//! Checkpoint format definitions for different frameworks

#![allow(unused_variables)] // Checkpoint format implementations

use anyhow::{anyhow, Result};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::path::Path;

/// Supported checkpoint formats
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum CheckpointFormat {
    PyTorch,
    TensorFlow,
    JAX,
    Trustformers,
    SafeTensors,
    ONNX,
}

impl CheckpointFormat {
    pub fn extension(&self) -> &'static str {
        match self {
            Self::PyTorch => ".pt",
            Self::TensorFlow => ".ckpt",
            Self::JAX => ".jax",
            Self::Trustformers => ".trust",
            Self::SafeTensors => ".safetensors",
            Self::ONNX => ".onnx",
        }
    }

    pub fn from_path(path: &Path) -> Option<Self> {
        let ext = path.extension()?.to_str()?;
        match ext {
            "pt" | "pth" | "bin" => Some(Self::PyTorch),
            "ckpt" | "h5" | "pb" => Some(Self::TensorFlow),
            "jax" | "msgpack" => Some(Self::JAX),
            "trust" => Some(Self::Trustformers),
            "safetensors" => Some(Self::SafeTensors),
            "onnx" => Some(Self::ONNX),
            _ => None,
        }
    }
}

/// Base trait for all checkpoint formats
pub trait Checkpoint: Send + Sync {
    /// Get the format type
    fn format(&self) -> CheckpointFormat;

    /// Get all weight names in the checkpoint
    fn weight_names(&self) -> Vec<String>;

    /// Get a specific weight tensor
    fn get_weight(&self, name: &str) -> Result<WeightTensor>;

    /// Set a weight tensor
    fn set_weight(&mut self, name: &str, weight: WeightTensor) -> Result<()>;

    /// Get metadata associated with the checkpoint
    fn metadata(&self) -> &HashMap<String, String>;

    /// Save the checkpoint to disk
    fn save(&self, path: &Path) -> Result<()>;

    /// Load from disk
    fn load(path: &Path) -> Result<Self>
    where
        Self: Sized;
}

/// Unified weight tensor representation
#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
pub struct WeightTensor {
    pub data: Vec<f32>,
    pub shape: Vec<usize>,
    pub dtype: DataType,
}

#[derive(Debug, Clone, Copy, PartialEq, Eq, serde::Serialize, serde::Deserialize)]
pub enum DataType {
    Float32,
    Float16,
    BFloat16,
    Int8,
    Int4,
}

impl WeightTensor {
    pub fn new(data: Vec<f32>, shape: Vec<usize>) -> Self {
        Self {
            data,
            shape,
            dtype: DataType::Float32,
        }
    }

    pub fn num_elements(&self) -> usize {
        self.shape.iter().product()
    }

    pub fn reshape(&mut self, new_shape: Vec<usize>) -> Result<()> {
        let new_size: usize = new_shape.iter().product();
        if new_size != self.num_elements() {
            return Err(anyhow!(
                "Cannot reshape tensor from {:?} to {:?}",
                self.shape,
                new_shape
            ));
        }
        self.shape = new_shape;
        Ok(())
    }

    pub fn transpose(&mut self, dims: &[usize]) -> Result<()> {
        if dims.len() != self.shape.len() {
            return Err(anyhow!("Transpose dimensions mismatch"));
        }

        // Create new shape based on transpose dimensions
        let mut new_shape = vec![0; self.shape.len()];
        for (i, &dim) in dims.iter().enumerate() {
            if dim >= self.shape.len() {
                return Err(anyhow!("Transpose dimension {} out of bounds", dim));
            }
            new_shape[i] = self.shape[dim];
        }

        // Calculate strides for original and transposed tensor
        let mut old_strides = vec![1; self.shape.len()];
        for i in (0..self.shape.len() - 1).rev() {
            old_strides[i] = old_strides[i + 1] * self.shape[i + 1];
        }

        let mut new_strides = vec![1; new_shape.len()];
        for i in (0..new_shape.len() - 1).rev() {
            new_strides[i] = new_strides[i + 1] * new_shape[i + 1];
        }

        // Transpose the data
        let mut new_data = vec![0.0; self.data.len()];
        let total_elements = self.data.len();

        for i in 0..total_elements {
            // Calculate old indices
            let mut old_indices = vec![0; self.shape.len()];
            let mut temp = i;
            for (j, &stride) in old_strides.iter().enumerate() {
                old_indices[j] = temp / stride;
                temp %= stride;
            }

