eson commited on
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2bd606a
1 Parent(s): f331792

remove vocabs; update compression_app; add character_app;

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  1. .gitattributes +0 -10
  2. .gitignore +5 -1
  3. vocab/README.md → README.2.md +3 -1
  4. README.md +16 -1
  5. app.py +7 -5
  6. character_app.py +80 -0
  7. character_util.py +213 -0
  8. app_compression.py → compression_app.py +42 -40
  9. utils/compression_util.py → compression_util.py +151 -61
  10. config.py +0 -20
  11. patcher/README.md +15 -0
  12. patcher/sptokenizer_patch_deprecated.py +0 -105
  13. patcher/sptokenizer_wrapper.py +0 -61
  14. patcher/tiktoken_patch.py +2 -2
  15. app_playground.py → playground_app.py +34 -19
  16. examples.py → playground_examples.py +9 -9
  17. util.py → playground_util.py +39 -35
  18. requirements.txt +3 -1
  19. stats/character_stats.json +1712 -0
  20. stats/compress_rate.json +0 -4286
  21. stats/compression_rate.json +0 -0
  22. utils/byte_util.py +0 -0
  23. utils/character_util.py +0 -231
  24. utils/convert_sp_to_json.py +0 -4
  25. utils/fn_util.py +0 -0
  26. utils/lang_util.py +26 -30
  27. utils/lang_util_2.py +0 -115
  28. utils/oov.md +202 -0
  29. utils/oov_util.py +109 -3
  30. utils/speed_util.py +0 -9
  31. utils/symbol.py +0 -35
  32. utils/text_util.py +12 -1
  33. utils/vocab.jd.txt.v2 +0 -10268
  34. vocab.py +453 -0
  35. vocab/Intern_gpt/README.md +0 -0
  36. vocab/__init__.py +0 -260
  37. vocab/_alpaca_7b/README.md +0 -0
  38. vocab/_goat/README.md +0 -0
  39. vocab/_goat/__init__.py +0 -0
  40. vocab/albert/__init__.py +0 -6
  41. vocab/aya_101/__init__.py +0 -5
  42. vocab/baichuan/Baichuan-7B/config.json +0 -26
  43. vocab/baichuan/Baichuan-7B/configuration_baichuan.py +0 -66
  44. vocab/baichuan/Baichuan-7B/special_tokens_map.json +0 -23
  45. vocab/baichuan/Baichuan-7B/tokenization_baichuan.py +0 -250
  46. vocab/baichuan/Baichuan-7B/tokenizer.model +0 -3
  47. vocab/baichuan/Baichuan-7B/tokenizer_config.json +0 -35
  48. vocab/baichuan/__init__.py +0 -19
  49. vocab/baichuan/demo.py +0 -6
  50. vocab/baichuan/error.md +0 -8
.gitattributes CHANGED
@@ -33,13 +33,3 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
36
- vocab/belle_7b_2m/belle-7b-2m/tokenizer.json filter=lfs diff=lfs merge=lfs -text
37
- vocab/bloom/tokenizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text
38
- vocab/gemma_7b/gemma-7b/tokenizer.model filter=lfs diff=lfs merge=lfs -text
39
- vocab/gemma_7b/gemma-7b/tokenizer.json filter=lfs diff=lfs merge=lfs -text
40
- vocab/grok_1/tokenizer.model filter=lfs diff=lfs merge=lfs -text
41
- vocab/llama3/Meta-Llama-3-70B/tokenizer.json filter=lfs diff=lfs merge=lfs -text
42
- vocab/mistral_7b/Mistral-7B-v0.1/tokenizer.json filter=lfs diff=lfs merge=lfs -text
43
- vocab/mistral_7b/Mistral-7B-v0.1/tokenizer.model filter=lfs diff=lfs merge=lfs -text
44
- vocab/mixtral_8_7b/Mixtral-8x7B-v0.1/tokenizer.json filter=lfs diff=lfs merge=lfs -text
45
- vocab/mixtral_8_7b/Mixtral-8x7B-v0.1/tokenizer.model filter=lfs diff=lfs merge=lfs -text
 
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
 
 
 
 
 
 
 
.gitignore CHANGED
@@ -14,4 +14,8 @@ downloads/
14
  eggs/
15
  .eggs/
16
  .idea/
17
- gradio_cached_examples
 
 
 
 
 
14
  eggs/
15
  .eggs/
16
  .idea/
17
+ gradio_cached_examples
18
+ stats/
19
+ test/
20
+ wip/
21
+ tools/
vocab/README.md → README.2.md RENAMED
@@ -67,7 +67,7 @@ carol
67
  ```
68
 
69
 
70
- ##
71
 
72
  https://github.com/pytorch/fairseq/blob/master/tests/test_noising.py#L37
73
 
@@ -77,6 +77,8 @@ https://github.com/pytorch/fairseq/blob/master/tests/test_noising.py#L37
77
 
78
  跟BERT类似,只不过BERT是词后缀,这里是词前缀。
79
 
 
 
80
 
81
  ## GPT2
82
 
 
67
  ```
68
 
69
 
70
+ ## @@
71
 
72
  https://github.com/pytorch/fairseq/blob/master/tests/test_noising.py#L37
73
 
 
77
 
78
  跟BERT类似,只不过BERT是词后缀,这里是词前缀。
79
 
80
+ 这种应该是 https://github.com/rsennrich/subword-nmt
81
+
82
 
83
  ## GPT2
84
 
README.md CHANGED
@@ -7,6 +7,8 @@ sdk: gradio
7
  sdk_version: 4.28.3
8
  app_file: app.py
9
  pinned: false
 
 
10
  ---
11
 
12
 
@@ -210,4 +212,17 @@ python utils/compress_rate_util.py
210
 
211
  - Getting the most out of your tokenizer for pre-training and domain adaptation
212
  - Efficient and Effective Text Encoding for Chinese LLaMA and Alpaca
213
- - https://huggingface.co/spaces/Xenova/the-tokenizer-playground
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7
  sdk_version: 4.28.3
8
  app_file: app.py
9
  pinned: false
10
+ datasets:
11
+ - cc100
12
  ---
13
 
14
 
 
212
 
213
  - Getting the most out of your tokenizer for pre-training and domain adaptation
214
  - Efficient and Effective Text Encoding for Chinese LLaMA and Alpaca
215
+ - blog
216
+ - https://help.openai.com/en/articles/4936856-what-are-tokens-and-how-to-count-them
217
+ - https://huggingface.co/docs/transformers/tokenizer_summary#sentencepiece
218
+ - https://www.huaxiaozhuan.com/%E5%B7%A5%E5%85%B7/huggingface_transformer/chapters/1_tokenizer.html
219
+ - https://zhuanlan.zhihu.com/p/652520262
220
+ - https://github.com/QwenLM/Qwen/blob/main/tokenization_note_zh.md
221
+ - demo
222
+ - https://huggingface.co/spaces/Xenova/the-tokenizer-playground
223
+ - https://github.com/dqbd/tiktokenizer
224
+ - https://chat.lmsys.org/?leaderboard
225
+ - https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
226
+ - paper
227
+ - ss
228
+ -
app.py CHANGED
@@ -1,16 +1,18 @@
1
 
2
- import gradio as gr
3
- from app_playground import demo as tab_playground
4
- from app_compression import demo as tab_compression
5
  from patcher.gr_interface import TabbedInterface
6
 
7
 
8
  demo = TabbedInterface(
9
- [tab_playground, tab_compression],
10
- [" ⚔️ Playground", "🏆 Compression Leaderboard",], # 编码速度,解码速度,字符分类(zh、num等,支持正则),支持的语言,机构,。
11
  title='<div align="center">Tokenizer Arena ⚔️</div>',
12
  css="css/style.css"
13
  )
14
 
 
 
15
  if __name__ == "__main__":
16
  demo.launch()
 
1
 
2
+ from playground_app import demo as playground_tab
3
+ from compression_app import demo as compression_tab
4
+ from character_app import demo as character_tab
5
  from patcher.gr_interface import TabbedInterface
6
 
7
 
8
  demo = TabbedInterface(
9
+ [playground_tab, compression_tab, character_tab],
10
+ [" ⚔️ Playground", "🏆 Compression Leaderboard", "📊 Character Statistics"], # 编码速度,解码速度,字符分类(zh、num等,支持正则),支持的语言,机构,。
11
  title='<div align="center">Tokenizer Arena ⚔️</div>',
12
  css="css/style.css"
13
  )
14
 
15
+ demo.load(js=open("js/onload.js", "r", encoding="utf-8").read())
16
+
17
  if __name__ == "__main__":
18
  demo.launch()
character_app.py ADDED
@@ -0,0 +1,80 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import gradio as gr
2
+ from character_util import get_character_table
3
+
4
+ all_columns = [
5
+ ("digit", "digit"),
6
+ ("space", "space"),
7
+ ("lang-chinese", 'zh'),
8
+ ("lang-korea", 'ko'),
9
+ ("lang-japanese", 'ja'),
10
+ # ("byte", "byte"),
11
+ # ("oov", "oov")
12
+ ]
13
+ default_columns = ["digit", "zh"]
14
+
15
+ # columns = ["lang-zh", "lang-korea", "lang-japanese", "number", "space", "bytes", "oov"]
16
+
17
+ abbr2name = {column[1]: column[0].split('-')[-1] for column in all_columns}
18
+
19
+
20
+ def get_column_info(columns):
21
+ print(columns)
22
+ markdown = ""
23
+ for column in columns:
24
+ markdown += f"- `num({column})`: num of tokens containing {abbr2name[column]} characters\n" \
25
+ f"- `len({column})`: `min,median,max` length of tokens containing {abbr2name[column]} characters\n"
26
+ return markdown
27
+
28
+
29
+ with gr.Blocks() as demo:
30
+ gr.Markdown("## 🛠️ Setting") # ⚙
31
+ with gr.Accordion("Please select the type of character you want to count.", open=True):
32
+ # file size 💽 🖴, tokens 🧮
33
+ with gr.Row():
34
+ with gr.Column():
35
+ columns = gr.Checkboxgroup(
36
+ all_columns,
37
+ value=default_columns,
38
+ label="character type",
39
+ # info=""
40
+ )
41
+ gr.Markdown(
42
+ "To count other types of characters, you can modify [character_util.py]"
43
+ "(https://huggingface.co/spaces/eson/tokenizer-arena/blob/main/character_util.py). "
44
+ )
45
+ column_info = gr.Markdown(
46
+ get_column_info(default_columns)
47
+ )
48
+
49
+ gr.Markdown("## 📊 Character Statistics")
50
+ search_bar = gr.Textbox(
51
+ placeholder="🔍 Search by tokenizer or organization (e.g., 'llama', 'openai') and press ENTER...",
52
+ show_label=False,
53
+ elem_id="search-bar",
54
+ )
55
+ compress_rate_table = gr.Dataframe(datatype="html", wrap=True)
56
+
57
+ search_bar.submit(
58
+ get_character_table,
59
+ inputs=[search_bar, columns],
60
+ outputs=compress_rate_table
61
+ )
62
+ columns.change(
63
+ get_character_table,
64
+ inputs=[search_bar, columns],
65
+ outputs=compress_rate_table
66
+ )
67
+ columns.change(
68
+ get_column_info,
69
+ inputs=[columns],
70
+ outputs=column_info
71
+ )
72
+
73
+ demo.load(
74
+ get_character_table,
75
+ inputs=[search_bar, columns],
76
+ outputs=compress_rate_table
77
+ )
78
+
79
+ if __name__ == "__main__":
80
+ demo.launch()
character_util.py ADDED
@@ -0,0 +1,213 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """
2
+ TODO:
3
+ 1. 繁体、简体、语种、
4
+ 2. 确认 bert的space token数目
5
+ 3. add token_impl
6
+ 4.
7
+ """
8
+ import os
9
+ import json
10
+ import numpy as np
11
+ import pandas as pd
12
+ from collections import Counter, defaultdict
13
+ from vocab import tokenizer_factory
14
+ from typing import Optional, Union, Literal
15
+ from utils.log_util import logger
16
+ from utils.text_util import contains_digit, get_space_count
17
+ from utils.lang_util import detect_language, language_ranges
18
+
19
+ CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
20
+
21
+
22
+ def _to_unicode(text):
23
+ return ''.join(r'\u{:04X}'.format(ord(chr)) for chr in text)
24
+
25
+
26
+ def _get_coding_length(tokenizer, vocab, filter=None):
27
+ """
28
+ oov character may be tokenized into more than one token.
29
+ """
30
+ all_length = []
31
+ for word in vocab:
32
+ if len(word) > 1:
33
+ continue
34
+ if filter is not None and filter(word):
35
+ continue
36
+ try:
37
+ tokens = tokenizer.encode(word)
38
+ except Exception as e:
39
+ print(e)
40
+
41
+ all_length.append(len(tokens))
42
+ # if len(tokens.ids) > 1:
43
+ # if len(tokens) > 3:
44
+ # print(word, tokens)
45
+
46
+ dist_length = Counter(all_length)
47
+ mean_length = round(sum(all_length) / len(all_length), 2)
48
+ return dist_length, mean_length
49
+
50
+
51
+ cache = {}
52
+
53
+
54
+ def _dist(token_lens):
55
+ """
56
+ :param token_lens:
57
+ :return: min,median,max of token_lens
58
+ """
59
+ if not token_lens:
60
+ return "-"
61
+ return f"{min(token_lens)},{round(np.median(token_lens))},{max(token_lens)}"
62
+
63
+
64
+ def iter_vocab(
65
+ tokenizer_name: str,
66
+ from_cache: bool = True,
67
+ cache_dir: str = "stats",
68
+ ) -> Union[pd.DataFrame, dict]:
69
+ """
70
+ :param tokenizer_name:
71
+ :param from_cache:
72
+ :param cache_dir:
73
+ :return:
74
+ """
75
+ tokenizer_config = tokenizer_factory.get_tokenizer_config(tokenizer_name)
76
+
77
+ cache_dir = os.path.join(CURRENT_DIR, cache_dir)
78
+ os.makedirs(cache_dir, exist_ok=True)
79
+
80
+ # load from cache
81
+ cache_path = os.path.join(cache_dir, "character_stats.json")
82
+ if not cache and os.path.exists(cache_path):
83
+ with open(cache_path, "r", encoding="utf-8") as f_tmp:
84
+ cache.update(json.load(f_tmp))
85
+ if from_cache and tokenizer_name in cache:
86
+ logger.info(f"load {tokenizer_config.name_or_path} from cache")
87
+ return cache[tokenizer_name]
88
+
89
+ tokenizer = tokenizer_factory.get_tokenizer(tokenizer_name)
90
+
91
+ tokens_by_lang = {lang[1]: [] for lang in language_ranges.keys()}
92
+ digit_tokens = []
93
+ space_tokens = []
94
+ byte_tokens = []
95
+
96
+ buffer = []
97
+ for token_id in range(tokenizer.vocab_size):
98
+ # for token_id in tokenizer.get_vocab():
99
+ # for token_id in range(len(tokenizer)):
100
+ decode_str = tokenizer.decode([token_id], skip_special_tokens=False)
101
+ token = tokenizer.convert_ids_to_tokens([token_id], skip_special_tokens=False)[0]
102
+ tags = []
103
+ if token is None: # 有些词典有空的id(不连续)
104
+ continue
105
+ if isinstance(token, bytes):
106
+ token = token.decode("utf-8", errors="ignore")
107
+
108
+ if hasattr(tokenizer, "sp_model"): # 基于 sentencepiece 包
109
+ if tokenizer.sp_model.is_byte(token_id):
110
+ tags.append("is_byte")
111
+ byte_tokens.append(token)
112
+
113
+ language_tags = detect_language(decode_str)
114
+ for language in language_tags:
115
+ tokens_by_lang[language[1]].append(decode_str)
116
+
117
+ if contains_digit(decode_str):
118
+ tags.append("digit")
119
+ digit_tokens.append(decode_str)
120
+
121
+ space_count = get_space_count(decode_str)
122
+ if space_count > 0:
123
+ space_tokens.append(decode_str)
124
+
125
+ buffer.append(json.dumps(
126
+ {
127
+ "id": token_id,
128
+ "token": token,
129
+ "token_decode": decode_str,
130
+ "token_dumps": json.dumps(token),
131
+ "token_unicode": _to_unicode(token),
132
+ "token_len": len(decode_str),
133
+ },
134
+ ensure_ascii=False) + "\n")
135
+
136
+ result = {
137
+ "tokenizer": tokenizer_factory.get_name_with_hyperlink(tokenizer_name),
138
+ "organization": tokenizer_config.org,
139
+ # "impl": str(tokenizer.__class__),
140
+ # "vocab_size-": tokenizer.vocab_size, # vocab_size_without_added_token
141
+ "vocab_size": len(tokenizer),
142
+
143
+ # "中文汉字编码长度均值": mean_length, # 不用统计,因为字典包含中文字符多,一般就意味着 中文汉字编码长度短。
144
+ # "中文汉字编码长度分布": json.dumps(dist_length),
145
+
146
+ "num(digit)": len(digit_tokens),
147
+ "len(digit)": _dist([len(token) for token in digit_tokens]),
148
+ "num(space)": len(space_tokens),
149
+ "len(space)": _dist([len(token) for token in space_tokens]),
150
+
151
+ # "num(byte)": len(byte_tokens)
152
+ }
153
+
154
+ for lang, tokens in tokens_by_lang.items():
155
+ result[f"num({lang})"] = len(tokens)
156
+ result["len(" + lang + ")"] = _dist([len(token) for token in tokens])
157
+
158
+ out_path = os.path.join(cache_dir, f"iter_vocab/{tokenizer_name.replace('/', '_')}.vocab.jsonl")
159
+ with open(out_path, "w", encoding="utf-8") as f_out:
160
+ for line in buffer:
161
+ f_out.write(line)
162
+ len_before = len(cache)
163
+ cache[tokenizer_name] = result
164
+ len_after = len(cache)
165
+ logger.info(f"saving {tokenizer_name} to memory and file cache: {len_before}->{len_after}")
166
+ with open(cache_path, "w", encoding="utf-8") as f_out:
167
+ f_out.write(json.dumps(cache, ensure_ascii=False, indent=2))
168
+ return result
169
+
170
+
171
+ def to_dataframe(stats, columns):
172
+ table = []
173
+ for stat in stats.values():
174
+ filtered_stat = {}
175
+ for k, v in stat.items():
176
+ if not k.startswith("num") and not k.startswith("len"):
177
+ filtered_stat[k] = v
178
+ if any(column in k for column in columns):
179
+ k = k.replace("ja-kana", "kana")
180
+ filtered_stat[k] = v
181
+ table.append(filtered_stat)
182
+ df = pd.DataFrame(table)
183
+ return df
184
+
185
+
186
+ def get_character_table(
187
+ tokenizer_filter: Optional[str] = None,
188
+ columns: Optional[str] = None,
189
+ return_type: Optional[Literal["dict", "dataframe"]] = "dataframe"
190
+ ) -> Union[pd.DataFrame, dict]:
191
+ """
192
+ """
193
+ logger.info(f"columns: {columns}, tokenizer_filter: {tokenizer_filter}")
194
+ stats = {}
195
+ if tokenizer_filter is not None:
196
+ tokenizer_names = [tokenizer_config.name_or_path for tokenizer_config in tokenizer_factory.all_tokenizer_configs
197
+ if tokenizer_filter.lower() in tokenizer_config.name_or_path.lower()]
198
+ else:
199
+ tokenizer_names = tokenizer_factory.all_tokenizer_names
200
+
201
+ for tokenizer_name in tokenizer_names:
202
+ stat = iter_vocab(tokenizer_name)
203
+ stats[tokenizer_name] = stat
204
+
205
+ if return_type == "dataframe":
206
+ stats = to_dataframe(stats, columns)
207
+ return stats
208
+
209
+
210
+ if __name__ == "__main__":
211
+ # aa = get_character_table(tokenizer_filter="baichuan")
212
+ df = get_character_table()
213
+ logger.info(f"\n{df.to_markdown(index=False)}")
app_compression.py → compression_app.py RENAMED
@@ -1,6 +1,14 @@
 
 
 
 
 
 
 
 
1
  import gradio as gr
2
- from utils.compression_util import get_compression_leaderboard
3
- from utils.compression_util import common_corpuses
4
 
5
  with gr.Blocks() as demo:
6
  # gr.Markdown("## Convertor")
@@ -44,63 +52,56 @@ with gr.Blocks() as demo:
44
  # )
45
 
46
  gr.Markdown("## 🛠️ Setting") # ⚙
47
- with gr.Accordion("Please select corpus and measure of compression rate ...", open=True):
48
  # file size 💽 🖴, tokens 🧮
49
- # gr.Markdown(
50
- # "Please select corpus and measure of compression rate.\n"
51
- #"`num_of_trillion_tokens` `num_of_billion_tokens`\n"
52
- # "- `b_tokens/g_bytes` measures how many billion tokens per gigabytes corpus. \n"
53
- # "- `t_tokens/t_bytes` measures how many trillion tokens per terabytes corpus. \n"
54
- # "- `n_chars/n_tokens` measures how many chars per token in the current corpus. \n\n"
55
- # "All the above measures are depend on corpus. You can reproduce this "
56
- # "procedure at [github](https://github.com/xu-song/tokenizer-arena/)."
57
- # )
58
-
59
  with gr.Row():
60
- compress_rate_corpus = gr.Dropdown(
61
- common_corpuses, # , "code"
62
- value=["cc100-en", "cc100-zh-Hans"],
63
- label="corpus",
64
- multiselect=True
65
- # info=""
66
- )
 
67
 
 
 
 
 
 
 
 
 
68
 
69
- # unit of file_size: gigabyte terabyte
70
- # unit of token_num: million billion trillion
71
- # The most common units of measurement include length (meter, inch, foot), weight (gram, kilogram, pound), volume (liter, gallon, milliliter), time (second, minute, hour)
72
- compress_rate_unit = gr.Radio(
73
- ["b_tokens/g_bytes", "t_tokens/t_bytes"],
74
- value="b_tokens/g_bytes",
75
- label="measure",
 
 
76
  )
77
 
78
- gr.Markdown(
79
- # "`num_of_trillion_tokens` `num_of_billion_tokens`\n"
80
- "- `b_tokens/g_bytes` measures how many billion tokens per gigabytes corpus. \n"
81
- "- `t_tokens/t_bytes` measures how many trillion tokens per terabytes corpus. \n"
82
- "- `n_chars/n_tokens` measures how many chars per token in the tokenized corpus. \n"
83
- # "\nAll the above measures are depend on corpus. You can reproduce this "
84
- # "procedure at [github](https://github.com/xu-song/tokenizer-arena/)."
85
- )
86
-
87
  gr.Markdown("## 🏆 Compression Rate Leaderboard")
88
  search_bar = gr.Textbox(
89
- placeholder="🔍 Search tokenizers(e.g., 'llama') and press ENTER...",
90
  show_label=False,
91
  elem_id="search-bar",
92
  )
93
- compress_rate_table = gr.Dataframe()
94
 
95
  # func call
96
  compress_rate_corpus.change(
97
  get_compression_leaderboard,
98
- inputs=[compress_rate_corpus, compress_rate_unit],
99
  outputs=compress_rate_table
100
  )
101
  compress_rate_unit.change(
102
  get_compression_leaderboard,
103
- inputs=[compress_rate_corpus, compress_rate_unit],
104
  outputs=compress_rate_table
105
  )
106
  # file_size.change(
@@ -123,5 +124,6 @@ with gr.Blocks() as demo:
123
  inputs=[compress_rate_corpus, compress_rate_unit],
124
  outputs=compress_rate_table
125
  )
 
126
  if __name__ == "__main__":
127
  demo.launch()
 
1
+ """
2
+ TODO:
3
+ - 统计 tokenizer_impl
4
+ - 统计 OOV
5
+ - 统计 reversal
6
+ - 增加 math,code
7
+ """
8
+
9
  import gradio as gr
10
+ from compression_util import get_compression_leaderboard, common_corpuses
11
+
12
 
13
  with gr.Blocks() as demo:
14
  # gr.Markdown("## Convertor")
 
52
  # )
53
 
54
  gr.Markdown("## 🛠️ Setting") # ⚙
55
+ with gr.Accordion("Please select the corpus and measure of compression rate.", open=True):
56
  # file size 💽 🖴, tokens 🧮
57
+ # Total amount of disk used
 
 
 
 
 
 
 
 
 
58
  with gr.Row():
59
+ with gr.Column():
60
+ compress_rate_corpus = gr.Dropdown(
61
+ common_corpuses, # , "code"
62
+ value=["cc100/en", "cc100/zh-Hans", "cc100/fr", "cc100/es"],
63
+ label="corpus",
64
+ multiselect=True
65
+ # info=""
66
+ )
67
 
68
+ # unit of file_size: gigabyte terabyte
69
+ # unit of token_num: million billion trillion
70
+ # The most common units of measurement include length (meter, inch, foot), weight (gram, kilogram, pound), volume (liter, gallon, milliliter), time (second, minute, hour)
71
+ compress_rate_unit = gr.Radio(
72
+ ["b_tokens/g_bytes", "t_tokens/t_bytes"],
73
+ value="b_tokens/g_bytes",
74
+ label="measure", # evaluation metric
75
+ )
76
 
77
+ gr.Markdown(
78
+ "- `corpus`: tokenization is performed on the selected subsets of [cc100](https://huggingface.co/datasets/cc100) corpus.\n"
79
+ "- `b_tokens/g_bytes` measures how many billion tokens per gigabytes corpus.\n"
80
+ "- `t_tokens/t_bytes` measures how many trillion tokens per terabytes corpus.\n"
81
+ # "- `g_bytes/b_tokens` measures how many gigabytes corpus per billion tokens.\n"
82
+ # "- `t_bytes/t_tokens` measures how many terabytes corpus per trillion tokens.\n"
83
+ "- `char/token` measures how many chars per token on the tokenized corpus.\n"
84
+ "- `oov_ratio`: out-of-vocabulary ratio on the selected corpus. 👉 get [oov charset](https://huggingface.co/spaces/eson/tokenizer-arena/blob/main/stats/compression_rate.json)\n\n"
85
+ "You can reproduce this procedure with [compression_util.py](https://huggingface.co/spaces/eson/tokenizer-arena/blob/main/compression_util.py)."
86
  )
87
 
 
 
 
 
 
 
 
 
 
88
  gr.Markdown("## 🏆 Compression Rate Leaderboard")
89
  search_bar = gr.Textbox(
90
+ placeholder="🔍 Search by tokenizer or organization (e.g., 'llama', 'openai') and press ENTER...",
91
  show_label=False,
92
  elem_id="search-bar",
93
  )
94
+ compress_rate_table = gr.Dataframe(datatype="html")
95
 
96
  # func call
97
  compress_rate_corpus.change(
98
  get_compression_leaderboard,
99
+ inputs=[compress_rate_corpus, compress_rate_unit, search_bar],
100
  outputs=compress_rate_table
101
  )
102
  compress_rate_unit.change(
103
  get_compression_leaderboard,
104
+ inputs=[compress_rate_corpus, compress_rate_unit, search_bar],
105
  outputs=compress_rate_table
106
  )
107
  # file_size.change(
 
124
  inputs=[compress_rate_corpus, compress_rate_unit],
125
  outputs=compress_rate_table
126
  )
127
+
128
  if __name__ == "__main__":
129
  demo.launch()
utils/compression_util.py → compression_util.py RENAMED
@@ -2,8 +2,8 @@
2
 
3
  中文数据:clue superclue
4
  英文数据:glue cnn_dailymail gigaword
5
- 代码数据:
6
- 数字:
7
 
8
  """
9
 
@@ -13,15 +13,15 @@ import sys
13
  import pandas as pd
14
  from datasets import load_dataset
15
  from utils.log_util import logger
16
- from vocab import load_tokener
17
- from vocab import all_tokenizers
18
  from typing import List, Optional, Union, Literal
19
 
20
  CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
21
 
22
  common_units = ["g_bytes/b_tokens", "b_tokens/g_bytes", "t_bytes/t_tokens", "t_tokens/t_bytes", "n_chars/n_tokens", ]
23
- common_corpuses = sorted(["cc100-en", "cc100-zh-Hans", "cc100-es", "cc100-fr", "cc100-de", "cc100-ko",
24
- "cc100-fa", "cc100-ar", "cc100-ja"])
 
25
 
26
  VALID_CODES_CC100 = [
27
  "am", "ar", "as", "az", "be", "bg", "bn", "bn_rom", "br", "bs", "ca", "cs", "cy", "da", "de",
@@ -44,9 +44,12 @@ def get_n_bytes_of_string(string_text):
44
 
45
 
46
  def unit_convertor(stat, unit):
47
- n_tokens = stat["n_tokens"]
48
- n_chars = stat["n_chars"]
49
- n_bytes = stat["n_bytes"]
 
 
 
50
 
51
  n_tokens_in_billion = n_tokens / (1000 * 1000 * 1000)
52
  n_tokens_in_trillion = n_tokens / (1000 * 1000 * 1000 * 1000)
@@ -57,11 +60,9 @@ def unit_convertor(stat, unit):
57
 
58
  if unit == "n_tokens/n_bytes":
59
  value = n_tokens / n_bytes
60
-
61
- # the average number of characters per token
62
- elif unit in ["n_chars/n_tokens", "chars_per_token"]: # 重要:平均一个token包含多少个字符。
63
  value = n_chars / n_tokens
64
- elif unit == "n_tokens/n_chars": # 一个中文汉字需要几个token?
65
  value = n_tokens / n_chars
66
  elif unit == "g_bytes/b_tokens":
67
  value = n_bytes_in_gb / n_tokens_in_billion
@@ -76,14 +77,48 @@ def unit_convertor(stat, unit):
76
  return round(value, 3)
77
 
78
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
79
  def to_dataframe(stats, units=None):
80
  if units is None:
81
  units = common_units
82
  elif not isinstance(units, list):
83
  units = [units]
84
  table = []
85
- for tokenizer_name, stat in stats.items():
86
- columns = {"tokenizer": tokenizer_name, "vocab_size": stat["vocab_size"]}
 
87
  for unit in units:
88
  if unit not in stat:
89
  columns[unit] = unit_convertor(stat, unit)
@@ -98,105 +133,159 @@ cache = {}
98
 
99
 
100
  def tokenize_corpus(
101
- tokenizer_name: str,
102
  corpuses: List[str],
103
- cache_path: str = "stats/compress_rate.json"
104
  ) -> dict:
105
  """
106
  这个要独立的cache,因为速度慢。
107
- :param tokenizer_name:
108
  :param corpuses:
109
  :param cache_path:
110
  :return:
111
  """
112
 
