"""
Model configuration and loading for time series forecasting.
Supports multiple Chronos model variants with different architectures.
"""

import torch
from chronos import Chronos2Pipeline, ChronosPipeline

# Try to import TiRex forecasting library
try:
    from tirex import load_model as load_tirex_model
    TIREX_AVAILABLE = True
except ImportError:
    TIREX_AVAILABLE = False


class ModelConfig:
    """Configuration for available forecasting models"""
    
    CHRONOS_2_MODELS = {
        "Chronos-2 (Latest, 120M params)": {
            "model_id": "amazon/chronos-2",
            "pipeline_class": Chronos2Pipeline,
            "description": "Latest Chronos-2 model with 120M parameters"
        }
    }
    
    CHRONOS_T5_MODELS = {
        "Chronos-T5 Tiny (8M params)": {
            "model_id": "amazon/chronos-t5-tiny",
            "pipeline_class": ChronosPipeline,
            "description": "Smallest Chronos-T5 model, fastest inference"
        },
        "Chronos-T5 Mini (20M params)": {
            "model_id": "amazon/chronos-t5-mini",
            "pipeline_class": ChronosPipeline,
            "description": "Mini Chronos-T5 model"
        },
        "Chronos-T5 Small (46M params)": {
            "model_id": "amazon/chronos-t5-small",
            "pipeline_class": ChronosPipeline,
            "description": "Small Chronos-T5 model"
        },
        "Chronos-T5 Base (200M params)": {
            "model_id": "amazon/chronos-t5-base",
            "pipeline_class": ChronosPipeline,
            "description": "Base Chronos-T5 model"
        },
        "Chronos-T5 Large (710M params)": {
            "model_id": "amazon/chronos-t5-large",
            "pipeline_class": ChronosPipeline,
            "description": "Largest Chronos-T5 model, best accuracy"
        }
    }
    
    TIREX_MODELS = {
        "TiRex (35M params)": {
            "model_id": "NX-AI/TiRex",
            "pipeline_class": "TiRex",
            "description": "TiRex xLSTM-based model, excellent for both short and long-term forecasting"
        }
    } if TIREX_AVAILABLE else {}
    
    @classmethod
    def get_all_models(cls):
        """Get all available models"""
        all_models = {}
        all_models.update(cls.CHRONOS_2_MODELS)
        all_models.update(cls.CHRONOS_T5_MODELS)
        all_models.update(cls.TIREX_MODELS)
        return all_models
    
    @classmethod
    def get_model_names(cls):
        """Get list of model names for dropdown"""
        return list(cls.get_all_models().keys())
    
    @classmethod
    def get_model_config(cls, model_name):
        """Get configuration for a specific model"""
        return cls.get_all_models().get(model_name)


def load_model_pipeline(model_name, device_map="cpu", dtype=torch.float32):
    """
    Load a forecasting model pipeline.
    
    Args:
        model_name: Display name of the model
        device_map: Device to load model on (default: "cpu")
        dtype: Data type for model weights (default: torch.float32)
    
    Returns:
        Loaded pipeline instance or model
    """
    config = ModelConfig.get_model_config(model_name)
    
    if config is None:
        raise ValueError(f"Unknown model: {model_name}")
    
    pipeline_class = config["pipeline_class"]
    model_id = config["model_id"]
    
    # Load TiRex model differently
    if pipeline_class == "TiRex":
        if not TIREX_AVAILABLE:
            raise ImportError(
                "TiRex library not installed. Install with: pip install tirex-ts\n"
                "Note: TiRex requires GPU support (CUDA-enabled GPU recommended)"
            )
        # TiRex uses load_model from tirex library
        # backend="torch" for CPU/GPU, device="cuda" or "cpu"
        import torch
        device = "cuda" if torch.cuda.is_available() and device_map == "cuda" else "cpu"
        model = load_tirex_model(model_id, backend="torch", device=device)
        return TiRexWrapper(model)
    
    # Load Chronos pipelines
    pipeline = pipeline_class.from_pretrained(
        model_id,
        device_map=device_map,
        dtype=dtype,
    )
    
    return pipeline


class TiRexWrapper:
    """Wrapper to make TiRex compatible with Chronos pipeline API"""
    
    def __init__(self, model):
        self.model = model
    
    def predict_df(self, context_df, prediction_length, quantile_levels, **kwargs):
        """
        Wrapper to make TiRex work with the same API as Chronos
        TiRex.forecast() may return a tuple of (forecast, metadata) or just tensor
        """
        import pandas as pd
        import torch
        
        # Convert dataframe to tensor (batch_size=1, sequence_length)
        context = torch.tensor(context_df['target'].values, dtype=torch.float32).unsqueeze(0)
        
        # TiRex forecast may return tuple or tensor
        with torch.no_grad():
            result = self.model.forecast(context=context, prediction_length=prediction_length)
        
        # Handle tuple return (forecast, metadata)
        if isinstance(result, tuple):
            forecast = result[0]
        else:
            forecast = result
        
        # forecast is shape (batch, prediction_length) or (batch, prediction_length, samples)
        if forecast.dim() == 3:  # (batch, pred_len, samples)
            forecast = forecast[0]  # Take first batch
            # Calculate quantiles from samples
            quantiles = {}
            for q in quantile_levels:
                quantiles[str(q)] = torch.quantile(forecast, q, dim=-1).cpu().numpy()
            median = torch.median(forecast, dim=-1).values.cpu().numpy()
        elif forecast.dim() == 2:  # (batch, pred_len) - single prediction
            forecast = forecast[0].cpu().numpy()  # Take first batch
            median = forecast
            # Use same value for all quantiles since we don't have distribution
            quantiles = {str(q): median for q in quantile_levels}
        else:  # (pred_len,)
            median = forecast.cpu().numpy()
            quantiles = {str(q): median for q in quantile_levels}
        
        # Create output dataframe matching Chronos format
        result_df = pd.DataFrame({
            'predictions': median,
            **quantiles
        })
        
        return result_df


def get_model_info(model_name):
    """
    Get information about a model.
    
    Args:
        model_name: Display name of the model
    
    Returns:
        Dictionary with model information
    """
    config = ModelConfig.get_model_config(model_name)
    
    if config is None:
        return None
    
    return {
        "name": model_name,
        "model_id": config["model_id"],
        "description": config["description"],
        "pipeline": config["pipeline_class"].__name__
    }