            // Calculate new indices after transpose
            let mut new_indices = vec![0; new_shape.len()];
            for (j, &dim) in dims.iter().enumerate() {
                new_indices[j] = old_indices[dim];
            }

            // Calculate new linear index
            let mut new_idx = 0;
            for (j, &idx) in new_indices.iter().enumerate() {
                new_idx += idx * new_strides[j];
            }

            new_data[new_idx] = self.data[i];
        }

        self.data = new_data;
        self.shape = new_shape;
        Ok(())
    }
}

/// PyTorch checkpoint format
pub struct PyTorchCheckpoint {
    weights: HashMap<String, WeightTensor>,
    metadata: HashMap<String, String>,
}

impl Default for PyTorchCheckpoint {
    fn default() -> Self {
        Self::new()
    }
}

impl PyTorchCheckpoint {
    pub fn new() -> Self {
        Self {
            weights: HashMap::new(),
            metadata: HashMap::new(),
        }
    }
}

impl Checkpoint for PyTorchCheckpoint {
    fn format(&self) -> CheckpointFormat {
        CheckpointFormat::PyTorch
    }

    fn weight_names(&self) -> Vec<String> {
        self.weights.keys().cloned().collect()
    }

    fn get_weight(&self, name: &str) -> Result<WeightTensor> {
        self.weights
            .get(name)
            .cloned()
            .ok_or_else(|| anyhow!("Weight '{}' not found", name))
    }

    fn set_weight(&mut self, name: &str, weight: WeightTensor) -> Result<()> {
        self.weights.insert(name.to_string(), weight);
        Ok(())
    }

    fn metadata(&self) -> &HashMap<String, String> {
        &self.metadata
    }

    fn save(&self, path: &Path) -> Result<()> {
        // Implement PyTorch-compatible checkpoint saving using JSON format
        // This provides a practical implementation that can be extended to actual pickle format later
        let checkpoint_data = serde_json::json!({
            "weights": self.weights.iter().map(|(key, tensor)| {
                (key.clone(), serde_json::json!({
                    "data": tensor.data,
                    "shape": tensor.shape,
                    "dtype": format!("{:?}", tensor.dtype)
                }))
            }).collect::<HashMap<String, serde_json::Value>>(),
            "metadata": self.metadata,
            "format": "pytorch_compatible"
        });

        std::fs::write(path, serde_json::to_string_pretty(&checkpoint_data)?)?;
        Ok(())
    }

    fn load(path: &Path) -> Result<Self> {
        // Implement PyTorch-compatible checkpoint loading
        let content = std::fs::read_to_string(path)?;
        let checkpoint_data: serde_json::Value = serde_json::from_str(&content)?;

        let mut checkpoint = Self::new();

        // Load weights
        if let Some(weights) = checkpoint_data.get("weights").and_then(|w| w.as_object()) {
            for (key, tensor_data) in weights {
                if let (Some(data), Some(shape), Some(dtype)) = (
                    tensor_data.get("data").and_then(|d| d.as_array()),
                    tensor_data.get("shape").and_then(|s| s.as_array()),
                    tensor_data.get("dtype").and_then(|d| d.as_str()),
                ) {
                    let data: Vec<f32> =
                        data.iter().filter_map(|v| v.as_f64().map(|f| f as f32)).collect();
                    let shape: Vec<usize> =
                        shape.iter().filter_map(|v| v.as_u64().map(|u| u as usize)).collect();

                    checkpoint.weights.insert(key.clone(), WeightTensor::new(data, shape));
                }
            }
        }

        // Load metadata
        if let Some(metadata) = checkpoint_data.get("metadata").and_then(|m| m.as_object()) {
            for (key, value) in metadata {
                if let Some(value_str) = value.as_str() {
                    checkpoint.metadata.insert(key.clone(), value_str.to_string());
                }
            }
        }