113
- def _tokenize(tokenizer, datasets):
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
114
  n_tokens = 0
115
  n_chars = 0
116
- n_bytes = 0
 
 
 
 
 
117
  for dataset in datasets:
118
  for item in dataset:
119
  text = item["text"]
120
  n_bytes += get_n_bytes_of_string(text)
121
  n_chars += len(text)
122
- encodings = tokenizer.encode(text)
123
- n_tokens += len(encodings)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
124
  stat = {
125
- # "vocab_size": len(tokenizer.vocab_size,
126
- "vocab_size": len(tokenizer),
127
- "n_bytes": n_bytes,
128
- "n_tokens": n_tokens,
129
- "n_chars": n_chars,
 
 
130
  }
 
 
 
 
 
 
 
 
 
 
 
 
 
131
  return stat
132
 
133
  # load from cache
134
- cache_id = f"{tokenizer_name}.{'.'.join(corpuses)}"
 
135
  if not cache and os.path.exists(cache_path):
136
  with open(cache_path, "r", encoding="utf-8") as f_tmp:
137
  cache.update(json.load(f_tmp))
138
  if cache_id in cache:
139
- logger.info(f"loading {cache_id} from in-memory cache")
140
  return cache[cache_id]
141
 
142
  # tokenize corpus
143
- tokenizer = load_tokener(tokenizer_name)
144
- datasets = [load_dataset("eson/cc100-samples", corpus.replace("cc100-", ""), split="train") for corpus in corpuses]
145
- stat = _tokenize(tokenizer, datasets)
 
 
 
 
 
 
 
 
 
 
146
 
147
  # save to cache
148
  len_before = len(cache)
149
  cache[cache_id] = stat
150
  len_after = len(cache)
151
- logger.info(f"saving {cache_id} to in-memory and file cache: {len_before}->{len_after}")
152
  with open(cache_path, "w", encoding="utf-8") as f_tmp:
153
- json.dump(cache, f_tmp, indent=2)
154
  return stat
155
 
156
 
157
  def get_compression_leaderboard(
158
- corpuses: List[str] = ['cc100-en'],
159
  unit: str = "b_tokens/g_bytes",
160
  tokenizer_filter: Optional[str] = None,
161
  return_type: Optional[Literal["dict", "dataframe"]] = "dataframe"
162
  ) -> Union[pd.DataFrame, dict]:
163
  """
164
- ## TODO
165
- - search by organization,
166
  """
167
  logger.info(f"corpuses: {corpuses}; unit: {unit}; tokenizer_filter: {tokenizer_filter}")
168
  stats = {}
169
  if tokenizer_filter is not None:
170
- tokenizers = [tokenizer_name for tokenizer_name in all_tokenizers if tokenizer_filter in tokenizer_name]
 
171
  else:
172
- tokenizers = all_tokenizers
173
- for lang in corpuses:
174
- for tokenizer_name in tokenizers:
175
- stat = tokenize_corpus(tokenizer_name, [lang])
176
- stats[tokenizer_name] = stat
 
177
 
178
  if return_type == "dataframe":
179
  token_number_unit, file_size_unit = unit.split("/")
180
  reverse_unit = f"{file_size_unit}/{token_number_unit}"
181
- stats = to_dataframe(stats, [unit, reverse_unit, "n_chars/n_tokens"])
182
- stats = stats.sort_values(unit)
183
- stats = stats.rename(columns={unit: f' ⬆️{unit}'})
184
  return stats
185
 
186
 
187
- def update_compress_rate():
188
- pass
189
-
190
-
191
- def test():
192
- tokenizer_name = "gpt_4"
193
- tokenizer = load_tokener(tokenizer_name)
194
- stats = {tokenizer_name: tokenize_corpus(tokenizer, ["cc100-en", "cc100-zh-Hans"])}
195
- df = to_dataframe(stats)
196
- # print(df.to_markdown(index=False, tablefmt='fancy_grid'))
197
- logger.info(f"\n{df.to_markdown(index=False)}")
198
-
199
-
200
  def main():
201
  if len(sys.argv) == 3:
202
  tokenizer_filter = [sys.argv[1]]
@@ -204,11 +293,12 @@ def main():
204
  else:
205
  tokenizer_filter = None
206
  corpuses = common_corpuses
207
- df = get_compression_leaderboard(corpuses)
 
 
208
  # print(df.to_markdown(index=False, tablefmt='fancy_grid'))
209
  logger.info(f"\n{df.to_markdown(index=False)}")
210
 
211
 
212
  if __name__ == "__main__":
213
  main()
214
- # test()
 
2
 
3
  中文数据:clue superclue
4
  英文数据:glue cnn_dailymail gigaword
5
+ code:
6
+ math:
7
 
8
  """
9
 
 
13
  import pandas as pd
14
  from datasets import load_dataset
15
  from utils.log_util import logger
16
+ from vocab import tokenizer_factory, TokenizerConfig
 
17
  from typing import List, Optional, Union, Literal
18
 
19
  CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
20
 
21
  common_units = ["g_bytes/b_tokens", "b_tokens/g_bytes", "t_bytes/t_tokens", "t_tokens/t_bytes", "n_chars/n_tokens", ]
22
+
23
+ common_corpuses = sorted(["cc100/en", "cc100/zh-Hans", "cc100/es", "cc100/fr", "cc100/de", "cc100/ko",
24
+ "cc100/fa", "cc100/ar", "cc100/ja"])
25
 
26
  VALID_CODES_CC100 = [
27
  "am", "ar", "as", "az", "be", "bg", "bn", "bn_rom", "br", "bs", "ca", "cs", "cy", "da", "de",
 
44
 
45
 
46
  def unit_convertor(stat, unit):
47
+ n_tokens = stat["_n_tokens"]
48
+ n_chars = stat["_n_chars"]
49
+ n_bytes = stat["_n_bytes"]
50
+
51
+ if n_tokens is None:
52
+ return None
53
 
54
  n_tokens_in_billion = n_tokens / (1000 * 1000 * 1000)
55
  n_tokens_in_trillion = n_tokens / (1000 * 1000 * 1000 * 1000)
 
60
 
61
  if unit == "n_tokens/n_bytes":
62
  value = n_tokens / n_bytes
63
+ elif unit in ["char/token", "chars_per_token"]: # 重要:平均一个token包含多少个字符。
 
 
64
  value = n_chars / n_tokens
65
+ elif unit in ["token/char", "tokens_per_char"]: # 一个中文汉字需要几个token?
66
  value = n_tokens / n_chars
67
  elif unit == "g_bytes/b_tokens":
68
  value = n_bytes_in_gb / n_tokens_in_billion
 
77
  return round(value, 3)
78
 
79
 
80
+ def _merge_stats_by_corpus(stats_by_corpus, oov_threshold=0.3):
81
+ """
82
+ """
83
+ all_stats = list(stats_by_corpus.values())
84
+ assert len(set([stats["tokenizer"] for stats in all_stats])) == 1
85
+ reversible = all(stat['reversible'] for stat in all_stats)
86
+ is_support = all(stat['oov_ratio'] < oov_threshold for stat in all_stats)
87
+
88
+ merged_stats = {
89
+ "tokenizer": all_stats[0]["tokenizer"],
90
+ "organization": all_stats[0]["organization"],
91
+ "vocab_size": all_stats[0]["vocab_size"],
92
+ "_n_bytes": 0,
93
+ "_n_tokens": 0 if is_support else None,
94
+ "_n_chars": 0,
95
+ "_n_oov_chars": 0,
96
+ "reversible": True,
97
+ }
98
+ for stats in all_stats:
99
+ merged_stats["_n_bytes"] += stats["_n_bytes"]
100
+ merged_stats["_n_chars"] += stats["_n_chars"]
101
+ if is_support: # The number of tokens cannot be accurately counted, when there are too many UNKs.
102
+ merged_stats["_n_tokens"] += stats["_n_tokens"]
103
+ merged_stats["_n_oov_chars"] += stats["_n_oov_chars"]
104
+ merged_stats["reversible"] &= stats['reversible']
105
+
106
+ merged_stats.update({
107
+ "oov_ratio": float("%.4g" % (stats["_n_oov_chars"] / stats["_n_chars"])),
108
+ "reversible": reversible
109
+ })
110
+ return merged_stats
111
+
112
+
113
  def to_dataframe(stats, units=None):
114
  if units is None:
115
  units = common_units
116
  elif not isinstance(units, list):
117
  units = [units]
118
  table = []
119
+
120
+ for stat in stats.values():
121
+ columns = {k: v for k, v in stat.items() if not k.startswith("_")}
122
  for unit in units:
123
  if unit not in stat:
124
  columns[unit] = unit_convertor(stat, unit)
 
133
 
134
 
135
  def tokenize_corpus(
136
+ tokenizer_name: str, # 可以免加载tokenizer直接出结果
137
  corpuses: List[str],
138
+ cache_dir: str = "stats"
139
  ) -> dict:
140
  """
141
  这个要独立的cache,因为速度慢。
142
+ :param tokenizer_config: 可以不加载就
143
  :param corpuses:
144
  :param cache_path:
145
  :return:
146
  """
147
 
148
+ def _char_based_oov(src_text, decode_text):
149
+ oov_chars = []
150
+ for char in src_text:
151
+ if char not in decode_text:
152
+ oov_chars.append(char)
153
+
154
+ n_oov_chars = len(oov_chars)
155
+ oov_charset = list(dict.fromkeys(oov_chars))
156
+ return n_oov_chars, oov_charset
157
+
158
+ def _tokenize(tokenizer, datasets, detail_path=None):
159
+ """
160
+ export_diff: true | false
161
+ :param tokenizer:
162
+ :param datasets:
163
+ :param detail_path:
164
+ :return:
165
+ """
166
+ n_bytes = 0
167
  n_tokens = 0
168
  n_chars = 0
169
+ n_oov_chars = 0
170
+ diff_details = []
171
+ oov_charset = set()
172
+ unk_token_id = None
173
+ if hasattr(tokenizer, "unk_token"):
174
+ unk_token_id = tokenizer.unk_token_id
175
  for dataset in datasets:
176
  for item in dataset:
177
  text = item["text"]
178
  n_bytes += get_n_bytes_of_string(text)
179
  n_chars += len(text)
180
+ ids = tokenizer.encode(text, add_special_tokens=False)
181
+
182
+ # detect oov
183
+ decode_text = tokenizer.decode(ids)
184
+ decode_text_without_unk = tokenizer.decode([token_id for token_id in ids if token_id != unk_token_id])
185
+ if decode_text != text:
186
+ _n_oov_chars, _oov_charset = _char_based_oov(text, decode_text_without_unk)
187
+ diff_details.append(
188
+ {
189
+ "text": text,
190
+ "decode_text": decode_text,
191
+ "decode_text_without_unk": decode_text_without_unk,
192
+ "n_oov_chars": _n_oov_chars,
193
+ 'oov_ratio': _n_oov_chars / len(text),
194
+ 'oov_charset': json.dumps(_oov_charset, ensure_ascii=False),
195
+ }
196
+ )
197
+ n_oov_chars += _n_oov_chars
198
+ oov_charset.update(_oov_charset)
199
+ n_tokens += len(ids)
200
  stat = {
201
+ "_n_bytes": n_bytes,
202
+ "_n_tokens": n_tokens,
203
+ "_n_chars": n_chars,
204
+ "_n_oov_chars": n_oov_chars,
205
+ "oov_ratio": n_oov_chars / n_chars,
206
+ '_oov_charset': json.dumps(list(oov_charset), ensure_ascii=False),
207
+ "reversible": len(diff_details) == 0
208
  }
209
+
210
+ if detail_path and diff_details:
211
+ logger.info(f"saving tokenization detail to '{detail_path}'")
212
+ with open(detail_path, "w", encoding="utf-8") as f:
213
+ f.write(json.dumps(diff_details, ensure_ascii=False, indent=2))
214
+ # print(f"{tokenizer_config.name_or_path}, {infer_tokenizer_type(tokenizer_config)}\n"
215
+ # f"reversible: false; unk_token: {get_unk(tokenizer_config)},"
216
+ # f" unk_ratio: {unk_count / len(encoding):.4f}; oov: []")
217
+ # for diff_detail in diff_details:
218
+ # # print(f"text[{i}] = {str(bytes(text[i:], 'utf-8'))}\n"
219
+ # # f"decoding[{i}] = {str(bytes(decoding[i:], 'utf-8'))}")
220
+ # f.write(f"text= {json.dumps(text[i:], ensure_ascii=False)}, \n"
221
+ # f"decoding[{i}] = {json.dumps(decoding[i:], ensure_ascii=False)}")
222
  return stat
223
 
224
  # load from cache
225
+ cache_id = f"{tokenizer_name} @ {'.'.join(corpuses)}"
226
+ cache_path = os.path.join(cache_dir, "compression_rate.json")
227
  if not cache and os.path.exists(cache_path):
228
  with open(cache_path, "r", encoding="utf-8") as f_tmp:
229
  cache.update(json.load(f_tmp))
230
  if cache_id in cache:
231
+ # logger.info(f"loading {cache_id} from in-memory cache")
232
  return cache[cache_id]
233
 
234
  # tokenize corpus
235
+ tokenizer = tokenizer_factory.get_tokenizer(tokenizer_name)
236
+ datasets = [load_dataset("eson/cc100-samples", corpus.replace("cc100/", ""), split="train") for corpus in corpuses]
237
+
238
+ stat = {
239
+ "tokenizer": tokenizer_factory.get_name_with_hyperlink(tokenizer_name),
240
+ "organization": tokenizer_factory.get_tokenizer_config(tokenizer_name).org,
241
+ "vocab_size": len(tokenizer),
242
+ }
243
+ tokenize_detail_dir = os.path.join(cache_dir, "compression_rate")
244
+ os.makedirs(tokenize_detail_dir, exist_ok=True)
245
+ tokenize_detail_path = os.path.join(tokenize_detail_dir, cache_id.replace("/", ".") + ".diff.json")
246
+ stat.update(_tokenize(tokenizer, datasets, detail_path=tokenize_detail_path))
247
+ # add basic info
248
 
249
  # save to cache
250
  len_before = len(cache)
251
  cache[cache_id] = stat
252
  len_after = len(cache)
253
+ logger.info(f"saving '{cache_id}' to memory and file cache '{cache_path}': {len_before}->{len_after}")
254
  with open(cache_path, "w", encoding="utf-8") as f_tmp:
255
+ json.dump(cache, f_tmp, ensure_ascii=False, indent=2)
256
  return stat
257
 
258
 
259
  def get_compression_leaderboard(
260
+ corpuses: List[str] = ['cc100/en'],
261
  unit: str = "b_tokens/g_bytes",
262
  tokenizer_filter: Optional[str] = None,
263
  return_type: Optional[Literal["dict", "dataframe"]] = "dataframe"
264
  ) -> Union[pd.DataFrame, dict]:
265
  """
 
 
266
  """
267
  logger.info(f"corpuses: {corpuses}; unit: {unit}; tokenizer_filter: {tokenizer_filter}")
268
  stats = {}
269
  if tokenizer_filter is not None:
270
+ tokenizer_names = [tokenizer_name for tokenizer_name in tokenizer_factory.all_tokenizer_names
271
+ if tokenizer_filter.lower() in tokenizer_name.lower()]
272
  else:
273
+ tokenizer_names = tokenizer_factory.all_tokenizer_names
274
+ for tokenizer_name in tokenizer_names:
275
+ stats_by_corpus = {}
276
+ for corpus in corpuses:
277
+ stats_by_corpus[corpus] = tokenize_corpus(tokenizer_name, [corpus])
278
+ stats[tokenizer_name] = _merge_stats_by_corpus(stats_by_corpus)
279
 
280
  if return_type == "dataframe":
281
  token_number_unit, file_size_unit = unit.split("/")
282
  reverse_unit = f"{file_size_unit}/{token_number_unit}"
283
+ stats = to_dataframe(stats, [unit, reverse_unit, "char/token"])
284
+ stats = stats.sort_values(["oov_ratio", unit], ascending=[True, True])
285
+ stats = stats.rename(columns={"oov_ratio": f' ⬆️oov_ratio'}).rename(columns={unit: f' ⬆️{unit}'}) # ⬇
286
  return stats
287
 
288
 
 
 
 
 
 
 
 
 
 
 
 
 
 
289
  def main():
290
  if len(sys.argv) == 3:
291
  tokenizer_filter = [sys.argv[1]]
 
293
  else:
294
  tokenizer_filter = None
295
  corpuses = common_corpuses
296
+ # tokenizer_filter = "openai"
297
+ # corpuses = ["cc100/en", "cc100/zh-Hans"]
298
+ df = get_compression_leaderboard(corpuses, tokenizer_filter=tokenizer_filter)
299
  # print(df.to_markdown(index=False, tablefmt='fancy_grid'))
300
  logger.info(f"\n{df.to_markdown(index=False)}")
301
 
302
 
303
  if __name__ == "__main__":
304
  main()
 
config.py DELETED
@@ -1,20 +0,0 @@
1
- USE_REMOTE = False # use remote tokenizer or local tokenizer
2
-
3
- # load_vocab_with_SPECIAL_TOKEN = True # 如果不包含会导致计算词典大小错误、overlap_token计算不一致。
4
-
5
- # encoding config
6
- ADD_SPECIAL_TOKEN = False
7
-
8
- #
9
- LAZY_IMPORT = True
10
-
11
- # DEBUG: 设置环境变量 RUST_BACKTRACE=full
12
- #
13
-
14
- default_user_input = """\
15
- Replace this text in the input field to see how tokenization works.
16
- Buenos días!
17
- 华为发布Mate60手机。
18
- ラグビーワールドカップ2023フランス"""
19
- default_tokenizer_type_1 = "llama3"
20
- default_tokenizer_type_2 = "gpt_4"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
patcher/README.md ADDED
@@ -0,0 +1,15 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+
3
+ ## vocabsize不一致问题
4
+
5
+
6
+ - .vcab_size
7
+ - Size of the base vocabulary (without the added tokens)
8
+ - 来自 https://huggingface.co/transformers/v2.11.0/main_classes/tokenizer.html
9
+ - len(tokenizer)
10
+ - Size of the full vocabulary with the added tokens.
11
+ - https://github.com/huggingface/transformers/issues/12632
12
+ - max(tokenizer.get_vocab().values())
13
+ - 包括不连续的 token_id
14
+ - https://github.com/huggingface/transformers/issues/4875
15
+
patcher/sptokenizer_patch_deprecated.py DELETED
@@ -1,105 +0,0 @@
1
- """
2
-
3
- ## adapt to transformer tokenizer
4
-
5
- https://github.com/huggingface/transformers/blob/v4.40.1/src/transformers/tokenization_utils.py#L379
6
-
7
- ## usage
8
-
9
- - grok
10
-
11
- ## 风险评估
12
-
13
- - 可能会干扰 sentencepiece.SentencePieceProcessor的正常使用,比如 .vocab_size 原来是个方法,patch后是个property
14
-
15
-
16
- ## TODO
17
-
18
- 不用patch,改用wrapper。常见的 tokenizer通常是封装的 sentencepiece,
19
- """
20
-
21
- import sentencepiece
22
-
23
-
24
- @property
25
- def vocab_size(self):
26
- """Returns vocab size"""
27
- return self.get_piece_size()
28
-
29
-
30
- def get_vocab(self):
31
- """Returns vocab as a dict"""
32
- vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
33
- # vocab.update(self.added_tokens_encoder)
34
- return vocab
35
-
36
-
37
- def _tokenize(self, text):
38
- """Returns a tokenized string."""
39
- return self.encode(text, out_type=str)
40
-
41
-
42
- def _convert_token_to_id(self, token):
43
- """Converts a token (str) in an id using the vocab."""
44
- return self.piece_to_id(token)
45
-
46
-
47
- def _convert_id_to_token(self, index):
48
- """Converts an index (integer) in a token (str) using the vocab."""
49
- token = self.IdToPiece(index)
50
- return token
51
-
52
-
53
- def convert_ids_to_tokens(self, ids, skip_special_tokens=False):
54
- """ copy from transformers.PreTrainedTokenizer
55
- Converts a single index or a sequence of indices in a token or a sequence of tokens, using the vocabulary and
56
- added tokens.
57
-
58
- Args:
59
- ids (`int` or `List[int]`):
60
- The token id (or token ids) to convert to tokens.
61
- skip_special_tokens (`bool`, *optional*, defaults to `False`):
62
- Whether or not to remove special tokens in the decoding.
63
-
64
- Returns:
65
- `str` or `List[str]`: The decoded token(s).
66
- """
67
- self._added_tokens_decoder = {} # add by xs
68
- if isinstance(ids, int):
69
- if ids in self._added_tokens_decoder:
70
- return self._added_tokens_decoder[ids].content
71
- else:
72
- return self._convert_id_to_token(ids)
73
- tokens = []
74
- for index in ids:
75
- index = int(index)
76
- if skip_special_tokens and index in self.all_special_ids:
77
- continue
78
- if index in self._added_tokens_decoder:
79
- tokens.append(self._added_tokens_decoder[index].content)
80
- else:
81
- tokens.append(self._convert_id_to_token(index))
82
- return tokens
83
-
84
-
85
- def encode(self, *args, **kwargs):
86
- """
87
- add_special_token 是为了兼容 hf_tokenizer
88
- """
89
- kwargs.pop("add_special_tokens", None)
90
- kwargs.pop("allowed_special", None)
91
- return self.Encode(*args, **kwargs)
92
-
93
-
94
- def decode(self, *args, **kwargs):
95
- kwargs.pop("skip_special_tokens", None)
96
- return self.Decode(*args, **kwargs)
97
-
98
-
99
- sentencepiece.SentencePieceProcessor.vocab_size = vocab_size #
100
- sentencepiece.SentencePieceProcessor.get_vocab = get_vocab
101
- sentencepiece.SentencePieceProcessor._convert_id_to_token = _convert_id_to_token
102
- sentencepiece.SentencePieceProcessor.convert_ids_to_tokens = convert_ids_to_tokens
103
- # sentencepiece.SentencePieceProcessor.tokenize = _tokenize
104
- sentencepiece.SentencePieceProcessor.encode = encode
105
- sentencepiece.SentencePieceProcessor.decode = decode
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
patcher/sptokenizer_wrapper.py DELETED
@@ -1,61 +0,0 @@
1
- """ 封装 sentencepiece.SentencePieceProcessor,以便符合transformers中的tokenizer标准
2
-
3
- ## reference
4
-
5
-
6
- ## usage
7
-
8
- - grok
9
-
10
- """
11
-
12
- import sentencepiece as spm
13
- from transformers import PreTrainedTokenizer
14
-
15
-
16
- class SPTokenizerWrapper(PreTrainedTokenizer):
17
- """
18
-
19
- ## impl in PreTrainedTokenizer
20
- - convert_ids_to_tokens
21
- """
22
-
23
- def __init__(self, vocab_file):
24
- self.vocab_file = vocab_file
25
- self.sp_model = spm.SentencePieceProcessor(self.vocab_file)
26
- super().__init__()
27
-
28
- @property
29
- def vocab_size(self):
30
- """Returns vocab size"""
31
- return self.sp_model.get_piece_size()
32
-
33
- def get_vocab(self):
34
- """Returns vocab as a dict"""
35
- vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
36
- return vocab
37
-
38
- def _convert_token_to_id(self, token):
39
- """Converts a token (str) in an id using the vocab."""
40
- return self.sp_model.piece_to_id(token)
41
-
42
- def _convert_id_to_token(self, index):
43
- """Converts an index (integer) in a token (str) using the vocab."""
44
- token = self.sp_model.IdToPiece(index)
45
- return token
46
-
47
- # def (self, ids, skip_special_tokens=False): # impl in PreTrainedTokenizer
48
-
49
-
50
- def encode(self, *args, **kwargs):
51
- kwargs.pop("add_special_tokens", None)
52
- kwargs.pop("allowed_special", None)
53
- return self.sp_model.Encode(*args, **kwargs)
54
-
55
- def decode(self, *args, **kwargs):
56
- kwargs.pop("skip_special_tokens", None)
57
- return self.sp_model.Decode(*args, **kwargs)
58
-
59
-
60
-
61
- # PreTrainedTokenizer.convert_ids_to_tokens
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
patcher/tiktoken_patch.py CHANGED
@@ -70,8 +70,8 @@ def get_vocab(self, token_type="str"):
70
 
71
  @property
72
  def vocab_size(self):
73
- """Returns vocab size"""
74
- return self.n_vocab
75
 
76
 
77
  def encode(self, *args, **kwargs):
 
70
 
71
  @property
72
  def vocab_size(self):
73
+ """Returns vocab size without special tokens"""
74
+ return len(self._mergeable_ranks)
75
 
76
 
77
  def encode(self, *args, **kwargs):
app_playground.py → playground_app.py RENAMED
@@ -36,9 +36,12 @@ table
36
  """
37
 
38
  import gradio as gr
39
- from vocab import all_tokenizers
40
- from util import *
41
- from examples import example_fn, example_types
 
 
 
42
 
43
  get_window_url_params = """
44
  function(url_params) {
@@ -48,6 +51,8 @@ get_window_url_params = """
48
  }
49
  """
50
 
 
 
51
  with gr.Blocks() as demo:
52
  # links: https://www.coderstool.com/utf8-encoding-decoding
53
  # 功能:输入文本,进行分词
@@ -60,6 +65,7 @@ with gr.Blocks() as demo:
60
  example_types,
61
  value="Examples",
62
  type="index",
 
63
  show_label=False,
64
  container=False,
65
  scale=0,
@@ -102,21 +108,26 @@ with gr.Blocks() as demo:
102
  with gr.Column(scale=6):
103
  with gr.Group():
104
  tokenizer_name_1 = gr.Dropdown(
105
- all_tokenizers,
106
  label="Tokenizer 1",
107
  )
108
  with gr.Group():
109
  with gr.Row():
 
 
 
 
 
110
  stats_vocab_size_1 = gr.TextArea(
111
  label="Vocab Size",
112
  lines=1,
113
  elem_classes="statistics"
114
  )
115
- stats_zh_token_size_1 = gr.TextArea(
116
- label="ZH char/word",
117
- lines=1,
118
- elem_classes="statistics",
119
- )
120
  # stats_compress_rate_1 = gr.TextArea(
121
  # label="Compress Rate",
122
  # lines=1,
@@ -140,21 +151,26 @@ with gr.Blocks() as demo:
140
  with gr.Column(scale=6):
141
  with gr.Group():
142
  tokenizer_name_2 = gr.Dropdown(
143
- all_tokenizers,
144
  label="Tokenizer 2",
145
  )
146
  with gr.Group():
147
  with gr.Row():
148
- stats_vocab_size_2 = gr.TextArea(
149
- label="VocabSize",
150
  lines=1,
151
- elem_classes="statistics"
152
  )
153
- stats_zh_token_size_2 = gr.TextArea(
154
- label="ZH char/word", # 中文字/词
155
  lines=1,
156
- elem_classes="statistics",
157
  )
 
 
 
 
 
158
  # stats_compress_rate_2 = gr.TextArea(
159
  # label="Compress Rate",
160
  # lines=1,
@@ -196,7 +212,7 @@ with gr.Blocks() as demo:
196
 
197
  tokenizer_name_1.change(tokenize, [user_input, tokenizer_name_1],
198
  [output_text_1, output_table_1])
199
- tokenizer_name_1.change(basic_count, [tokenizer_name_1], [stats_vocab_size_1, stats_zh_token_size_1])
200
  tokenizer_name_1.change(get_overlap_token_size, [tokenizer_name_1, tokenizer_name_2],
201
  [stats_overlap_token_size_1, stats_overlap_token_size_2])
202
  # tokenizer_type_1.change(get_compress_rate, [tokenizer_type_1, compress_rate_corpus, compress_rate_unit],
@@ -209,7 +225,7 @@ with gr.Blocks() as demo:
209
 
210
  tokenizer_name_2.change(tokenize, [user_input, tokenizer_name_2],
211
  [output_text_2, output_table_2])
212
- tokenizer_name_2.change(basic_count, [tokenizer_name_2], [stats_vocab_size_2, stats_zh_token_size_2])
213
  tokenizer_name_2.change(get_overlap_token_size, [tokenizer_name_1, tokenizer_name_2],
214
  [stats_overlap_token_size_1, stats_overlap_token_size_2])
215
  # tokenizer_type_2.change(get_compress_rate,
@@ -235,7 +251,6 @@ with gr.Blocks() as demo:
235
  [user_input, tokenizer_name_1, tokenizer_name_2]
236
  )
237
 
238
- demo.load(js=open("js/onload.js", "r", encoding="utf-8").read())
239
  demo.load(
240
  fn=on_load,
241
  inputs=[user_input], # 这里只需要传个空object即可。
 
36
  """
37
 
38
  import gradio as gr
39
+ from vocab import tokenizer_factory
40
+ from playground_examples import example_types, example_fn
41
+ from playground_util import tokenize, tokenize_pair, basic_count, get_overlap_token_size, on_load
42
+
43
+
44
+
45
 
46
  get_window_url_params = """
47
  function(url_params) {
 
51
  }
52
  """
53
 
54
+ all_tokenizer_name = [(config.name_display, config.name_or_path) for config in tokenizer_factory.all_tokenizer_configs]
55
+
56
  with gr.Blocks() as demo:
57
  # links: https://www.coderstool.com/utf8-encoding-decoding
58
  # 功能:输入文本,进行分词
 
65
  example_types,
66
  value="Examples",
67
  type="index",
68
+ allow_custom_value=True,
69
  show_label=False,
70
  container=False,
71
  scale=0,
 
108
  with gr.Column(scale=6):
109
  with gr.Group():
110
  tokenizer_name_1 = gr.Dropdown(
111
+ all_tokenizer_name,
112
  label="Tokenizer 1",
113
  )
114
  with gr.Group():
115
  with gr.Row():
116
+ organization_1 = gr.TextArea(
117
+ label="Organization",
118
+ lines=1,
119
+ elem_classes="statistics",
120
+ )
121
  stats_vocab_size_1 = gr.TextArea(
122
  label="Vocab Size",
123
  lines=1,
124
  elem_classes="statistics"
125
  )
126
+ # stats_zh_token_size_1 = gr.TextArea(
127
+ # label="ZH char/word",
128
+ # lines=1,
129
+ # elem_classes="statistics",
130
+ # )
131
  # stats_compress_rate_1 = gr.TextArea(
132
  # label="Compress Rate",
133
  # lines=1,
 
