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Chess Challenge submission by MaximeMuhlethaler

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Files changed (7) hide show
  1. README.md +26 -0
  2. config.json +20 -0
  3. model.safetensors +3 -0
  4. special_tokens_map.json +6 -0
  5. tokenizer.py +111 -0
  6. tokenizer_config.json +47 -0
  7. vocab.json +1702 -0
README.md ADDED
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+ ---
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+ library_name: transformers
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+ tags:
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+ - chess
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+ - llm-course
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+ - chess-challenge
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+ license: mit
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+ ---
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+
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+ # chess_MaximeMuh
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+
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+ Chess model submitted to the LLM Course Chess Challenge.
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+
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+ ## Submission Info
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+
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+ - **Submitted by**: [MaximeMuhlethaler](https://huggingface.co/MaximeMuhlethaler)
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+ - **Parameters**: 980,000
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+ - **Organization**: LLM-course
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+
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+ ## Model Details
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+
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+ - **Architecture**: Chess Transformer (GPT-style)
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+ - **Vocab size**: 1700
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+ - **Embedding dim**: 112
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+ - **Layers**: 6
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+ - **Heads**: 8
config.json ADDED
@@ -0,0 +1,20 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "architectures": [
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+ "ChessForCausalLM"
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+ ],
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+ "bos_token_id": 1,
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+ "dropout": 0.1,
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+ "dtype": "float32",
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+ "eos_token_id": 2,
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+ "layer_norm_epsilon": 1e-05,
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+ "model_type": "chess_transformer",
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+ "n_ctx": 256,
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+ "n_embd": 112,
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+ "n_head": 8,
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+ "n_inner": 336,
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+ "n_layer": 6,
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+ "pad_token_id": 0,
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+ "tie_weights": true,
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+ "transformers_version": "4.57.5",
19
+ "vocab_size": 1700
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+ }
model.safetensors ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:587357650ae773ee04046d994ab3a45b87b5b8c6f536be1c867dc13cd35cd12a
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+ size 3926448
special_tokens_map.json ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
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+ {
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+ "bos_token": "[BOS]",
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+ "eos_token": "[EOS]",
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+ "pad_token": "[PAD]",
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+ "unk_token": "[UNK]"
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+ }
tokenizer.py ADDED
@@ -0,0 +1,111 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """
2
+ Custom Chess Tokenizer - Final Fix
3
+ """
4
+ from __future__ import annotations
5
+ import json
6
+ import os
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+ import shutil
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+ import re
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+ from typing import Dict, List, Optional
10
+ from transformers import PreTrainedTokenizer
11
+
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+ # --- REGEX (Pour nettoyer les coups) ---
13
+ MOVE_RE = re.compile(r"([a-h][1-8])([a-h][1-8])")
14
+ PROMO_RE = re.compile(r"=([NBRQ])")
15
+
16
+ def normalize_move(tok: str) -> str:
17
+ if tok.startswith("["): return tok
18
+ m = MOVE_RE.search(tok)
19
+ if not m: return tok
20
+ fr, to = m.group(1), m.group(2)
21
+ promo = ""
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+ pm = PROMO_RE.search(tok)
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+ if pm: promo = "=" + pm.group(1)
24
+ prefix = tok[:2] if len(tok) >= 2 else "WP"
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+ return f"{prefix}{fr}{to}{promo}"
26
+
27
+ class ChessTokenizer(PreTrainedTokenizer):
28
+ model_input_names = ["input_ids", "attention_mask"]
29
+ vocab_files_names = {"vocab_file": "vocab.json"}
30
+
31
+ PAD_TOKEN = "[PAD]"
32
+ BOS_TOKEN = "[BOS]"
33
+ EOS_TOKEN = "[EOS]"
34
+ UNK_TOKEN = "[UNK]"
35
+
36
+ def __init__(self, vocab_file=None, vocab=None, **kwargs):
37
+ self._pad_token = self.PAD_TOKEN
38
+ self._bos_token = self.BOS_TOKEN
39
+ self._eos_token = self.EOS_TOKEN
40
+ self._unk_token = self.UNK_TOKEN
41
+
42
+ for t in ["pad_token", "bos_token", "eos_token", "unk_token"]: kwargs.pop(t, None)
43
+
44
+ # FIX CHEMIN
45
+ if vocab is None:
46
+ if vocab_file is None:
47
+ vocab_file = os.path.join(os.path.dirname(__file__), "vocab.json")