        Ok(checkpoint)
    }
}

/// TensorFlow checkpoint format
pub struct TensorFlowCheckpoint {
    weights: HashMap<String, WeightTensor>,
    metadata: HashMap<String, String>,
}

impl Default for TensorFlowCheckpoint {
    fn default() -> Self {
        Self::new()
    }
}

impl TensorFlowCheckpoint {
    pub fn new() -> Self {
        Self {
            weights: HashMap::new(),
            metadata: HashMap::new(),
        }
    }
}

impl Checkpoint for TensorFlowCheckpoint {
    fn format(&self) -> CheckpointFormat {
        CheckpointFormat::TensorFlow
    }

    fn weight_names(&self) -> Vec<String> {
        self.weights.keys().cloned().collect()
    }

    fn get_weight(&self, name: &str) -> Result<WeightTensor> {
        self.weights
            .get(name)
            .cloned()
            .ok_or_else(|| anyhow!("Weight '{}' not found", name))
    }

    fn set_weight(&mut self, name: &str, weight: WeightTensor) -> Result<()> {
        self.weights.insert(name.to_string(), weight);
        Ok(())
    }

    fn metadata(&self) -> &HashMap<String, String> {
        &self.metadata
    }

    fn save(&self, path: &Path) -> Result<()> {
        // Implement TensorFlow-compatible checkpoint saving using JSON format
        // This provides a practical implementation that can be extended to actual protobuf format later
        let checkpoint_data = serde_json::json!({
            "weights": self.weights.iter().map(|(key, tensor)| {
                (key.clone(), serde_json::json!({
                    "data": tensor.data,
                    "shape": tensor.shape,
                    "dtype": format!("{:?}", tensor.dtype)
                }))
            }).collect::<HashMap<String, serde_json::Value>>(),
            "metadata": self.metadata,
            "format": "tensorflow_compatible"
        });

        std::fs::write(path, serde_json::to_string_pretty(&checkpoint_data)?)?;
        Ok(())
    }

    fn load(path: &Path) -> Result<Self> {
        // Implement TensorFlow-compatible checkpoint loading
        let content = std::fs::read_to_string(path)?;
        let checkpoint_data: serde_json::Value = serde_json::from_str(&content)?;

        let mut checkpoint = Self::new();

        // Load weights
        if let Some(weights) = checkpoint_data.get("weights").and_then(|w| w.as_object()) {
            for (key, tensor_data) in weights {
                if let (Some(data), Some(shape), Some(dtype)) = (
                    tensor_data.get("data").and_then(|d| d.as_array()),
                    tensor_data.get("shape").and_then(|s| s.as_array()),
                    tensor_data.get("dtype").and_then(|d| d.as_str()),
                ) {
                    let data: Vec<f32> =
                        data.iter().filter_map(|v| v.as_f64().map(|f| f as f32)).collect();
                    let shape: Vec<usize> =
                        shape.iter().filter_map(|v| v.as_u64().map(|u| u as usize)).collect();

                    checkpoint.weights.insert(key.clone(), WeightTensor::new(data, shape));
                }
            }
        }

        // Load metadata
        if let Some(metadata) = checkpoint_data.get("metadata").and_then(|m| m.as_object()) {
            for (key, value) in metadata {
                if let Some(value_str) = value.as_str() {
                    checkpoint.metadata.insert(key.clone(), value_str.to_string());
                }
            }
        }

        Ok(checkpoint)
    }
}

/// JAX checkpoint format
pub struct JaxCheckpoint {
    weights: HashMap<String, WeightTensor>,
    metadata: HashMap<String, String>,
}

impl Default for JaxCheckpoint {
    fn default() -> Self {
        Self::new()
    }
}

impl JaxCheckpoint {
    pub fn new() -> Self {
        Self {
            weights: HashMap::new(),
            metadata: HashMap::new(),
        }
    }
}

impl Checkpoint for JaxCheckpoint {
    fn format(&self) -> CheckpointFormat {
        CheckpointFormat::JAX
    }

    fn weight_names(&self) -> Vec<String> {
        self.weights.keys().cloned().collect()
    }

    fn get_weight(&self, name: &str) -> Result<WeightTensor> {
        self.weights
            .get(name)
            .cloned()
            .ok_or_else(|| anyhow!("Weight '{}' not found", name))
    }

    fn set_weight(&mut self, name: &str, weight: WeightTensor) -> Result<()> {
        self.weights.insert(name.to_string(), weight);
        Ok(())
    }

    fn metadata(&self) -> &HashMap<String, String> {
        &self.metadata
    }

    fn save(&self, path: &Path) -> Result<()> {
        // Implement JAX-compatible checkpoint saving using JSON format
        // This provides a practical implementation that can be extended to actual msgpack format later
        let checkpoint_data = serde_json::json!({
            "weights": self.weights.iter().map(|(key, tensor)| {
                (key.clone(), serde_json::json!({
                    "data": tensor.data,
                    "shape": tensor.shape,
                    "dtype": format!("{:?}", tensor.dtype)
                }))
            }).collect::<HashMap<String, serde_json::Value>>(),
            "metadata": self.metadata,
            "format": "jax_compatible"
        });

        std::fs::write(path, serde_json::to_string_pretty(&checkpoint_data)?)?;
        Ok(())
    }

    fn load(path: &Path) -> Result<Self> {
        // Implement JAX-compatible checkpoint loading
        let content = std::fs::read_to_string(path)?;
        let checkpoint_data: serde_json::Value = serde_json::from_str(&content)?;

        let mut checkpoint = Self::new();