151
  with gr.Column(scale=6):
152
  with gr.Group():
153
  tokenizer_name_2 = gr.Dropdown(
154
+ all_tokenizer_name,
155
  label="Tokenizer 2",
156
  )
157
  with gr.Group():
158
  with gr.Row():
159
+ organization_2 = gr.TextArea(
160
+ label="Organization",
161
  lines=1,
162
+ elem_classes="statistics",
163
  )
164
+ stats_vocab_size_2 = gr.TextArea(
165
+ label="Vocab Size",
166
  lines=1,
167
+ elem_classes="statistics"
168
  )
169
+ # stats_zh_token_size_2 = gr.TextArea(
170
+ # label="ZH char/word", # 中文字/词
171
+ # lines=1,
172
+ # elem_classes="statistics",
173
+ # )
174
  # stats_compress_rate_2 = gr.TextArea(
175
  # label="Compress Rate",
176
  # lines=1,
 
212
 
213
  tokenizer_name_1.change(tokenize, [user_input, tokenizer_name_1],
214
  [output_text_1, output_table_1])
215
+ tokenizer_name_1.change(basic_count, [tokenizer_name_1], [stats_vocab_size_1, organization_1])
216
  tokenizer_name_1.change(get_overlap_token_size, [tokenizer_name_1, tokenizer_name_2],
217
  [stats_overlap_token_size_1, stats_overlap_token_size_2])
218
  # tokenizer_type_1.change(get_compress_rate, [tokenizer_type_1, compress_rate_corpus, compress_rate_unit],
 
225
 
226
  tokenizer_name_2.change(tokenize, [user_input, tokenizer_name_2],
227
  [output_text_2, output_table_2])
228
+ tokenizer_name_2.change(basic_count, [tokenizer_name_2], [stats_vocab_size_2, organization_2])
229
  tokenizer_name_2.change(get_overlap_token_size, [tokenizer_name_1, tokenizer_name_2],
230
  [stats_overlap_token_size_1, stats_overlap_token_size_2])
231
  # tokenizer_type_2.change(get_compress_rate,
 
251
  [user_input, tokenizer_name_1, tokenizer_name_2]
252
  )
253
 
 
254
  demo.load(
255
  fn=on_load,
256
  inputs=[user_input], # 这里只需要传个空object即可。
examples.py → playground_examples.py RENAMED
@@ -19,11 +19,11 @@ https://www.computerhope.com/jargon/s/specchar.htm
19
 
20
  examples = {
21
  "en": [
22
- ["number: (10086 + 98) = 100184", "llama", "bloom"], #
23
- ["whitespace: 2spaces 8spaces\t1tab\t\t2tab\n1newline", "llama", "bert_base_cased"], # chatglm 有blank_n, bert丢掉了空格,
24
  # !?。"#$%&'()*+,-/:;<=>@[\]^_`{|}~⦅⦆「」、、〃》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏.
25
- ["punctuation: ,.:/?+=\",。!?;【】〔〕〖〗", "gemma_7b", "llama"], # llama词典有点小
26
- ["symbol: 🦙❤❥웃유♋☮✊☏☢☚✔☑♚▢♪✈✞÷↑↓▤▥⊙■□▣▽¿─│♥❣▬▫☿Ⓐ ✋✉☣☤", "baichuan", "llama"],
27
  # ["special: [PAD] [UNK] [CLS] [SEP] [MASK] <|system|> <|user|> <|assistant|> <|endoftext|>", "", ""],
28
  ],
29
  "zh": [
@@ -37,16 +37,16 @@ examples = {
37
 
38
  more_examples = [
39
  # bert系列
40
- ("bert_base_cased", "bert_base_uncased", "", ""), # # clue VS kplug, bert VS clue
41
- ("bert_base_cased", "clue", "", "增加了[]()"),
42
- ("clue", "kplug", "", ""),
43
 
44
  # llama系列 (基于sentencepiece)
45
  ("baichuan", "baichuan2", "baichuan2支持多空格 ,多个换行\n\n\n,do not add dummy prefix as Baichuan1"),
46
  ("llama", "baichuan2", "baichuan2支持多空格 ,多个换行\n\n"),
47
- ("llama", "chinese_llama2", ""),
48
  ("llama", "llama3", "扩充词典"),
49
- ("chinese_llama", "chinese_llama2", ""),
50
 
51
  # glm系列 (基于sentencepiece)
52
  ("glm", "chatglm1", ""),
 
19
 
20
  examples = {
21
  "en": [
22
+ ["number: (10086 + 98) = 100184", "huggyllama/llama-7b", "bigscience/bloom"], #
23
+ ["whitespace: 2spaces 8spaces\t1tab\t\t2tab\n1newline", "huggyllama/llama-7b", "google-bert/bert-base-cased"], # chatglm 有blank_n, bert丢掉了空格,
24
  # !?。"#$%&'()*+,-/:;<=>@[\]^_`{|}~⦅⦆「」、、〃》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏.
25
+ ["punctuation: ,.:/?+=\",。!?;【】〔〕〖〗", "google/gemma-7b", "huggyllama/llama-7b"], # llama词典有点小
26
+ ["symbol: 🦙❤❥웃유♋☮✊☏☢☚✔☑♚▢♪✈✞÷↑↓▤▥⊙■□▣▽¿─│♥❣▬▫☿Ⓐ ✋✉☣☤", "baichuan-inc/Baichuan-7B", "huggyllama/llama-7b"],
27
  # ["special: [PAD] [UNK] [CLS] [SEP] [MASK] <|system|> <|user|> <|assistant|> <|endoftext|>", "", ""],
28
  ],
29
  "zh": [
 
37
 
38
  more_examples = [
39
  # bert系列
40
+ ("bert-base-cased", "bert-base-uncased", "", ""), # # clue VS kplug, bert VS clue
41
+ ("bert-base-cased", "clue", "", "增加了[]()"),
42
+ ("roberta-chinese-clue", "kplug", "", ""),
43
 
44
  # llama系列 (基于sentencepiece)
45
  ("baichuan", "baichuan2", "baichuan2支持多空格 ,多个换行\n\n\n,do not add dummy prefix as Baichuan1"),
46
  ("llama", "baichuan2", "baichuan2支持多空格 ,多个换行\n\n"),
47
+ ("llama", "chinese-llama-2-7b", ""),
48
  ("llama", "llama3", "扩充词典"),
49
+ ("chinese-llama-lora-7b", "chinese-llama-2-7b", ""),
50
 
51
  # glm系列 (基于sentencepiece)
52
  ("glm", "chatglm1", ""),
util.py → playground_util.py RENAMED
@@ -1,22 +1,33 @@
1
  import gradio as gr
2
  import json
3
  import pandas as pd
4
- import config
5
- from vocab import load_tokener
6
- from utils.character_util import iter_vocab
7
  from utils.log_util import logger
8
- from utils.compression_util import tokenize_corpus, unit_convertor
9
  from functools import lru_cache
10
 
 
 
 
 
 
 
 
 
11
 
12
  @lru_cache
13
- def tokenize(text, tokenizer_name, color_num=5):
 
 
 
 
 
14
  """
15
  """
16
  logger.info("param=" + json.dumps({"text": text, "tokenizer_type": tokenizer_name}, ensure_ascii=False))
17
  pos_tokens = []
18
- tokenizer = load_tokener(tokenizer_name)
19
- if config.ADD_SPECIAL_TOKEN:
20
  encoding = tokenizer.encode(text, add_special_tokens=True)
21
  else:
22
  encoding = tokenizer.encode(text, add_special_tokens=False)
@@ -34,7 +45,7 @@ def tokenize(text, tokenizer_name, color_num=5):
34
  token_str = token.decode("utf-8")
35
  except:
36
  token_str = token.decode("utf-8", errors="ignore")
37
- logger.error(f"{idx}: decode_error: " + json.dumps( # gpt_35_turbo 经常有token会decode error,这里用来记录一下
38
  {"tokenizer_type": tokenizer_name, "token": str(token), "token_str": token_str},
39
  ensure_ascii=False))
40
 
@@ -45,7 +56,8 @@ def tokenize(text, tokenizer_name, color_num=5):
45
  token_bytes = bytes(token_str, "utf-8")
46
  # json_dumps = json.dumps(token_str)
47
  else:
48
- logger.error(f"{idx}: wrong type for token {token_id} {type(token)} " + json.dumps({"text": text, "tokenizer_type": tokenizer_name}, ensure_ascii=False))
 
49
  token_str = token
50
  token_bytes = token
51
  # continue
@@ -82,30 +94,22 @@ def tokenize_pair(text, tokenizer_type_1, tokenizer_type_2):
82
  @lru_cache
83
  def basic_count(tokenizer_name):
84
  stats = iter_vocab(tokenizer_name)
85
- return stats['vocab_size'], f'{stats["中文token数"]}'
86
  # return tokenizer.vocab_size, f'{stats["中文汉字数"]["中文单字"]}/{stats["中文汉字数"]["中文多字"]}'
87
 
88
- def get_compress_rate(tokenizer_type, all_corpus, unit):
89
- tokenizer = load_tokener(tokenizer_type)
90
- compress_rate_stats = tokenize_corpus(tokenizer, all_corpus)
91
- compress_rate = unit_convertor(compress_rate_stats, unit)
92
- return compress_rate
93
 
94
- # def get_all_compress_rate(corpuses, unit):
95
- # stats = {}
96
- # for lang in corpuses:
97
- # print("###" * 10 + lang)
98
- # for tokenizer_name in tokenizers:
99
- # tokenizer = load_tokener(tokenizer_name)
100
- # stat = tokenize_corpus(tokenizer, [lang])
101
- # stats[tokenizer_name] = stat
102
- # pprint(stats)
103
 
104
 
105
  @lru_cache
106
- def get_overlap_token_size(tokenizer_type_1, tokenizer_type_2):
107
- tokenizer1 = load_tokener(tokenizer_type_1)
108
- tokenizer2 = load_tokener(tokenizer_type_2)
109
 
110
  vocab_set_1 = tokenizer1.get_vocab().keys()
111
  vocab_set_2 = tokenizer2.get_vocab().keys()
@@ -121,11 +125,10 @@ def get_overlap_token_size(tokenizer_type_1, tokenizer_type_2):
121
  overlap_tokens = vocab_set_1 & vocab_set_2
122
  overlap_token_size = len(overlap_tokens)
123
  logger.info(
124
- f"{overlap_token_size} OverlapTokens of {tokenizer_type_1} {tokenizer_type_2}: {list(overlap_tokens)[:10]}")
125
  return overlap_token_size, overlap_token_size
126
 
127
 
128
-
129
  def on_load(url_params, request: gr.Request):
130
  """
131
  onLoad
@@ -148,15 +151,16 @@ def on_load(url_params, request: gr.Request):
148
  # if "referer" in request.headers: # not work for huggingface-space
149
  # url_params = parse_qs(urlparse(request.headers["referer"]).query)
150
  # url_params = {k: v[0] for k, v in url_params.items() if len(v) > 0}
151
- tokenizer_type_1 = url_params.get("tokenizer1", config.default_tokenizer_type_1)
152
- tokenizer_type_2 = url_params.get("tokenizer2", config.default_tokenizer_type_2)
153
- text = url_params.get("text", config.default_user_input)
154
  logger.info(f"client_ip: {client_ip}; params: {url_params}")
155
  return text, tokenizer_type_1, tokenizer_type_2
156
 
157
 
158
- def compress_rate_unit_change(unit):
159
- return gr.update(label=f"Compress Rate: {unit}"), gr.update(label=f"Compress Rate: {unit}"),
 
160
 
161
  def test_coding():
162
  bytes1 = b'\xe4\xb8\xad'
@@ -164,5 +168,5 @@ def test_coding():
164
 
165
 
166
  if __name__ == "__main__":
167
- print(get_overlap_token_size("gpt_35_turbo", "gpt_4"))
168
  # print(basic_count("internlm_chat_7b"))
 
1
  import gradio as gr
2
  import json
3
  import pandas as pd
4
+ from vocab import tokenizer_factory
5
+ from character_util import iter_vocab
 
6
  from utils.log_util import logger
 
7
  from functools import lru_cache
8
 
9
+ default_user_input = """\
10
+ Replace this text in the input field to see how tokenization works.
11
+ Buenos días!
12
+ 华为发布Mate60手机。
13
+ ラグビーワールドカップ2023フランス"""
14
+ # default_tokenizer_name_1 = "Meta/llama3"
15
+ default_tokenizer_name_1 = "gradientai/Llama-3-8B-Instruct-Gradient-1048k"
16
+ default_tokenizer_name_2 = "openai/gpt-4"
17
 
18
  @lru_cache
19
+ def tokenize(
20
+ text: str,
21
+ tokenizer_name: str,
22
+ color_num: int = 5,
23
+ add_special_token: bool = False
24
+ ):
25
  """
26
  """
27
  logger.info("param=" + json.dumps({"text": text, "tokenizer_type": tokenizer_name}, ensure_ascii=False))
28
  pos_tokens = []
29
+ tokenizer = tokenizer_factory.get_tokenizer(tokenizer_name)
30
+ if add_special_token:
31
  encoding = tokenizer.encode(text, add_special_tokens=True)
32
  else:
33
  encoding = tokenizer.encode(text, add_special_tokens=False)
 
45
  token_str = token.decode("utf-8")
46
  except:
47
  token_str = token.decode("utf-8", errors="ignore")
48
+ logger.error(f"{idx}: decode_error: " + json.dumps( # gpt_35_turbo 经常有token会decode error,这里用来记录一下
49
  {"tokenizer_type": tokenizer_name, "token": str(token), "token_str": token_str},
50
  ensure_ascii=False))
51
 
 
56
  token_bytes = bytes(token_str, "utf-8")
57
  # json_dumps = json.dumps(token_str)
58
  else:
59
+ logger.error(f"{idx}: wrong type for token {token_id} {type(token)} " + json.dumps(
60
+ {"text": text, "tokenizer_type": tokenizer_name}, ensure_ascii=False))
61
  token_str = token
62
  token_bytes = token
63
  # continue
 
94
  @lru_cache
95
  def basic_count(tokenizer_name):
96
  stats = iter_vocab(tokenizer_name)
97
+ return stats['vocab_size'], f'{stats["organization"]}'
98
  # return tokenizer.vocab_size, f'{stats["中文汉字数"]["中文单字"]}/{stats["中文汉字数"]["中文多字"]}'
99
 
 
 
 
 
 
100
 
101
+ # def get_compress_rate(tokenizer_name, all_corpus, unit):
102
+ # tokenizer = tokenizer_factory.get_tokenizer(tokenizer_name)
103
+ # compress_rate_stats = tokenize_corpus(tokenizer, all_corpus)
104
+ # compress_rate = unit_convertor(compress_rate_stats, unit)
105
+ # return compress_rate
106
+
 
 
 
107
 
108
 
109
  @lru_cache
110
+ def get_overlap_token_size(tokenizer_name_1, tokenizer_name_2):
111
+ tokenizer1 = tokenizer_factory.get_tokenizer(tokenizer_name_1)
112
+ tokenizer2 = tokenizer_factory.get_tokenizer(tokenizer_name_2)
113
 
114
  vocab_set_1 = tokenizer1.get_vocab().keys()
115
  vocab_set_2 = tokenizer2.get_vocab().keys()
 
125
  overlap_tokens = vocab_set_1 & vocab_set_2
126
  overlap_token_size = len(overlap_tokens)
127
  logger.info(
128
+ f"{overlap_token_size} OverlapTokens of {tokenizer_name_1} {tokenizer_name_2}: {list(overlap_tokens)[:10]}")
129
  return overlap_token_size, overlap_token_size
130
 
131
 
 
132
  def on_load(url_params, request: gr.Request):
133
  """
134
  onLoad
 
151
  # if "referer" in request.headers: # not work for huggingface-space
152
  # url_params = parse_qs(urlparse(request.headers["referer"]).query)
153
  # url_params = {k: v[0] for k, v in url_params.items() if len(v) > 0}
154
+ tokenizer_type_1 = url_params.get("tokenizer1", default_tokenizer_name_1)
155
+ tokenizer_type_2 = url_params.get("tokenizer2", default_tokenizer_name_2)
156
+ text = url_params.get("text", default_user_input)
157
  logger.info(f"client_ip: {client_ip}; params: {url_params}")
158
  return text, tokenizer_type_1, tokenizer_type_2
159
 
160
 
161
+ # def compress_rate_unit_change(unit):
162
+ # return gr.update(label=f"Compress Rate: {unit}"), gr.update(label=f"Compress Rate: {unit}"),
163
+
164
 
165
  def test_coding():
166
  bytes1 = b'\xe4\xb8\xad'
 
168
 
169
 
170
  if __name__ == "__main__":
171
+ print(get_overlap_token_size("gpt-35-turbo", "gpt-4"))
172
  # print(basic_count("internlm_chat_7b"))
requirements.txt CHANGED
@@ -6,4 +6,6 @@ torch
6
  zhon
7
  nltk
8
  boto3
9
- ai2-olmo==0.2.4
 
 
 
6
  zhon
7
  nltk
8
  boto3
9
+ ai2-olmo
10
+ ipadic
11
+ fugashi
stats/character_stats.json ADDED
@@ -0,0 +1,1712 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "FacebookAI/xlm-roberta-base": {
3
+ "tokenizer": "<a target=\"_blank\" href=\"https://huggingface.co/FacebookAI/xlm-roberta-base\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">xlm-roberta-base</a>",
4
+ "organization": "Facebook",
5
+ "vocab_size": 250002,
6
+ "num(digit)": 2728,
7
+ "len(digit)": "1,3,9",
8
+ "num(space)": 1,
9
+ "len(space)": "1,1,1",
10
+ "num(ar)": 14644,
11
+ "len(ar)": "1,4,16",
12
+ "num(zh)": 18457,
13
+ "len(zh)": "1,2,16",
14
+ "num(ja)": 20572,
15
+ "len(ja)": "1,2,16",
16
+ "num(ja-kana)": 3434,
17
+ "len(ja-kana)": "1,3,12",
18
+ "num(ko)": 5373,
19
+ "len(ko)": "1,2,8"
20
+ },
21
+ "clue/roberta_chinese_clue_tiny": {
22
+ "tokenizer": "<a target=\"_blank\" href=\"https://huggingface.co/clue/roberta_chinese_clue_tiny\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">roberta-chinese-clue</a>",
23
+ "organization": "CLUE",
24
+ "vocab_size": 8021,
25
+ "num(digit)": 230,
26
+ "len(digit)": "1,4,10",
27
+ "num(space)": 0,
28
+ "len(space)": "-",
29
+ "num(ar)": 30,
30
+ "len(ar)": "1,2,3",
31
+ "num(zh)": 5689,
32
+ "len(zh)": "1,1,1",
33
+ "num(ja)": 5691,
34
+ "len(ja)": "1,1,3",
35
+ "num(ja-kana)": 0,
36
+ "len(ja-kana)": "-",
37
+ "num(ko)": 0,
38
+ "len(ko)": "-"
39
+ },
40
+ "dbmdz/bert-base-german-uncased": {
41
+ "tokenizer": "<a target=\"_blank\" href=\"https://huggingface.co/dbmdz/bert-base-german-uncased\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">bert-base-german-uncased</a>",
42
+ "organization": "dbmdz",
43
+ "vocab_size": 31102,
44
+ "num(digit)": 1733,
45
+ "len(digit)": "1,4,12",
46
+ "num(space)": 0,
47
+ "len(space)": "-",
48
+ "num(ar)": 0,
49
+ "len(ar)": "-",
50
+ "num(zh)": 0,
51
+ "len(zh)": "-",
52
+ "num(ja)": 0,
53
+ "len(ja)": "-",
54
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- "vocab_size": 32001,
4234
- "n_bytes": 1524839,
4235
- "n_tokens": 964428,
4236
- "n_chars": 655190
4237
- },
4238
- "wizardlm_7b_v1.cc100-ko": {
4239
- "vocab_size": 32001,
4240
- "n_bytes": 1524839,
4241
- "n_tokens": 964428,
4242
- "n_chars": 655190
4243
- },
4244
- "wizardmath_70b_v1.cc100-ko": {
4245
- "vocab_size": 32002,
4246
- "n_bytes": 1524839,
4247
- "n_tokens": 964428,
4248
- "n_chars": 655190
4249
- },
4250
- "xlm_roberta.cc100-ko": {
4251
- "vocab_size": 250002,
4252
- "n_bytes": 1524839,
4253
- "n_tokens": 374571,
4254
- "n_chars": 655190
4255
- },
4256
- "yi_34b.cc100-ko": {
4257
- "vocab_size": 64000,
4258
- "n_bytes": 1524839,
4259
- "n_tokens": 1203134,
4260
- "n_chars": 655190
4261
- },
4262
- "yi_6b.cc100-ko": {
4263
- "vocab_size": 64000,
4264
- "n_bytes": 1524839,
4265
- "n_tokens": 1203134,
4266
- "n_chars": 655190
4267
- },
4268
- "yi_vl34b.cc100-ko": {
4269
- "vocab_size": 64000,
4270
- "n_bytes": 1524839,
4271
- "n_tokens": 1210021,
4272
- "n_chars": 655190
4273
- },
4274
- "zephyr_7b_beta.cc100-ko": {
4275
- "vocab_size": 32000,
4276
- "n_bytes": 1524839,
4277
- "n_tokens": 728766,
4278
- "n_chars": 655190
4279
- },
4280
- "llama_3_chinese_8b.cc100-zh-Hans": {
4281
- "vocab_size": 128256,
4282
- "n_bytes": 2633047,
4283
- "n_tokens": 757405,
4284
- "n_chars": 927311
4285
- }
4286
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
stats/compression_rate.json ADDED
The diff for this file is too large to render. See raw diff
 
utils/byte_util.py DELETED
File without changes
utils/character_util.py DELETED
@@ -1,231 +0,0 @@
1
- """
2
- TODO: 繁体、简体、语种、
3
- """
4
- import os
5
- import json
6
- from collections import Counter
7
- from vocab import load_tokener
8
- from utils.log_util import logger
9
- from utils.text_util import is_all_digit, has_digit, get_digit_count, get_space_count
10
- from utils.lang_util import detect_language
11
- from utils.lang_util_2 import is_zh_char, is_all_zh, get_zh_count
12
-
13
- CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
14
-
15
- zh_tokens = [line.strip() for line in open(os.path.join(CURRENT_DIR, "vocab.jd.txt.v2"), "r", encoding="utf-8") if
16
- is_zh_char(line.strip())]
17
-
18
-
19
- def digit_():
20
- """
21
- qwen segments numbers by single digits.
22
- """
23
- pass
24
-
25
-
26
- def to_unicode(text):
27
- return ''.join(r'\u{:04X}'.format(ord(chr)) for chr in text)
28
-
29
- def zh_iterator():
30
- for idx in range(ord(u'\u4e00'), ord(u'\u9fa5')):
31
- yield (chr(idx))
32
-
33
-
34
- def get_coding_length(tokenizer, vocab, filter=None):
35
- """
36
- 计算编码长度。(有些中文汉字被解码成多个token)
37
- """
38
- all_length = []
39
- for word in vocab:
40
- if len(word) > 1:
41
- continue
42
- if filter is not None and filter(word):
43
- continue
44
- try:
45
- tokens = tokenizer.encode(word)
46
- except Exception as e:
47
- print(e)
48
-
49
- all_length.append(len(tokens))
50
- # if len(tokens.ids) > 1:
51
- # if len(tokens) > 3:
52
- # print(word, tokens)
53
-
54
- dist_length = Counter(all_length)
55
- mean_length = round(sum(all_length) / len(all_length), 2)
56
- return dist_length, mean_length
57
-
58
-
59
-
60
- def remove_special_char():
61
- """
62
- :return:
63
- """
64
- # bert词典有 ##开头的
65
- # byteBPE词典有带空格的
66
- # decode_str = decode_str.strip().replace("#", "") # TODO, 按类型
67
- pass
68
-
69
-
70
- cache = {}
71
-
72
- def _mean(datas):
73
- return sum(datas) / len(datas)
74
-
75
- def iter_vocab(tokenizer_name, from_cache=True, cache_dir="stats/iter_vocab"):
76
- """
77
- 由于速度较快,建议不采用文件缓存。
78
- :param tokenizer:
79
- :param from_cache:
80
- :return:
81
- """
82
- cache_dir = os.path.join(CURRENT_DIR, f"../{cache_dir}")
83
- os.makedirs(cache_dir, exist_ok=True)
84
-
85
- tokenizer = load_tokener(tokenizer_name)
86
-
87
-
88
- # load from cache
89
- if from_cache and tokenizer_name in cache:
90
- logger.info(f"load {tokenizer_name} from cache")
91
- return cache[tokenizer_name]
92
-
93
- has_zh_tokens = []
94
- all_zh_tokens = []
95
- has_digit_tokens = []
96
- all_digit_tokens = []
97
- has_space_tokens = []
98
- all_space_tokens = []
99
-
100
- # zh_tags = ["all_zh", "has_zh"]
101
- # digit_tags = ["all_digit", "has_digit"]
102
-
103
- # zh_token_count = {"total": 0, "包含1个中文单字": 0, "中文多字": 0}
104
-
105
- # symbol_count = 0
106
-
107
- all_single_zh_tokens = set()
108
- zh_symbol_count = 0
109
- buffer = []
110
- for token_id in range(tokenizer.vocab_size):
111
- decode_str = tokenizer.decode([token_id], skip_special_tokens=False)
112
- token = tokenizer.convert_ids_to_tokens([token_id], skip_special_tokens=False)[0]
113
- # tokenizer.convert_tokens_to_string(tokens)
114
-
115
- tags = []
116
-
117
- if token is None: # 有些词典有空的id(不连续)
118
- continue
119
- if isinstance(token, bytes):
120
- token = token.decode("utf-8", errors="ignore")
121
-
122
- digit_count = get_digit_count(decode_str)
123
- language_tags = detect_language(decode_str)
124
-
125
- if "Chinese" in language_tags:
126
- has_zh_tokens.append(decode_str)
127
-
128
- if is_all_zh(decode_str):
129
- tags.append("all_zh")
130
- all_zh_tokens.append(decode_str)
131
-
132
-
133
- if is_all_digit(decode_str):
134
- tags.append("all_digit")
135
- all_digit_tokens.append(decode_str)
136
- if has_digit(decode_str):
137
- tags.append("has_digit")
138
- has_digit_tokens.append(decode_str)
139
-
140
-
141
- space_count = get_space_count(decode_str)
142
- if space_count > 0:
143
- has_space_tokens.append(decode_str)
144
- if space_count == len(decode_str):
145
- all_space_tokens.append(decode_str)
146
-
147
- zh_count = get_zh_count(decode_str)
148
-
149
- buffer.append(json.dumps(
150
- {"id": token_id,
151
- "token": token,
152
- "token_decode": decode_str,
153
- "token_dumps": json.dumps(token),
154
- "token_unicode": to_unicode(token),
155
- "token_len": len(decode_str),
156
- "zh_count": zh_count, # 包含汉字的数目
157
- # "zh-smpli": zh_hans_count, # 简体中文 zh-Hans
158
- "tags": tags,
159
- "zh_symbol_count": zh_symbol_count,
160
- },
161
- ensure_ascii=False) + "\n")
162
-
163
- # if zh_count >= 1:
164
- # zh_token_count["total"] += 1
165
- # if zh_count > 1:
166
- # zh_token_count["中文多字"] += 1
167
- # else:
168
- # zh_token_count["中文单字"] += 1
169
- # all_single_zh_tokens.add(decode_str.strip().replace("#", ""))
170
- #
171
- # zh_token_count["中文单字-去重���"] = len(all_single_zh_tokens)
172
-
173
- dist_length, mean_length = get_coding_length(tokenizer, zh_tokens, filter=lambda k: not is_zh_char(k))
174
-
175
- # TODO: 繁体字,简体字
176
-
177
- result = {
178
- "name": tokenizer_name,
179
- "impl": str(tokenizer.__class__),
180
- "vocab_size": len(tokenizer),
181
- "中文token数": len(has_zh_tokens),
182
- "中文token的平均长度": None,
183
- "纯中文token的平均长度": None,
184
- "中文标点数": zh_symbol_count,
185
- "中文汉字编码长度均值": mean_length,
186
- "中文汉字编码长度分布": json.dumps(dist_length),
187
- "纯数字token数": len(all_digit_tokens),
188
- "包含数字token数": len(has_digit_tokens),
189
- "纯数字token的平均长度": round(_mean([len(item) for item in all_digit_tokens]), 2),
190
- "纯中文token数": None, # all_zh
191
- "纯space的token数": len(all_space_tokens),
192
- "纯space的token数": len(all_space_tokens), # "#"
193
- "纯space的token的平均长度": None, # avg_len( tokens_contains_space)
194
- "contains_korea": None,
195
- }
196
- out_path = os.path.join(cache_dir, f"{tokenizer_name}.vocab.jsonl")
197
- logger.info(f"saving vocab to {out_path}")
198
- with open(out_path, "w", encoding="utf-8") as f_out:
199
- f_out.write(json.dumps(result, ensure_ascii=False) + "\n")
200
- for line in buffer:
201
- f_out.write(line)
202
- cache[tokenizer_name] = result
203
- return result
204
-
205
-
206
-
207
-
208
-
209
-
210
- if __name__ == "__main__":
211
- # test_coding_length(jd_vocab_tokens, filter=lambda k: not is_chinese(k))
212
- # test_coding_length(zh_punc)
213
- # test_coding_length(zh_iterator())
214
-
215
- # from vocab.chatglm2_6b import tokenizer; name = "chatglm2_6b"
216
- # from vocab.chatglm_6b import tokenizer; name="chatglm_6b"
217
- # from vocab.baichuan2 import tokenizer; name="baichuan2"
218
- name="gpt_4"
219
- # name="gpt2"
220
- # name="qwen1_5_14b_chat"
221
- # name="gpt_nexo_20b"
222
- # name="fastchat_t5_3b"
223
-
224
-
225
- print(iter_vocab(name))
226
-
227
-
228
-
229
-
230
-
231
-
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
utils/convert_sp_to_json.py DELETED
@@ -1,4 +0,0 @@
1
-
2
- from vocab.baichuan_7b import tokenizer
3
-
4
- tokenizer.sp
 
 
 
