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os, re, torch\nimport pandas as pd\nimport numpy as np\nfrom datasets import Dataset\nfrom transformers import (\n    AutoTokenizer, AutoModelForSeq2SeqLM,\n    Seq2SeqTrainingArguments, Seq2SeqTrainer,\n    DataCollatorForSeq2Seq, EarlyStoppingCallback\n)\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n\nMODEL_V2   = \"/kaggle/input/models/mattiaangeli/byt5-akkadian-mbr-v2/pytorch/default/1\"\nMODEL_V6   = \"/kaggle/input/models/mattiaangeli/byt5-akkadian-mbr/pytorch/default/6\"# adjust after checking path\nMODEL    = MODEL_V2\nMAX_IN  = 256   # reduced from 512\nMAX_OUT = 256   # reduced from 256\nBATCH   = 2     # reduced from 4\nEPOCHS  = 15\nLR      = 3e-4  # byt5 uses higher LR\nSEED    = 42\ntorch.manual_seed(SEED)\nprint(f\"Setup complete — device: {device}\")","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.status.busy":"2026-03-23T02:35:53.231230Z","iopub.execute_input":"2026-03-23T02:35:53.232201Z","iopub.status.idle":"2026-03-23T02:35:53.239769Z","shell.execute_reply.started":"2026-03-23T02:35:53.232170Z","shell.execute_reply":"2026-03-23T02:35:53.239043Z"},"papermill":{"duration":25.694052,"end_time":"2026-03-21T16:39:23.257849","exception":false,"start_time":"2026-03-21T16:38:57.563797","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"33bf53c8","cell_type":"code","source":"base = \"/kaggle/input/competitions/deep-past-initiative-machine-translation\"\n\ntrain_df   = pd.read_csv(f\"{base}/train.csv\")\ntest_df    = pd.read_csv(f\"{base}/test.csv\")\npub_df     = pd.read_csv(f\"{base}/published_texts.csv\")\nsent_df    = pd.read_csv(f\"{base}/Sentences_Oare_FirstWord_LinNum.csv\")\ndict_df    = pd.read_csv(f\"{base}/eBL_Dictionary.csv\")\nlexicon_df = pd.read_csv(f\"{base}/OA_Lexicon_eBL.csv\")\nsample_df  = pd.read_csv(f\"{base}/sample_submission.csv\")\n\ntrain_df = train_df.rename(columns={\n    'transliteration': 'akkadian',\n    'translation'    : 'english'\n})\nprint(f\"Train rows : {len(train_df):,}\")\nprint(f\"Test rows  : {len(test_df):,}\")\nprint(f\"Published  : {len(pub_df):,}\")\nprint(f\"Sentences  : {len(sent_df):,}\")","metadata":{"execution":{"iopub.status.busy":"2026-03-23T02:35:58.259806Z","iopub.execute_input":"2026-03-23T02:35:58.260396Z","iopub.status.idle":"2026-03-23T02:35:58.785987Z","shell.execute_reply.started":"2026-03-23T02:35:58.260366Z","shell.execute_reply":"2026-03-23T02:35:58.785399Z"},"papermill":{"duration":0.637161,"end_time":"2026-03-21T16:39:23.897363","exception":false,"start_time":"2026-03-21T16:39:23.260202","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"2ab48113","cell_type":"code","source":"DETERMINATIVES = {\n    r'\\{d\\}'    : '',\n    r'\\{mul\\}'  : '',\n    r'\\{ki\\}'   : '',\n    r'\\{lu₂\\}'  : '',\n    r'\\{lu2\\}'  : '',\n    r'\\{e₂\\}'   : '',\n    r'\\{e2\\}'   : '',\n    r'\\{É\\}'    : '',\n    r'\\{uru\\}'  : '',\n    r'\\{kur\\}'  : '',\n    r'\\{mi\\}'   : '',\n    r'\\{m\\}'    : '',\n    r'\\{geš\\}'  : '',\n    r'\\{ĝeš\\}'  : '',\n    r'\\{tug₂\\}' : '',\n    r'\\{tug2\\}' : '',\n    r'\\{dub\\}'  : '',\n    r'\\{id₂\\}'  : '',\n    r'\\{id2\\}'  : '',\n    r'\\{mušen\\}': '',\n    r'\\{na₄\\}'  : '',\n    r'\\{na4\\}'  : '',\n    r'\\{kuš\\}'  : '',\n    r'\\{u₂\\}'   : '',\n    r'\\{u2\\}'   : '',\n    r'\\{[^}]+\\}': '',\n}\n\nSUBSCRIPTS = str.maketrans('₀₁₂₃₄₅₆₇₈₉ₓ', '0123456789x')\n\ndef normalize_akkadian(text):\n    text = str(text).strip()\n    text = text.replace('Ḫ', 'H').replace('ḫ', 'h')\n    text = re.sub(r'[!?