#!/bin/bash
set -euo pipefail
export PATH="/venv/main/bin:$PATH"
export PYTHONDONTWRITEBYTECODE=1
export CUDA_VISIBLE_DEVICES=0
export WORK=/dev/shm/eval

log() { echo "[$(date '+%Y-%m-%d %H:%M:%S')] $*"; }

log "============================================"
log "  Setup: Polish LLM Eval for QuIP# Bielik"
log "============================================"

mkdir -p "$WORK"
cd "$WORK"

# 1. HF login first (needed for private model download)
log "HuggingFace login..."
python -c "
from huggingface_hub import login
login(token='HF_TOKEN_REDACTED')
print('Login OK')
"

# 2. Clone speakleash lm-eval fork with Polish tasks
log "Cloning speakleash/lm-evaluation-harness (polish3 branch)..."
if [ ! -d "$WORK/lm-evaluation-harness" ]; then
    git clone https://github.com/speakleash/lm-evaluation-harness.git -b polish3
fi
cd "$WORK/lm-evaluation-harness"
pip install -e . 2>&1 | tail -5
log "lm-eval installed"

# 3. Clone quip-sharp for model loading
cd "$WORK"
if [ ! -d "$WORK/quip-sharp" ]; then
    git clone https://github.com/Cornell-RelaxML/quip-sharp.git
fi
cd "$WORK/quip-sharp"

# 4. Build CUDA kernels
log "Building quiptools CUDA kernels..."
cd quiptools && python setup.py install 2>&1 | tail -5 && cd ..
log "quiptools built"

# 5. Install deps
pip install glog primefac protobuf sentencepiece 2>&1 | tail -3

# 6. Apply patches
log "Applying quip-sharp patches..."

# torch.load compat for PyTorch 2.10+
python -c "
for f in ['lib/utils/unsafe_import.py', 'eval/eval_zeroshot.py']:
    try:
        path = '$WORK/quip-sharp/' + f
        with open(path, 'r') as fh:
            c = fh.read()
        if 'weights_only' not in c and 'torch.load' in c:
            c = c.replace('import torch\n', 'import torch\n_orig_load = torch.load\ndef _compat_load(*a, **kw):\n    kw.setdefault(\"weights_only\", False)\n    return _orig_load(*a, **kw)\ntorch.load = _compat_load\n', 1)
            with open(path, 'w') as fh:
                fh.write(c)
            print(f'Patched {f}')
    except FileNotFoundError:
        pass
"

# fast_hadamard_transform fallback
python -c "
path = '$WORK/quip-sharp/lib/utils/matmul_had.py'
with open(path, 'r') as f:
    content = f.read()
if content.startswith('import fast_hadamard_transform'):
    content = 'try:\n    import fast_hadamard_transform\n    HAS_FAST_HAD = True\nexcept ImportError:\n    HAS_FAST_HAD = False\n' + content.split('\n', 1)[1]
    with open(path, 'w') as f:
        f.write(content)
    print('matmul_had.py patched')
"

# hadamard fallback implementation
python -c "
path = '$WORK/quip-sharp/lib/utils/matmul_had.py'
with open(path, 'r') as f:
    c = f.read()
if 'HAS_FAST_HAD' in c and 'Walsh-Hadamard' not in c:
    old = 'return fast_hadamard_transform.hadamard_transform(x, scale)'
    new = '''if HAS_FAST_HAD:
        return fast_hadamard_transform.hadamard_transform(x, scale)
    else:
        # Pure PyTorch Walsh-Hadamard fallback
        n = x.shape[-1]
        orig_shape = x.shape
        x = x.contiguous().view(-1, n)
        h = 1
        while h < n:
            x = x.view(-1, n // (2 * h), 2, h)
            a = x[:, :, 0, :] + x[:, :, 1, :]
            b = x[:, :, 0, :] - x[:, :, 1, :]
            x = torch.stack([a, b], dim=2).view(-1, n)
            h *= 2
        return (x * scale).view(orig_shape)'''
    if old in c:
        c = c.replace(old, new)
        with open(path, 'w') as f:
            f.write(c)
        print('hadamard fallback added')
"

# 7. Download QuIP# model from HuggingFace
log "Downloading QuIP# model..."
mkdir -p "$WORK/model"
python -c "
from huggingface_hub import snapshot_download
path = snapshot_download('Jakubrd4/bielik-q2-variant-a', local_dir='$WORK/model')
print(f'Model downloaded to: {path}')
"
log "Model downloaded"

# 8. Download tokenizer (base model)
log "Pre-downloading tokenizer..."
python -c "
from transformers import AutoTokenizer
tok = AutoTokenizer.from_pretrained('speakleash/Bielik-11B-v2.3-Instruct')
print(f'Tokenizer loaded: {tok.name_or_path}, vocab_size={tok.vocab_size}')
"

# 9. List available Polish tasks
log "Available Polish tasks:"
cd "$WORK/lm-evaluation-harness"
python -c "
from lm_eval import tasks
mgr = tasks.TaskManager()
polish = [t for t in mgr.all_tasks if 'polish' in t.lower() or 'polemo' in t.lower()]
for t in sorted(polish):
    print(f'  {t}')
print(f'Total: {len(polish)} Polish tasks')
" 2>/dev/null || echo "Task listing skipped"

log "============================================"
log "  Setup complete! Ready for eval."
log "============================================"