 
 
utils/fn_util.py DELETED
File without changes
utils/lang_util.py CHANGED
@@ -18,43 +18,39 @@ import re
18
  # 由于大部分是'latin',所以就不统计了。
19
  common = ['Chinese', 'Japanese-Kana', 'Korean', 'Arabic', 'number']
20
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
21
  def detect_language(s):
22
  # 定义各语言字符的Unicode范围
23
- language_ranges = {
24
- 'Arabic': r'[\u0600-\u06FF\u0750-\u077F\u08A0-\u08FF\uFB50-\uFDFF\uFE70-\uFEFF]',
25
- # 'CJK' https://en.wikipedia.org/wiki/CJK_Unified_Ideographs
26
- 'Chinese': r'[\u4e00-\u9fff]',
27
- 'Japanese': r'[\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF]', # https://stackoverflow.com/questions/19899554/unicode-range-for-japanese
28
- 'Japanese-Kana': r'[\u3040-\u309F\u30A0-\u30FF]', # Hiragana & Katakana
29
- # 'Korean': r'[\uac00-\ud7a3]',
30
- 'Hangul': r'[\uac00-\ud7a3]',
31
-
32
-
33
- # 拉丁字母系列
34
- 'Latin': r'[\u0000-\u007F\u0080-\u00FF]',
35
- 'English': r'[A-Za-z]', # 这可能会与其他使用基本拉丁字母的语言重叠
36
- 'French': r'[\u00C0-\u00FF]',
37
- 'German': r'[\u00C4\u00D6\u00DC\u00E4\u00F6\u00FC\u00DF]',
38
- 'Spanish-': r'[\u00C1\u00E1\u00C9\u00E9\u00CD\u00ED\u00D3\u00F3\u00DA\u00FA\u00D1\u00F1\u00FC]', # 西班牙语特有字符集合
39
-
40
-
41
- # 斯拉夫语族
42
- 'Cyrillic': r'[\u0400-\u04FF\u0500-\u052F\u2DE0-\u2DFF\uA640-\uA69F]',
43
-
44
- #
45
- 'Greek': r'[\u0370-\u03FF\u1F00-\u1FFF]', # 希腊字母
46
- 'Hebrew': r'[\u0590-\u05FF\uFB1D-\uFB4F]', # 希伯来语
47
-
48
-
49
- }
50
-
51
  detected_languages = []
52
-
53
  for language, pattern in language_ranges.items():
54
  if re.search(pattern, s):
55
  detected_languages.append(language)
56
 
57
- return detected_languages if detected_languages else ['Unknown']
58
 
59
 
60
  if __name__ == "__main__":
 
18
  # 由于大部分是'latin',所以就不统计了。
19
  common = ['Chinese', 'Japanese-Kana', 'Korean', 'Arabic', 'number']
20
 
21
+ language_ranges = {
22
+ ('Arabic', 'ar'): r'[\u0600-\u06FF\u0750-\u077F\u08A0-\u08FF\uFB50-\uFDFF\uFE70-\uFEFF]',
23
+ # 'CJK' https://en.wikipedia.org/wiki/CJK_Unified_Ideographs
24
+ ('Chinese', 'zh'): r'[\u4e00-\u9fff]',
25
+ ('Japanese', 'ja'): r'[\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF]',
26
+ # https://stackoverflow.com/questions/19899554/unicode-range-for-japanese
27
+ # Kana type refers to Japanese hiragana and katakana characters that represent phonetic sounds in the Japanese language.
28
+ ('Japanese-Kana', 'ja-kana'): r'[\u3040-\u309F\u30A0-\u30FF]', # Hiragana & Katakana
29
+ ('Korean', 'ko'): r'[\uac00-\ud7a3]',
30
+
31
+ # 拉丁字母系列
32
+ # ('Latin', 'la'): r'[\u0000-\u007F\u0080-\u00FF]',
33
+ # ('English', 'en'): r'[A-Za-z]', # 这可能会与其他使用基本拉丁字母的语言重叠
34
+ # ('French', 'fr'): r'[\u00C0-\u00FF]',
35
+ # ('German', 'de'): r'[\u00C4\u00D6\u00DC\u00E4\u00F6\u00FC\u00DF]',
36
+ # ('Spanish-特有'): r'[\u00C1\u00E1\u00C9\u00E9\u00CD\u00ED\u00D3\u00F3\u00DA\u00FA\u00D1\u00F1\u00FC]', # 西班牙语特有字符集合
37
+
38
+ # 斯拉夫语系列
39
+ # ('Cyrillic', ''): r'[\u0400-\u04FF\u0500-\u052F\u2DE0-\u2DFF\uA640-\uA69F]',
40
+
41
+ #
42
+ # 'Greek': r'[\u0370-\u03FF\u1F00-\u1FFF]', # 希腊字母
43
+ # 'Hebrew': r'[\u0590-\u05FF\uFB1D-\uFB4F]', # 希伯来语
44
+ }
45
+
46
  def detect_language(s):
47
  # 定义各语言字符的Unicode范围
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
48
  detected_languages = []
 
49
  for language, pattern in language_ranges.items():
50
  if re.search(pattern, s):
51
  detected_languages.append(language)
52
 
