"""
Hub utilities for downloading and managing Chiluka TTS models.

Supports:
- HuggingFace Hub integration
- Automatic model downloading
- Local caching
- Multiple model variants
"""

import os
import shutil
from pathlib import Path
from typing import Optional, Union

# Default HuggingFace Hub repository
DEFAULT_HF_REPO = "Seemanth/chiluka-tts"

# Cache directory for downloaded models
CACHE_DIR = Path.home() / ".cache" / "chiluka"

# ============================================
# Model Registry
# ============================================
# Maps model names to their config + checkpoint paths
# relative to the repo root.
MODEL_REGISTRY = {
    "telugu": {
        "config": "configs/config_ft.yml",
        "checkpoint": "checkpoints/epoch_2nd_00017.pth",
        "languages": ["te", "en"],
        "description": "Telugu + English single-speaker TTS",
    },
    "hindi_english": {
        "config": "configs/config_hindi_english.yml",
        "checkpoint": "checkpoints/epoch_2nd_00029.pth",
        "languages": ["hi", "en"],
        "description": "Hindi + English multi-speaker TTS (5 speakers)",
    },
}

DEFAULT_MODEL = "hindi_english"

# Shared pretrained sub-models (same across all variants)
PRETRAINED_FILES = {
    "asr_config": "pretrained/ASR/config.yml",
    "asr_model": "pretrained/ASR/epoch_00080.pth",
    "f0_model": "pretrained/JDC/bst.t7",
    "plbert_config": "pretrained/PLBERT/config.yml",
    "plbert_model": "pretrained/PLBERT/step_1000000.t7",
}


def list_models() -> dict:
    """
    List all available model variants.

    Returns:
        Dictionary of model names and their info.

    Example:
        >>> from chiluka import hub
        >>> hub.list_models()
        {'telugu': {...}, 'hindi_english': {...}}
    """
    return {
        name: {
            "languages": info["languages"],
            "description": info["description"],
        }
        for name, info in MODEL_REGISTRY.items()
    }


def get_cache_dir() -> Path:
    """Get the cache directory for Chiluka models."""
    cache_dir = Path(os.environ.get("CHILUKA_CACHE", CACHE_DIR))
    cache_dir.mkdir(parents=True, exist_ok=True)
    return cache_dir


def is_model_cached(repo_id: str = DEFAULT_HF_REPO) -> bool:
    """Check if a model is already cached locally."""
    cache_path = get_cache_dir() / repo_id.replace("/", "_")
    if not cache_path.exists():
        return False

    # Check if shared pretrained files exist
    for file_path in PRETRAINED_FILES.values():
        if not (cache_path / file_path).exists():
            return False

    # Check if at least one model variant exists
    for model_info in MODEL_REGISTRY.values():
        config_exists = (cache_path / model_info["config"]).exists()
        checkpoint_exists = (cache_path / model_info["checkpoint"]).exists()
        if config_exists and checkpoint_exists:
            return True

    return False


def download_from_hf(
    repo_id: str = DEFAULT_HF_REPO,
    revision: str = "main",
    force_download: bool = False,
    token: Optional[str] = None,
) -> Path:
    """
    Download model files from HuggingFace Hub.

    Args:
        repo_id: HuggingFace Hub repository ID (e.g., 'Seemanth/chiluka-tts')
        revision: Git revision to download (branch, tag, or commit hash)
        force_download: If True, re-download even if cached
        token: HuggingFace API token for private repos

    Returns:
        Path to the downloaded model directory
    """
    try:
        from huggingface_hub import snapshot_download
    except ImportError:
        raise ImportError(
            "huggingface_hub is required for downloading models. "
            "Install with: pip install huggingface_hub"
        )

    cache_path = get_cache_dir() / repo_id.replace("/", "_")

    if is_model_cached(repo_id) and not force_download:
        print(f"Using cached model from {cache_path}")
        return cache_path

    print(f"Downloading model from HuggingFace Hub: {repo_id}...")

    downloaded_path = snapshot_download(
        repo_id=repo_id,
        revision=revision,
        cache_dir=get_cache_dir() / "hf_cache",
        token=token,
        local_dir=cache_path,
        local_dir_use_symlinks=False,
    )

    print(f"Model downloaded to {cache_path}")
    return Path(downloaded_path)


def get_model_paths(
    model: str = DEFAULT_MODEL,
    repo_id: str = DEFAULT_HF_REPO,
) -> dict:
    """
    Get paths to all model files after downloading.

