#!/usr/bin/env python3
"""
Script untuk generate README.md dari template
Penggunaan: python generate_readme.py config.yaml
"""

import argparse
from pathlib import Path

import yaml


def load_config(config_path):
    """Load konfigurasi dari file YAML"""
    with open(config_path, "r", encoding="utf-8") as f:
        return yaml.safe_load(f)


def load_template(template_path):
    """Load template README"""
    with open(template_path, "r", encoding="utf-8") as f:
        return f.read()


def replace_placeholders(template, config):
    """Replace placeholder dengan nilai dari config"""
    content = template

    # Replace semua placeholder dengan nilai dari config
    for key, value in config.items():
        placeholder = f"{{{{{key}}}}}"
        if isinstance(value, (list, dict)):
            # Convert list/dict ke string YAML format
            value = yaml.dump(
                value, default_flow_style=False, allow_unicode=True
            ).strip()
        content = content.replace(placeholder, str(value))

    return content


def generate_readme(config_path, template_path, output_path):
    """Generate README dari template dan config"""
    config = load_config(config_path)
    template = load_template(template_path)

    readme_content = replace_placeholders(template, config)

    with open(output_path, "w", encoding="utf-8") as f:
        f.write(readme_content)

    print(f"README berhasil digenerate: {output_path}")


def create_sample_config(output_path):
    """Buat sample config file"""
    sample_config = {
        # Metadata
        "LICENSE": "mit",
        "LANGUAGE": "id",
        "LIBRARY_NAME": "transformers",
        "PIPELINE_TAG": "text-classification",
        "DATASET_TYPE": "custom",
        "INFERENCE_ENABLED": True,
        # Model Info
        "MODEL_NAME": "BERT Indonesian Topic Classification (16 labels)",
        "MODEL_TITLE": "BERT Indonesian Topic Classification (16 labels)",
        "BASE_MODEL": "cahya/bert-base-indonesian-1.5G",
        "TASK_TYPE": "text-classification",
        "TASK_NAME": "Topic Classification",
        "TASK_DESCRIPTION": "Topic classification (single-label)",
        "NUM_LABELS": 16,
        "LABELS_INLINE": "Politik, Ekonomi, Olahraga, Teknologi, dll.",
        "DATASET_NAME": "Custom Dataset (ID)",
        "SPLIT_TYPE": "validation",
        # Visualization
        "VISUALIZATION_TYPE": "Confusion Matrix",
        "VISUALIZATION_FILENAME": "confusion_matrix.png",
        # Tags (sebagai list)
        "TAGS": [
            "  - indonesian",
            "  - indonesia",
            "  - topic-classification",
            "  - bert",
        ],
        # Metrics (sebagai list)
        "METRICS": [
            "          - type: accuracy",
            "            value: 0.921",
            "          - type: f1",
            "            name: f1_macro",
            "            value: 0.893",
            "          - type: f1",
            "            name: f1_micro",
            "            value: 0.912",
        ],
        # Content sections
        "INTENDED_USE": "- Klasifikasi topik untuk teks berbahasa Indonesia pada domain umum.",
        "LIMITATIONS": """- Performa bergantung pada distribusi label dataset Anda.
- Teks OOD (di luar domain data latih) bisa turun akurasinya.""",
        "TRAINING_DETAILS": """- Framework: 🤗 Transformers (PyTorch)
- Max length: 512
- Batch size: 16
- Epochs: 3
- Learning rate: 2e-5
- Weight decay: 0.01
- Warmup ratio: 0.1
- Scheduler: linear
- Mixed precision: true""",
        "EVALUATION_DETAILS": """- Split: 80/20 stratified
- Accuracy (val): **92.1%**
- F1 Macro (val): **89.3%**
- F1 Micro (val): **91.2%**

Per-label report tersedia pada artifact `eval_results.json`.""",
        "USAGE_CODE": """from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

repo_id = "your-username/model-name"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForSequenceClassification.from_pretrained(repo_id).eval()

text = "Contoh teks untuk diklasifikasi."
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
    logits = model(**inputs).logits
pred_id = logits.argmax(-1).item()
label = model.config.id2label[pred_id]
print(label)""",
        "ADDITIONAL_INFO": """## Citation

Jika menggunakan model ini, mohon kutip:
```bibtex
@misc{your-model-2025,
  title={Model Title},
  author={Your Name},
  year={2025},
  url={https://huggingface.co/your-username/model-name}
}
```""",
    }

    with open(output_path, "w", encoding="utf-8") as f:
        yaml.dump(
            sample_config, f, default_flow_style=False, allow_unicode=True, indent=2
        )

    print(f"Sample config dibuat: {output_path}")


def main():
    parser = argparse.ArgumentParser(description="Generate README dari template")
    parser.add_argument("--config", "-c", help="Path ke file config YAML")
    parser.add_argument(
        "--template",
        "-t",
        default="README.md",
        help="Path ke template README (default: README.md)",
    )
    parser.add_argument(
        "--output",
        "-o",
        default="README_generated.md",
        help="Path output README (default: README_generated.md)",
    )
    parser.add_argument(
        "--create-sample", action="store_true", help="Buat sample config file"
    )

    args = parser.parse_args()

    if args.create_sample:
        create_sample_config("sample_config.yaml")
        return

    if not args.config:
        print("Error: --config diperlukan kecuali menggunakan --create-sample")
        parser.print_help()
        return

    if not Path(args.config).exists():
        print(f"Error: Config file tidak ditemukan: {args.config}")
        return

    if not Path(args.template).exists():
        print(f"Error: Template file tidak ditemukan: {args.template}")
        return

    generate_readme(args.config, args.template, args.output)


if __name__ == "__main__":
    main()