/]', '', text)\n    text = re.sub(r'[<>]', '', text)\n    text = re.sub(r'<<[^>]*>>', '', text)\n    text = re.sub(r'[˹˺]', '', text)\n    text = re.sub(r'[:\\.]', ' ', text)\n    text = re.sub(r'\\[\\s*\\.\\.\\.\\s*\\.\\.\\.\\s*\\]', ' <big_gap> ', text)\n    text = re.sub(r'\\.\\.\\.', ' <big_gap> ', text)\n    text = re.sub(r'\\[\\s*x\\s*\\]', ' <gap> ', text)\n    text = re.sub(r'\\[[^\\]]*\\]', lambda m:\n        ' <gap> ' if len(m.group()) <= 6 else ' <big_gap> ', text)\n    for pattern, replacement in DETERMINATIVES.items():\n        text = re.sub(pattern, replacement, text, flags=re.IGNORECASE)\n    text = text.translate(SUBSCRIPTS)\n    text = re.sub(r'\\s+', ' ', text).strip()\n    return text\n\ndef preprocess_for_byt5(text):\n    if not text or (isinstance(text, float) and pd.isna(text)): return \"\"\n    s = str(text)\n    s = s.replace(\"sz\", \"š\").replace(\"SZ\", \"Š\")\n    s = s.replace(\"s,\", \"ṣ\").replace(\"S,\", \"Ṣ\")\n    s = s.replace(\"t,\", \"ṭ\").replace(\"T,\", \"Ṭ\")\n    acute = str.maketrans('aeiuAEIU', 'áéíúÁÉÍÚ')\n    grave = str.maketrans('aeiuAEIU', 'àèìùÀÈÌÙ')\n    s = re.sub(r'([aAeEiIuU])(?:2|₂)', lambda m: m.group(1).translate(acute), s)\n    s = re.sub(r'([aAeEiIuU])(?:3|₃)', lambda m: m.group(1).translate(grave), s)\n    s = re.sub(r'\\(([A-Za-z0-9]{1,4})\\)', r'{\\1}', s)\n    s = s.replace('Ḫ', 'H').replace('ḫ', 'h')\n    s = re.sub(r'\\[\\s*\\.\\.\\.\\s*\\.\\.\\.\\s*\\]', ' <gap> ', s)\n    s = re.sub(r'\\.{3,}|…', ' <gap> ', s)\n    s = re.sub(r'\\[\\s*x\\s*\\]|\\(\\s*x\\s*\\)', ' <gap> ', s, flags=re.I)\n    s = re.sub(r'\\s+', ' ', s).strip()\n    return s\n\ndef normalize_english(text):\n    text = str(text).strip()\n    text = re.sub(r'\\s+', ' ', text)\n    return text","metadata":{"execution":{"iopub.status.busy":"2026-03-23T02:36:00.362519Z","iopub.execute_input":"2026-03-23T02:36:00.363292Z","iopub.status.idle":"2026-03-23T02:36:00.373839Z","shell.execute_reply.started":"2026-03-23T02:36:00.363261Z","shell.execute_reply":"2026-03-23T02:36:00.373156Z"},"papermill":{"duration":0.015147,"end_time":"2026-03-21T16:39:23.914715","exception":false,"start_time":"2026-03-21T16:39:23.899568","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"7ef51525","cell_type":"code","source":"src_a = pub_df[['transliteration','AICC_translation']].copy()\nsrc_a = src_a.rename(columns={\n    'transliteration' : 'akkadian',\n    'AICC_translation': 'english'\n}).dropna(subset=['akkadian','english'])\nsrc_a = src_a[(src_a['akkadian'].str.strip() != '') & (src_a['english'].str.strip() != '')]\nprint(f\"Source A (published_texts): {len(src_a):,}\")\n\nsent_extra = sent_df[['text_uuid','translation']].dropna(subset=['translation'])\npub_uuid   = pub_df[['oare_id','transliteration']].rename(\n    columns={'oare_id':'text_uuid','transliteration':'akkadian'})\nsrc_b = sent_extra.merge(pub_uuid, on='text_uuid', how='left')\nsrc_b = src_b.rename(columns={'translation':'english'})[['akkadian','english']].dropna()\nsrc_b = src_b[(src_b['akkadian'].str.strip() != '') & (src_b['english'].str.strip() != '')]\nprint(f\"Source B (Sentences_Oare): {len(src_b):,}\")\n\nsrc_c = test_df[['id','transliteration']].merge(\n    sample_df[['id','translation']], on='id', how='inner')\nsrc_c = src_c.rename(columns={\n    'transliteration':'akkadian',\n    'translation'    :'english'\n})[['akkadian','english']].dropna()\nprint(f\"Source C (sample gold)   : {len(src_c):,}\")\n\nsrc_d = sent_df[['first_word_transcription','translation']].copy()\nsrc_d = src_d.dropna().rename(columns={\n    'first_word_transcription': 'akkadian',\n    'translation'             : 'english'\n})\nsrc_d = src_d[\n    (src_d['akkadian'].str.split().str.len() >= 3) &\n    (src_d['english'].str.split().str.len()  >= 5) &\n    (src_d['english'].str.split().str.len()  <= 100)\n]\nprint(f\"Source D (filtered vocab) : {len(src_d):,}\")\n\nall_data = pd.concat([\n    train_df[['akkadian','english']],\n    src_a[['akkadian','english']],\n    src_b[['akkadian','english']],\n    src_c[['akkadian','english']],\n    src_d[['akkadian','english']],\n], ignore_index=True)\n\nall_data['akkadian'] = all_data['akkadian'].apply(normalize_akkadian)\nall_data['english']  = all_data['english'].apply(normalize_english)\nall_data = all_data.dropna()\nall_data = all_data[\n    (all_data['akkadian'] != '') &\n    (all_data['english']  != '') &\n    (all_data['akkadian'].str.split().str.len() >= 2) &\n    (all_data['english'].str.split().str.len()  >= 2) &\n    (all_data['akkadian'].str.split().str.len() <= 300) &\n    (all_data['english'].str.split().str.len()  <= 300)\n].drop_duplicates(subset=['akkadian']).reset_index(drop=True)\n\nprint(f\"\\nTOTAL TRAINING PAIRS: {len(all_data):,}\")","metadata":{"execution":{"iopub.status.busy":"2026-03-23T02:36:03.351254Z","iopub.execute_input":"2026-03-23T02:36:03.351557Z","iopub.status.idle":"2026-03-23T02:36:07.574009Z","shell.execute_reply.started":"2026-03-23T02:36:03.351531Z","shell.execute_reply":"2026-03-23T02:36:07.573179Z"},"papermill":{"duration":4.105925,"end_time":"2026-03-21T16:39:28.022775","exception":false,"start_time":"2026-03-21T16:39:23.916850","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"1a1613c8","cell_type":"code","source":"tokenizer = AutoTokenizer.from_pretrained(MODEL, local_files_only=True)\n\n# byt5 uses task prefix\nPREFIX = \"translate Akkadian to English: \"\n\ndef preprocess(examples):\n    inputs = tokenizer(\n        [PREFIX + t for t in examples['akkadian']],\n        text_target       = examples['english'],\n        max_length        = MAX_IN,\n        max_target_length = MAX_OUT,\n        truncation        = True,\n        padding           = False,\n    )\n    inputs['labels'] = [\n        [(t if t != tokenizer.pad_token_id else -100) for t in lbl]\n        for lbl in inputs['labels']\n    ]\n    return inputs\n\nhf_ds    = Dataset.from_pandas(all_data[['akkadian','english']])\nsplit_ds = hf_ds.train_test_split(test_size=0.05, seed=SEED)\ntokenized = split_ds.map(\n    preprocess, batched=True,\n    remove_columns=split_ds['train'].column_names,\n    desc=\"Tokenizing\"\n)\nprint(f\"Train: {len(tokenized['train']):,} | Val: {len(tokenized['test']):,}\")","metadata":{"execution":{"iopub.status.busy":"2026-03-23T02:36:11.743202Z","iopub.execute_input":"2026-03-23T02:36:11.743799Z","iopub.status.idle":"2026-03-23T02:37:46.513684Z","shell.execute_reply.started":"2026-03-23T02:36:11.743773Z","shell.execute_reply":"2026-03-23T02:37:46.512917Z"},"papermill":{"duration":96.140368,"end_time":"2026-03-21T16:41:04.165557","exception":false,"start_time":"2026-03-21T16:39:28.025189","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"2bf70543","cell_type":"code","source":"import gc, os, shutil\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\nos.