Y%m%d")

    if args.mode == "simple":
        for x in battles:
            for key in [
                "conversation_a",
                "conversation_b",
                "question_id",
            ]:
                if key in x:
                    del x[key]
        print("Samples:")
        for i in range(min(4, len(battles))):
            print(battles[i])
        output = f"clean_battle_{args.task_name}_{cutoff_date}.json"
    elif args.mode == "conv_release":
        # new_battles = []
        # for x in battles:
        #     if not x["anony"]:
        #         continue
        #     for key in []:
        #         del x[key]
        #     new_battles.append(x)
        # battles = new_battles
        output = f"clean_battle_{args.task_name}_conv_{cutoff_date}.json"

    with open(output, "w") as fout:
        json.dump(battles, fout, indent=2, ensure_ascii=False)
    print(f"Write cleaned data to {output}")
    
    with open("cut_off_date.txt", "w") as fout:
        fout.write(cutoff_date)