        // Load weights
        if let Some(weights) = checkpoint_data.get("weights").and_then(|w| w.as_object()) {
            for (key, tensor_data) in weights {
                if let (Some(data), Some(shape), Some(dtype)) = (
                    tensor_data.get("data").and_then(|d| d.as_array()),
                    tensor_data.get("shape").and_then(|s| s.as_array()),
                    tensor_data.get("dtype").and_then(|d| d.as_str()),
                ) {
                    let data: Vec<f32> =
                        data.iter().filter_map(|v| v.as_f64().map(|f| f as f32)).collect();
                    let shape: Vec<usize> =
                        shape.iter().filter_map(|v| v.as_u64().map(|u| u as usize)).collect();

                    checkpoint.weights.insert(key.clone(), WeightTensor::new(data, shape));
                }
            }
        }

        // Load metadata
        if let Some(metadata) = checkpoint_data.get("metadata").and_then(|m| m.as_object()) {
            for (key, value) in metadata {
                if let Some(value_str) = value.as_str() {
                    checkpoint.metadata.insert(key.clone(), value_str.to_string());
                }
            }
        }

        Ok(checkpoint)
    }
}

/// TrustformeRS native checkpoint format
pub struct TrustformersCheckpoint {
    weights: HashMap<String, WeightTensor>,
    metadata: HashMap<String, String>,
    version: String,
}

impl Default for TrustformersCheckpoint {
    fn default() -> Self {
        Self::new()
    }
}

impl TrustformersCheckpoint {
    pub fn new() -> Self {
        Self {
            weights: HashMap::new(),
            metadata: HashMap::new(),
            version: env!("CARGO_PKG_VERSION").to_string(),
        }
    }
}

impl Checkpoint for TrustformersCheckpoint {
    fn format(&self) -> CheckpointFormat {
        CheckpointFormat::Trustformers
    }

    fn weight_names(&self) -> Vec<String> {
        self.weights.keys().cloned().collect()
    }

    fn get_weight(&self, name: &str) -> Result<WeightTensor> {
        self.weights
            .get(name)
            .cloned()
            .ok_or_else(|| anyhow!("Weight '{}' not found", name))
    }

    fn set_weight(&mut self, name: &str, weight: WeightTensor) -> Result<()> {
        self.weights.insert(name.to_string(), weight);
        Ok(())
    }

    fn metadata(&self) -> &HashMap<String, String> {
        &self.metadata
    }

    fn save(&self, path: &Path) -> Result<()> {
        // Use bincode for efficient serialization
        let data = bincode::serialize(&(&self.weights, &self.metadata, &self.version))?;
        std::fs::write(path, data)?;
        Ok(())
    }

    fn load(path: &Path) -> Result<Self> {
        let data = std::fs::read(path)?;
        let (weights, metadata, version): (
            HashMap<String, WeightTensor>,
            HashMap<String, String>,
            String,
        ) = bincode::deserialize(&data)?;
        Ok(Self {
            weights,
            metadata,
            version,
        })
    }
}

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

    #[test]
    fn test_format_detection() {
        assert_eq!(
            CheckpointFormat::from_path(Path::new("model.pt")),
            Some(CheckpointFormat::PyTorch)
        );
        assert_eq!(
            CheckpointFormat::from_path(Path::new("model.ckpt")),
            Some(CheckpointFormat::TensorFlow)
        );
        assert_eq!(
            CheckpointFormat::from_path(Path::new("model.jax")),
            Some(CheckpointFormat::JAX)
        );
    }

    #[test]
    fn test_weight_tensor() {
        let mut tensor = WeightTensor::new(vec![1.0, 2.0, 3.0, 4.0], vec![2, 2]);
        assert_eq!(tensor.num_elements(), 4);

        assert!(tensor.reshape(vec![4]).is_ok());
        assert_eq!(tensor.shape, vec![4]);

        assert!(tensor.reshape(vec![2, 3]).is_err());
    }
}