53
+ return detected_languages
54
 
55
 
56
  if __name__ == "__main__":
utils/lang_util_2.py DELETED
@@ -1,115 +0,0 @@
1
- """
2
- 日语、韩语 等
3
- https://www.cnblogs.com/luoganttcc/p/16605150.html
4
- https://zhuanlan.zhihu.com/p/618684374
5
- - https://zhuanlan.zhihu.com/p/84625185 赞
6
-
7
-
8
- ## 相关包
9
-
10
- import opencc
11
- import langid
12
- imort langdetect
13
- https://github.com/pemistahl/lingua-py
14
- - 原理:
15
-
16
-
17
- """
18
-
19
-
20
-
21
- from zhon.hanzi import punctuation as zh_punc
22
-
23
- def is_zh_char(uchar):
24
- """
25
- https://github.com/fxsjy/jieba/blob/master/jieba/__init__.py#L48
26
- re.compile("([\u4E00-\u9FD5]+)", re.U)
27
- """
28
- return u'\u4e00' <= uchar <= u'\u9fa5'
29
-
30
- def has_zh_punc(text):
31
- """
32
- 是否包含中文标点
33
- """
34
- return any(ch in zh_punc for ch in text)
35
-
36
-
37
- def has_zh(text):
38
- """ contains Chinese characters """
39
- return any(is_zh_char(ch) for ch in text)
40
-
41
-
42
- def get_zh_count(text):
43
- return sum([is_zh_char(uchar) for uchar in text])
44
-
45
-
46
- def is_all_zh(text):
47
- return all(is_zh_char(char) for char in text)
48
-
49
-
50
- def is_all_en(text):
51
- return text.encode('utf-8').isalpha()
52
-
53
-
54
-
55
-
56
- ranges = [
57
- {"from": ord(u"\u3300"), "to": ord(u"\u33ff")}, # compatibility ideographs
58
- {"from": ord(u"\ufe30"), "to": ord(u"\ufe4f")}, # compatibility ideographs
59
- {"from": ord(u"\uf900"), "to": ord(u"\ufaff")}, # compatibility ideographs
60
- {"from": ord(u"\U0002F800"), "to": ord(u"\U0002fa1f")}, # compatibility ideographs
61
- {'from': ord(u'\u3040'), 'to': ord(u'\u309f')}, # Japanese Hiragana 日本平假名 96个
62
- {"from": ord(u"\u30a0"), "to": ord(u"\u30ff")}, # Japanese Katakana 日语片假名 96个
63
- {"from": ord(u"\u2e80"), "to": ord(u"\u2eff")}, # cjk radicals supplement
64
- {"from": ord(u"\u4e00"), "to": ord(u"\u9fff")}, # 中文 u"\u4e00"-'\u9fa5',
65
- {"from": ord(u"\u3400"), "to": ord(u"\u4dbf")}, #
66
- {"from": ord(u"\U00020000"), "to": ord(u"\U0002a6df")},
67
- {"from": ord(u"\U0002a700"), "to": ord(u"\U0002b73f")},
68
- {"from": ord(u"\U0002b740"), "to": ord(u"\U0002b81f")},
69
- {"from": ord(u"\U0002b820"), "to": ord(u"\U0002ceaf")} # included as of Unicode 8.0
70
- ]
71
-
72
- # 韩语 [\uac00-\ud7ff]
73
-
74
-
75
- def is_cjk(char):
76
- """
77
- CJK(Chinese、Japanese、Korean)
78
- 日语中有很多汉字,日本汉字超过2万。
79
- 韩语有谚文,超过50个,有朝鲜汉字超过2万。
80
- """
81
- return any([range["from"] <= ord(char) <= range["to"] for range in ranges])
82
-
83
-
84
- def cjk_substrings(string):
85
- i = 0
86
- while i < len(string):
87
- if is_cjk(string[i]):
88
- start = i
89
- while is_cjk(string[i]): i += 1
90
- yield string[start:i]
91
- i += 1
92
-
93
-
94
- def aa():
95
- # string = "sdf344asfasf天地方益3権sdfsdf".decode("utf-8")
96
- for idx, item in enumerate(ranges):
97
- print(idx, end=": ")
98
- for j in range(10):
99
- print(chr(item["from"] + j), end=", ")
100
- print("")
101
- # for sub in cjk_substrings(string):
102
- # string = string.replace(sub, "(" + sub + ")")
103
- # print(string)
104
-
105
-
106
- def is_traditional_chinese(text):
107
- cc = opencc.OpenCC('t2s')
108
- converted_text = cc.convert(text)
109
- if converted_text != text:
110
- return True
111
- return False
112
-
113
-
114
-
115
- # aa()
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
utils/oov.md ADDED
@@ -0,0 +1,202 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ ```sh
3
+ ###################################
4
+ ClueAI/ChatYuan-large-v2, <class 'tokenizers.models.Unigram'>
5
+ reversible: false; unk_token: <unk>, 2, unk_ratio: 0.2000; oov: []
6
+ text[7] = "Замглавы управления развития; 특히 주소 15~17번 홀에선 3연속; 確実に春が近づいてること; a közoktatással? _ Belföld; pumë, i vjetër, vjeç; ئەردوغان ۋە قىرغىزىستان ; निम्न में से कौन सा हारडवेयर; ተለዋዋጭ የግድግዳ ; Дзейныя асобы:; « અમરેલીનાં મહિલા વિકાસ; \t\n\r🦙❤❥웃유♋☮✊;װיקיװערטערבוך ",
7
+ decoding[7] = "<unk>амглав<unk> у<unk>равления развития; <unk> <unk> 15~17<unk> <unk> 3<unk>; 確実に春が近づいてること; a közoktatással? _ Belföld; pum<unk>, i vjet<unk>r, vjeç; <unk>ا<unk> <unk> <unk>ا<unk> ; <unk> <unk> <unk> <unk> <unk> <unk>; <unk> <unk> ; <unk>зейн<unk>я асо<unk>:; <unk> <unk> <unk> <unk>; <unk>;<unk>"
8
+
9
+
10
+ ###################################
11
+ ClueAI/PromptCLUE-base, <class 'tokenizers.models.Unigram'>
12
+ reversible: false; unk_token: <unk>, 2, unk_ratio: 0.2000; oov: []
13
+ text[7] = "Замглавы управления развития; 특히 주소 15~17번 홀에선 3연속; 確実に春が近づいてること; a közoktatással? _ Belföld; pumë, i vjetër, vjeç; ئەردوغان ۋە قىرغىزىستان ; निम्न में से कौन सा हारडवेयर; ተለዋዋጭ የግድግዳ ; Дзейныя асобы:; « અમરેલીનાં મહિલા વિકાસ; \t\n\r🦙❤❥웃유♋☮✊;װיקיװערטערבוך ",
14
+ decoding[7] = "<unk>амглав<unk> у<unk>равления развития; <unk> <unk> 15~17<unk> <unk> 3<unk>; 確実に春が近づいてること; a közoktatással? _ Belföld; pum<unk>, i vjet<unk>r, vjeç; <unk>ا<unk> <unk> <unk>ا<unk> ; <unk> <unk> <unk> <unk> <unk> <unk>; <unk> <unk> ; <unk>зейн<unk>я асо<unk>:; <unk> <unk> <unk> <unk>; <unk>;<unk>"
15
+ ###################################
16
+ CohereForAI/aya-101, <class 'tokenizers.models.Unigram'>
17
+ reversible: false; unk_token: <unk>, 2, unk_ratio: 0.0079; oov: []
18
+ text[73] = " a közoktatással? _ Belföld; pumë, i vjetër, vjeç; ئەردوغان ۋە قىرغىزىستان ; निम्न में से कौन सा हारडवेयर; ተለዋዋጭ የግድግዳ ; Дзейныя асобы:; « અમરેલીનાં મહિલા વિકાસ; \t\n\r🦙❤❥웃유♋☮✊;װיקיװערטערבוך ",
19
+ decoding[73] = "a közoktatással? _ Belföld; pumë, i vjetër, vjeç; ئەردوغان ۋە قىرغىزىستان ; निम्न में से कौन सा हारडवेयर; ተለዋዋጭ የግድግዳ ; Дзейныя асобы:; « અમરેલીનાં મહિલા વિકાસ; <unk>❤❥웃유♋☮✊;װיקיװערטערבוך"
20
+ ###################################
21
+ FacebookAI/xlm-roberta-base, <class 'tokenizers.models.Unigram'>
22
+ reversible: false; unk_token: <unk>, 3, unk_ratio: 0.0096; oov: []
23
+ text[73] = " a közoktatással? _ Belföld; pumë, i vjetër, vjeç; ئەردوغان ۋە قىرغىزىستان ; निम्न में से कौन सा हारडवेयर; ተለዋዋጭ የግድግዳ ; Дзейныя асобы:; « અમરેલીનાં મહિલા વિકાસ; \t\n\r🦙❤❥웃유♋☮✊;װיקיװערטערבוך ",
24
+ decoding[73] = "a közoktatással? _ Belföld; pumë, i vjetër, vjeç; ئەردوغان ۋە قىرغىزىستان ; निम्न में से कौन सा हारडवेयर; ተለዋዋጭ የግድግዳ ; Дзейныя асобы:; « અમરેલીનાં મહિલા વિકાસ; <unk>❤❥웃유♋☮✊;װיקיװערטערבוך"
25
+ ###################################
26
+ OrionStarAI/Orion-14B-Chat, sp_model, byte_num: 0
27
+ reversible: false; unk_token: <unk>, 0, unk_ratio: 0.0495; oov: []
28
+ text[71] = "; a közoktatással? _ Belföld; pumë, i vjetër, vjeç; ئەردوغان ۋە قىرغىزىستان ; निम्न में से कौन सा हारडवेयर; ተለዋዋጭ የግድግዳ ; Дзейныя асобы:; « અમરેલીનાં મહિલા વિકાસ; \t\n\r🦙❤❥웃유♋☮✊;װיקיװערטערבוך ",
29
+ decoding[71] = "; a közoktatással? _ Belföld; pumë, i vjetër, vjeç; ئ<unk> ⁇ ردوغان <unk> ⁇ قىرغىزىستان ; निम्न में से कौन सा हारडवेयर; ተለ<unk> ⁇ ጭ የግድግ<unk> ⁇ ; Дзейныя асобы:; « <unk> ⁇ <unk> ⁇ <unk> ⁇ ; \t\n <unk> ⁇ ❤❥웃유♋☮✊; <unk> ⁇ יקי<unk> ⁇ ערטערבוך "
30
+ ###################################
31
+ THUDM/chatglm-6b, byte_num: 256
32
+ reversible: false; unk_token: <unk>, 0, unk_ratio: 0.0000; oov: []
33
+ text[237] = "\r🦙❤❥웃유♋☮✊;װיקיװערטערבוך ",
34
+ decoding[237] = " 🦙❤❥웃유♋☮✊;װיקיװערטערבוך"
35
+ ###################################
36
+ abeja/gpt-neox-japanese-2.7b, japanese-bpe: https://github.com/tanreinama/Japanese-BPEEncoder_V2
37
+ reversible: false; unk_token: <|endoftext|>, 31999, unk_ratio: 0.0000; oov: []
38
+ text[7] = "Замглавы управления развития; 특히 주소 15~17번 홀에선 3연속; 確実に春が近づいてること; a közoktatással? _ Belföld; pumë, i vjetër, vjeç; ئەردوغان ۋە قىرغىزىستان ; निम्न में से कौन सा हारडवेयर; ተለዋዋጭ የግድግዳ ; Дзейныя асобы:; « અમરેલીનાં મહિલા વિકાસ; \t\n\r🦙❤❥웃유♋☮✊;װיקיװערטערבוך ",
39
+ decoding[7] = "���������������� �������������������� ����������������; ������ ������ 15~17��� ��������� 3������; 確実に春が近づいてること; a k��zoktat��ssal? _ Belf��ld; pum��, i vjet��r, vje��; ���������������� ���� ���������������������� ; ��������������� ��������� ������ ��������� ������ ������������������������; ��������������� ��������������� ; �������������� ����������:; ǀ ��������������������������� ��������������� ���������������; \t\n\n🐯❤‖������🟥🟥🤚;��������������������������"
40
+
41
+
42
+ ###################################
43
+ baichuan-inc/Baichuan-7B, sp_model, byte_num: 256
44
+ reversible: false; unk_token: <unk>, 0, unk_ratio: 0.0000; oov: []
45
+ text[237] = "\r🦙❤❥웃유♋☮✊;װיקיװערטערבוך ",
46
+ decoding[237] = " 🦙❤❥웃유♋☮✊;װיקיװערטערבוך "
47
+ ###################################
48
+ ckiplab/gpt2-base-chinese, <class 'tokenizers.models.WordPiece'>
49
+ reversible: false; unk_token: [UNK], 100, unk_ratio: 0.1185; oov: []
50
+ text[5] = "; Замглавы управления развития; 특히 주소 15~17번 홀에선 3연속; 確実に春が近づいてること; a közoktatással? _ Belföld; pumë, i vjetër, vjeç; ئەردوغان ۋە قىرغىزىستان ; निम्न में से कौन सा हारडवेयर; ተለዋዋጭ የግድግዳ ; Дзейныя асобы:; « અમરેલીનાં મહિલા વિકાસ; \t\n\r🦙❤❥웃유♋☮✊;װיקיװערטערבוך ",
51
+ decoding[5] = " ; замглавы управления развития ; 특히 주소 15 ~ 17번 홀에선 3연속 ; 確 実 に 春 か 近 ついてること ; a kozoktatassal? _ belfold ; pume, i vjeter, vjec ; [UNK] [UNK] [UNK] ; [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] ; [UNK] [UNK] ; дзеиныя асобы : ; « [UNK] [UNK] [UNK] ; [UNK] ; [UNK]"
52
+
53
+
54
+ ###################################
55
+ cl-tohoku/bert-base-japanese, wordpiece.MecabTokenizer, 支持byte-level https://github.com/polm/fugashi
56
+ reversible: false; unk_token: [UNK], 1, unk_ratio: 0.3951; oov: []
57
+ text[5] = "; Замглавы управления развития; 특히 주소 15~17번 홀에선 3연속; 確実に春が近づいてること; a közoktatással? _ Belföld; pumë, i vjetër, vjeç; ئەردوغان ۋە قىرغىزىستان ; निम्न में से कौन सा हारडवेयर; ተለዋዋጭ የግድግዳ ; Дзейныя асобы:; « અમરેલીનાં મહિલા વિકાસ; \t\n\r🦙❤❥웃유♋☮✊;װיקיװערטערבוך ",
58
+ decoding[5] = " ; [UNK] [UNK] [UNK] ; [UNK] [UNK] 15 ~ 17 [UNK] [UNK] 3 [UNK] ; 確実 に 春 が 近づい てる こと ; a közoktatással? _ Belföld ; [UNK], i [UNK], vjeç ; [UNK] [UNK] [UNK] ; [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] ; [UNK] [UNK] ; [UNK] [UNK] :; [UNK] [UNK] [UNK] [UNK] ; [UNK] [UNK] [UNK] [UNK] [UNK]"
59
+
60
+
61
+ ###################################
62
+ clue/roberta_chinese_clue_tiny, <class 'tokenizers.models.WordPiece'>
63
+ reversible: false; unk_token: [UNK], 100, unk_ratio: 0.3580; oov: []
64
+ text[5] = "; Замглавы управления развития; 특히 주소 15~17번 홀에선 3연속; 確実に春が近づいてること; a közoktatással? _ Belföld; pumë, i vjetër, vjeç; ئەردوغان ۋە قىرغىزىستان ; निम्न में से कौन सा हारडवेयर; ተለዋዋጭ የግድግዳ ; Дзейныя асобы:; « અમરેલીનાં મહિલા વિકાસ; \t\n\r🦙❤❥웃유♋☮✊;װיקיװערטערבוך ",
65
+ decoding[5] = " ; [UNK] [UNK] [UNK] ; [UNK] [UNK] 15 ~ [UNK] [UNK] [UNK] ; [UNK] 実 [UNK] 春 [UNK] 近 [UNK] ; a kozoktatassal? _ belfold ; pume, i vjeter, vjec ; [UNK] [UNK] [UNK] ; [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] ; [UNK] [UNK] ; [UNK] асобы : ; « [UNK] [UNK] [UNK] ; [UNK] ; [UNK]"
66
+
67
+
68
+ ###################################
69
+ dbmdz/bert-base-german-uncased, <class 'tokenizers.models.WordPiece'>
70
+ reversible: false; unk_token: [UNK], 101, unk_ratio: 0.4459; oov: []
71
+ text[5] = "; Замглавы управления развития; 특히 주소 15~17번 홀에선 3연속; 確実に春が近づいてること; a közoktatással? _ Belföld; pumë, i vjetër, vjeç; ئەردوغان ۋە قىرغىزىستان ; निम्न में से कौन सा हारडवेयर; ተለዋዋጭ የግድግዳ ; Дзейныя асобы:; « અમરેલીનાં મહિલા વિકાસ; \t\n\r🦙❤❥웃유♋☮✊;װיקיװערטערבוך ",
72
+ decoding[5] = " ; [UNK] [UNK] [UNK] ; [UNK] [UNK] 15 ~ [UNK] [UNK] [UNK] ; [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] ; a kozoktatassal? _ belfold ; pume, i vjeter, vjec ; [UNK] [UNK] [UNK] ; [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] ; [UNK] [UNK] ; [UNK] [UNK] : ; « [UNK] [UNK] [UNK] ; [UNK] ; [UNK]"
73
+ ###################################
74
+ deepseek-ai/deepseek-coder-33b-instruct, <class 'tokenizers.models.BPE'>
75
+ reversible: false; unk_token: None, None, unk_ratio: 0.0000; oov: []
76
+ text[77] = "özoktatással? _ Belföld; pumë, i vjetër, vjeç; ئەردوغان ۋە قىرغىزىستان ; निम्न में से कौन सा हारडवेयर; ተለዋዋጭ የግድግዳ ; Дзейныя асобы:; « અમરેલીનાં મહિલા વિકાસ; \t\n\r🦙❤❥웃유♋☮✊;װיקיװערטערבוך ",
77
+ decoding[77] = "�zoktatással? _ Belf�ld; pumë, i vjetër, vjeç; ئەردوغان ۋە قىرغىزىستان ; निम्न में से कौन सा हारडवेयर; ተለዋዋጭ የግድግዳ ; Дзейныя асобы:; « અમરેલીનાં મહિલા વિકાસ; \t\n\r🦙❤❥웃유♋☮✊;װיקיװערטערבוך "
78
+ Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
79
+ [2024-05-12 00:30:36] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer eson/kplug-base-encoder
80
+ ###################################
81
+ deepseek-ai/deepseek-llm-7b-base, <class 'tokenizers.models.BPE'>
82
+ reversible: false; unk_token: None, None, unk_ratio: 0.0000; oov: []
83
+ text[77] = "özoktatással? _ Belföld; pumë, i vjetër, vjeç; ئەردوغان ۋە قىرغىزىستان ; निम्न में से कौन सा हारडवेयर; ተለዋዋጭ የግድግዳ ; Дзейныя асобы:; « અમરેલીનાં મહિલા વિકાસ; \t\n\r🦙❤❥웃유♋☮✊;װיקיװערטערבוך ",
84
+ decoding[77] = "�zoktatással? _ Belf�ld; pumë, i vjetër, vjeç; ئەردوغان ۋە قىرغىزىستان ; निम्न में से कौन सा हारडवेयर; ተለዋዋጭ የግድግዳ ; Дзейныя асобы:; « અમરેલીનાં મહિલા વિકાસ; \t\n\r🦙❤❥웃유♋☮✊;װיקיװערטערבוך "
85
+ [2024-05-12 00:30:56] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer fnlp/moss-moon-003-sft
86
+ ###################################
87
+ eson/kplug-base-encoder, <class 'tokenizers.models.WordPiece'>
88
+ reversible: false; unk_token: [UNK], 100, unk_ratio: 0.3625; oov: []
89
+ text[5] = "; Замглавы управления развития; 특히 주소 15~17번 홀에선 3연속; 確実に春が近づいてること; a közoktatással? _ Belföld; pumë, i vjetër, vjeç; ئەردوغان ۋە قىرغىزىستان ; निम्न में से कौन सा हारडवेयर; ተለዋዋጭ የግድግዳ ; Дзейныя асобы:; « અમરેલીનાં મહિલા વિકાસ; \t\n\r🦙❤❥웃유♋☮✊;װיקיװערטערבוך ",
90
+ decoding[5] = " ; [UNK] [UNK] [UNK] ; [UNK] [UNK] 15 ~ [UNK] [UNK] [UNK] ; [UNK] 実 [UNK] 春 [UNK] 近 [UNK] ; a kozoktatassal? _ belfold ; pume, i vjeter, vjec ; [UNK] [UNK] [UNK] ; [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] ; [UNK] [UNK] ; [UNK] асобы : ; « [UNK] [UNK] [UNK] ; [UNK] ; [UNK]"
91
+ Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
92
+ [2024-05-12 00:31:36] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer google-bert/bert-base-cased
93
+ ###################################
94
+ fnlp/moss-moon-003-sft, 应该是 sentencepiece.byte_bpe,待确认
95
+ reversible: false; unk_token: <|endoftext|>, 106028, unk_ratio: 0.0000; oov: []
96
+ text[74] = "a közoktatással? _ Belföld; pumë, i vjetër, vjeç; ئەردوغان ۋە قىرغىزىستان ; निम्न में से कौन सा हारडवेयर; ተለዋዋጭ የግድግዳ ; Дзейныя асобы:; « અમરેલીનાં મહિલા વિકાસ; \t\n\r🦙❤❥웃유♋☮✊;װיקיװערטערבוך ",
97
+ decoding[74] = " a közoktatással? _ Belföld; pumë, i vjetër, vjeç; ئەردوغان ۋە قىرغىزىستان ; निम्न में से कौन सा हारडवेयर; ተለዋዋጭ የግድግዳ ; Дзейныя асобы:; « અમરેલીનાં મહિલા વિકાસ; \t\n\r🦙❤❥웃유♋☮✊;װיקיװערטערבוך "
98
+ ###################################
99
+ google-bert/bert-base-cased, <class 'tokenizers.models.WordPiece'>
100
+ reversible: false; unk_token: [UNK], 100, unk_ratio: 0.1732; oov: []
101
+ text[5] = "; Замглавы управления развития; 특히 주소 15~17번 홀에선 3연속; 確実に春が近づいてること; a közoktatással? _ Belföld; pumë, i vjetër, vjeç; ئەردوغان ۋە قىرغىزىستان ; निम्न में से कौन सा हारडवेयर; ተለዋዋጭ የግድግዳ ; Дзейныя асобы:; « અમરેલીનાં મહિલા વિકાસ; \t\n\r🦙❤❥웃유♋☮✊;װיקיװערטערבוך ",
102
+ decoding[5] = " ; Замглавы управления развития ; [UNK] [UNK] 15 ~ [UNK] [UNK] [UNK] ; [UNK] [UNK] に [UNK] [UNK] [UNK] [UNK] ; a közoktatással? _ Belföld ; pumë, i vjetër, vjeç ; [UNK] [UNK] قىرغىزىستان ; निम्न में से [UNK] सा [UNK] ; [UNK] [UNK] ; Дзейныя асобы : ; « [UNK] [UNK] [UNK] ; [UNK] ; [UNK]"
103
+ [2024-05-12 00:31:56] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer google-bert/bert-base-chinese
104
+ [2024-05-12 00:32:16] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer google-bert/bert-base-german-cased
105
+ ###################################
106
+ google-bert/bert-base-chinese, <class 'tokenizers.models.WordPiece'>
107
+ reversible: false; unk_token: [UNK], 100, unk_ratio: 0.3704; oov: []
108
+ text[5] = "; Замглавы управления развития; 특히 주소 15~17번 홀에선 3연속; 確実に春が近づいてること; a közoktatással? _ Belföld; pumë, i vjetër, vjeç; ئەردوغان ۋە قىرغىزىستان ; निम्न में से कौन सा हारडवेयर; ተለዋዋጭ የግድግዳ ; Дзейныя асобы:; « અમરેલીનાં મહિલા વિકાસ; \t\n\r🦙❤❥웃유♋☮✊;װיקיװערטערבוך ",
109
+ decoding[5] = " ; [UNK] управления развития ; [UNK] [UNK] 15 ~ [UNK] [UNK] [UNK] ; 確 実 に 春 [UNK] 近 [UNK] ; a [UNK]? _ [UNK] ; [UNK], i [UNK], [UNK] ; [UNK] [UNK] [UNK] ; [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] ; [UNK] [UNK] ; [UNK] асобы : ; « [UNK] [UNK] [UNK] ; [UNK] ; [UNK]"
110
+ ###################################
111
+ google-bert/bert-base-german-cased, <class 'tokenizers.models.WordPiece'>
112
+ reversible: false; unk_token: [UNK], 2, unk_ratio: 0.5938; oov: []
113
+ text[5] = "; Замглавы управления развития; 특히 주소 15~17번 홀에선 3연속; 確実に春が近づいてること; a közoktatással? _ Belföld; pumë, i vjetër, vjeç; ئەردوغان ۋە قىرغىزىستان ; निम्न में से कौन सा हारडवेयर; ተለዋዋጭ የግድግዳ ; Дзейныя асобы:; « અમરેલીનાં મહિલા વિકાસ; \t\n\r🦙❤❥웃유♋☮✊;װיקיװערטערבוך ",
114
+ decoding[5] = " ; [UNK] [UNK] [UNK] ; [UNK] [UNK] 15 ~ [UNK] [UNK] [UNK] ; [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] ; a [UNK]? _ Belföld ; [UNK], i [UNK], [UNK] ; [UNK] [UNK] [UNK] ; [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] ; [UNK] [UNK] ; [UNK] [UNK] : ; [UNK] [UNK] [UNK] [UNK] ; [UNK] ; [UNK]"
115
+ [2024-05-12 00:32:36] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer google-bert/bert-base-multilingual-cased
116
+ [2024-05-12 00:32:57] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer google-bert/bert-base-multilingual-uncased
117
+ ###################################
118
+ google-bert/bert-base-multilingual-cased, <class 'tokenizers.models.WordPiece'>
119
+ reversible: false; unk_token: [UNK], 100, unk_ratio: 0.0531; oov: []
120
+ text[5] = "; Замглавы управления развития; 특히 주소 15~17번 홀에선 3연속; 確実に春が近づいてること; a közoktatással? _ Belföld; pumë, i vjetër, vjeç; ئەردوغان ۋە قىرغىزىستان ; निम्न में से कौन सा हारडवेयर; ተለዋዋጭ የግድግዳ ; Дзейныя асобы:; « અમરેલીનાં મહિલા વિકાસ; \t\n\r🦙❤❥웃유♋☮✊;װיקיװערטערבוך ",
121
+ decoding[5] = " ; Замглавы управления развития ; 특히 주소 15 ~ 17번 홀에선 3연속 ; 確 実 に 春 が 近 づいてること ; a közoktatással? _ Belföld ; pumë, i vjetër, vjeç ; [UNK] [UNK] قىرغىزىستان ; निम्न में से कौन सा हारडवेयर ; [UNK] [UNK] ; Дзейныя асобы : ; « અમરેલીનાં મહિલા વિકાસ ; [UNK] ; [UNK]"
122
+ [2024-05-12 00:33:17] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer google-bert/bert-base-uncased
123
+ ###################################
124
+ google-bert/bert-base-multilingual-uncased, <class 'tokenizers.models.WordPiece'>
125
+ reversible: false; unk_token: [UNK], 100, unk_ratio: 0.0360; oov: []
126
+ text[5] = "; Замглавы управления развития; 특히 주소 15~17번 홀에선 3연속; 確実に春が近づいてること; a közoktatással? _ Belföld; pumë, i vjetër, vjeç; ئەردوغان ۋە قىرغىزىستان ; निम्न में से कौन सा हारडवेयर; ተለዋዋጭ የግድግዳ ; Дзейныя асобы:; « અમરેલીનાં મહિલા વિકાસ; \t\n\r🦙❤❥웃유♋☮✊;װיקיװערטערבוך ",
127
+ decoding[5] = " ; замглавы управления развития ; 특히 주소 15 ~ 17번 홀에선 3연속 ; 確 実 に 春 か 近 ついてること ; a kozoktatassal? _ belfold ; pume, i vjeter, vjec ; يەردوغان ۋە قىرغىزىستان ; निमन म स कौन सा हारडवयर ; [UNK] [UNK] ; дзеиныя асобы : ; « અમરલીના મહિલા વિકાસ ; [UNK] ; [UNK]"
128
+ [2024-05-12 00:33:37] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer google-t5/t5-large
129
+ ###################################
130
+ google-bert/bert-base-uncased, <class 'tokenizers.models.WordPiece'>
131
+ reversible: false; unk_token: [UNK], 100, unk_ratio: 0.0867; oov: []
132
+ text[5] = "; Замглавы управления развития; 특히 주소 15~17번 홀에선 3연속; 確実に春が近づいてること; a közoktatással? _ Belföld; pumë, i vjetër, vjeç; ئەردوغان ۋە قىرغىزىستان ; निम्न में से कौन सा हारडवेयर; ተለዋዋጭ የግድግዳ ; Дзейныя асобы:; « અમરેલીનાં મહિલા વિકાસ; \t\n\r🦙❤❥웃유♋☮✊;װיקיװערטערבוך ",
133
+ decoding[5] = " ; замглавы управления развития ; 특히 주소 15 ~ 17번 홀에선 3연속 ; [UNK] [UNK] に 春 か [UNK] ついてること ; a kozoktatassal? _ belfold ; pume, i vjeter, vjec ; [UNK] [UNK] قىرغىزىستان ; निमन म स [UNK] सा हारडवयर ; [UNK] [UNK] ; дзеиныя асобы : ; « [UNK] [UNK] [UNK] ; [UNK] ; [UNK]"
134
+ ###################################
135
+ google-t5/t5-large, <class 'tokenizers.models.Unigram'>
136
+ reversible: false; unk_token: <unk>, 2, unk_ratio: 0.2769; oov: []
137
+ text[7] = "Замглавы управления развития; 특히 주소 15~17번 홀에선 3연속; 確実に春が近づいてること; a közoktatással? _ Belföld; pumë, i vjetër, vjeç; ئەردوغان ۋە قىرغىزىستان ; निम्न में से कौन सा हारडवेयर; ተለዋዋጭ የግድግዳ ; Дзейныя асобы:; « અમરેલીનાં મહિલા વિકાસ; \t\n\r🦙❤❥웃유♋☮✊;װיקיװערטערבוך ",
138
+ decoding[7] = "<unk>ам<unk>лав<unk> у<unk>равлени<unk> ра<unk>вити<unk>; <unk> <unk> 15<unk>17<unk> <unk> 3<unk>; <unk>; a közoktatással? _ Belföld; pum<unk>, i vjet<unk>r, vjeç; <unk> <unk> <unk> ; <unk> <unk> <unk> <unk> <unk> <unk>; <unk> <unk> ; <unk>е<unk>н<unk> асо<unk>:; « <unk> <unk> <unk>; <unk>;<unk>"
139
+ [2024-05-12 00:34:57] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer google/byt5-small
140
+ [2024-05-12 00:35:18] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer google/gemma-7b
141
+ [2024-05-12 00:35:39] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer google/mobilebert-uncased
142
+ [2024-05-12 00:36:59] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer google/mt5-large
143
+ ###################################
144
+ google/mobilebert-uncased, <class 'tokenizers.models.WordPiece'>
145
+ reversible: false; unk_token: [UNK], 100, unk_ratio: 0.0867; oov: []
146
+ text[5] = "; Замглавы управления развития; 특히 주소 15~17번 홀에선 3연속; 確実に春が近づいてること; a közoktatással? _ Belföld; pumë, i vjetër, vjeç; ئەردوغان ۋە قىرغىزىستان ; निम्न में से कौन सा हारडवेयर; ተለዋዋጭ የግድግዳ ; Дзейныя асобы:; « અમરેલીનાં મહિલા વિકાસ; \t\n\r🦙❤❥웃유♋☮✊;װיקיװערטערבוך ",
147
+ decoding[5] = " ; замглавы управления развития ; 특히 주소 15 ~ 17번 홀에선 3연속 ; [UNK] [UNK] に 春 か [UNK] ついてること ; a kozoktatassal? _ belfold ; pume, i vjeter, vjec ; [UNK] [UNK] قىرغىزىستان ; निमन म स [UNK] सा हारडवयर ; [UNK] [UNK] ; дзеиныя асобы : ; « [UNK] [UNK] [UNK] ; [UNK] ; [UNK]"
148
+ C:\Users\xusong28\Miniconda3\envs\py3.10-torch1.13-hf.latest\lib\site-packages\transformers\convert_slow_tokenizer.py:560: UserWarning: The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option which is not implemented in the fast tokenizers. In practice this means that the fast version of the tokenizer can produce unknown tokens whereas the sentencepiece version would have converted these unknown tokens into a sequence of byte tokens matching the original piece of text.
149
+ warnings.warn(
150
+ [2024-05-12 00:37:23] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer google/switch-c-2048
151
+ ###################################
152
+ google/mt5-large, <class 'tokenizers.models.Unigram'>
153
+ reversible: false; unk_token: <unk>, 2, unk_ratio: 0.0079; oov: []
154
+ text[73] = " a közoktatással? _ Belföld; pumë, i vjetër, vjeç; ئەردوغان ۋە قىرغىزىستان ; निम्न में से कौन सा हारडवेयर; ተለዋዋጭ የግድግዳ ; Дзейныя асобы:; « અમરેલીનાં મહિલા વિકાસ; \t\n\r🦙❤❥웃유♋☮✊;װיקיװערטערבוך ",
155
+ decoding[73] = "a közoktatással? _ Belföld; pumë, i vjetër, vjeç; ئەردوغان ۋە قىرغىزىستان ; निम्न में से कौन सा हारडवेयर; ተለዋዋጭ የግድግዳ ; Дзейныя асобы:; « અમરેલીનાં મહિલા વિકાસ; <unk>❤❥웃유♋☮✊;װיקיװערטערבוך"
156
+ [2024-05-12 00:37:43] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer hfl/chinese-alpaca-lora-7b
157
+ ###################################
158
+ google/switch-c-2048, <class 'tokenizers.models.Unigram'>
159
+ reversible: false; unk_token: <unk>, 2, unk_ratio: 0.2769; oov: []
160
+ text[7] = "Замглавы управления развития; 특히 주소 15~17번 홀에선 3연속; 確実に春が近づいてること; a közoktatással? _ Belföld; pumë, i vjetër, vjeç; ئەردوغان ۋە قىرغىزىستان ; निम्न में से कौन सा हारडवेयर; ተለዋዋጭ የግድግዳ ; Дзейныя асобы:; « અમરેલીનાં મહિલા વિકાસ; \t\n\r🦙❤❥웃유♋☮✊;װיקיװערטערבוך ",
161
+ decoding[7] = "<unk>ам<unk>лав<unk> у<unk>равлени<unk> ра<unk>вити<unk>; <unk> <unk> 15<unk>17<unk> <unk> 3<unk>; <unk>; a közoktatással? _ Belföld; pum<unk>, i vjet<unk>r, vjeç; <unk> <unk> <unk> ; <unk> <unk> <unk> <unk> <unk> <unk>; <unk> <unk> ; <unk>е<unk>н<unk> асо<unk>:; « <unk> <unk> <unk>; <unk>;<unk>"
162
+ You are using the default legacy behaviour of the <class 'transformers.models.llama.tokenization_llama.LlamaTokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565
163
+ [2024-05-12 00:38:04] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer hfl/chinese-llama-2-7b
164
+ [2024-05-12 00:38:25] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer hfl/chinese-llama-lora-7b
165
+ [2024-05-12 00:38:46] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer hfl/llama-3-chinese-8b
166
+ Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
167
+ [2024-05-12 00:39:07] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer hpcai-tech/grok-1
168
+ [2024-05-12 00:39:28] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer internlm/internlm-chat-7b
169
+ [2024-05-12 00:40:09] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer internlm/internlm-xcomposer-7b
170
+ [2024-05-12 00:40:31] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer internlm/internlm2-chat-7b
171
+ [2024-05-12 00:41:13] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer internlm/internlm2-math-7b
172
+ [2024-05-12 00:41:35] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer lmsys/fastchat-t5-3b-v1.0
173
+ Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
174
+ ###################################
175
+ [2024-05-12 00:41:55] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer meta-llama/Llama-2-7b-chat
176
+ lmsys/fastchat-t5-3b-v1.0, sp_model, byte_num: 0
177
+ reversible: false; unk_token: <unk>, 2, unk_ratio: 0.2105; oov: []
178
+ text[7] = "Замглавы управления развития; 특히 주소 15~17번 홀에선 3연속; 確実に春が近づいてること; a közoktatással? _ Belföld; pumë, i vjetër, vjeç; ئەردوغان ۋە قىرغىزىستان ; निम्न में से कौन सा हारडवेयर; ተለዋዋጭ የግድግዳ ; Дзейныя асобы:; « અમરેલીનાં મહિલા વિકાસ; \t\n\r🦙❤❥웃유♋☮✊;װיקיװערטערבוך ",
179
+ decoding[7] = " <unk> ам<unk> лав<unk> у<unk> равлени<unk> ра<unk> вити<unk>; <unk> <unk> 15<unk> 17<unk> <unk> 3<unk>; <unk>; a közoktatással? _ Belföld; pum<unk>, i vjet<unk>r, vjeç; <unk> <unk> <unk> ; <unk> <unk> <unk> <unk> <unk> <unk>; <unk> <unk> ; <unk> е<unk> н<unk> асо<unk>:; « <unk> <unk> <unk>; \t \n <unk> ;<unk> "
180
+ [2024-05-12 00:41:55] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer meta-llama/Meta-Llama-3-8B
181
+ [2024-05-12 00:41:55] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer microsoft/Phi-3-mini-4k-instruct
182
+ Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
183
+ [2024-05-12 00:42:16] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer microsoft/phi-1
184
+ [2024-05-12 00:42:36] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer microsoft/phi-2
185
+ Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
186
+ [2024-05-12 00:42:56] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer mistralai/Mistral-7B-v0.1
187
+ [2024-05-12 00:43:16] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer mistralai/Mixtral-8x7B-v0.1
188
+ [2024-05-12 00:43:37] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer openai-community/gpt2
189
+ [2024-05-12 00:43:57] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer openai/code-davinci-002
190
+ [2024-05-12 00:43:57] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer openai/gpt-3.5-turbo
191
+ [2024-05-12 00:43:57] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer openai/gpt-4
192
+ [2024-05-12 00:43:57] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer openai/text-davinci-003
193
+ [2024-05-12 00:43:57] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer paust/pko-t5-large
194
+ Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
195
+ [2024-05-12 00:44:18] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer thu-coai/CharacterGLM-6B
196
+ [2024-05-12 00:44:58] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer tiiuae/falcon-180b
197
+ [2024-05-12 00:45:19] [INFO] [34044:7360] [__init__.py:343:load_tokenizer] loading tokenizer tiiuae/falcon-7b
198
+
199
+ Process finished with exit code 0
200
+
201
+
202
+ ```
utils/oov_util.py CHANGED
@@ -2,11 +2,117 @@
2
 
3
 
4
  import os
 
 
5
 
6
- CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
7
 
8
- space_tokens = ["空格 ,两个空格 ,三个空格 ,制表符\t,换行符\n"]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9
 
10
 
11
 
12
- docs = [line.strip() for line in open(os.path.join(CURRENT_DIR, "test.txt"), "r", encoding="utf-8")]
 
2
 
3
 
4
  import os
5
+ import json
6
+ from vocab import all_tokenizer_config, load_tokenizer, TokenizerImpl
7
 
 
8
 
9
+ text = "hello; Замглавы управления развития; 특히 주소 15~17번 홀에선 3연속;" \
10
+ " 確実に春が近づいてること; a közoktatással? _ Belföld;" \
11
+ " pumë, i vjetër, vjeç; ئەردوغان ۋە قىرغىزىستان ;" \
12
+ " निम्न में से कौन सा हारडवेयर; ተለዋዋጭ የግድግዳ ; Дзейныя асобы:;" \
13
+ " « અમરેલીનાં મહિલા વિકાસ; 🦙❤❥웃유♋☮✊;" \
14
+ "װיקיװערטערבוך "
15
+ whitespace = "\t \n\n\r "
16
+ bytes = b"\x00\x01\x02\x03\x04".decode('utf-8')
17
+
18
+ text += whitespace
19
+
20
+
21
+ def get_unk(tokenizer_config):
22
+ tokenizer = load_tokenizer(tokenizer_config)
23
+ if hasattr(tokenizer, "unk_token"):
24
+ return f"{tokenizer.unk_token}, {tokenizer.unk_token_id}"
25
+ else:
26
+ return "unk_token not found"
27
+
28
+
29
+ # def infer_tokenizer_impl(tokenizer_config):
30
+ def infer_tokenizer_type(tokenizer_config):
31
+ tokenizer = load_tokenizer(tokenizer_config)
32
+ if tokenizer_config.impl == TokenizerImpl.TikToken:
33
+ return "tiktoken"
34
+ if hasattr(tokenizer, "backend_tokenizer"):
35
+ return str(type(tokenizer.backend_tokenizer.model)) # type(tokenizer._tokenizer.model))
36
+ # orion: sp_model.Load(vocab_file),继承 PreTrainedTokenizer
37
+ elif hasattr(tokenizer, "sp_model"): # 基于 sentencepiece 包
38
+ # for i in range(tokenizer.sp_model.piece_size()):
39
+ # if tokenizer.sp_model.is_byte(i):
40
+ # print("")
41
+ return f"sp_model, byte_num: {sum([tokenizer.sp_model.is_byte(i) for i in range(tokenizer.sp_model.piece_size())])}"
42
+
43
+ # sp.Load(model_path) ,并且包括image_tokenizer
44
+ elif "glm-" in tokenizer_config.name_or_path:
45
+ return f"byte_num: {sum([tokenizer.sp_tokenizer.text_tokenizer.sp.is_byte(i) for i in range(tokenizer.sp_tokenizer.text_tokenizer.sp.piece_size())])}"
