//! Candle model wrapper and utilities

use crate::error::{CandleError, Result};
use crate::session::CandleConfig;
use candle_core::Tensor as CandleTensor;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::path::Path;
use tracing::{debug, info};

/// Model architecture type
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum ModelArchitecture {
    /// Generic model
    Generic,
    /// BERT-style encoder
    Bert,
    /// GPT-style decoder
    Gpt,
    /// `LLaMA` architecture
    Llama,
    /// Mistral architecture
    Mistral,
    /// Phi architecture
    Phi,
    /// Vision Transformer
    ViT,
    /// Stable Diffusion
    StableDiffusion,
    /// Whisper (speech)
    Whisper,
}

/// Information about a loaded model
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelInfo {
    /// Model name
    pub name: String,
    /// Model architecture
    pub architecture: ModelArchitecture,
    /// Number of parameters
    pub num_parameters: usize,
    /// Model size in bytes
    pub size_bytes: usize,
    /// Hidden size
    pub hidden_size: usize,
    /// Number of layers
    pub num_layers: usize,
    /// Number of attention heads
    pub num_heads: usize,
    /// Vocabulary size (for language models)
    pub vocab_size: Option<usize>,
    /// Maximum sequence length
    pub max_seq_len: Option<usize>,
    /// Additional metadata
    pub metadata: HashMap<String, String>,
}

impl Default for ModelInfo {
    fn default() -> Self {
        Self {
            name: "model".to_string(),
            architecture: ModelArchitecture::Generic,
            num_parameters: 0,
            size_bytes: 0,
            hidden_size: 768,
            num_layers: 12,
            num_heads: 12,
            vocab_size: None,
            max_seq_len: None,
            metadata: HashMap::new(),
        }
    }
}

/// A loaded Candle model ready for inference
pub struct CandleModel {
    /// Model information
    info: ModelInfo,
    /// Model weights
    weights: HashMap<String, CandleTensor>,
    /// Device the model is on
    device: candle_core::Device,
}

impl CandleModel {
    /// Load a model from a file path
    pub fn load(path: impl AsRef<Path>, config: &CandleConfig) -> Result<Self> {
        let path = path.as_ref();
        info!("Loading Candle model from: {}", path.display());

        if !path.exists() {
            return Err(CandleError::model_load(format!(
                "Model file not found: {}",
                path.display()
            )));
        }

        let device = config.device.to_candle()?;

        // Determine file type and load accordingly
        let extension = path.extension().and_then(|e| e.to_str()).unwrap_or("");

        match extension {
            "safetensors" => Self::load_safetensors(path, device),
            "bin" | "pt" | "pth" => Self::load_pytorch(path, device),
            "gguf" => Self::load_gguf(path, device),
            _ => Err(CandleError::model_load(format!(
                "Unsupported model format: {extension}"
            ))),
        }
    }

    /// Load `SafeTensors` format
    fn load_safetensors(path: &Path, device: candle_core::Device) -> Result<Self> {
        use safetensors::SafeTensors;

        let data = std::fs::read(path)?;
        let size_bytes = data.len();

        // Parse SafeTensors
        let tensors = SafeTensors::deserialize(&data)?;

        // Convert to Candle tensors
        let mut weights = HashMap::new();
        let mut num_parameters = 0;

        for (name, tensor_view) in tensors.tensors() {
            let shape: Vec<usize> = tensor_view.shape().to_vec();
            let numel: usize = shape.iter().product();
            num_parameters += numel;

            // Get dtype and convert
            let dtype = match tensor_view.dtype() {
                safetensors::Dtype::F32 => candle_core::DType::F32,
                safetensors::Dtype::F16 => candle_core::DType::F16,
                safetensors::Dtype::BF16 => candle_core::DType::BF16,
                safetensors::Dtype::F64 => candle_core::DType::F64,
                safetensors::Dtype::I64 => candle_core::DType::I64,
                safetensors::Dtype::U32 => candle_core::DType::U32,
                _ => candle_core::DType::F32,
            };

            // Create Candle tensor (simplified - real implementation would handle all dtypes)
            if dtype == candle_core::DType::F32 {
                let data: &[f32] = bytemuck::cast_slice(tensor_view.data());
                let tensor = CandleTensor::from_slice(data, shape.as_slice(), &device)?;
                weights.insert(name.clone(), tensor);
            }
        }

        debug!(
            "Loaded {} tensors, {} parameters",
            weights.len(),
            num_parameters
        );

        let info = ModelInfo {
            name: path
                .file_stem()
                .and_then(|s| s.to_str())
                .unwrap_or("model")
                .to_string(),
            num_parameters,
            size_bytes,
            ..Default::default()
        };

        Ok(Self {
            info,
            weights,
            device,
        })
    }

    /// Load `PyTorch` format (placeholder)
    fn load_pytorch(path: &Path, device: candle_core::Device) -> Result<Self> {
        let size_bytes = std::fs::metadata(path)
            .map(|m| m.len() as usize)
            .unwrap_or(0);

        let info = ModelInfo {
            name: path
                .file_stem()
                .and_then(|s| s.to_str())
                .unwrap_or("model")
                .to_string(),
            size_bytes,
            ..Default::default()
        };

        Ok(Self {
            info,
            weights: HashMap::new(),
            device,
        })
    }

    /// Load GGUF format (for quantized models)
    fn load_gguf(path: &Path, device: candle_core::Device) -> Result<Self> {
        let size_bytes = std::fs::metadata(path)
            .map(|m| m.len() as usize)
            .unwrap_or(0);

        let info = ModelInfo {
            name: path
                .file_stem()
                .and_then(|s| s.to_str())
                .unwrap_or("model")
                .to_string(),
            size_bytes,
            ..Default::default()
        };

        Ok(Self {
            info,
            weights: HashMap::new(),
            device,
        })
    }

    /// Get model information
    pub fn info(&self) -> &ModelInfo {
        &self.info
    }

    /// Get model name
    pub fn name(&self) -> &str {
        &self.info.name
    }

    /// Get number of parameters
    pub fn num_parameters(&self) -> usize {
        self.info.num_parameters
    }

    /// Get model size in bytes
    pub fn size_bytes(&self) -> usize {
        self.info.size_bytes
    }

    /// Get a weight tensor by name
    pub fn get_weight(&self, name: &str) -> Option<&CandleTensor> {
        self.weights.get(name)
    }

    /// Run forward pass
    pub fn forward(
        &self,
        inputs: HashMap<String, CandleTensor>,
    ) -> Result<HashMap<String, CandleTensor>> {
        debug!("Running forward pass with {} inputs", inputs.len());

        // Simulated forward pass
        // In real implementation, this would execute the model graph
        let mut outputs = HashMap::new();

        // Create dummy output based on first input
        if let Some((_, input)) = inputs.iter().next() {
            let batch_size = input.dims().first().copied().unwrap_or(1);
            let output_shape = vec![batch_size, 1000]; // Assume classification output
            let output = CandleTensor::zeros(
                output_shape.as_slice(),
                candle_core::DType::F32,
                &self.device,
            )?;
            outputs.insert("output".to_string(), output);
        }

        Ok(outputs)
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_model_info_default() {
        let info = ModelInfo::default();
        assert_eq!(info.name, "model");
        assert_eq!(info.hidden_size, 768);
        assert_eq!(info.num_layers, 12);
    }

    #[test]
    fn test_model_architecture() {
        let arch = ModelArchitecture::Llama;
        assert_eq!(arch, ModelArchitecture::Llama);
    }
}