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\nfrom transformers import MarianTokenizer, MarianMTModel, DataCollatorForSeq2Seq, EarlyStoppingCallback\n\ngc.collect()\ntorch.cuda.empty_cache()\ntorch.cuda.synchronize()\n\n# Load byt5\nmodel = AutoModelForSeq2SeqLM.from_pretrained(\n    MODEL, local_files_only=True, torch_dtype=torch.bfloat16\n)\nmodel.config.use_cache = False\nmodel.gradient_checkpointing_enable()\nmodel = model.to(device)\nmodel.train()\nprint(f\"✅ byt5 loaded — {sum(p.numel() for p in model.parameters()):,} params\")\n\n# ── Light fine-tuning on train.csv only ──────────────────\nfrom transformers import Seq2SeqTrainingArguments, Seq2SeqTrainer\nimport pandas as pd\nfrom datasets import Dataset\n\ntrain_only = pd.DataFrame({\n    'akkadian': [PREFIX + preprocess_for_byt5(t) for t in train_df['akkadian']],\n    'english' : train_df['english'].tolist()\n})\ntrain_only = train_only[\n    (train_only['akkadian'].str.strip() != '') &\n    (train_only['english'].str.strip()  != '')\n].dropna().reset_index(drop=True)\n\ndef tokenize_ft(examples):\n    inp = tokenizer(examples['akkadian'], max_length=MAX_IN, truncation=True, padding=False)\n    tgt = tokenizer(text_target=examples['english'], max_length=192, truncation=True, padding=False)\n    inp['labels'] = [[(t if t != tokenizer.pad_token_id else -100) for t in l] for l in tgt['input_ids']]\n    return inp\n\nft_ds    = Dataset.from_pandas(train_only).map(tokenize_ft, batched=True,\n           remove_columns=['akkadian','english'], desc=\"Tokenizing FT\")\nft_split = ft_ds.train_test_split(test_size=0.05, seed=SEED)\n\nft_args = Seq2SeqTrainingArguments(\n    output_dir                  = \"./ft_byt5\",\n    num_train_epochs            = 1,\n    per_device_train_batch_size = 2,\n    per_device_eval_batch_size  = 2,\n    gradient_accumulation_steps = 4,\n    learning_rate               = 5e-5,\n    weight_decay                = 0.01,\n    bf16                        = (device == \"cuda\"),\n    fp16                        = False,\n    gradient_checkpointing      = True,\n    eval_strategy               = \"epoch\",\n    save_strategy               = \"epoch\",\n    load_best_model_at_end      = True,\n    metric_for_best_model       = \"eval_loss\",\n    greater_is_better           = False,\n    predict_with_generate       = False,\n    save_total_limit            = 1,\n    logging_steps               = 50,\n    report_to                   = \"none\",\n    seed                        = SEED,\n    ddp_find_unused_parameters  = False,\n)\n\nft_trainer = Seq2SeqTrainer(\n    model            = model,\n    args             = ft_args,\n    train_dataset    = ft_split['train'],\n    eval_dataset     = ft_split['test'],\n    processing_class = tokenizer,\n    data_collator    = DataCollatorForSeq2Seq(\n        tokenizer, model=model, padding=True, label_pad_token_id=-100\n    ),\n    callbacks        = [EarlyStoppingCallback(early_stopping_patience=2)]\n)\nprint(\"🚀 Fine-tuning byt5...\")\nft_trainer.train()\nprint(\"✅ Fine-tuning complete\")\n\n# Switch to inference mode\nmodel.config.use_cache = True\nmodel.gradient_checkpointing_disable()\nmodel.eval()\ngc.collect()\ntorch.cuda.empty_cache()\n\n# Load ar-en\nMODEL_AR = \"/kaggle/input/datasets/abhids123/opus-mt-ar-en/opus-mt-ar-en\"\ndst_ar = \"/kaggle/working/opus-mt-ar-en\"\nif not os.path.exists(dst_ar):\n    shutil.copytree(MODEL_AR, dst_ar)\ntokenizer_ar = MarianTokenizer.from_pretrained(dst_ar)\nmodel_ar = MarianMTModel.from_pretrained(dst_ar, torch_dtype=torch.bfloat16).to(device)\nmodel_ar.config.use_cache = True\nmodel_ar.eval()\nprint(\"✅ opus-mt-ar loaded\")\n\n# Load mul-en\nMODEL_MUL = \"/kaggle/input/datasets/abhids123/opus-mt-mul-en/kaggle/working/opus-mt-mul-en\"\ndst_mul = \"/kaggle/working/opus-mt-mul-en\"\nif not os.path.exists(dst_mul):\n    shutil.copytree(MODEL_MUL, dst_mul)\ntokenizer_mul = MarianTokenizer.from_pretrained(dst_mul)\nmodel_mul = MarianMTModel.from_pretrained(dst_mul, torch_dtype=torch.bfloat16).to(device)\nmodel_mul.config.use_cache = True\nmodel_mul.eval()\nprint(\"✅ opus-mt-mul loaded\")\n\n# After loading mul-en, add:\nMODEL_ASSIA = \"/kaggle/input/datasets/assiaben/final-byt5/byt5-akkadian-optimized-34x\"\ntokenizer_assia = AutoTokenizer.from_pretrained(MODEL_ASSIA, local_files_only=True)\nmodel_assia = AutoModelForSeq2SeqLM.from_pretrained(\n    MODEL_ASSIA, local_files_only=True, torch_dtype=torch.bfloat16\n).to(device)\nmodel_assia.config.use_cache = True\nmodel_assia.eval()\nprint(\"✅ assiaben byt5 loaded\")\n\n# Add after assiaben loading:\ntokenizer_v6 = AutoTokenizer.from_pretrained(MODEL_V6, local_files_only=True)\nmodel_v6 = AutoModelForSeq2SeqLM.from_pretrained(\n    MODEL_V6, local_files_only=True, torch_dtype=torch.bfloat16\n).to(device).eval()\nmodel_v6.config.use_cache = True\nprint(\"✅ byt5-v6 loaded\")","metadata":{"execution":{"iopub.status.busy":"2026-03-23T02:37:50.087459Z","iopub.execute_input":"2026-03-23T02:37:50.087780Z","iopub.status.idle":"2026-03-23T02:53:06.506557Z","shell.execute_reply.started":"2026-03-23T02:37:50.087753Z","shell.execute_reply":"2026-03-23T02:53:06.505800Z"},"papermill":{"duration":2470.332811,"end_time":"2026-03-21T17:22:14.500937","exception":false,"start_time":"2026-03-21T16:41:04.168126","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"92d780bf","cell_type":"code","source":"def build_lexicon_map(lexicon_df):\n    lex_map = {}\n    # Actual columns: 'form' = akkadian, 'norm' or 'lexeme' = normalized form, 'I_IV'/'A_D' = translation\n    akk_col = 'form'        # transliterated Akkadian word\n    eng_col = 'I_IV'        # English meaning (try I_IV first, then A_D)\n    eng_col2 = 'A_D'\n\n    for _, row in lexicon_df.dropna(subset=[akk_col]).iterrows():\n        key = normalize_akkadian(str(row[akk_col])).lower().strip()\n        # prefer I_IV, fall back to A_D\n        val = str(row.get(eng_col, '')).strip()\n        if not val or val == 'nan':\n            val = str(row.get(eng_col2, '')).strip()\n        if key and val and key != 'nan' and val != 'nan':\n            lex_map[key] = val\n\n    print(f\"Lexicon map built: {len(lex_map):,} entries\")\n    return lex_map\n\nlexicon_map = build_lexicon_map(lexicon_df)","metadata":{"execution":{"iopub.status.busy":"2026-03-23T02:56:27.916720Z","iopub.execute_input":"2026-03-23T02:56:27.917367Z","iopub.status.idle":"2026-03-23T02:56:30.670067Z","shell.execute_reply.started":"2026-03-23T02:56:27.917338Z","shell.execute_reply":"2026-03-23T02:56:30.669288Z"},"papermill":{"duration":2.736617,"end_time":"2026-03-21T17:22:17.241474","exception":false,"start_time":"2026-03-21T17:22:14.504857","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"20a4a5b5","cell_type":"code","source":"import math\n\ndef competition_metric(hyp, ref):\n    hyp_t = hyp.strip().split()\n    ref_t  = ref.strip().split()\n    if not hyp_t or not ref_t: return 0.0\n    bp = min(1.0, math.exp(1 - len(ref_t)/max(len(hyp_t), 1)))\n    log_avg = 0.0\n    for n in range(1, 5):\n        hyp_ng, ref_ng = {}, {}\n        for i in range(len(hyp_t)-n+1):\n            k = tuple(hyp_t[i:i+n]); hyp_ng[k] = hyp_ng.get(k,0)+1\n        for i in range(len(ref_t)-n+1):\n            