46
+ # sp.Load(model_path) ,没有image_tokenizer
47
+ elif "glm2-" in tokenizer_config.name_or_path \
48
+ or "glm3-" in tokenizer_config.name_or_path \
49
+ or "CharacterGLM-6B" in tokenizer_config.name_or_path:
50
+ return f"byte_num: {sum([tokenizer.tokenizer.sp_model.is_byte(i) for i in range(tokenizer.tokenizer.sp_model.piece_size())])}"
51
+ elif "abeja/gpt-neox-japanese-2.7b" == tokenizer_config.name_or_path: # 支持 byte-level,解决oov问题
52
+ return f"japanese-bpe: https://github.com/tanreinama/Japanese-BPEEncoder_V2"
53
+ # bert-base-japanese: 特殊的地方在于 "word_tokenizer_type": "mecab",见 https://huggingface.co/tohoku-nlp/bert-base-japanese/blob/main/tokenizer_config.json
54
+ elif "bert-base-japanese" in tokenizer_config.name_or_path:
55
+ return "wordpiece.MecabTokenizer, 支持byte-level https://taku910.github.io/mecab/"
56
+ elif "moss" in tokenizer_config.name_or_path:
57
+ return "应该是 sentencepiece.byte_bpe,待确认"
58
+ elif "byt5" in tokenizer_config.name_or_path:
59
+ return "未知,待定"
60
+ else:
61
+ print("catch", tokenizer_config.name_or_path)
62
+ raise "error"
63
+
64
+
65
+
66
+
67
+
68
+ def test_reversible(tokenizer_config):
69
+ """
70
+ xlm-roberta-base 为什么oov这么少?是因为有 byte吗?
71
+ :param tokenizer_config:
72
+ :return:
73
+ """
74
+ tokenizer = load_tokenizer(tokenizer_config)
75
+ encoding = tokenizer.encode(text, add_special_tokens=False)
76
+ decoding = tokenizer.decode(encoding)
77
+
78
+ if text in decoding:
79
+ # print(tokenizer_config.name, tokenizer_config.impl, "reversible: true")
80
+ pass
81
+ else:
82
+ unk_count = sum([1 for token_id in encoding if token_id == tokenizer.unk_token_id])
83
+ oov_tokens = []
84
+ # if tokenizer_config.impl == TokenizerImpl.SentencePiece:
85
+ # print(sum([tokenizer.is_byte(i) for i in range(tokenizer.piece_size())]))
86
+
87
+ print("#######"*5)
88
+ print(f"{tokenizer_config.name_or_path}, {infer_tokenizer_type(tokenizer_config)}\n"
89
+ f"reversible: false; unk_token: {get_unk(tokenizer_config)},"
90
+ f" unk_ratio: {unk_count/len(encoding):.4f}; oov: []")
91
+ for i in range(len(text)):
92
+ if text[i] != decoding[i]:
93
+ # print(f"text[{i}] = {str(bytes(text[i:], 'utf-8'))}\n"
94
+ # f"decoding[{i}] = {str(bytes(decoding[i:], 'utf-8'))}")
95
+ print(f"text[{i}] = {json.dumps(text[i:], ensure_ascii=False)}, \n"
96
+ f"decoding[{i}] = {json.dumps(decoding[i:], ensure_ascii=False)}")
97
+
98
+ break
99
+
100
+
101
+
102
+ for config in all_tokenizer_config:
103
+ # if "xlm-roberta-base" in config.name:
104
+ # if "xlm-roberta-base" in config.name:
105
+ # if "chatglm3-6b" in config.name:
106
+ # if "bert-base-japanese" in config.name:
107
+ # if "moss" in config.name:
108
+ # if "byt5" in config.name:
109
+ if "baichuan" in config.name_or_path:
110
+ # if "CharacterGLM-6B" in config.name:
111
+ # if "fastchat-t5" in config.name: # 报错 pyo3_runtime.PanicException: AddedVocabulary bad split
112
+ # if True:
113
+ # test_unk(config)
114
+ test_reversible(config)
115
+
116
 
117
 
118
 
 
utils/speed_util.py DELETED
@@ -1,9 +0,0 @@
1
- """
2
- encode速度
3
- decode速度
4
-
5
- ## examples
6
-
7
- qwen的encode速度有点慢
8
-
9
- """
 
 
 
 
 
 
 
 
 
 
utils/symbol.py DELETED
@@ -1,35 +0,0 @@
1
- """
2
- special_symbols: https://github.com/google/sentencepiece/blob/master/doc/special_symbols.md
3
- emoji:
4
- """
5
-
6
- import sys
7
-
8
-
9
- # 来自 https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/tokenizer/gpt2_tokenization.py#L55
10
- # 啥意思?
11
- def bytes_to_unicode():
12
- """
13
- Returns list of utf-8 byte and a corresponding list of unicode strings.
14
- The reversible bpe codes work on unicode strings.
15
- This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
16
- When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
17
- This is a signficant percentage of your normal, say, 32K bpe vocab.
18
- To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
19
- And avoids mapping to whitespace/control characters the bpe code barfs on.
20
- """
21
- _chr = unichr if sys.version_info[0] == 2 else chr
22
- bs = list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + \
23
- list(range(ord("®"), ord("ÿ") + 1))
24
- cs = bs[:]
25
- n = 0
26
- for b in range(2**8):
27
- if b not in bs:
28
- bs.append(b)
29
- cs.append(2**8 + n)
30
- n += 1
31
- cs = [_chr(n) for n in cs]
32
- return dict(zip(bs, cs))
33
-
34
- aa = bytes_to_unicode()
35
- print(aa)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
utils/text_util.py CHANGED
@@ -1,12 +1,23 @@
 
 
 
 
 
 
 
 
1
 
2
  def is_digit_char(uchar):
3
  return uchar in "0123456789"
4
 
5
 
6
- def has_digit(text):
7
  return any(is_digit_char(ch) for ch in text)
8
 
9
 
 
 
 
10
  def is_all_digit(text):
11
  return all(is_digit_char(char) for char in text)
12
 
 
1
+ """
2
+ char_
3
+ """
4
+
5
+
6
+ def detect_lang_from_unicode():
7
+ pass
8
+
9
 
10
  def is_digit_char(uchar):
11
  return uchar in "0123456789"
12
 
13
 
14
+ def contains_digit(text):
15
  return any(is_digit_char(ch) for ch in text)
16
 
17
 
18
+ def get_digit_count(text):
19
+ pass
20
+
21
  def is_all_digit(text):
22
  return all(is_digit_char(char) for char in text)
23
 
utils/vocab.jd.txt.v2 DELETED
@@ -1,10268 +0,0 @@
1
- [PAD]
2
- [unused1]
3
- [unused2]
4
- [unused3]
5
- [unused4]
6
- [unused5]
7
- [unused6]
8
- [unused7]
9
- [unused8]
10
- [unused9]
11
- [unused10]
12
- [unused11]
13
- [unused12]
14
- [unused13]
15
- [unused14]
16
- [unused15]
17
- [unused16]
18
- [unused17]
19
- [unused18]
20
- [unused19]
21
- [unused20]
22
- [unused21]
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- [unused22]
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- [unused23]
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- [unused24]
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- [unused25]
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- [unused26]
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- [unused27]
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- [unused28]
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- [unused29]
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- [unused30]
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- [unused31]
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- [unused32]
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- [unused33]
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- [unused34]
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- [unused35]
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- [unused36]
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- [unused37]
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- [unused38]
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- [unused39]
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- [unused40]
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- [unused41]
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- [unused42]
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- [unused43]
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- [unused44]
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- [unused45]
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- [unused46]
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- [unused47]
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- [unused48]
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- [unused49]
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- [unused50]
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- [unused51]
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- [unused52]
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- [unused53]
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- [unused54]
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- [unused55]
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- [unused56]
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- [unused57]
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- [unused58]
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- [unused59]
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- [unused60]
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- [unused61]
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- [unused62]
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- [unused63]
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- [unused64]
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- [unused65]
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- [unused66]
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- [unused67]
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- [unused68]
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- [unused69]
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- [unused70]
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- [unused71]
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- [unused72]
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- [unused73]
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- [unused74]
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- [unused75]
77
- [unused76]
78
- [unused77]
79
- [unused78]
80
- [unused79]
81
- [unused80]
82
- [unused81]
83
- [unused82]
84
- [unused83]
85
- [unused84]
86
- [unused85]
87
- [unused86]
88
- [unused87]
89
- [unused88]
90
- [unused89]
91
- [unused90]
92
- [unused91]
93
- [unused92]
94
- [unused93]
95
- [unused94]
96
- [unused95]
97
- [unused96]
98
- [unused97]
99
- [unused98]
100
- [unused99]
101
- [UNK]
102
- [CLS]
103
- [SEP]
104
- [MASK]
105
- <S>
106
- <T>
107
- !
108
- "
109
-
110
-
111
-
112
-
113
-
114
-
115
-
116
- #
117
- $
118
- %
119
- &
120
- '
121
- (
122
- )
123
- *
124
- +
125
- ,
126
- -
127
- .
128
- /
129
- 0
130
- 1
131
- 2
132
- 3
133
- 4
134
- 5
135
- 6
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- 90
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- 93
223
- 94
224
- 95
225
- 96
226
- 97
227
- 98
228
- 99
229
- 100
230
- 120
231
- 128
232
- 180
233
- 200
234
- 256
235
- 304
236
- 360
237
- 500
238
- 512
239
- 1000
240
- 1080
241
- 2000
242
- 2014
243
- 2015
244
- 2016
245
- 2017
246
- 2018
247
- 2019
248
- 2020
249
- 2021
250
- 2022
251
- :
252
- ;
253
- <
254
- =
255
- >
256
- ?
257
- @
258
- [
259
- \
260
- ]
261
- ^
262
- _
263
- a
264
- b
265
- c
266
- d
267
- e
268
- f
269
- g
270
- h
271
- i
272
- j
273
- k
274
- l
275
- m
276
- n
277
- o
278
- p
279
- q
280
- r
281
- s
282
- t
283
- u
284
- v
285
- w
286
- x
287
- y
288
- z
289
- {
290
- |
291
- }
292
- ~
293
- £
294
- ¤
295
- ¥
296
- §
297
- «
298
- °
299
- ±
300
- ²
301
- ³
302
- µ
303
- ·
304
- ¹
305
- º
306
- »
307
- ¼
308
- ×
309
- ß
310
- æ
311
- ÷
312
- ø
313
- đ
314
- ŋ
315
- ɔ
316
- ə
317
- ɡ
318
- ʰ
319
- ˇ
320
- ˈ
321
- ˊ
322
- ˋ
323
- ˍ
324
- ː
325
- ˙
326
- ˚
327
- ˢ
328
- α
329
- β
330
- γ
331
- δ
332
- ε
333
- η
334
- θ
335
- ι
336
- κ
337
- λ
338
- μ
339
- ν
340
- ο
341
- π
342
- ρ
343
- ς
344
- σ
345
- τ
346
- υ
347
- φ
348
- χ
349
- ψ
350
- ω
351
- а
352
- б
353
- в
354
- г
355
- д
356
- е
357
- ж
358
- з
359
- и
360
- к
361
- л
362
- м
363
- н
364
- о
365
- п
366
- р
367
- с
368
- т
369
- у
370
- ф
371
- х
372
- ц
373
- ч
374
- ш
375
- ы
376
- ь
377
- я
378
- і
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
-
452
-
453
-
454
-
455
-
456
-
457
-
458
-
459
-
460
-
461
-
462
-
463
-
464
-
465
-
466
-
467
-
468
-
469
-
470
-
471
-
472
-
473
-
474
-
475
-
476
-
477
-
478
-
479
-
480
-
481
-
482
-
483
-
484
-
485
-
486
-
487
-
488
-
489
-
490
-
491
-
492
-
493
-
494
-
495
-
496
-
497
-
498
-
499
-
500
-
501
-
502
-
503
-
504
-
505
-
506
-
507
-
508
-
509
-
510
-
511
-
512
-
513
-
514
-
515
-
516
-
517
-
518
-
519
-
520
-
521
-
522
-
523
-
524
-
525
-
526
-
527
-
528
-
529
-
530
-
531
-
532
-
533
-
534
-
535
-
536
-
537
-
538
-
539
-
540
-
541
-
542
-
543
-
544
-
545
-
546
-
547
-
548
- ⦿
549
-
550
-
551
-
552
-
553
-
554
-
555
-
556
-
557
-
558
-
559
-
560
-
561
-
562
-
563
-
564
-
565
-
566
-
567
-
568
-
569
-
570
-
571
-
572
-
573
-
574
-
575
-
576
-
577
-
578
-
579
-
580
-
581
-
582
-
583
-
584
-
585
-
586
-
587
-
588
-
589
-
590
-
591
-
592
-
593
-
594
-
595
-
596
-
597
-
598
-
599
-
600
-
601
-
602
-
603
-
604
-
605
-
606
-
607
-
608
-
609
-
610
-
611
-
612
-
613
-
614
-
615
-
616
-
617
-
618
-
619
-
620
-
621
- 丿
622
-
623
-
624
-
625
-
626
-
627
-
628
-
629
-
630
-
631
-
632
-
633
-
634
-
635
-
636
-
637
-
638
-
639
-
640
-
641
-
642
-
643
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644
-
645
-
646
-
647
-
648
-
649
-
650
-
651
-
652
-
653
-
654
-
655
-
656
-
657
-
658
-
659
-
660
-
661
-
662
-
663
-
664
-
665
-
666
-
667
-
668
-
669
-
670
-
671
-
672
-
673
-
674
-
675
-
676
-
677
-
678
-
679
-
680
-
681
-
682
-
683
-
684
- 亿
685
-
686
-
687
-
688
-
689
-
690
-
691
-
692
-
693
-
694
-
695
-
696
-
697
-
698
-
699
-
700
-
701
-
702
-
703
-
704
-
705
-
706
-
707
-
708
-
709
-
710
-
711
-
712
-
713
-
714
-
715
-
716
-
717
-
718
-
719
-
720
-
721
- 仿
722
-
723
-
724
-
725
-
726
-
727
-
728
-
729
-
730
-
731
-
732
-
733
-
734
-
735
-
736
-
737
-
738
-
739
-
740
-
741
-
742
-
743
-
744
-
745
-
746
-
747
-
748
-
749
-
750
-
751
-
752
-
753
-
754
-
755
-
756
-
757
-
758
-
759
-
760
-
761
-
762
-
763
-
764
-
765
-
766
-
767
-
768
-
769
-
770
-
771
-
772
-
773
-
774
-
775
-
776
-
777
-
778
-
779
-
780
-
781
-
782
-
783
-
784
- 使
785
-
786
-
787
-
788
-
789
-
790
-
791
-
792
-
793
-
794
-
795
-
796
-
797
-
798
-
799
-
800
-
801
-
802
-
803
-
804
-
805
-
806
-
807
- 便
808
-
809
-
810
-
811
-
812
-
813
-
814
-
815
-
816
-
817
-
818
-
819
-
820
-
821
-
822
-
823
-
824
-
825
-
826
-
827
-
828
-
829
-
830
-
831
-
832
-
833
-
834
-
835
-
836
-
837
-
838
-
839
-
840
-
841
-
842
-
843
-
844
-
845
-
846
-
847
-
848
-
849
-
850
-
851
-
852
-
853
-
854
-
855
-
856
-
857
-
858
-
859
-
860
-
861
-
862
-
863
-
864
-
865
-
866
-
867
-
868
-
869
-
870
-
871
-
872
-
873
-
874
-
875
-
876
-
877
-
878
-
879
-
880
-
881
-
882
-
883
-
884
-
885
-
886
-
887
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888
-
889
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890
-
891
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892
-
893
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894
-
895
-
896
-
897
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898
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899
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900
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901
-
902
-
903
-
904
-
905
-
906
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907
-
908
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909
-
910
-
911
-
912
-
913
-
914
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915
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916
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917
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918
-
919
-
920
-
921
-
922
-
923
-
924
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925
-
926
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927
-
928
-
929
-
930
-
931
-
932
-
933
-
934
-
935
-
936
-
937
-
938
-
939
-
940
-
941
- ��
942
-
943
-
944
-
945
-
946
-
947
-
948
-
949
-
950
-
951
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952
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953
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954
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955
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956
-
957
-
958
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959
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960
-
961
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962
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963
-
964
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965
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966
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967
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968
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969
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970
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971
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972
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973
-
974
-
975
-
976
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977
-
978
-
979
-
980
-
981
-
982
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983
-
984
-
985
-
986
-
987
-
988
-
989
-
990
-
991
-
992
-
993
-
994
-
995
-
996
-
997
-
998
-
999
-
1000
-
1001
-
1002
-
1003
-
1004
-
1005
-
1006
-
1007
-
1008
-
1009
-
1010
-
1011
-
1012
-
1013
-
1014
-
1015
-
1016
-
1017
-
1018
-
1019
-
1020
-
1021
-
1022
-
1023
-
1024
-
1025
-
1026
-
1027
-
1028
-
1029
-
1030
-
1031
-
1032
-
1033
-
1034
-
1035
-
1036
-
1037
-
1038
-
1039
-
1040
-
1041
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1042
-
1043
-
1044
-
1045
-
1046
-
1047
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1048
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1049
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1050
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1051
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1052
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1053
-
1054
-
1055
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1056
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1057
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1058
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1059
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1060
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1061
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1062
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1063
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1064
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1065
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1066
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1067
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1068
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1069
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1070
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1071
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1072
-
1073
-
1074
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1075
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1076
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1077
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1078
-
1079
-
1080
-
1081
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1082
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1083
-
1084
-
1085
-
1086
-
1087
-
1088
-
1089
-
1090
-
1091
-
1092
-
1093
-
1094
-
1095
-
1096
-
1097
-
1098
-
1099
-
1100
-
1101
-
1102
-
1103
-
1104
-
1105
-
1106
-
1107
-
1108
-
1109
-
1110
-
1111
-
1112
-
1113
-
1114
-
1115
-
1116
-
1117
-
1118
-
1119
-
1120
-
1121
-
1122
-
1123
-
1124
-
1125
-
1126
-
1127
-
1128
-
1129
-
1130
-
1131
-
1132
-
1133
-
1134
-
1135
-
1136
-
1137
-
1138
-
1139
-
1140
-
1141
-
1142
-
1143
-
1144
-
1145
-
1146
-
1147
-
1148
-
1149
-
1150
-
1151
-
1152
-
1153
-
1154
-
1155
-
1156
-
1157
-
1158
-
1159
-
1160
-
1161
-
1162
-
1163
-
1164
-
1165
-
1166
-
1167
-
1168
-
1169
-
1170
-
1171
-
1172
-
1173
-
1174
-
1175
-
1176
-
1177
-
1178
-
1179
-
1180
-
1181
-
1182
-
1183
-
1184
-
1185
-
1186
-
1187
-
1188
-
1189
-
1190
-
1191
-
1192
-
1193
-
1194
-
1195
-
1196
-
1197
-
1198
-
1199
-
1200
-
1201
-
1202
-
1203
-
1204
-
1205
-
1206
-
1207
-
1208
-
1209
-
1210
-
1211
-
1212
-
1213
-
1214
-
1215
-
1216
-
1217
-
1218
-
1219
-
1220
-
1221
-
1222
-
1223
-
1224
-
1225
-
1226
-
1227
-
1228
-
1229
-
1230
-
1231
-
1232
-
1233
-
1234
-
1235
-
1236
-
1237
-
1238
-
1239
-
1240
-
1241
-
1242
-
1243
-
1244
-
1245
-
1246
-
1247
-
1248
-
1249
-
1250
-
1251
-
1252
-
1253
-
1254
-
1255
-
1256
-
1257
-
1258
-
1259
-
1260
-
1261
-
1262
-
1263
-
1264
-
1265
-
1266
-
1267
-
1268
-
1269
-
1270
-
1271
-
1272
-
1273
-
1274
-
1275
-
1276
-
1277
-
1278
-
1279
-
1280
-
1281
-
1282
-
1283
-
1284
-
1285
-
1286
-
1287
-
1288
-
1289
-
1290
-
1291
-
1292
-
1293
-
1294
-
1295
-
1296
-
1297
-
1298
-
1299
-
1300
-
1301
-
1302
-
1303
-
1304
-
1305
-
1306
-
1307
-
1308
-
1309
-
1310
-
1311
-
1312
-
1313
-
1314
-
1315
-
1316
-
1317
-
1318
-
1319
-
1320
-
1321
-
1322
-
1323
-
1324
-
1325
-
1326
-
1327
-
1328
-
1329
-
1330
-
1331
-
1332
-
1333
-
1334
-
1335
-
1336
-
1337
-
1338
-
1339
-
1340
-
1341
-
1342
-
1343
-
1344
-
1345
-
1346
-
1347
-
1348
-
1349
-
1350
-
1351
-
1352
-
1353
-
1354
-
1355
-
1356
-
1357
-
1358
-
1359
-
1360
-
1361
-
1362
-
1363
-
1364
-
1365
-
1366
-
1367
-
1368
-
1369
-
1370
-
1371
-
1372
-
1373
-
1374
-
1375
-
1376
-
1377
-
1378
-
1379
-
1380
-
1381
-
1382
-
1383
-
1384
-
1385
-
1386
-
1387
-
1388
-
1389
-
1390
-
1391
-
1392
-
1393
-
1394
-
1395
-
1396
-
1397
-
1398
-
1399
-
1400
-
1401
-
1402
-
1403
-
1404
-
1405
-
1406
-
1407
-
1408
-
1409
-
1410
-
1411
-
1412
-
1413
-
1414
-
1415
-
1416
-
1417
-
1418
-
1419
-
1420
-
1421
-
1422
-
1423
-
1424
-
1425
-
1426
-
1427
-
1428
-
1429
-
1430
-
1431
-
1432
-
1433
-
1434
-
1435
-
1436
-
1437
-
1438
-
1439
-
1440
-
1441
-
1442
-
1443
-
1444
-
1445
-
1446
-
1447
-
1448
-
1449
-
1450
-
1451
-
1452
-
1453
-
1454
-
1455
-
1456
-
1457
-
1458
-
1459
-
1460
-
1461
-
1462
-
1463
-
1464
-
1465
-
1466
-
1467
-
1468
-
1469
-
1470
-
1471
-
1472
-
1473
-
1474
-
1475
-
1476
-
1477
-
1478
-
1479
-
1480
-
1481
-
1482
-
1483
-
1484
-
1485
-
1486
-
1487
-
1488
-
1489
-
1490
-
1491
-
1492
-
1493
-
1494
-
1495
-
1496
-
1497
-
1498
-
1499
-
1500
-
1501
-
1502
-
1503
-
1504
-
1505
-
1506
-
1507
-
1508
-
1509
-
1510
-
1511
-
1512
-
1513
-
1514
-
1515
-
1516
-
1517
-
1518
-
1519
-
1520
-
1521
-
1522
-
1523
-
1524
-
1525
-
1526
-
1527
-
1528
-
1529
-
1530
-
1531
-
1532
-
1533
-
1534
-
1535
-
1536
-
1537
-
1538
-
1539
-
1540
-
1541
-
1542
-
1543
-
1544
-
1545
-
1546
-
1547
-
1548
-
1549
-
1550
-
1551
-
1552
-
1553
-
1554
-
1555
-
1556
-
1557
-
1558
-
1559
-
1560
-
1561
-
1562
-
1563
-
1564
-
1565
-
1566
-
1567
-
1568
-
1569
-
1570
-
1571
-
1572
-
1573
-
1574
-
1575
-
1576
-
1577
-
1578
-
1579
-
1580
-
1581
-
1582
-
1583
-
1584
-
1585
-
1586
-
1587
-
1588
-
1589
-
1590
-
1591
-
1592
-
1593
-
1594
-
1595
-
1596
-
1597
-
1598
-
1599
-
1600
-
1601
-
1602
-
1603
-
1604
-
1605
-
1606
-
1607
-
1608
-
1609
-
1610
-
1611
-
1612
-
1613
-
1614
-
1615
-
1616
-
1617
-
1618
-
1619
-
1620
-
1621
-
1622
-
1623
-
1624
-
1625
-
1626
-
1627
-
1628
-
1629
-
1630
-
1631
-
1632
-
1633
-
1634
-
1635
-
1636
-
1637
-
1638
-
1639
-
1640
-
1641
-
1642
-
1643
-
1644
-
1645
-
1646
-
1647
-
1648
-
1649
-
1650
-
1651
-
1652
-
1653
-
1654
-
1655
-
1656
-
1657
-
1658
-
1659
-
1660
-
1661
-
1662
-
1663
-
1664
-
1665
-
1666
-
1667
-
1668
-
1669
-
1670
-
1671
-
1672
-
1673
-
1674
-
1675
-
1676
-
1677
-
1678
-
1679
-
1680
-
1681
-
1682
-
1683
-
1684
-
1685
-
1686
-
1687
-
1688
-
1689
-
1690
-
1691
-
1692
-
1693
-
1694
-
1695
-
1696
-
1697
-
1698
-
1699
-
1700
-
1701
-
1702
-
1703
-
1704
-
1705
-
1706
-
1707
-
1708
-
1709
-
1710
-
1711
-
1712
-
1713
-
1714
-
1715
-
1716
-
1717
-
1718
-
1719
-
1720
-
1721
-
1722
-
1723
-
1724
-
1725
-
1726
-
1727
-
1728
-
1729
-
1730
-
1731
-
1732
-
1733
-
1734
-
1735
-
1736
- 姿
1737
-
1738
-
1739
-
1740
-
1741
-
1742
-
1743
-
1744
-
1745
-
1746
-
1747
-
1748
-
1749
-
1750
-
1751
-
1752
-
1753
-
1754
-
1755
-
1756
-
1757
-
1758
-
1759
-
1760
-
1761
-
1762
-
1763
-
1764
-
1765
-
1766
-
1767
-
1768
-
1769
-
1770
-
1771
-
1772
- 婿
1773
-
1774
-
1775
-
1776
-
1777
-
1778
-
1779
-
1780
-
1781
-
1782
-
1783
-
1784
-
1785
-
1786
-
1787
-
1788
-
1789
-
1790
-
1791
-
1792
-
1793
-
1794
-
1795
-
1796
-
1797
-
1798
-
1799
-
1800
-
1801
- 嬿
1802
-
1803
-
1804
-
1805
-
1806
-
1807
-
1808
-
1809
-
1810
-
1811
-
1812
-
1813
-
1814
-
1815
-
1816
-
1817
-
1818
-
1819
-
1820
-
1821
-
1822
-
1823
-
1824
-
1825
-
1826
-
1827
-
1828
-
1829
-
1830
-
1831
-
1832
-
1833
-
1834
-
1835
-
1836
-
1837
-
1838
-
1839
-
1840
-
1841
-
1842
-
1843
-
1844
-
1845
-
1846
-
1847
-
1848
-
1849
-
1850
-
1851
-
1852
-
1853
-
1854
-
1855
-
1856
-
1857
-
1858
-
1859
-
1860
-
1861
-
1862
-
1863
-
1864
-
1865
-
1866
-
1867
-
1868
- 宿
1869
-
1870
-
1871
-
1872
-
1873
-
1874
-
1875
-
1876
-
1877
-
1878
-
1879
-
1880
-
1881
-
1882
-
1883
-
1884
-
1885
-
1886
-
1887
-
1888
-
1889
-
1890
-
1891
-
1892
-
1893
- 寿
1894
-
1895
-
1896
-
1897
-
1898
-
1899
-
1900
-
1901
-
1902
-
1903
-
1904
-
1905
-
1906
-
1907
-
1908
-
1909
-
1910
-
1911
-
1912
-
1913
-
1914
-
1915
-
1916
-
1917
-
1918
-
1919
-
1920
- 尿
1921
-
1922
-
1923
-
1924
-
1925
-
1926
-
1927
-
1928
-
1929
-
1930
-
1931
-
1932
-
1933
-
1934
-
1935
-
1936
-
1937
-
1938
-
1939
-
1940
-
1941
-
1942
-
1943
- 屿
1944
-
1945
-
1946
-
1947
-
1948
-
1949
-
1950
-
1951
-
1952
-
1953
-
1954
-
1955
-
1956
-
1957
-
1958
-
1959
-
1960
-
1961
-
1962
-
1963
-
1964
-
1965
- ��
1966
-
1967
-
1968
-
1969
-
1970
-
1971
-
1972
-
1973
-
1974
-
1975
-
1976
-
1977
-
1978
-
1979
-
1980
-
1981
-
1982
-
1983
-
1984
-
1985
-
1986
-
1987
-
1988
-
1989
-
1990
-
1991
-
1992
-
1993
-
1994
-
1995
-
1996
-
1997
-
1998
-
1999
-
2000
-
2001
-
2002
-
2003
-
2004
-
2005
-
2006
-
2007
-
2008
-
2009
-
2010
-
2011
-
2012
-
2013
-
2014
-
2015
-
2016
-
2017
-
2018
-
2019
-
2020
-
2021
-
2022
-
2023
-
2024
-
2025
-
2026
-
2027
- 巿
2028
-
2029
-
2030
-
2031
-
2032
-
2033
-
2034
-
2035
-
2036
-
2037
-
2038
-
2039
-
2040
-
2041
-
2042
-
2043
-
2044
-
2045
-
2046
-
2047
-
2048
-
2049
-
2050
-
2051
-
2052
-
2053
-
2054
-
2055
-
2056
-
2057
-
2058
-
2059
-
2060
-
2061
-
2062
-
2063
-
2064
-
2065
-
2066
-
2067
-
2068
-
2069
-
2070
- 广
2071
-
2072
-
2073
-
2074
-
2075
-
2076
-
2077
-
2078
-
2079
-
2080
-
2081
-
2082
-
2083
-
2084
-
2085
-
2086
-
2087
-
2088
-
2089
-
2090
-
2091
-
2092
-
2093
-
2094
-
2095
-
2096
-
2097
-
2098
-
2099
-
2100
-
2101
-
2102
-
2103
-
2104
-
2105
-
2106
-
2107
- 廿
2108
-
2109
-
2110
-
2111
-
2112
-
2113
-
2114
-
2115
-
2116
-
2117
-
2118
-
2119
-
2120
-
2121
-
2122
-
2123
-
2124
-
2125
-
2126
-
2127
-
2128
-
2129
-
2130
-
2131
-
2132
-
2133
-
2134
-
2135
-
2136
-
2137
-
2138
-
2139
-
2140
-
2141
-
2142
-
2143
-
2144
-
2145
-
2146
-
2147
-
2148
-
2149
-
2150
-
2151
-
2152
-
2153
-
2154
-
2155
-
2156
-
2157
-
2158
- 彿
2159
-
2160
-
2161
-
2162
-
2163
-
2164
-
2165
-
2166
-
2167
-
2168
-
2169
-
2170
-
2171
-
2172
-
2173
-
2174
-
2175
-
2176
-
2177
-
2178
-
2179
-
2180
-
2181
-
2182
-
2183
-
2184
-
2185
-
2186
-
2187
-
2188
-
2189
-
2190
-
2191
-
2192
-
2193
-
2194
-
2195
-
2196
-
2197
-
2198
-
2199
-
2200
-
2201
-
2202
-
2203
-
2204
-
2205
-
2206
-
2207
-
2208
-
2209
-
2210
- 忿
2211
- 怀
2212
-
2213
-
2214
-
2215
-
2216
-
2217
-
2218
-
2219
-
2220
-
2221
-
2222
-
2223
-
2224
-
2225
-
2226
-
2227
-
2228
-
2229
-
2230
-
2231
-
2232
-
2233
-
2234
-
2235
-
2236
-
2237
-
2238
-
2239
-
2240
-
2241
-
2242
-
2243
-
2244
-
2245
-
2246
-
2247
-
2248
-
2249
-
2250
-
2251
-
2252
-
2253
-
2254
-
2255
-
2256
-
2257
-
2258
-
2259
-
2260
-
2261
-
2262
-
2263
-
2264
-
2265
-
2266
-
2267
-
2268
-
2269
-
2270
-
2271
-
2272
-
2273
-
2274
-
2275
-
2276
-
2277
-
2278
-
2279
-
2280
-
2281
-
2282
-
2283
-
2284
-
2285
-
2286
-
2287
-
2288
-
2289
-
2290
-
2291
-
2292
-
2293
-
2294
-
2295
-
2296
-
2297
-
2298
-
2299
-
2300
-
2301
-
2302
-
2303
-
2304
-
2305
-
2306
-
2307
-
2308
-
2309
-
2310
-
2311
-
2312
-
2313
-
2314
-
2315
-
2316
-
2317
-
2318
-
2319
-
2320
-
2321
-
2322
-
2323
-
2324
-
2325
-
2326
-
2327
-
2328
-
2329
-
2330
-
2331
-
2332
-
2333
-
2334
-
2335
-
2336
-
2337
-
2338
-
2339
-
2340
-
2341
-
2342
-
2343
-
2344
-
2345
-
2346
-
2347
-
2348
-
2349
-
2350
-
2351
-
2352
-
2353
-
2354
-
2355
-
2356
-
2357
-
2358
-
2359
-
2360
-
2361
-
2362
-
2363
-
2364
-
2365
-
2366
-
2367
-
2368
-
2369
-
2370
-
2371
-
2372
-
2373
-
2374
-
2375
-
2376
-
2377
-
2378
-
2379
-
2380
-
2381
-
2382
-
2383
-
2384
-
2385
-
2386
-
2387
-
2388
-
2389
-
2390
-
2391
-
2392
-
2393
-
2394
-
2395
-
2396
-
2397
-
2398
-
2399
-
2400
-
2401
-
2402
-
2403
-
2404
-
2405
-
2406
-
2407
-
2408
-
2409
-
2410
-
2411
-
2412
-
2413
-
2414
-
2415
-
2416
-
2417
-
2418
-
2419
-
2420
-
2421
-
2422
-
2423
-
2424
-
2425
-
2426
-
2427
-
2428
-
2429
-
2430
-
2431
-
2432
-
2433
-
2434
-
2435
-
2436
-
2437
-
2438
-
2439
-
2440
-
2441
-
2442
-
2443
-
2444
-
2445
-
2446
-
2447
-
2448
-
2449
-
2450
-
2451
-
2452
-
2453
-
2454
-
2455
-
2456
-
2457
-
2458
-
2459
-
2460
-
2461
-
2462
-
2463
-
2464
-
2465
-
2466
-
2467
-
2468
-
2469
-
2470
-
2471
-
2472
-
2473
-
2474
-
2475
-
2476
-
2477
-
2478
-
2479
-
2480
-
2481
-
2482
-
2483
-
2484
-
2485
-
2486
-
2487
-
2488
-
2489
-
2490
-
2491
-
2492
-
2493
-
2494
-
2495
-
2496
-
2497
-
2498
-
2499
-
2500
-
2501
-
2502
-
2503
-
2504
-
2505
-
2506
-
2507
-
2508
-
2509
-
2510
-
2511
-
2512
-
2513
-
2514
-
2515
-
2516
-
2517
-
2518
-
2519
-
2520
-
2521
-
2522
-
2523
-
2524
-
2525
-
2526
-
2527
-
2528
-
2529
-
2530
-
2531
-
2532
-
2533
-
2534
-
2535
-
2536
-
2537
-
2538
-
2539
-
2540
-
2541
-
2542
-
2543
-
2544
-
2545
-
2546
-
2547
-
2548
-
2549
-
2550
-
2551
-
2552
-
2553
-
2554
-
2555
-
2556
-
2557
-
2558
-
2559
-
2560
-
2561
-
2562
-
2563
-
2564
-
2565
-
2566
-
2567
-
2568
-
2569
-
2570
-
2571
-
2572
-
2573
-
2574
-
2575
-
2576
-
2577
-
2578
-
2579
-
2580
-
2581
-
2582
-
2583
-
2584
-
2585
-
2586
-
2587
-
2588
-
2589
-
2590
-
2591
-
2592
-
2593
-
2594
-
2595
-
2596
-
2597
-
2598
-
2599
-
2600
-
2601
-
2602
-
2603
-
2604
-
2605
-
2606
-
2607
-
2608
-
2609
-
2610
-
2611
-
2612
-
2613
-
2614
-
2615
-
2616
-
2617
-
2618
-
2619
-
2620
-
2621
-
2622
-
2623
-
2624
-
2625
-
2626
-
2627
-
2628
-
2629
-
2630
-
2631
-
2632
-
2633
-
2634
-
2635
-
2636
-
2637
-
2638
-
2639
-
2640
-
2641
-
2642
-
2643
-
2644
-
2645
-
2646
-
2647
-
2648
-
2649
-
2650
-
2651
-
2652
-
2653
-
2654
-
2655
-
2656
-
2657
-
2658
-
2659
-
2660
-
2661
-
2662
-
2663
-
2664
-
2665
-
2666
-
2667
-
2668
-
2669
-
2670
-
2671
-
2672
-
2673
-
2674
-
2675
-
2676
-
2677
-
2678
-
2679
-
2680
-
2681
-
2682
-
2683
-
2684
-
2685
-
2686
-
2687
-
2688
-
2689
-
2690
-
2691
-
2692
-
2693
-
2694
-
2695
-
2696
-
2697
-
2698
-
2699
-
2700
-
2701
-
2702
-
2703
-
2704
-
2705
-
2706
-
2707
-
