//! Model conversion utilities
//!
//! This module provides utilities for converting models between different frameworks
//! and formats, including PyTorch compatibility and migration tools.
//!
//! Note: This module requires std for file operations and is only available with the "std" feature.

#[cfg(feature = "std")]
use crate::Module;
#[cfg(feature = "std")]
use std::{collections::HashMap, path::Path, string::String, vec::Vec};
#[cfg(feature = "std")]
use torsh_core::error::{Result, TorshError};
#[cfg(feature = "std")]
use torsh_tensor::Tensor;

/// Metadata for a model
#[cfg(feature = "std")]
#[derive(Debug, Clone)]
pub struct ModelMetadata {
    /// Model name
    pub name: String,
    /// Model version
    pub version: String,
    /// Framework it was originally created in
    pub framework: String,
    /// Input shapes
    pub input_shapes: Vec<Vec<usize>>,
    /// Output shapes
    pub output_shapes: Vec<Vec<usize>>,
    /// Additional metadata
    pub metadata: HashMap<String, String>,
}

impl Default for ModelMetadata {
    fn default() -> Self {
        Self {
            name: "Unnamed".to_string(),
            version: "1.0.0".to_string(),
            framework: "Unknown".to_string(),
            input_shapes: vec![],
            output_shapes: vec![],
            metadata: HashMap::new(),
        }
    }
}

/// Information about a layer
#[derive(Debug, Clone)]
pub struct LayerInfo {
    /// Layer name
    pub name: String,
    /// Layer type
    pub layer_type: String,
    /// Input shapes
    pub input_shapes: Vec<Vec<usize>>,
    /// Output shapes
    pub output_shapes: Vec<Vec<usize>>,
    /// Layer parameters
    pub parameters: HashMap<String, String>,
}

impl Default for LayerInfo {
    fn default() -> Self {
        Self {
            name: "Unnamed".to_string(),
            layer_type: "Unknown".to_string(),
            input_shapes: vec![],
            output_shapes: vec![],
            parameters: HashMap::new(),
        }
    }
}

/// Result of model conversion
#[derive(Debug)]
pub struct ConvertedModel {
    /// Converted layers
    pub layers: Vec<LayerInfo>,
    /// Converted parameters
    pub parameters: HashMap<String, Tensor>,
    /// Model metadata
    pub metadata: ModelMetadata,
    /// Conversion log
    pub conversion_log: Vec<String>,
}

impl ConvertedModel {
    /// Get the total number of parameters
    pub fn total_parameters(&self) -> usize {
        self.parameters
            .values()
            .map(|tensor| tensor.shape().numel())
            .sum()
    }

    /// Get conversion warnings
    pub fn warnings(&self) -> Vec<&str> {
        self.conversion_log
            .iter()
            .filter(|log| log.contains("warning") || log.contains("Warning"))
            .map(|s| s.as_str())
            .collect()
    }

    /// Get conversion errors
    pub fn errors(&self) -> Vec<&str> {
        self.conversion_log
            .iter()
            .filter(|log| log.contains("error") || log.contains("Error"))
            .map(|s| s.as_str())
            .collect()
    }

    /// Check if conversion was successful
    pub fn is_successful(&self) -> bool {
        self.errors().is_empty()
    }
}

/// Framework source for model conversion
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum FrameworkSource {
    /// PyTorch model
    PyTorch,
    /// TensorFlow model
    TensorFlow,
    /// ONNX model
    Onnx,
    /// Keras model
    Keras,
    /// JAX/Flax model
    Jax,
}

/// Conversion configuration
#[derive(Debug, Clone)]
pub struct ConversionConfig {
    /// Source framework
    pub source: FrameworkSource,
    /// Preserve layer names during conversion
    pub preserve_names: bool,
    /// Strict type conversion (fail on incompatible types)
    pub strict_types: bool,
    /// Convert training-specific layers to inference equivalents
    pub inference_mode: bool,
    /// Handle unsupported operations
    pub fallback_mode: bool,
}

impl Default for ConversionConfig {
    fn default() -> Self {
        Self {
            source: FrameworkSource::PyTorch,
            preserve_names: true,
            strict_types: true,
            inference_mode: false,
            fallback_mode: false,
        }
    }
}

/// Model converter for migrating between frameworks
pub struct ModelConverter {
    config: ConversionConfig,
}

impl ModelConverter {
    /// Create a new model converter with the given configuration
    pub fn new(config: ConversionConfig) -> Self {
        Self { config }
    }

    /// Create a converter specifically for PyTorch models
    pub fn from_pytorch() -> Self {
        Self::new(ConversionConfig {
            source: FrameworkSource::PyTorch,
            ..Default::default()
        })
    }

    /// Create a converter specifically for TensorFlow models
    pub fn from_tensorflow() -> Self {
        Self::new(ConversionConfig {
            source: FrameworkSource::TensorFlow,
            ..Default::default()
        })
    }