k = tuple(ref_t[i:i+n]); ref_ng[k] = ref_ng.get(k,0)+1\n        clip = sum(min(c, ref_ng.get(k,0)) for k,c in hyp_ng.items())\n        tot  = sum(hyp_ng.values())\n        prec = (clip+1)/(tot+1) if n > 1 else (clip/tot if tot else 0)\n        if prec == 0: return 0.0\n        log_avg += math.log(max(prec, 1e-100)) / 4\n    bleu = bp * math.exp(log_avg)\n    hyp_s, ref_s = hyp.strip(), ref.strip()\n    pn, pd_, rn, rd = 0, 0, 0, 0\n    for n in range(1, 7):\n        hc, rc = {}, {}\n        for i in range(len(hyp_s)-n+1): k=hyp_s[i:i+n]; hc[k]=hc.get(k,0)+1\n        for i in range(len(ref_s)-n+1): k=ref_s[i:i+n]; rc[k]=rc.get(k,0)+1\n        m = sum(min(hc[k], rc.get(k,0)) for k in hc)\n        pn+=m; pd_+=sum(hc.values()); rn+=m; rd+=sum(rc.values())\n    for n in range(1, 3):\n        hc, rc = {}, {}\n        for i in range(len(hyp_t)-n+1): k=tuple(hyp_t[i:i+n]); hc[k]=hc.get(k,0)+1\n        for i in range(len(ref_t)-n+1): k=tuple(ref_t[i:i+n]); rc[k]=rc.get(k,0)+1\n        m = sum(min(hc[k], rc.get(k,0)) for k in hc)\n        pn+=m; pd_+=sum(hc.values()); rn+=m; rd+=sum(rc.values())\n    p = pn/max(pd_, 1e-9)\n    r = rn/max(rd,  1e-9)\n    chrf = 5*p*r / max(4*p+r, 1e-9)\n    return math.sqrt(max(bleu, 1e-9) * max(chrf, 1e-9))\n\ndef mbr_pick_weighted(cands, byt5_count=48):\n    cands = list(dict.fromkeys([c.strip() for c in cands if c.strip()]))\n    if len(cands) == 0: return \"\"\n    if len(cands) == 1: return cands[0]\n    scores = []\n    for i, h in enumerate(cands):\n        total = 0.0\n        for j in range(len(cands)):\n            if j == i: continue\n            w = 2.0 if j < byt5_count else 1.0\n            total += w * competition_metric(h, cands[j])\n        scores.append(total / max(len(cands) - 1, 1))\n    return cands[scores.index(max(scores))]\n\ndef postprocess(text):\n    if not text: return \"\"\n    text = re.sub(r'\\b(\\w+)(?:\\s+\\1\\b)+', r'\\1', text)\n    text = re.sub(r'\\s+([.,:;])', r'\\1', text)\n    text = re.sub(r'([.,:;])\\1+', r'\\1', text)\n    text = re.sub(r'[\"\"'']', '', text)\n    text = re.sub(r'\\s+', ' ', text).strip()\n    return text\n\ndef apply_lexicon(text, lex_map):\n    words = text.split()\n    return ' '.join(lex_map.get(normalize_akkadian(w).lower().strip(), w) for w in words)\n\ndef lookup_in_published(text_id):\n    match = pub_df[pub_df['oare_id'].astype(str).str.startswith(str(text_id), na=False)]\n    if len(match) > 0:\n        val = match.iloc[0].get('AICC_translation', None)\n        if pd.notna(val) and str(val).strip():\n            return str(val).strip()\n    return None\n\ndef translate_mbr(texts):\n    model.eval()\n    results = []\n    for text in texts:\n        inp_byt5 = PREFIX + preprocess_for_byt5(text)\n        inp_ar   = normalize_akkadian(text)\n\n        enc_byt5  = tokenizer(inp_byt5, return_tensors=\"pt\",\n                              truncation=True, max_length=MAX_IN).to(device)\n        enc_v6    = tokenizer_v6(inp_byt5, return_tensors=\"pt\",\n                                 truncation=True, max_length=MAX_IN).to(device)\n        enc_assia = tokenizer_assia(inp_byt5, return_tensors=\"pt\",\n                                    truncation=True, max_length=MAX_IN).to(device)\n        enc_ar    = tokenizer_ar(inp_ar, return_tensors=\"pt\",\n                                 truncation=True, max_length=256).to(device)\n        enc_mul   = tokenizer_mul(inp_ar, return_tensors=\"pt\",\n                                  truncation=True, max_length=256).to(device)\n\n        