2708
-
2709
-
2710
-
2711
-
2712
-
2713
-
2714
-
2715
-
2716
-
2717
-
2718
-
2719
-
2720
-
2721
-
2722
-
2723
-
2724
-
2725
-
2726
-
2727
-
2728
-
2729
-
2730
-
2731
-
2732
-
2733
-
2734
-
2735
-
2736
-
2737
-
2738
-
2739
-
2740
-
2741
-
2742
-
2743
-
2744
-
2745
-
2746
-
2747
-
2748
-
2749
-
2750
-
2751
-
2752
-
2753
-
2754
-
2755
-
2756
-
2757
-
2758
-
2759
-
2760
-
2761
-
2762
-
2763
-
2764
-
2765
-
2766
-
2767
-
2768
-
2769
-
2770
-
2771
-
2772
-
2773
-
2774
-
2775
-
2776
-
2777
-
2778
-
2779
-
2780
-
2781
-
2782
-
2783
-
2784
-
2785
-
2786
-
2787
-
2788
-
2789
-
2790
-
2791
-
2792
-
2793
-
2794
-
2795
-
2796
-
2797
-
2798
-
2799
-
2800
-
2801
-
2802
-
2803
-
2804
-
2805
-
2806
-
2807
-
2808
-
2809
-
2810
-
2811
-
2812
-
2813
-
2814
-
2815
-
2816
-
2817
-
2818
-
2819
-
2820
-
2821
-
2822
-
2823
-
2824
-
2825
-
2826
-
2827
-
2828
-
2829
-
2830
-
2831
-
2832
-
2833
-
2834
-
2835
-
2836
-
2837
-
2838
-
2839
-
2840
-
2841
-
2842
-
2843
-
2844
-
2845
-
2846
-
2847
-
2848
-
2849
-
2850
-
2851
-
2852
-
2853
-
2854
-
2855
-
2856
-
2857
-
2858
-
2859
-
2860
-
2861
-
2862
-
2863
-
2864
-
2865
-
2866
-
2867
-
2868
-
2869
-
2870
-
2871
-
2872
-
2873
-
2874
-
2875
-
2876
-
2877
-
2878
-
2879
-
2880
-
2881
-
2882
-
2883
-
2884
-
2885
-
2886
-
2887
-
2888
-
2889
-
2890
-
2891
-
2892
-
2893
-
2894
-
2895
-
2896
-
2897
-
2898
-
2899
-
2900
-
2901
-
2902
-
2903
-
2904
-
2905
-
2906
-
2907
-
2908
-
2909
-
2910
-
2911
-
2912
-
2913
-
2914
-
2915
-
2916
-
2917
-
2918
-
2919
-
2920
-
2921
-
2922
-
2923
-
2924
-
2925
-
2926
-
2927
-
2928
-
2929
-
2930
-
2931
-
2932
-
2933
-
2934
-
2935
-
2936
-
2937
-
2938
-
2939
-
2940
-
2941
-
2942
-
2943
-
2944
-
2945
-
2946
-
2947
-
2948
-
2949
-
2950
-
2951
-
2952
-
2953
-
2954
-
2955
-
2956
-
2957
-
2958
-
2959
-
2960
-
2961
-
2962
-
2963
-
2964
-
2965
-
2966
-
2967
-
2968
-
2969
-
2970
-
2971
-
2972
-
2973
-
2974
-
2975
-
2976
-
2977
-
2978
-
2979
-
2980
-
2981
-
2982
-
2983
-
2984
-
2985
-
2986
-
2987
-
2988
-
2989
- ��
2990
-
2991
-
2992
-
2993
-
2994
-
2995
-
2996
-
2997
-
2998
- 椿
2999
-
3000
-
3001
-
3002
-
3003
-
3004
-
3005
-
3006
-
3007
-
3008
-
3009
-
3010
-
3011
-
3012
-
3013
-
3014
-
3015
-
3016
-
3017
-
3018
-
3019
-
3020
-
3021
-
3022
-
3023
-
3024
-
3025
-
3026
-
3027
-
3028
-
3029
-
3030
-
3031
-
3032
-
3033
-
3034
-
3035
-
3036
-
3037
-
3038
-
3039
-
3040
-
3041
-
3042
-
3043
- 槿
3044
-
3045
-
3046
-
3047
-
3048
-
3049
-
3050
-
3051
-
3052
-
3053
-
3054
-
3055
-
3056
-
3057
-
3058
-
3059
-
3060
-
3061
-
3062
-
3063
-
3064
-
3065
-
3066
-
3067
-
3068
-
3069
-
3070
-
3071
-
3072
-
3073
-
3074
-
3075
-
3076
-
3077
-
3078
-
3079
-
3080
-
3081
-
3082
-
3083
-
3084
-
3085
-
3086
-
3087
-
3088
-
3089
-
3090
-
3091
-
3092
-
3093
-
3094
-
3095
-
3096
-
3097
-
3098
-
3099
-
3100
-
3101
-
3102
-
3103
-
3104
-
3105
-
3106
-
3107
-
3108
-
3109
-
3110
-
3111
-
3112
-
3113
-
3114
-
3115
-
3116
-
3117
- 殿
3118
-
3119
-
3120
-
3121
-
3122
-
3123
-
3124
-
3125
-
3126
-
3127
-
3128
-
3129
-
3130
-
3131
-
3132
-
3133
-
3134
-
3135
-
3136
-
3137
-
3138
-
3139
-
3140
-
3141
-
3142
-
3143
-
3144
-
3145
-
3146
-
3147
-
3148
-
3149
-
3150
-
3151
-
3152
-
3153
-
3154
-
3155
-
3156
-
3157
-
3158
-
3159
-
3160
-
3161
-
3162
-
3163
-
3164
-
3165
-
3166
-
3167
-
3168
-
3169
-
3170
-
3171
-
3172
-
3173
-
3174
-
3175
-
3176
-
3177
-
3178
-
3179
-
3180
-
3181
-
3182
-
3183
-
3184
-
3185
-
3186
-
3187
-
3188
-
3189
-
3190
-
3191
-
3192
-
3193
-
3194
-
3195
-
3196
-
3197
-
3198
-
3199
-
3200
-
3201
-
3202
-
3203
-
3204
-
3205
-
3206
-
3207
-
3208
-
3209
-
3210
-
3211
-
3212
-
3213
-
3214
-
3215
-
3216
-
3217
-
3218
-
3219
-
3220
-
3221
- 沿
3222
-
3223
-
3224
-
3225
-
3226
-
3227
-
3228
-
3229
-
3230
-
3231
-
3232
-
3233
-
3234
-
3235
-
3236
-
3237
-
3238
-
3239
-
3240
-
3241
-
3242
-
3243
-
3244
-
3245
-
3246
-
3247
-
3248
-
3249
-
3250
-
3251
-
3252
-
3253
-
3254
-
3255
-
3256
-
3257
-
3258
-
3259
-
3260
-
3261
-
3262
-
3263
-
3264
-
3265
-
3266
-
3267
-
3268
-
3269
-
3270
-
3271
-
3272
-
3273
-
3274
-
3275
-
3276
-
3277
-
3278
-
3279
-
3280
-
3281
-
3282
-
3283
-
3284
-
3285
-
3286
-
3287
-
3288
-
3289
-
3290
-
3291
-
3292
-
3293
-
3294
-
3295
-
3296
-
3297
-
3298
-
3299
-
3300
-
3301
-
3302
-
3303
-
3304
-
3305
-
3306
-
3307
-
3308
-
3309
-
3310
-
3311
-
3312
-
3313
-
3314
-
3315
-
3316
-
3317
-
3318
-
3319
-
3320
-
3321
-
3322
-
3323
-
3324
-
3325
-
3326
-
3327
- 涿
3328
-
3329
-
3330
-
3331
-
3332
-
3333
-
3334
-
3335
-
3336
-
3337
-
3338
-
3339
-
3340
-
3341
-
3342
-
3343
-
3344
-
3345
-
3346
-
3347
-
3348
-
3349
-
3350
-
3351
-
3352
-
3353
-
3354
-
3355
-
3356
-
3357
-
3358
-
3359
-
3360
-
3361
-
3362
-
3363
-
3364
-
3365
-
3366
-
3367
-
3368
-
3369
-
3370
-
3371
-
3372
-
3373
-
3374
-
3375
-
3376
-
3377
-
3378
-
3379
-
3380
-
3381
-
3382
-
3383
-
3384
-
3385
-
3386
-
3387
-
3388
- 湿
3389
-
3390
-
3391
-
3392
-
3393
-
3394
-
3395
-
3396
-
3397
-
3398
-
3399
-
3400
-
3401
-
3402
-
3403
-
3404
-
3405
-
3406
-
3407
-
3408
-
3409
-
3410
-
3411
-
3412
-
3413
-
3414
-
3415
-
3416
-
3417
-
3418
-
3419
-
3420
-
3421
-
3422
-
3423
-
3424
-
3425
-
3426
-
3427
-
3428
-
3429
-
3430
-
3431
-
3432
-
3433
-
3434
-
3435
-
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6454
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6457
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6459
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6460
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6461
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6463
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6466
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6468
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6469
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6470
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6475
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6477
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6498
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6503
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6504
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6563
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6602
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6606
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6610
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6611
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6612
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6615
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6620
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6621
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6624
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6625
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6628
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6629
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6630
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6631
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6633
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6636
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6637
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6641
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6650
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6651
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6652
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6654
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6655
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6656
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6658
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6659
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6660
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6661
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6662
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6663
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6665
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6668
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6669
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6670
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6671
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6675
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6680
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6685
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6688
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6689
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6695
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6724
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6725
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6728
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6735
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6737
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6740
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6741
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6742
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6743
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6744
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6746
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6749
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6750
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6752
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6754
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6755
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6756
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6760
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6762
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6765
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6767
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6768
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6769
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6770
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6772
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6774
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6775
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6776
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6777
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6778
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6779
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6780
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6781
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6782
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6783
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6784
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6785
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6786
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6787
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6788
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6789
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6790
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6791
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6792
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6793
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6794
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6795
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6796
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6797
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6798
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6799
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6800
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6801
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6802
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6803
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6804
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6805
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6806
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6807
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6808
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6809
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6810
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6811
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6812
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6813
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6814
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6815
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6816
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6817
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6818
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6819
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6820
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6821
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6822
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6823
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6824
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6825
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6826
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6827
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 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
vocab.py ADDED
@@ -0,0 +1,453 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from patcher import tiktoken_patch
2
+ import tiktoken
3
+ from transformers import AutoTokenizer
4
+ from enum import Enum, auto
5
+ from dataclasses import dataclass, field
6
+
7
+ from utils.log_util import logger
8
+ from typing import Dict, Any, Union
9
+
10
+ """Interface:
11
+ tokenizer.encode
12
+ tokenizer.decode
13
+ tokenizer.convert_tokens_to_string # gpt4 没有这个方法
14
+ tokenizer.convert_ids_to_tokens
15
+
16
+
17
+ tokenizer.parent = ""
18
+ tokenizer.vocab_size
19
+ tokenizer.get_vocab() # gpt-neox-20b, llama
20
+ tokenizer.type = TokenizerType.ByteBPE.name
21
+ tokenizer.implementation = TokenizerImpl.SentencePiece.name # https://github.com/facebookresearch/llama/blob/main/llama/tokenizer.py
22
+ "HFGPT2Tokenizer", "HFTokenizer", "GPT2BPETokenizer", "CharLevelTokenizer", "TiktokenTokenizer", "SPMTokenizer", https://github.com/EleutherAI/gpt-neox/blob/main/tools/preprocess_data.py
23
+
24
+
25
+ tokenizer.comments = "split all numbers into individual digits, " \
26
+ "and fallback to bytes to decompose unknown UTF-8 characters"
27
+
28
+ tokenizer.all_special_tokens # baichuan
29
+ tokenizer.special_tokens_set # gpt3.5_turbo
30
+ tokenizer.special_tokens_map
31
+ """
32
+
33
+
34
+ class TokenizerImpl(Enum):
35
+ """
36
+ - https://github.com/huggingface/tokenizers/blob/main/bindings/python/py_src/tokenizers/implementations/__init__.py
37
+ - https://huggingface.co/docs/transformers/tokenizer_summary
38
+ - https://github.com/EleutherAI/gpt-neox/blob/main/megatron/tokenizer/tokenizer.py
39
+
40
+ ## google/BertTokenizer
41
+ - https://github.com/huggingface/tokenizers/blob/main/bindings/python/py_src/tokenizers/implementations/bert_wordpiece.py
42
+ - 特征
43
+ - 算法:BERT的编码器是 BPE-WordPiece,将单词拆分成多个前缀符号(比如BERT中的##)最小单元
44
+ - 词典:有##开头的token,表示subword,
45
+ - 中文采用char粒度分词
46
+ - 英文采用 WordPiece
47
+
48
+
49
+
50
+
51
+ ## google/sentencepiece
52
+ - https://github.com/google/sentencepiece/blob/3863f7648e5d8edb571ac592f3ac4f5f0695275a/src/sentencepiece_model.proto#L48
53
+ - 支持 sentencepiece 和 wordpiece
54
+ - sentencepiece 有byte-bpe吗?
55
+ - UNIGRAM = 1; // Unigram language model with dynamic algorithm
56
+ - BPE = 2; // Byte Pair Encoding
57
+ - WORD = 3; // Delimitered by whitespace.
58
+ - CHAR = 4; // tokenizes into character sequence
59
+ - wordpiece
60
+ - 特征:
61
+ - 训练: spm_train --model_type unigram/bpe/char/word
62
+ - 特殊符号: Ġ
63
+ - 文件: *.sp_model 或 *.model (可选文件 .vocab,) spm简称 (其他格式比如 tokenizer.json是给hf_tokenizer兼容用的)
64
+ - 实现:
65
+ - 依赖: protobuf
66
+ - 训练: `import sentencepiece as spm; spm.SentencePieceTrainer.train` 或 `spm_train`
67
+ - 加载: `import sentencepiece as spm; spm.SentencePieceProcessor().Load(vocab_file)`
68
+ - 方法: 是SentencePieceProcessor类型,sp_model.id_to_piece,有tokenizer.json tokenizer.model,
69
+ - 分词:
70
+ - pre_tokenizers.ByteLevel(add_prefix_space=True, use_regex=False)
71
+ - 词典: 词典字符有 ▁ (U+2581) ,表示空格或句首。
72
+ - 示例:google-t5, llama,baichuan, orion,
73
+ - llama: tokenizer.json(包含model.vocab model.merges) tokenizer.model
74
+ - grok: 原始是 .model文件,后面转成了 tokenizer.json
75
+ - google-t5: tokenizer.json, spiece.model
76
+ - Skywork-13B-Math: tokenizer.model
77
+ - xlm_roberta: sentencepiece.bpe.model
78
+ - GPT2Tokenizer
79
+ - tokenizer.json, vocab.json, merges.txt (https://huggingface.co/openai-community/gpt2)
80
+ - vocab.bpe, encoder.json, dict.txt (fairseq版本,不常用,可以忽略这个版本)
81
+
82
+
83
+
84
+ ## thu/icetk
85
+ - icetk: sentencepiece的分支,支持image_tokenizer。
86
+ - glm, chatglm1, chatglm2
87
+
88
+ ## huggingface/tokenizers
89
+ - https://github.com/huggingface/tokenizers
90
+ - VS sentencepiece
91
+ - 支持sentencepiece
92
+ - .model转化为 (merges.txt + vocab.json) 或者 tokenizer.json
93
+ - https://github.com/huggingface/tokenizers/blob/main/bindings/python/scripts/sentencepiece_extractor.py
94
+ - 加载 merges.txt, vocab.json
95
+ - SentencePieceBPETokenizer https://github.com/huggingface/tokenizers/blob/v0.19.1/bindings/python/py_src/tokenizers/implementations/sentencepiece_bpe.py#L10
96
+ - 在 sentencepiece基础上,hf_tokenizer支持pre-tokenization的正则表达式,对tab和换行支持更好,支持special token
97
+ - 类型: 支持 BBPE, WordPiece or Unigram
98
+ - 特征:
99
+ - 文件: tokenizer.json(包含后两个文件的内容), merges.txt, vocab.json
100
+ - added_tokens 在vocab中不一定存在。
101
+ - 实现:
102
+ - 训练: `from tokenizers.trainers import BpeTrainer, UnigramTrainer, WordLevelTrainer, WordPieceTrainer`
103
+ - 加载:
104
+ - 方法: .model.from_file .model.save .model.token_to_id .model.tokenize
105
+ - .model 是 tokenizer.models.BPE 类型
106
+ - 词典有 Ġ "\u0120" 开头
107
+ - 优势
108
+ -
109
+ - 示例:gpt2, gpt_neox_20b, moss, bloom, qwen2
110
+ - 优势:相对sentence piece,
111
+ - ss
112
+
113
+ ## openai/tiktoken
114
+ - 特征:空格就是空格,
115
+ - 示例:gpt3.5 gpt4, qwen,
116
+ """
117
+ """ 算法体系 https://www.huaxiaozhuan.com/%E5%B7%A5%E5%85%B7/huggingface_transformer/chapters/1_tokenizer.html
118
+ - word-base tokenizer:
119
+ - char-base tokenizer:
120
+ - subword-based Tokenizer
121
+ - BPE
122
+ - byte-bpe: base vocabulary大小是256
123
+ - WordPiece:
124
+ - 相比BPE,WordPiece 仅保存最终词表,而不保存学到的 merge rule
125
+ - Unigram
126
+ - SentencePiece
127
+
128
+ """
129
+
130
+ # 分类体系:https://github.com/huggingface/tokenizers/blob/main/bindings/python/py_src/tokenizers/implementations/
131
+ BertTokenizer = "wordpiece.BertTokenizer"
132
+ JapaneseTokenizer = ("wordpiece.MecabTokenizer", "https://github.com/polm/fugashi") # 常用日语包 ipadic,fugashi,
133
+ ByteLevelBPETokenizer = "byte_level_bpe" # BBPE
134
+ SentencePieceBPETokenizer = "sentencepiece_bpe"
135
+
136
+ # 分类体系
137
+
138
+ # SentencePeice(BPE)
139
+ SentencePiece = auto() # sentencepiece.bpe, sentencepiece.unigram, sentencepiece.char, sentencepiece.word,
140
+ byte_level_bpe = auto()
141
+ # HFTokenizer = auto() # , 支持
142
+ TikToken = auto()
143
+ # subword-nmt
144
+ # WordPiece
145
+
146
+
147
+ # load_vocab_with_SPECIAL_TOKEN = True # 如果不包含会导致计算词典大小错误、overlap_token计算不一致。
148
+
149
+
150
+ @dataclass
151
+ class TokenizerConfig:
152
+ """
153
+ https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/blob/main/src/leaderboard/read_evals.py
154
+ """
155
+ name_or_path: str # org/model (path on hub), as unique id
156
+ name_display: str = None #
157
+ impl: TokenizerImpl = None # implementation, tokenizer_class/type
158
+ org: str = None
159
+ link: str = None # http://**
160
+ desc: str = None # description
161
+ meta: str = None
162
+ level: str = None # char-level, word-level, byte-level
163
+ init_kwargs: Dict[str, Any] = field(default_factory=dict, )
164
+
165
+ def __post_init__(self):
166
+ if self.link is None:
167
+ self.link = "https://huggingface.co/" + self.name_or_path # TODO + revision
168
+ if self.name_display is None:
169
+ self.name_display = self.name_or_path
170
+
171
+ @classmethod
172
+ def init_from_json_file(cls, json_filepath: str) -> 'TokenizerConfig':
173
+ pass
174
+
175
+ def __eq__(self, other):
176
+ if isinstance(other, self.__class__):
177
+ return self.__dict__ == other.__dict__
178
+ else:
179
+ return False
180
+
181
+ def __hash__(self):
182
+ return hash(self.name_or_path)
183
+
184
+
185
+ # format: , description, hf_path, tokenizer_class/type, comments, Organization
186
+ # TODO: append link and description to the end of dropdown button.
187
+ _all_tokenizer_config = [
188
+ ##### bert 系列
189
+ TokenizerConfig("google-bert/bert-base-cased", impl=TokenizerImpl.BertTokenizer, org="Google",
190
+ desc="first add whitespace around any CJK character, then perform wordpiece tokenization."),
191
+ TokenizerConfig("google-bert/bert-base-uncased", impl=TokenizerImpl.BertTokenizer, org="Google",
192
+ desc="first add whitespace around any CJK character, then perform wordpiece tokenization."),
193
+ TokenizerConfig("google-bert/bert-base-chinese", impl=TokenizerImpl.BertTokenizer, org="Google",
194
+ desc="first add whitespace around any CJK character, then perform wordpiece tokenization."),
195
+ TokenizerConfig("google-bert/bert-base-german-cased", impl=TokenizerImpl.BertTokenizer, org="Google"),
196
+ TokenizerConfig("dbmdz/bert-base-german-uncased", impl=TokenizerImpl.BertTokenizer, org="dbmdz"),
197
+ TokenizerConfig("google-bert/bert-base-multilingual-uncased", impl=TokenizerImpl.BertTokenizer, org="Google"),
198
+ TokenizerConfig("google-bert/bert-base-multilingual-cased", impl=TokenizerImpl.BertTokenizer, org="Google"),
199
+ TokenizerConfig("tohoku-nlp/bert-base-japanese", impl=TokenizerImpl.BertTokenizer, org="Tohoku",
200
+ desc="The texts are first tokenized by MeCab morphological parser with the IPA dictionary, "
201
+ "then split into subwords by the WordPiece algorithm."),
202
+ TokenizerConfig("clue/roberta_chinese_clue_tiny", name_display="clue/roberta-chinese-clue",
203
+ impl=TokenizerImpl.BertTokenizer, org="CLUE",
204
+ init_kwargs={"revision": "refs/pr/1"},
205
+ desc="",
206
+ meta="去掉了繁体字, https://github.com/CLUEbenchmark/CLUEPretrainedModels/blob/master/README.md"),
207
+ TokenizerConfig("eson/kplug-base-encoder", name_display="eson/kplug", impl=TokenizerImpl.BertTokenizer, org="JD"),
208
+ TokenizerConfig("ckiplab/gpt2-base-chinese", impl=TokenizerImpl.BertTokenizer, org="SINICA"), # 台湾中央研究院
209
+ # WoBERT
210
+ # WoBERT Plus https://github.com/ZhuiyiTechnology/WoBERT
211
+
212
+
213
+ ##### GPT2Tokenizer
214
+ TokenizerConfig("openai-community/gpt2", impl=TokenizerImpl.SentencePiece, org="OpenAI"),
215
+ # byte-level BPE,没有byte,是unicode-level的吗?
216
+ TokenizerConfig("ClassCat/gpt2-base-french", impl=TokenizerImpl.SentencePiece, org="ClassCat"),
217
+ TokenizerConfig("ClassCat/gpt2-base-spanish", impl=TokenizerImpl.SentencePiece, org="ClassCat"),
218
+ TokenizerConfig("fnlp/moss-moon-003-sft", impl=TokenizerImpl.SentencePiece, init_kwargs={"revision": "refs/pr/6"},
219
+ org="Fudan",
220
+ desc="This tokenizer has been trained to treat spaces like parts of the tokens "
221
+ "(a bit like sentencepiece) so a word will be encoded differently whether "
222
+ "it is at the beginning of the sentence (without space) or not",
223
+ meta="在gpt2词典基础上,扩充了5万中文"),
224
+ TokenizerConfig("bigscience/bloom", impl=TokenizerImpl.SentencePiece, org="BigScience",
225
+ meta="比gpt_neox的词典 对中文支持更好。"),
226
+ # ("bloomz_6b4_zh",
227
+ # ("BelleGroup/BELLE-7B-2M", # 模型和词典都基于bloom
228
+ #
229
+ TokenizerConfig("EleutherAI/gpt-neox-20b", impl=TokenizerImpl.SentencePiece, org="EleutherAI"), # 5万
230
+ TokenizerConfig("cyberagent/open-calm-7b", impl=TokenizerImpl.SentencePiece, org="CyberAgent"), # GPTNeoXTokenizer
231
+ TokenizerConfig("abeja/gpt-neox-japanese-2.7b", impl=TokenizerImpl.SentencePiece, org="ABEJA"),
232
+ TokenizerConfig("Qwen/Qwen1.5-14B-Chat", name_display="Qwen/Qwen1.5", impl=TokenizerImpl.SentencePiece, org="Alibaba"), # 15万,速度有点慢
233
+ TokenizerConfig("HuggingFaceH4/starchat-alpha", impl=TokenizerImpl.SentencePiece, org="-"),
234
+
235
+ ####### google/sentencepiece tokenizer:
236
+ # T5 llama internlm
237
+ TokenizerConfig("google-t5/t5-large", name_display="google-t5/t5", impl=TokenizerImpl.SentencePiece, org="Google"),
238
+ # t5_small, t5_base, t5_large, flan_t5_base,
239
+ # ("t5_base", "", "sentencepiece"),
240
+ # TokenizerConfig("google/flan-t5-base", impl=TokenizerImpl.SentencePiece, ),
241
+ TokenizerConfig("lmsys/fastchat-t5-3b-v1.0", impl=TokenizerImpl.SentencePiece,
242
+ org="LMSYS",
243
+ init_kwargs={"use_fast": False} # 解决 pyo3_runtime.PanicException: AddedVocabulary bad split
244
+ ),
245
+ TokenizerConfig("CohereForAI/aya-101", org="Cohere For AI"), # "tokenizer_class": "T5Tokenizer",
246
+
247
+ TokenizerConfig("ClueAI/ChatYuan-large-v2", impl=TokenizerImpl.SentencePiece, org="CLUE"),
248
+ TokenizerConfig("ClueAI/PromptCLUE-base", impl=TokenizerImpl.SentencePiece, org="CLUE"),
249
+ TokenizerConfig("gradientai/Llama-3-8B-Instruct-Gradient-1048k", name_display="Meta/llama3",
250
+ impl=TokenizerImpl.SentencePiece, org="Meta",
251
+ desc="llama split all numbers into individual digits, and fallback to bytes to decompose unknown UTF-8 characters"),
252
+ # byte-level BPE
253
+ # '中文单字': 700, '中文多字': 0
254
+ TokenizerConfig("NousResearch/Llama-2-7b-chat-hf", name_display="Meta/llama2", impl=TokenizerImpl.SentencePiece,
255
+ org="Meta"),
256
+ TokenizerConfig("huggyllama/llama-7b", name_display="Meta/llama", impl=TokenizerImpl.SentencePiece, org="Meta"),
257
+ TokenizerConfig("hpcai-tech/grok-1", name_display="xai-org/grok-1", impl=TokenizerImpl.SentencePiece, org="xAI"),
258
+ # 由.model文件转化为了
259
+ TokenizerConfig("hfl/chinese-llama-lora-7b", impl=TokenizerImpl.SentencePiece, org="-",
260
+ meta="向原始LLaMA的词汇表中添加2w个中文词汇,针对原版LLaMA模型扩充了中文词表, 提升了中文编解码效率"),
261
+ #
262
+ TokenizerConfig("hfl/chinese-llama-2-7b", impl=TokenizerImpl.SentencePiece, org="-",
263
+ meta="重新设计了新词表(大小:55296),进一步提升了中文字词的覆盖程度"), #
264
+ TokenizerConfig("hfl/llama-3-chinese-8b", impl=TokenizerImpl.SentencePiece, org="-"),
265
+ TokenizerConfig("hfl/chinese-alpaca-lora-7b", impl=TokenizerImpl.SentencePiece, org="-"),
266
+ # 中文Alpaca模型在上述中文LLaMA模型的基础上进一步使用了指令数据进行精调。 "比chinese_llama词典多一个`[PAD]`,请勿混用"
267
+ #
268
+ # ("belle_llama_ext_7b",
269
+ # ("alpaca_7b",
270
+ TokenizerConfig("baichuan-inc/Baichuan-7B", name_display="baichuan-inc/baichuan",
271
+ impl=TokenizerImpl.SentencePiece,
272
+ level="byte-level", org="Baichuan"),
273
+ TokenizerConfig("baichuan-inc/Baichuan2-7B-Chat", name_display="baichuan-inc/baichuan2",
274
+ impl=TokenizerImpl.SentencePiece, org="Baichuan",
275
+ desc="expand the vocabulary size from 64000 in Baichuan1 to 125696"),
276
+ TokenizerConfig("internlm/internlm-chat-7b", impl=TokenizerImpl.SentencePiece, org="Shanghai AI Lab"),
277
+ # 上海AI实验室 + 商汤
278
+ TokenizerConfig("internlm/internlm2-chat-7b", impl=TokenizerImpl.SentencePiece, org="Shanghai AI Lab"),
279
+ TokenizerConfig("internlm/internlm2-math-7b", impl=TokenizerImpl.SentencePiece, org="Shanghai AI Lab"),
280
+ TokenizerConfig("internlm/internlm-xcomposer-7b", impl=TokenizerImpl.SentencePiece, org="Shanghai AI Lab"),
281
+ TokenizerConfig("tiiuae/falcon-7b", impl=TokenizerImpl.SentencePiece, org="TII"),
282
+ TokenizerConfig("tiiuae/falcon-180b", impl=TokenizerImpl.SentencePiece, org="TII"),
283
+ TokenizerConfig("Skywork/Skywork-13B-base", impl=TokenizerImpl.SentencePiece, org="Kunlun"),
284
+ TokenizerConfig("Skywork/Skywork-13B-Math", impl=TokenizerImpl.SentencePiece, org="Kunlun"), # 文件:tokenizer.model
285
+ TokenizerConfig("FacebookAI/xlm-roberta-base", impl=TokenizerImpl.SentencePiece, org="Facebook"),
286
+ # 这个的tokenizer.json 为什么没有merges? vocab里为什么有概率值?
287
+ # "goat",
288
+
289
+ # ##### glm系列
290
+ # "glm_chinese",),
291
+ TokenizerConfig("THUDM/chatglm-6b", impl=TokenizerImpl.SentencePiece, org="Tsinghua",
292
+ meta=f"num_image_tokens: {12}; num_image_tokens: {34} ",
293
+ init_kwargs={"revision": "refs/pr/100"}),
294
+ TokenizerConfig("THUDM/chatglm2-6b", impl=TokenizerImpl.SentencePiece, org="Tsinghua", ),
295
+ TokenizerConfig("THUDM/chatglm3-6b", impl=TokenizerImpl.SentencePiece, org="Tsinghua", ),
296
+ TokenizerConfig("thu-coai/CharacterGLM-6B", impl=TokenizerImpl.SentencePiece, org="Tsinghua", ),
297
+
298
+ # tiktoken 系列
299
+ TokenizerConfig("openai/text-davinci-003", impl=TokenizerImpl.TikToken, org="OpenAI",
300
+ link="https://github.com/openai/tiktoken"),
301
+ #
302
+ TokenizerConfig("openai/code-davinci-002", impl=TokenizerImpl.TikToken, org="OpenAI",
303
+ link="https://github.com/openai/tiktoken"),
304
+ TokenizerConfig("openai/gpt-3.5-turbo", impl=TokenizerImpl.TikToken, org="OpenAI",
305
+ link="https://github.com/openai/tiktoken",
306
+ desc="tiktoken is a fast BPE tokeniser for use with OpenAI's models. There are 16 tokens KeyError"),
307
+ TokenizerConfig("openai/gpt-4", impl=TokenizerImpl.TikToken, org="OpenAI",
308
+ link="https://github.com/openai/tiktoken", ),
309
+ TokenizerConfig("openai/gpt-4o", impl=TokenizerImpl.TikToken, org="OpenAI",
310
+ link="https://github.com/openai/tiktoken", ),
311
+ TokenizerConfig("Qwen/Qwen-7B-Chat", name_display="Qwen/Qwen", impl=TokenizerImpl.TikToken, org="Alibaba",
312
+ init_kwargs={"revision": "refs/pr/56"},
313
+ meta="在gpt4词典基础上,删除了100个多数字token,增加10000中文词token;并优化了special_token的分词"),
314
+ # https://huggingface.co/Qwen/Qwen-7B-Chat#%E6%A8%A1%E5%9E%8B%E7%BB%86%E8%8A%82%EF%BC%88model%EF%BC%89
315
+ # 该词表在GPT-4使用的BPE词表cl100k_base基础上,对中文、多语言进行了优化,在对中、英、代码数据的高效编解码的基础上,
316
+ # 对部分多语言更加友好,方便用户在不扩展词表的情况下对部分语种进行能力增强。 词表对数字按单个数字位切分。
317
+
318
+ # TokenizerConfig("Qwen/Qwen-72B-Chat", impl=TokenizerImpl.TikToken),
319
+
320
+ # 未分类
321
+ # ("amber", ""),
322
+ TokenizerConfig("LLM360/CrystalCoder", org="MBZUAI"),
323
+ TokenizerConfig("mistralai/Mistral-7B-v0.1", org="Mistral"),
324
+ TokenizerConfig("mistralai/Mixtral-8x7B-v0.1", org="Mistral"),
325
+
326
+ TokenizerConfig("paust/pko-t5-large", org="PAUST"),
327
+
328
+ TokenizerConfig("01-ai/Yi-6B", org="Yi"),
329
+ TokenizerConfig("01-ai/Yi-34B", org="Yi"),
