#!/usr/bin/env python3
# /// script
# dependencies = [
#     "transformers>=4.36.0",
#     "peft>=0.7.0",
#     "torch>=2.0.0",
#     "accelerate>=0.24.0",
#     "huggingface_hub>=0.20.0",
#     "sentencepiece>=0.1.99",
#     "protobuf>=3.20.0",
#     "numpy",
#     "gguf",
# ]
# ///

import os
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
from huggingface_hub import HfApi, snapshot_download
import subprocess

print("🔄 GGUF Conversion Script")
print("=" * 60)

# Configuration
ADAPTER_MODEL = "evalstate/qwen-capybara-medium"
BASE_MODEL = "Qwen/Qwen2.5-0.5B"
OUTPUT_MODEL_NAME = "evalstate/qwen-capybara-medium-gguf"
username = os.environ.get("HF_USERNAME", "evalstate")

print(f"\n📦 Configuration:")
print(f"   Base model: {BASE_MODEL}")
print(f"   Adapter model: {ADAPTER_MODEL}")
print(f"   Output repo: {OUTPUT_MODEL_NAME}")

# Step 1: Load base model and adapter
print("\n🔧 Step 1: Loading base model and LoRA adapter...")
print("   (This may take a few minutes)")

base_model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL,
    dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True,
)
print("   ✅ Base model loaded")

# Load and merge adapter
print("   Loading LoRA adapter...")
model = PeftModel.from_pretrained(base_model, ADAPTER_MODEL)
print("   ✅ Adapter loaded")

print("   Merging adapter with base model...")
merged_model = model.merge_and_unload()
print("   ✅ Models merged!")

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(ADAPTER_MODEL, trust_remote_code=True)
print("   ✅ Tokenizer loaded")

# Step 2: Save merged model temporarily
print("\n💾 Step 2: Saving merged model...")
merged_dir = "/tmp/merged_model"
merged_model.save_pretrained(merged_dir, safe_serialization=True)
tokenizer.save_pretrained(merged_dir)
print(f"   ✅ Merged model saved to {merged_dir}")

# Step 3: Install llama.cpp for conversion
print("\n📥 Step 3: Setting up llama.cpp for GGUF conversion...")
print("   Cloning llama.cpp repository...")
subprocess.run(
    ["git", "clone", "https://github.com/ggerganov/llama.cpp.git", "/tmp/llama.cpp"],
    check=True,
    capture_output=True
)
print("   ✅ llama.cpp cloned")

print("   Installing Python dependencies...")
subprocess.run(
    ["pip", "install", "-r", "/tmp/llama.cpp/requirements.txt"],
    check=True,
    capture_output=True
)
# Also need sentencepiece for tokenizer conversion
subprocess.run(
    ["pip", "install", "sentencepiece", "protobuf"],
    check=True,
    capture_output=True
)
print("   ✅ Dependencies installed")

# Step 4: Convert to GGUF (FP16)
print("\n🔄 Step 4: Converting to GGUF format (FP16)...")
gguf_output_dir = "/tmp/gguf_output"
os.makedirs(gguf_output_dir, exist_ok=True)

convert_script = "/tmp/llama.cpp/convert_hf_to_gguf.py"
gguf_file = f"{gguf_output_dir}/qwen-capybara-medium-f16.gguf"

print(f"   Running: python {convert_script} {merged_dir}")
try:
    result = subprocess.run(
        [
            "python", convert_script,
            merged_dir,
            "--outfile", gguf_file,
            "--outtype", "f16"
        ],
        check=True,
        capture_output=True,
        text=True
    )
    print(result.stdout)
    if result.stderr:
        print("Warnings:", result.stderr)
except subprocess.CalledProcessError as e:
    print(f"❌ Conversion failed!")
    print("STDOUT:", e.stdout)
    print("STDERR:", e.stderr)
    raise
print(f"   ✅ FP16 GGUF created: {gguf_file}")

# Step 5: Quantize to different formats
print("\n⚙️  Step 5: Creating quantized versions...")
quantize_bin = "/tmp/llama.cpp/llama-quantize"

# Build quantize tool first
print("   Building quantize tool...")
subprocess.run(
    ["make", "-C", "/tmp/llama.cpp", "llama-quantize"],
    check=True,
    capture_output=True
)
print("   ✅ Quantize tool built")

# Common quantization formats
quant_formats = [
    ("Q4_K_M", "4-bit, medium quality (recommended)"),
    ("Q5_K_M", "5-bit, higher quality"),
    ("Q8_0", "8-bit, very high quality"),
]

quantized_files = []
for quant_type, description in quant_formats:
    print(f"   Creating {quant_type} quantization ({description})...")
    quant_file = f"{gguf_output_dir}/qwen-capybara-medium-{quant_type.lower()}.gguf"

    subprocess.run(
        [quantize_bin, gguf_file, quant_file, quant_type],
        check=True,
        capture_output=True
    )
    quantized_files.append((quant_file, quant_type))