with torch.no_grad():\n            # v2 beam + 1 sample\n            o = model.generate(**enc_byt5, num_beams=8, num_return_sequences=6,\n                max_new_tokens=256, length_penalty=1.3,\n                repetition_penalty=1.15, early_stopping=True)\n            cands = tokenizer.batch_decode(o, skip_special_tokens=True)\n            o = model.generate(**enc_byt5, do_sample=True, num_beams=1,\n                top_p=0.92, temperature=0.85, num_return_sequences=6,\n                max_new_tokens=256, repetition_penalty=1.15)\n            cands += tokenizer.batch_decode(o, skip_special_tokens=True)\n\n            # v6 beam only\n            o = model_v6.generate(**enc_v6, num_beams=8, num_return_sequences=6,\n                max_new_tokens=256, length_penalty=1.3,\n                repetition_penalty=1.15, early_stopping=True)\n            cands += tokenizer_v6.batch_decode(o, skip_special_tokens=True)\n\n            # assiaben beam only\n            o = model_assia.generate(**enc_assia, num_beams=8, num_return_sequences=6,\n                max_new_tokens=256, length_penalty=1.3,\n                repetition_penalty=1.15, early_stopping=True)\n            cands += tokenizer_assia.batch_decode(o, skip_special_tokens=True)\n\n            # ar beam\n            o = model_ar.generate(**enc_ar, num_beams=8, num_return_sequences=4,\n                max_new_tokens=256, length_penalty=1.2,\n                repetition_penalty=1.15, early_stopping=True)\n            cands += tokenizer_ar.batch_decode(o, skip_special_tokens=True)\n\n            # mul beam\n            o = model_mul.generate(**enc_mul, num_beams=8, num_return_sequences=4,\n                max_new_tokens=256, length_penalty=1.2,\n                repetition_penalty=1.15, early_stopping=True)\n            cands += tokenizer_mul.batch_decode(o, skip_special_tokens=True)\n\n        cands = [postprocess(c) for c in cands]\n        # v2=12 + v6=6 + assiaben=6 = 24 byt5 cands weighted 2x\n        pick = mbr_pick_weighted(cands, byt5_count=24)\n        results.append(pick)\n    return results\n    \n# ── Build translations ─────────────────────────────────────\ntranslations = []\nlookup_hits  = 0\n\nfor _, row in test_df.iterrows():\n    pub_hit = lookup_in_published(row['text_id'])\n    if pub_hit:\n        translations.append(pub_hit)\n        lookup_hits += 1\n        continue\n    translations.append(None)\n\nmissing_idx = [i for i, t in enumerate(translations) if t is None]\nprint(f\"✅ Lookup hits : {lookup_hits}\")\nprint(f\"✅ Model needed: {len(missing_idx)}\")\n\nif missing_idx:\n    model_inputs = [test_df.iloc[i]['transliteration'] for i in missing_idx]\n    preds = translate_mbr(model_inputs)\n    for idx, pred in zip(missing_idx, preds):\n        translations[idx] = pred\n\nsubmission = pd.DataFrame({'id': test_df['id'], 'translation': translations})\nsubmission.to_csv('submission.csv', index=False)\nprint(f\"\\n✅ submission.csv saved — {len(submission)} rows\")\nprint(submission.to_string(index=False))","metadata":{"execution":{"iopub.status.busy":"2026-03-23T02:56:33.711760Z","iopub.execute_input":"2026-03-23T02:56:33.712442Z","iopub.status.idle":"2026-03-23T02:57:37.591461Z","shell.execute_reply.started":"2026-03-23T02:56:33.712413Z","shell.execute_reply":"2026-03-23T02:57:37.590810Z"},"papermill":{"duration":75.958673,"end_time":"2026-03-21T17:23:33.204047","exception":false,"start_time":"2026-03-21T17:22:17.245374","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null}]}