330
+ TokenizerConfig("01-ai/Yi-VL-34B", org="Yi"),
331
+ TokenizerConfig("OrionStarAI/Orion-14B-Chat", org="OrionStar"),
332
+ TokenizerConfig("microsoft/phi-1", org="Microsoft"),
333
+ TokenizerConfig("microsoft/phi-2", org="Microsoft"),
334
+ TokenizerConfig("microsoft/Phi-3-mini-4k-instruct", org="Microsoft", meta="即llama vocab"),
335
+ TokenizerConfig("Upstage/SOLAR-10.7B-v1.0", org="-"),
336
+ TokenizerConfig("google/mobilebert-uncased", org="Google"),
337
+ # ("google/mobilenet_v2_1.0_224",), # error
338
+ TokenizerConfig("google/switch-c-2048", org="Google"),
339
+ TokenizerConfig("google/byt5-small", org="Google"),
340
+ TokenizerConfig("google/mt5-large", org="Google"),
341
+ TokenizerConfig("WizardLM/WizardCoder-Python-7B-V1.0", org="Microsoft"),
342
+ TokenizerConfig("WizardLM/WizardCoder-15B-V1.0", org="Microsoft"),
343
+ TokenizerConfig("WizardLM/WizardLM-7B-V1.0", org="Microsoft"),
344
+ TokenizerConfig("WizardLM/WizardMath-70B-V1.0", org="Microsoft"),
345
+ TokenizerConfig("TigerResearch/tigerbot-70b-chat-v4-4k", org="Tigerobo"),
346
+ TokenizerConfig("TigerResearch/tigerbot-13b-chat-v2", org="Tigerobo"),
347
+ TokenizerConfig("deepseek-ai/deepseek-coder-33b-instruct", org="DeepSeek"),
348
+ TokenizerConfig("deepseek-ai/deepseek-llm-7b-base", org="DeepSeek"),
349
+ TokenizerConfig("deepseek-ai/DeepSeek-V2", org="DeepSeek"),
350
+ TokenizerConfig("google/gemma-7b", org="Google"),
351
+ TokenizerConfig("allenai/OLMo-7B", org="Allen AI"),
352
+ TokenizerConfig("HuggingFaceH4/zephyr-7b-beta", org="HuggingFace"),
353
+ TokenizerConfig("ai21labs/Jamba-v0.1", org="AI21"),
354
+ TokenizerConfig("databricks/dbrx-instruct", org="Databricks"),
355
+
356
+ # ("claude",),
357
+ # https://github.com/Duxiaoman-DI/XuanYuan
358
+
359
+ # https://huggingface.co/apple/OpenELM-3B-Instruct https://huggingface.co/apple/OpenELM-3B
360
+
361
+ ]
362
+
363
+ assert len(set([config.name_display for config in _all_tokenizer_config])) == len(_all_tokenizer_config)
364
+ assert len(set([config.name_or_path for config in _all_tokenizer_config])) == len(_all_tokenizer_config)
365
+ assert len(set([config.name_or_path.split("/")[-1] for config in _all_tokenizer_config])) == len(_all_tokenizer_config)
366
+
367
+
368
+ class TokenizerFactory:
369
+
370
+ def __init__(self):
371
+ self.all_tokenizer_configs = sorted(_all_tokenizer_config, key=lambda k: k.name_or_path)
372
+ self.all_tokenizer_names = [config.name_or_path for config in self.all_tokenizer_configs]
373
+ self.name_to_config_list = [
374
+ {config.name_or_path: config for config in self.all_tokenizer_configs},
375
+ {config.name_display: config for config in self.all_tokenizer_configs},
376
+ {config.name_display.split("/")[-1]: config for config in self.all_tokenizer_configs},
377
+ ]
378
+ self.tokenizer_cache = {}
379
+
380
+ def get_tokenizer_config(self, tokenizer_name: str) -> TokenizerConfig:
381
+ for name_to_config in self.name_to_config_list:
382
+ if tokenizer_name in name_to_config:
383
+ return name_to_config[tokenizer_name]
384
+ return None
385
+
386
+ def get_tokenizer(self, tokenizer_name: str):
387
+ """
388
+ :param tokenizer_config:
389
+ :return:
390
+ """
391
+ tokenizer_config = self.get_tokenizer_config(tokenizer_name)
392
+
393
+ # 1. load from cache
394
+ if tokenizer_config in self.tokenizer_cache:
395
+ return self.tokenizer_cache[tokenizer_config]
396
+
397
+ # 2. load tokenizer
398
+ logger.info(f"loading tokenizer {tokenizer_config.name_or_path}")
399
+ if tokenizer_config.impl == TokenizerImpl.TikToken and "openai" in tokenizer_config.name_or_path:
400
+ tokenizer = tiktoken.encoding_for_model(tokenizer_config.name_or_path.replace("openai/", ""))
401
+ else:
402
+ tokenizer = AutoTokenizer.from_pretrained(
403
+ tokenizer_config.name_or_path,
404
+ trust_remote_code=True,
405
+ **tokenizer_config.init_kwargs
406
+ )
407
+ self.tokenizer_cache[tokenizer_config] = tokenizer
408
+ return tokenizer
409
+
410
+ def get_name_with_hyperlink(self, tokenizer_name):
411
+ def model_hyperlink(link, model_name):
412
+ model_name = model_name
413
+ return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
414
+
415
+ tokenizer_config = self.get_tokenizer_config(tokenizer_name)
416
+ return model_hyperlink(tokenizer_config.link, tokenizer_config.name_display.split("/")[-1])
417
+
418
+
419
+ tokenizer_factory = TokenizerFactory()
420
+
421
+ # class TokenizerType(Enum):
422
+ #
423
+ # # BERTTokenizer
424
+ # # 依赖一个txt文件
425
+ #
426
+ #
427
+ # # https://github.com/EleutherAI/gpt-neox/blob/v2.0/megatron/tokenizer/tokenizer.py#L231
428
+ # # 依赖一个json文件,Tokenizer.from_file(vocab_file)
429
+ # # 案例:gpt-neox-20B
430
+ # HFTokenizer = auto()
431
+ #
432
+ # # 依赖: model_file, sentencepiece.SentencePieceProcessor(model_file)
433
+ # # 案例:
434
+ # SentencePieceTokenizer = auto()
435
+ #
436
+ #
437
+ # # 依赖: 3个json文件:vocab.json, merges.txt, special_tokens.txt
438
+ # # 源码:
439
+ # # - https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/tokenizer/gpt2_tokenization.py#L92
440
+ # # Byte-level BPE
441
+ # GPT2BPETokenizer = auto()
442
+
443
+
444
+ if __name__ == "__main__":
445
+
446
+ for tokenizer_config in tokenizer_factory.all_tokenizer_configs:
447
+ if True:
448
+ # if "t5" in tokenizer_config.name_or_path:
449
+ tokenizer1 = tokenizer_factory.get_tokenizer(tokenizer_config.name_or_path)
450
+ tokenizer2 = tokenizer_factory.get_tokenizer(tokenizer_config.name_display)
451
+ tokenizer3 = tokenizer_factory.get_tokenizer(tokenizer_config.name_display.split("/")[-1])
452
+ assert tokenizer1 == tokenizer2 == tokenizer3
453
+ print(tokenizer_config.name_or_path, len(tokenizer1))
vocab/Intern_gpt/README.md DELETED
File without changes
vocab/__init__.py DELETED
@@ -1,260 +0,0 @@
1
- import importlib
2
- from enum import Enum, auto
3
-
4
- """Interface:
5
- tokenizer.encode
6
- tokenizer.decode
7
- tokenizer.convert_tokens_to_string # gpt4 没有这个方法
8
- tokenizer.convert_ids_to_tokens
9
-
10
-
11
- tokenizer.parent = ""
12
- tokenizer.vocab_size
13
- tokenizer.get_vocab() # gpt-neox-20b, llama
14
- tokenizer.type = TokenizerType.ByteBPE.name
15
- tokenizer.implementation = TokenizerImpl.SentencePiece.name # https://github.com/facebookresearch/llama/blob/main/llama/tokenizer.py
16
- "HFGPT2Tokenizer", "HFTokenizer", "GPT2BPETokenizer", "CharLevelTokenizer", "TiktokenTokenizer", "SPMTokenizer", https://github.com/EleutherAI/gpt-neox/blob/main/tools/preprocess_data.py
17
-
18
- - google/bert
19
- - 特征
20
- - 词典:有##开头的token,表示subword
21
- - 示例:
22
- - bpe-google/sentencepiece:
23
- - 特征:
24
- - 训练:
25
- - 文件: *.sp_model 或 *.model (可选文件 .vocab,) spm简称
26
- - 实现:
27
- - 依赖: protobuf
28
- - 训练: `import sentencepiece as spm; spm.SentencePieceTrainer.train` 或 `spm_train`
29
- - 加载: `import sentencepiece as spm; spm.SentencePieceProcessor().Load(vocab_file)`
30
- - 方法: 是SentencePieceProcessor类型,sp_model.id_to_piece,有tokenizer.json tokenizer.model,
31
- - 分词:
32
- - pre_tokenizers.ByteLevel(add_prefix_space=True, use_regex=False)
33
- - 词典: 词典字符有 ▁ (U+2581) ,表示空格或句首。
34
- - 示例:google-t5, llama,baichuan, orion,
35
- - icetk: sentencepiece的分支,支持image_tokenizer
36
- - glm, chatglm1, chatglm2
37
- - openai/tiktoken
38
- - bpe-hf_tokenizer
39
- - ss
40
- - 特征:
41
- - 文件: tokenizer.json(包含后两个文件的内容), merges.txt, vocab.json
42
- - added_tokens 在vocab中不一定存在。
43
- - 实现:
44
- - 训练: `from tokenizers.trainers import BpeTrainer, UnigramTrainer, WordLevelTrainer, WordPieceTrainer`
45
- - 加载:
46
- - 方法: .model.from_file .model.save .model.token_to_id .model.tokenize
47
- - .model 是 tokenizer.models.BPE 类型
48
- - 词典有 Ġ "\u0120" 开头
49
- - 优势
50
- -
51
- - 示例:gpt2, gpt_neox_20b, moss, bloom, qwen2
52
- - 优势:相对sentence piece,hf_tokenizer支持pre-tokenization的正则表达式,对tab和换行支持更好 ()
53
- - ss
54
- - tiktoken
55
- - 特征:空格就是空格,
56
- - 示例:gpt3.5 gpt4, qwen,
57
- tokenizer.comments = "split all numbers into individual digits, " \
58
- "and fallback to bytes to decompose unknown UTF-8 characters"
59
-
60
- tokenizer.all_special_tokens # baichuan
61
- tokenizer.special_tokens_set # gpt3.5_turbo
62
- tokenizer.special_tokens_map
63
-
64
- tokenizer.dependency [sentencepiece, tiktoken, icetk]
65
- """
66
-
67
- from utils.log_util import logger
68
-
69
- # Animal = Enum('Animal', 'ANT BEE CAT DOG')
70
-
71
- uniq_tokenizers = [
72
- ""
73
- ]
74
-
75
- # format: alias/abbr, description, hf_path, tokenizer_class/type, comments, Organization
76
- # TODO: append link and description to the end of dropdown button.
77
- all_tokenizers = [
78
- ##### bert 系列
79
- ("bert_base_cased", "", "bert"),
80
- ("bert_base_uncased", "", "bert"),
81
- ("bert_base_chinese", "", "bert"),
82
- ("roberta_chinese_clue", "", "bert"),
83
- ("kplug",),
84
- ("gpt2_chinese",),
85
-
86
- ##### GPT2Tokenizer
87
- ("gpt2", "", "GPT2Tokenizer",), #
88
- ("moss", "", "GPT2Tokenizer",),
89
- ("bloom", "", "GPT2Tokenizer",),
90
- # ("bloomz_6b4_zh",
91
- # ("belle_7b_2m", # 模型和词典都基于bloom
92
- #
93
- ("gpt_nexo_20b", "", "GPT2Tokenizer",), # 5万
94
- ("qwen1_5_14b_chat", "", "GPT2Tokenizer",), # 15万,速度有点慢
95
- ("starchat_alpha", "", "GPT2Tokenizer",),
96
-
97
- ####### google/sentencepiece tokenizer:
98
- # T5 llama internlm
99
- ("t5_small", "", "sentencepiece"),
100
- ("t5_base", "", "sentencepiece"),
101
- ("t5_large", "", "sentencepiece"),
102
- ("chatyuan_large_v2", "", "sentencepiece"),
103
- ("prompt_clue", "", "sentencepiece"),
104
-
105
- ("llama", "", "sentencepiece", "llama use single digits and thus uses 4 tokens to encode the number 1000"), # '中文单字': 700, '中文多字': 0
106
- ("llama2", "", "sentencepiece"),
107
- ("llama3", "", "sentencepiece"),
108
- ("chinese_llama", "", "sentencepiece"), #
109
- ("chinese_llama2", "", "sentencepiece"), #
110
- ("llama_3_chinese_8b", "sentencepiece"),
111
- # ("chinese_alpaca_lora_7b", # 中文Alpaca模型在上述中文LLaMA模型的基础上进一步使用了指令数据进行精调。
112
- # ("belle_llama_ext_7b",
113
- # ("alpaca_7b",
114
- ("baichuan", "", "sentencepiece"),
115
- ("baichuan2", "", "sentencepiece"),
116
- ("internlm_chat_7b", "", "sentencepiece"),
117
- ("internlm2_chat_7b", "", "sentencepiece"),
118
- ("internlm2_math_7b", "", "sentencepiece"),
119
- ("internlm_xcomposer_7b", "", "sentencepiece"),
120
- ("falcon_7b", "", "sentencepiece"),
121
- ("falcon_180b", "", "sentencepiece"),
122
- ("skywork_13b_base",),
123
- ("skywork_13b_math",),
124
- ("xlm_roberta", ),
125
- # "goat",
126
-
127
- # ##### glm系列
128
- # "glm_chinese",),
129
- ("chatglm_6b", "", "sentencepiece"),
130
- ("chatglm2_6b", "", "sentencepiece"),
131
- ("chatglm3_6b", "", "sentencepiece"),
132
- ("character_glm_6b", "", "sentencepiece"),
133
-
134
- # tiktoken 系列
135
- ("qwen_1_8b_chat", "", "tiktoken"),
136
- ("qwen_7b_chat", "", "tiktoken"),
137
- ("qwen_72b_chat", "", "tiktoken"),
138
- ("text_davinci_003", "", "tiktoken"),
139
- ("code_davinci_002", "", "tiktoken"),
140
- ("gpt_35_turbo", "", "tiktoken"),
141
- ("gpt_4", "", "tiktoken"),
142
-
143
- # 未分类
144
- # ("amber", ""),
145
- ("crystal_coder", ""),
146
- ("mistral_7b",),
147
- ("mixtral_8_7b",),
148
-
149
-
150
- ("flan_t5_base",),
151
- ("fastchat_t5_3b",),
152
- ("pko_t5_large",),
153
- ("wizardcoder_15b_v1",),
154
- ("yi_6b",),
155
- ("yi_34b",),
156
- ("yi_vl34b",),
157
- ("orion_14b_chat",),
158
- ("phi_1",),
159
- ("phi_2",),
160
- ("phi_3_mini", "即llama vocab"),
161
- ("solar_10_7b",),
162
- ("mobilebert_uncased",),
163
- # ("mobilenet_v2",), # error
164
- ("switch_c_2048",),
165
- ("byt5_small",),
166
- ("mt5_large",),
167
- ("wizardcoder_python_7b_v1",),
168
- ("wizardlm_7b_v1",),
169
- ("wizardmath_70b_v1",),
170
- ("tigerbot_70b_chat_v4_4k",),
171
- ("tigerbot_13b_chat_v2",),
172
- ("deepseek_coder_33b_instruct",),
173
- ("deepseek_llm_7b_base",),
174
- ("gemma_7b",),
175
- ("olmo_7b",),
176
- ("aya_101",),
177
- ("zephyr_7b_beta",),
178
- ("jamba_v0_1", ),
179
- ("dbrx_instruct", ),
180
- ("grok_1",),
181
- # ("claude",),
182
- ("gpt_nexo_20b", ),
183
- ("gpt_neox_japanese_2_7b", ),
184
-
185
- ]
186
-
187
- all_tokenizers = [tokenizer[0] for tokenizer in all_tokenizers]
188
- all_tokenizers = sorted(all_tokenizers)
189
-
190
-
191
- class TokenizerType(Enum):
192
- """
193
- - https://huggingface.co/docs/transformers/tokenizer_summary
194
- - https://github.com/EleutherAI/gpt-neox/blob/main/megatron/tokenizer/tokenizer.py
195
- - https://github.com/google/sentencepiece/blob/3863f7648e5d8edb571ac592f3ac4f5f0695275a/src/sentencepiece_model.proto#L48
196
- - UNIGRAM = 1; // Unigram language model with dynamic algorithm
197
- - BPE = 2; // Byte Pair Encoding
198
- - WORD = 3; // Delimitered by whitespace.
199
- - CHAR = 4; // tokenizes into character sequence
200
- """
201
- BPE = auto()
202
- ByteBPE = auto() # BBPE Byte-Level BPE
203
- GPT2BPETokenizer = auto() #
204
- BERTTokenizer = auto()
205
-
206
-
207
- # class TokenizerType(Enum):
208
- #
209
- # # BERTTokenizer
210
- # # 依赖一个txt文件
211
- #
212
- #
213
- # # https://github.com/EleutherAI/gpt-neox/blob/v2.0/megatron/tokenizer/tokenizer.py#L231
214
- # # 依赖一个json文件,Tokenizer.from_file(vocab_file)
215
- # # 案例:gpt-neox-20B
216
- # HFTokenizer = auto()
217
- #
218
- # # 依赖: model_file, sentencepiece.SentencePieceProcessor(model_file)
219
- # # 案例:
220
- # SentencePieceTokenizer = auto()
221
- #
222
- #
223
- # # 依赖: 3个json文件:vocab.json, merges.txt, special_tokens.txt
224
- # # 源码:
225
- # # - https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/tokenizer/gpt2_tokenization.py#L92
226
- # # Byte-level BPE
227
- # GPT2BPETokenizer = auto()
228
-
229
-
230
- class TokenizerImpl(Enum):
231
-
232
- """
233
- https://github.com/google/sentencepiece,支持 sentencepiece(BPE,unigram,char,word), wordpiece,
234
- spm_train --model_type unigram/bpe/char/word
235
- """
236
- SentencePiece = auto()
237
-
238
- # https://github.com/huggingface/transformers/blob/v4.30.2/src/transformers/models/gpt2/tokenization_gpt2.py#L104
239
- # 构造词典:
240
- # GPT2Tokenizer = auto()
241
- # BertTokenizer = auto() #
242
-
243
- """
244
- """
245
- HFTokenizer = auto() # https://github.com/huggingface/tokenizers, 支持
246
-
247
-
248
- cache = {}
249
-
250
- def load_tokener(model_name):
251
- if model_name in cache:
252
- return cache[model_name]
253
- logger.info(f"loading tokenizer {model_name}")
254
- tokenizer = importlib.import_module("." + model_name, 'vocab').tokenizer
255
- tokenizer.alias = model_name
256
- return tokenizer
257
-
258
-
259
- if __name__ == "__main__":
260
- pass
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
vocab/_alpaca_7b/README.md DELETED
File without changes
vocab/_goat/README.md DELETED
File without changes
vocab/_goat/__init__.py DELETED
File without changes
vocab/albert/__init__.py DELETED
@@ -1,6 +0,0 @@
1
- """
2
-
3
- SentencePiece(unigram)
4
-
5
- https://huggingface.co/docs/transformers/tokenizer_summary#sentencepiece
6
- """
 
 
 
 
 
 
 
vocab/aya_101/__init__.py DELETED
@@ -1,5 +0,0 @@
1
-
2
-
3
- from transformers import AutoTokenizer
4
-
5
- tokenizer = AutoTokenizer.from_pretrained("CohereForAI/aya-101")
 
 
 
 
 
 
vocab/baichuan/Baichuan-7B/config.json DELETED
@@ -1,26 +0,0 @@
1
- {
2
- "architectures": [
3
- "BaiChuanForCausalLM"
4
- ],
5
- "auto_map": {
6
- "AutoConfig": "configuration_baichuan.BaiChuanConfig",
7
- "AutoModelForCausalLM": "modeling_baichuan.BaiChuanForCausalLM"
8
- },
9
- "bos_token_id": 1,
10
- "eos_token_id": 2,
11
- "hidden_act": "silu",
12
- "hidden_size": 4096,
13
- "initializer_range": 0.02,
14
- "intermediate_size": 11008,
15
- "max_position_embeddings": 4096,
16
- "model_type": "baichuan",
17
- "num_attention_heads": 32,
18
- "num_hidden_layers": 32,
19
- "pad_token_id": 0,
20
- "rms_norm_eps": 1e-06,
21
- "tie_word_embeddings": false,
22
- "torch_dtype": "float32",
23
- "transformers_version": "4.29.1",
24
- "use_cache": true,
25
- "vocab_size": 64000
26
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
vocab/baichuan/Baichuan-7B/configuration_baichuan.py DELETED
@@ -1,66 +0,0 @@
1
- # coding=utf-8
2
- # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
3
- #
4
- # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
5
- # and OPT implementations in this library. It has been modified from its
6
- # original forms to accommodate minor architectural differences compared
7
- # to GPT-NeoX and OPT used by the Meta AI team that trained the model.
8
- #
9
- # Licensed under the Apache License, Version 2.0 (the "License");
10
- # you may not use this file except in compliance with the License.
11
- # You may obtain a copy of the License at
12
- #
13
- # http://www.apache.org/licenses/LICENSE-2.0
14
- #
15
- # Unless required by applicable law or agreed to in writing, software
16
- # distributed under the License is distributed on an "AS IS" BASIS,
17
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
18
- # See the License for the specific language governing permissions and
19
- # limitations under the License.
20
-
21
- from transformers.configuration_utils import PretrainedConfig
22
- from transformers.utils import logging
23
-
24
-
25
- logger = logging.get_logger(__name__)
26
-
27
-
28
- class BaiChuanConfig(PretrainedConfig):
29
- model_type = "baichuan"
30
- keys_to_ignore_at_inference = ["past_key_values"]
31
-
32
- def __init__(
33
- self,
34
- vocab_size=64000,
35
- hidden_size=4096,
36
- intermediate_size=11008,
37
- num_hidden_layers=32,
38
- num_attention_heads=32,
39
- hidden_act="silu",
40
- max_position_embeddings=4096,
41
- initializer_range=0.02,
42
- rms_norm_eps=1e-6,
43
- use_cache=True,
44
- pad_token_id=0,
45
- bos_token_id=1,
46
- eos_token_id=2,
47
- tie_word_embeddings=False,
48
- **kwargs,
49
- ):
50
- self.vocab_size = vocab_size
51
- self.max_position_embeddings = max_position_embeddings
52
- self.hidden_size = hidden_size
53
- self.intermediate_size = intermediate_size
54
- self.num_hidden_layers = num_hidden_layers
55
- self.num_attention_heads = num_attention_heads
56
- self.hidden_act = hidden_act
57
- self.initializer_range = initializer_range
58
- self.rms_norm_eps = rms_norm_eps
59
- self.use_cache = use_cache
60
- super().__init__(
61
- pad_token_id=pad_token_id,
62
- bos_token_id=bos_token_id,
63
- eos_token_id=eos_token_id,
64
- tie_word_embeddings=tie_word_embeddings,
65
- **kwargs,
66
- )
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
vocab/baichuan/Baichuan-7B/special_tokens_map.json DELETED
@@ -1,23 +0,0 @@
1
- {
2
- "bos_token": {
3
- "content": "<s>",
4
- "lstrip": false,
5
- "normalized": true,
6
- "rstrip": false,
7
- "single_word": false
8
- },
9
- "eos_token": {
10
- "content": "</s>",
11
- "lstrip": false,
12
- "normalized": true,
13
- "rstrip": false,
14
- "single_word": false
15
- },
16
- "unk_token": {
17
- "content": "<unk>",
18
- "lstrip": false,
19
- "normalized": true,
20
- "rstrip": false,
21
- "single_word": false
22
- }
23
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
vocab/baichuan/Baichuan-7B/tokenization_baichuan.py DELETED
@@ -1,250 +0,0 @@
1
- # coding=utf-8
2
- # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
3
- #
4
- # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
5
- # and OPT implementations in this library. It has been modified from its
6
- # original forms to accommodate minor architectural differences compared
7
- # to GPT-NeoX and OPT used by the Meta AI team that trained the model.
8
- #
9
- # Licensed under the Apache License, Version 2.0 (the "License");
10
- # you may not use this file except in compliance with the License.
11
- # You may obtain a copy of the License at
12
- #
13
- # http://www.apache.org/licenses/LICENSE-2.0
14
- #
15
- # Unless required by applicable law or agreed to in writing, software
16
- # distributed under the License is distributed on an "AS IS" BASIS,
17
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
18
- # See the License for the specific language governing permissions and
19
- # limitations under the License.
20
-
21
- import os
22
- from shutil import copyfile
23
- from typing import Any, Dict, List, Optional, Tuple
24
-
25
- import sentencepiece as spm
26
-
27
- from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
28
- from transformers.utils import logging
29
-
30
-
31
- logger = logging.get_logger(__name__)
32
-
33
- VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
34
-
35
- PRETRAINED_VOCAB_FILES_MAP = {
36
- "vocab_file": {},
37
- "tokenizer_file": {},
38
- }
39
- PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {}
40
-
41
-
42
- class BaiChuanTokenizer(PreTrainedTokenizer):
43
- """
44
- Construct a BaiChuan tokenizer. Based on byte-level Byte-Pair-Encoding.
45
-
46
- Args:
47
- vocab_file (`str`):
48
- Path to the vocabulary file.
49
- """
50
-
51
- vocab_files_names = VOCAB_FILES_NAMES
52
- pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
53
- max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
54
- model_input_names = ["input_ids", "attention_mask"]
55
-
56
- def __init__(
57
- self,
58
- vocab_file,
59
- unk_token="<unk>",
60
- bos_token="<s>",
61
- eos_token="</s>",
62
- pad_token=None,
63
- sp_model_kwargs: Optional[Dict[str, Any]] = None,
64
- add_bos_token=True,
65
- add_eos_token=False,
66
- clean_up_tokenization_spaces=False,
67
- **kwargs,
68
- ):
69
- self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
70
- bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
71
- eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
72
- unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
73
- pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
74
- self.vocab_file = vocab_file
75
- self.add_bos_token = add_bos_token
76
- self.add_eos_token = add_eos_token
77
- self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
78
- self.sp_model.Load(vocab_file)
79
- super().__init__(
80
- bos_token=bos_token,
81
- eos_token=eos_token,
82
- unk_token=unk_token,
83
- pad_token=pad_token,
84
- add_bos_token=add_bos_token,
85
- add_eos_token=add_eos_token,
86
- sp_model_kwargs=self.sp_model_kwargs,
87
- clean_up_tokenization_spaces=clean_up_tokenization_spaces,
88
- **kwargs,
89
- )
90
-
91
- def __getstate__(self):
92
- state = self.__dict__.copy()
93
- state["sp_model"] = None
94
- return state
95
-
96
- def __setstate__(self, d):
97
- self.__dict__ = d
98
- self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
99
- self.sp_model.Load(self.vocab_file)
100
-
101
- @property
102
- def vocab_size(self):
103
- """Returns vocab size"""
104
- return self.sp_model.get_piece_size()
105
-
106
- def get_vocab(self):
107
- """Returns vocab as a dict"""
108
- vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
109
- vocab.update(self.added_tokens_encoder)
110
- return vocab
111
-
112
- def _tokenize(self, text):
113
- """Returns a tokenized string."""
114
- return self.sp_model.encode(text, out_type=str)
115
-
116
- def _convert_token_to_id(self, token):
117
- """Converts a token (str) in an id using the vocab."""
118
- return self.sp_model.piece_to_id(token)
119
-
120
- def _convert_id_to_token(self, index):
121
- """Converts an index (integer) in a token (str) using the vocab."""
122
- token = self.sp_model.IdToPiece(index)
123
- return token
124
-
125
- def convert_tokens_to_string(self, tokens):
126
- """Converts a sequence of tokens (string) in a single string."""
127
- current_sub_tokens = []
128
- out_string = ""
129
- prev_is_special = False
130
- for i, token in enumerate(tokens):
131
- # make sure that special tokens are not decoded using sentencepiece model
132
- if token in self.all_special_tokens:
133
- if not prev_is_special and i != 0:
134
- out_string += " "
135
- out_string += self.sp_model.decode(current_sub_tokens) + token
136
- prev_is_special = True
137
- current_sub_tokens = []
138
- else:
139
- current_sub_tokens.append(token)
140
- prev_is_special = False
141
- out_string += self.sp_model.decode(current_sub_tokens)
142
- return out_string
143
-
144
- def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
145
- """
146
- Save the vocabulary and special tokens file to a directory.
147
-
148
- Args:
149
- save_directory (`str`):
150
- The directory in which to save the vocabulary.
151
-
152
- Returns:
153
- `Tuple(str)`: Paths to the files saved.
154
- """
155
- if not os.path.isdir(save_directory):
156
- logger.error(f"Vocabulary path ({save_directory}) should be a directory")
157
- return
158
- out_vocab_file = os.path.join(
159
- save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
160
- )
161
-
162
- if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
163
- copyfile(self.vocab_file, out_vocab_file)
164
- elif not os.path.isfile(self.vocab_file):
165
- with open(out_vocab_file, "wb") as fi:
166
- content_spiece_model = self.sp_model.serialized_model_proto()
167
- fi.write(content_spiece_model)
168
-
169
- return (out_vocab_file,)
170
-
171
- def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
172
- bos_token_id = [self.bos_token_id] if self.add_bos_token else []
173
- eos_token_id = [self.eos_token_id] if self.add_eos_token else []
174
-
175
- output = bos_token_id + token_ids_0 + eos_token_id
176
-
177
- if token_ids_1 is not None:
178
- output = output + bos_token_id + token_ids_1 + eos_token_id
179
-
180
- return output
181
-
182
- def get_special_tokens_mask(
183
- self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
184
- ) -> List[int]:
185
- """
186
- Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
187
- special tokens using the tokenizer `prepare_for_model` method.
188
-
189
- Args:
190
- token_ids_0 (`List[int]`):
191
- List of IDs.
192
- token_ids_1 (`List[int]`, *optional*):
193
- Optional second list of IDs for sequence pairs.
194
- already_has_special_tokens (`bool`, *optional*, defaults to `False`):
195
- Whether or not the token list is already formatted with special tokens for the model.
196
-
197
- Returns:
198
- `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
199
- """
200
- if already_has_special_tokens:
201
- return super().get_special_tokens_mask(
202
- token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
203
- )
204
-
205
- bos_token_id = [1] if self.add_bos_token else []
206
- eos_token_id = [1] if self.add_eos_token else []
207
-
208
- if token_ids_1 is None:
209
- return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
210
- return (
211
- bos_token_id
212
- + ([0] * len(token_ids_0))
213
- + eos_token_id
214
- + bos_token_id
215
- + ([0] * len(token_ids_1))
216
- + eos_token_id
217
- )
218
-
219
- def create_token_type_ids_from_sequences(
220
- self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
221
- ) -> List[int]:
222
- """
223
- Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
224
- sequence pair mask has the following format:
225
-
226
- ```
227
- 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
228
- | first sequence | second sequence |
229
- ```
230
-
231
- if token_ids_1 is None, only returns the first portion of the mask (0s).
232
-
233
- Args:
234
- token_ids_0 (`List[int]`):
235
- List of ids.
236
- token_ids_1 (`List[int]`, *optional*):
237
- Optional second list of IDs for sequence pairs.
238
-
239
- Returns:
240
- `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
241
- """
242
- bos_token_id = [self.bos_token_id] if self.add_bos_token else []
243
- eos_token_id = [self.eos_token_id] if self.add_eos_token else []
244
-
245
- output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)
246
-
247
- if token_ids_1 is not None:
248
- output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)
249
-
250
- return output
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
vocab/baichuan/Baichuan-7B/tokenizer.model DELETED
@@ -1,3 +0,0 @@
1
- version https://git-lfs.github.com/spec/v1
2
- oid sha256:4be54af290d93c113bcbf421115ae9eed9d6340408f564898f1e966dc738ef01
3
- size 1136699
 
 
 
 
vocab/baichuan/Baichuan-7B/tokenizer_config.json DELETED
@@ -1,35 +0,0 @@
1
- {
2
- "auto_map": {
3
- "AutoTokenizer": ["tokenization_baichuan.BaiChuanTokenizer", null]
4
- },
5
- "add_bos_token": false,
6
- "add_eos_token": false,
7
- "bos_token": {
8
- "__type": "AddedToken",
9
- "content": "<s>",
10
- "lstrip": false,
11
- "normalized": true,
12
- "rstrip": false,
13
- "single_word": false
14
- },
15
- "clean_up_tokenization_spaces": false,
16
- "eos_token": {
17
- "__type": "AddedToken",
18
- "content": "</s>",
19
- "lstrip": false,
20
- "normalized": true,
21
- "rstrip": false,
22
- "single_word": false
23
- },
24
- "model_max_length": 1000000000000000019884624838656,
25
- "sp_model_kwargs": {},
26
- "tokenizer_class": "BaiChuanTokenizer",
27
- "unk_token": {
28
- "__type": "AddedToken",
29
- "content": "<unk>",
30
- "lstrip": false,
31
- "normalized": true,
32
- "rstrip": false,
33
- "single_word": false
34
- }
35
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
vocab/baichuan/__init__.py DELETED
@@ -1,19 +0,0 @@
1
- import os
2
- import config
3
- from transformers import AutoTokenizer
4
- from vocab import TokenizerType
5
-
6
-
7
- if config.USE_REMOTE:
8
- tokenizer = AutoTokenizer.from_pretrained("baichuan-inc/Baichuan-7B", trust_remote_code=True)
9
- else:
10
- CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
11
- TOKENIZER_DIR = os.path.join(CURRENT_DIR, "Baichuan-7B")
12
- tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_DIR, trust_remote_code=True)
13
-
14
-
15
- # byte-bpe sentencepiece
16
- tokenizer.type = TokenizerType.ByteBPE
17
-
18
- tokenizer.comments = "使用 SentencePiece 中的 Byte-Pair Encoding (BPE) 作为分词算法"
19
-
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
vocab/baichuan/demo.py DELETED
@@ -1,6 +0,0 @@
1
-
2
- from vocab.baichuan import tokenizer
3
-
4
- id1 = tokenizer.encode("<pad>")
5
- token1 = tokenizer.decode(125696)
6
-
 
 
 
 
 
 
 
vocab/baichuan/error.md DELETED
@@ -1,8 +0,0 @@
1
-
2
-
3
- ## AttributeError: 'BaichuanTokenizer' object has no attribute 'sp_model'
4
-
5
- https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat/discussions/18
6
-
7
-
8
- transfomers 4.34 doesn't work for me either. Degrading to 4.33.1 works in my case