    Args:
        model: Model variant name ('telugu', 'hindi_english')
        repo_id: HuggingFace Hub repository ID

    Returns:
        Dictionary with paths to config, checkpoint, and pretrained directory
    """
    if model not in MODEL_REGISTRY:
        available = ", ".join(MODEL_REGISTRY.keys())
        raise ValueError(
            f"Unknown model '{model}'. Available models: {available}"
        )

    model_dir = download_from_hf(repo_id)
    model_info = MODEL_REGISTRY[model]

    return {
        "config_path": str(model_dir / model_info["config"]),
        "checkpoint_path": str(model_dir / model_info["checkpoint"]),
        "pretrained_dir": str(model_dir / "pretrained"),
    }


def clear_cache(repo_id: Optional[str] = None):
    """
    Clear cached models.

    Args:
        repo_id: If specified, only clear cache for this repo.
                If None, clear entire cache.
    """
    cache_dir = get_cache_dir()

    if repo_id:
        cache_path = cache_dir / repo_id.replace("/", "_")
        if cache_path.exists():
            shutil.rmtree(cache_path)
            print(f"Cleared cache for {repo_id}")
    else:
        if cache_dir.exists():
            shutil.rmtree(cache_dir)
            print("Cleared entire Chiluka cache")


def push_to_hub(
    local_dir: str,
    repo_id: str,
    token: Optional[str] = None,
    private: bool = False,
    commit_message: str = "Upload Chiluka TTS model",
):
    """
    Push a local model to HuggingFace Hub.

    Args:
        local_dir: Local directory containing model files
        repo_id: Target HuggingFace Hub repository ID
        token: HuggingFace API token (or set HF_TOKEN env var)
        private: Whether to create a private repository
        commit_message: Commit message for the upload

    Example:
        >>> push_to_hub(
        ...     local_dir="./chiluka",
        ...     repo_id="Seemanth/chiluka-tts",
        ...     private=False
        ... )
    """
    try:
        from huggingface_hub import HfApi, create_repo
    except ImportError:
        raise ImportError(
            "huggingface_hub is required for pushing models. "
            "Install with: pip install huggingface_hub"
        )

    api = HfApi(token=token)

    # Create repo if it doesn't exist
    try:
        create_repo(repo_id, private=private, token=token, exist_ok=True)
    except Exception as e:
        print(f"Note: {e}")

    # Upload folder
    print(f"Uploading to {repo_id}...")
    api.upload_folder(
        folder_path=local_dir,
        repo_id=repo_id,
        commit_message=commit_message,
        ignore_patterns=["*.pyc", "__pycache__", "*.egg-info", ".git"],
    )

    print(f"Model uploaded to: https://huggingface.co/{repo_id}")


def create_model_card(repo_id: str, save_path: Optional[str] = None) -> str:
    """
    Generate a model card (README.md) for HuggingFace Hub.

    Args:
        repo_id: Repository ID for the model
        save_path: If provided, save the model card to this path

    Returns:
        Model card content as string
    """
    owner = repo_id.split("/")[0]

    # Build model table
    model_rows = ""
    for name, info in MODEL_REGISTRY.items():
        langs = ", ".join(info["languages"])
        model_rows += f"| `{name}` | {info['description']} | {langs} |\n"

    model_card = f"""---
language:
  - en
  - te
  - hi
license: mit
library_name: chiluka
tags:
  - text-to-speech
  - tts
  - styletts2
  - voice-cloning
  - multi-language
---

# Chiluka TTS

Chiluka (చిలుక - Telugu for "parrot") is a lightweight Text-to-Speech model based on StyleTTS2.

## Available Models

| Model | Description | Languages |
|-------|-------------|-----------|
{model_rows}

## Installation

```bash
pip install chiluka
```

Or install from source:

```bash
pip install git+https://github.com/{owner}/chiluka.git
```

## Usage

### Hindi + English (default)

```python
from chiluka import Chiluka

tts = Chiluka.from_pretrained()

wav = tts.synthesize(
    text="Hello, world!",
    reference_audio="reference.wav",
    language="en"
)
tts.save_wav(wav, "output.wav")
```

### Telugu

```python
tts = Chiluka.from_pretrained(model="telugu")

wav = tts.synthesize(
    text="నమస్కారం",
    reference_audio="reference.wav",
    language="te"
)
```

### PyTorch Hub

```python
import torch

tts = torch.hub.load('{owner}/chiluka', 'chiluka')
tts = torch.hub.load('{owner}/chiluka', 'chiluka_telugu')
```

## License

MIT License

## Citation

Based on StyleTTS2 by Yinghao Aaron Li et al.
"""

    if save_path:
        with open(save_path, "w") as f:
            f.write(model_card)
        print(f"Model card saved to {save_path}")

    return model_card