    # Get file size
    size_mb = os.path.getsize(quant_file) / (1024 * 1024)
    print(f"   ✅ {quant_type}: {size_mb:.1f} MB")

# Step 6: Upload to Hub
print("\n☁️  Step 6: Uploading to Hugging Face Hub...")
api = HfApi()

# Create repo
print(f"   Creating repository: {OUTPUT_MODEL_NAME}")
try:
    api.create_repo(repo_id=OUTPUT_MODEL_NAME, repo_type="model", exist_ok=True)
    print("   ✅ Repository created")
except Exception as e:
    print(f"   ℹ️  Repository may already exist: {e}")

# Upload FP16 version
print("   Uploading FP16 GGUF...")
api.upload_file(
    path_or_fileobj=gguf_file,
    path_in_repo="qwen-capybara-medium-f16.gguf",
    repo_id=OUTPUT_MODEL_NAME,
)
print("   ✅ FP16 uploaded")

# Upload quantized versions
for quant_file, quant_type in quantized_files:
    print(f"   Uploading {quant_type}...")
    api.upload_file(
        path_or_fileobj=quant_file,
        path_in_repo=f"qwen-capybara-medium-{quant_type.lower()}.gguf",
        repo_id=OUTPUT_MODEL_NAME,
    )
    print(f"   ✅ {quant_type} uploaded")

# Create README
print("\n📝 Creating README...")
readme_content = f"""---
base_model: {BASE_MODEL}
tags:
- gguf
- llama.cpp
- quantized
- trl
- sft
---

# {OUTPUT_MODEL_NAME.split('/')[-1]}

This is a GGUF conversion of [{ADAPTER_MODEL}](https://huggingface.co/{ADAPTER_MODEL}), which is a LoRA fine-tuned version of [{BASE_MODEL}](https://huggingface.co/{BASE_MODEL}).

## Model Details

- **Base Model:** {BASE_MODEL}
- **Fine-tuned Model:** {ADAPTER_MODEL}
- **Training:** Supervised Fine-Tuning (SFT) with TRL
- **Format:** GGUF (for llama.cpp, Ollama, LM Studio, etc.)

## Available Quantizations

| File | Quant | Size | Description | Use Case |
|------|-------|------|-------------|----------|
| qwen-capybara-medium-f16.gguf | F16 | ~1GB | Full precision | Best quality, slower |
| qwen-capybara-medium-q8_0.gguf | Q8_0 | ~500MB | 8-bit | High quality |
| qwen-capybara-medium-q5_k_m.gguf | Q5_K_M | ~350MB | 5-bit medium | Good quality, smaller |
| qwen-capybara-medium-q4_k_m.gguf | Q4_K_M | ~300MB | 4-bit medium | Recommended - good balance |

## Usage

### With llama.cpp

```bash
# Download model
huggingface-cli download {OUTPUT_MODEL_NAME} qwen-capybara-medium-q4_k_m.gguf

# Run with llama.cpp
./llama-cli -m qwen-capybara-medium-q4_k_m.gguf -p "Your prompt here"
```

### With Ollama

1. Create a `Modelfile`:
```
FROM ./qwen-capybara-medium-q4_k_m.gguf
```

2. Create the model:
```bash
ollama create qwen-capybara -f Modelfile
ollama run qwen-capybara
```

### With LM Studio

1. Download the `.gguf` file
2. Import into LM Studio
3. Start chatting!

## Training Details

This model was fine-tuned using:
- **Dataset:** trl-lib/Capybara (1,000 examples)
- **Method:** Supervised Fine-Tuning with LoRA
- **Epochs:** 3
- **LoRA rank:** 16
- **Hardware:** A10G Large GPU

## License

Inherits the license from the base model: {BASE_MODEL}

## Citation

```bibtex
@misc{{qwen-capybara-medium-gguf,
  author = {{{username}}},
  title = {{Qwen Capybara Medium GGUF}},
  year = {{2025}},
  publisher = {{Hugging Face}},
  url = {{https://huggingface.co/{OUTPUT_MODEL_NAME}}}
}}
```

---

*Converted to GGUF format using llama.cpp*
"""

api.upload_file(
    path_or_fileobj=readme_content.encode(),
    path_in_repo="README.md",
    repo_id=OUTPUT_MODEL_NAME,
)
print("   ✅ README uploaded")

print("\n" + "=" * 60)
print("✅ GGUF Conversion Complete!")
print(f"📦 Repository: https://huggingface.co/{OUTPUT_MODEL_NAME}")
print("\n📥 Download with:")
print(f"   huggingface-cli download {OUTPUT_MODEL_NAME} qwen-capybara-medium-q4_k_m.gguf")
print("\n🚀 Use with Ollama:")
print("   1. Download the GGUF file")
print("   2. Create Modelfile: FROM ./qwen-capybara-medium-q4_k_m.gguf")
print("   3. ollama create qwen-capybara -f Modelfile")
print("   4. ollama run qwen-capybara")
print("=" * 60)