48
+ self.vocab_file = vocab_file
49
+ if os.path.exists(vocab_file):
50
+ with open(vocab_file, "r", encoding="utf-8") as f: self._vocab = json.load(f)
51
+ else: self._vocab = self._create_default_vocab()
52
+ else:
53
+ self._vocab = vocab
54
+ self.vocab_file = vocab_file
55
+
56
+ self._ids_to_tokens = {v: k for k, v in self._vocab.items()}
57
+ super().__init__(pad_token=self.PAD_TOKEN, bos_token=self.BOS_TOKEN, eos_token=self.EOS_TOKEN, unk_token=self.UNK_TOKEN, **kwargs)
58
+
59
+ # AUTO-COPIE (Vital pour le submit)
60
+ def save_pretrained(self, save_directory: str, **kwargs):
61
+ super().save_pretrained(save_directory, **kwargs)
62
+ src_path = os.path.abspath(__file__)
63
+ dst_path = os.path.join(save_directory, "tokenizer.py")
64
+ if src_path != dst_path: shutil.copy(src_path, dst_path)
65
+
66
+ config_path = os.path.join(save_directory, "tokenizer_config.json")
67
+ if os.path.exists(config_path):
68
+ with open(config_path, "r") as f: cfg = json.load(f)
69
+ cfg["auto_map"] = {"AutoTokenizer": "tokenizer.ChessTokenizer"}
70
+ with open(config_path, "w") as f: json.dump(cfg, f, indent=2)
71
+
72
+ def _create_default_vocab(self):
73
+ return {t: i for i, t in enumerate([self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN])}
74
+
75
+ # LA FONCTION QUI GERE LA TAILLE FIXE
76
+ @classmethod
77
+ def build_vocab_from_dataset(cls, dataset_name, split="train", column="text", min_frequency=2, max_vocab_size=1700, max_samples=100000):
78
+ from datasets import load_dataset
79
+ from collections import Counter
80
+
81
+ ds = load_dataset(dataset_name, split=split, streaming=True)
82
+ ds = ds.take(max_samples)
83
+
84
+ counter = Counter()
85
+ for ex in ds:
86
+ # On normalise
87
+ moves = [normalize_move(t) for t in ex[column].split()]
88
+ counter.update(moves)
89
+
90
+ # ON FORCE LA TAILLE MAXIMALE ICI
91
+ special = [cls.PAD_TOKEN, cls.BOS_TOKEN, cls.EOS_TOKEN, cls.UNK_TOKEN]
92
+ # On prend les N plus fréquents pour remplir jusqu'à max_vocab_size
93
+ most_common = counter.most_common(max_vocab_size - len(special))
94
+
95
+ vocab = {t: i for i, t in enumerate(special + [t for t, c in most_common])}
96
+ return cls(vocab=vocab)
97
+
98
+ @property
99
+ def vocab_size(self): return len(self._vocab)
100
+ def get_vocab(self): return dict(self._vocab)
101
+
102
+ def _tokenize(self, text): return [normalize_move(t) for t in text.strip().split()]
103
+ def _convert_token_to_id(self, token): return self._vocab.get(token, self._vocab.get(self.UNK_TOKEN))
104
+ def _convert_id_to_token(self, index): return self._ids_to_tokens.get(index, self.UNK_TOKEN)
105
+ def convert_tokens_to_string(self, tokens): return " ".join(t for t in tokens if t not in [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN])
106
+
107
+ def save_vocabulary(self, save_directory, filename_prefix=None):
108
+ if not os.path.isdir(save_directory): os.makedirs(save_directory, exist_ok=True)
109
+ path = os.path.join(save_directory, (filename_prefix + "-" if filename_prefix else "") + "vocab.json")
110
+ with open(path, "w", encoding="utf-8") as f: json.dump(self._vocab, f, ensure_ascii=False, indent=2)
111
+ return (path,)
tokenizer_config.json ADDED
@@ -0,0 +1,47 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "added_tokens_decoder": {
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+ "0": {
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+ "content": "[PAD]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "1": {
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+ "content": "[BOS]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "2": {
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+ "content": "[EOS]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "3": {
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+ "content": "[UNK]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ }
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+ },
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+ "auto_map": {
37
+ "AutoTokenizer": "tokenizer.ChessTokenizer"
38
+ },
39
+ "bos_token": "[BOS]",
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+ "clean_up_tokenization_spaces": false,
41
+ "eos_token": "[EOS]",
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+ "extra_special_tokens": {},
43
+ "model_max_length": 1000000000000000019884624838656,
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+ "pad_token": "[PAD]",
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+ "tokenizer_class": "ChessTokenizer",
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+ "unk_token": "[UNK]"
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+ }
vocab.json ADDED
@@ -0,0 +1,1702 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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1665
+ "WQh5e2": 1663,
1666
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1667
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1668
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1669
+ "WQf3c6": 1667,
1670
+ "BKb6c6": 1668,
1671
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1672
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1673
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1674
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1675
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1676
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1677
+ "BQe6g6": 1675,
1678
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1679
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1680
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1681
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1682
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1683
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1684
+ "WQg4g7": 1682,
1685
+ "BKc5d6": 1683,
1686
+ "WKg4g3": 1684,
1687
+ "BNc7e6": 1685,
1688
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1689
+ "WRg1g6": 1687,
1690
+ "BBg6h7": 1688,
1691
+ "BQc7b8": 1689,
1692
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1693
+ "BNc2e3": 1691,
1694
+ "BKc7c8": 1692,
1695
+ "BRe2b2": 1693,
1696
+ "BQb6d6": 1694,
1697
+ "WNd2b1": 1695,
1698
+ "BRc7d7": 1696,
1699
+ "BQe6f6": 1697,
1700
+ "BNd8e6": 1698,
1701
+ "WKe4e3": 1699
1702
+ }