    /// Create a converter specifically for ONNX models
    pub fn from_onnx() -> Self {
        Self::new(ConversionConfig {
            source: FrameworkSource::Onnx,
            ..Default::default()
        })
    }

    /// Convert a model from external format to Torsh
    pub fn convert(&self, model_path: &Path) -> Result<ConvertedModel> {
        match self.config.source {
            FrameworkSource::PyTorch => self.convert_from_pytorch(model_path),
            FrameworkSource::TensorFlow => self.convert_from_tensorflow(model_path),
            FrameworkSource::Onnx => self.convert_from_onnx(model_path),
            FrameworkSource::Keras => self.convert_from_keras(model_path),
            FrameworkSource::Jax => self.convert_from_jax(model_path),
        }
    }

    /// Convert from PyTorch model
    fn convert_from_pytorch(&self, path: &Path) -> Result<ConvertedModel> {
        // PyTorch conversion implementation
        // This would parse PyTorch .pth or .pt files

        let metadata = self.parse_pytorch_metadata(path)?;
        let layers = self.convert_pytorch_layers(&metadata)?;
        let parameters = self.convert_pytorch_parameters(&metadata)?;

        Ok(ConvertedModel {
            layers,
            parameters,
            metadata,
            conversion_log: vec!["Converted from PyTorch".to_string()],
        })
    }

    /// Convert from TensorFlow model
    fn convert_from_tensorflow(&self, path: &Path) -> Result<ConvertedModel> {
        // TensorFlow conversion implementation
        // This would parse TensorFlow SavedModel or .pb files

        let metadata = self.parse_tensorflow_metadata(path)?;
        let layers = self.convert_tensorflow_layers(&metadata)?;
        let parameters = self.convert_tensorflow_parameters(&metadata)?;

        Ok(ConvertedModel {
            layers,
            parameters,
            metadata,
            conversion_log: vec!["Converted from TensorFlow".to_string()],
        })
    }

    /// Convert from ONNX model
    fn convert_from_onnx(&self, path: &Path) -> Result<ConvertedModel> {
        // ONNX conversion implementation
        // This would parse ONNX .onnx files

        let metadata = self.parse_onnx_metadata(path)?;
        let layers = self.convert_onnx_layers(&metadata)?;
        let parameters = self.convert_onnx_parameters(&metadata)?;

        Ok(ConvertedModel {
            layers,
            parameters,
            metadata,
            conversion_log: vec!["Converted from ONNX".to_string()],
        })
    }

    /// Convert from Keras model
    fn convert_from_keras(&self, _path: &Path) -> Result<ConvertedModel> {
        // Keras conversion implementation
        let metadata = ModelMetadata::default();
        Ok(ConvertedModel {
            layers: vec![],
            parameters: HashMap::new(),
            metadata,
            conversion_log: vec!["Converted from Keras (placeholder)".to_string()],
        })
    }

    /// Convert from JAX/Flax model
    fn convert_from_jax(&self, _path: &Path) -> Result<ConvertedModel> {
        // JAX conversion implementation
        let metadata = ModelMetadata::default();
        Ok(ConvertedModel {
            layers: vec![],
            parameters: HashMap::new(),
            metadata,
            conversion_log: vec!["Converted from JAX (placeholder)".to_string()],
        })
    }

    /// Parse PyTorch model metadata
    fn parse_pytorch_metadata(&self, _path: &Path) -> Result<ModelMetadata> {
        // This would parse PyTorch model files
        // For now, return placeholder metadata
        Ok(ModelMetadata {
            name: "PyTorch Model".to_string(),
            version: "Unknown".to_string(),
            framework: "PyTorch".to_string(),
            input_shapes: vec![],
            output_shapes: vec![],
            metadata: {
                let mut map = HashMap::new();
                map.insert("architecture".to_string(), "Unknown".to_string());
                map.insert("total_parameters".to_string(), "0".to_string());
                map
            },
        })
    }

    /// Parse TensorFlow model metadata
    fn parse_tensorflow_metadata(&self, _path: &Path) -> Result<ModelMetadata> {
        Ok(ModelMetadata {
            name: "TensorFlow Model".to_string(),
            version: "Unknown".to_string(),
            framework: "TensorFlow".to_string(),
            input_shapes: vec![],
            output_shapes: vec![],
            metadata: {
                let mut map = HashMap::new();
                map.insert("architecture".to_string(), "Unknown".to_string());
                map.insert("total_parameters".to_string(), "0".to_string());
                map
            },
        })
    }

    /// Parse ONNX model metadata
    fn parse_onnx_metadata(&self, _path: &Path) -> Result<ModelMetadata> {
        Ok(ModelMetadata {
            name: "ONNX Model".to_string(),
            version: "Unknown".to_string(),
            framework: "ONNX".to_string(),
            input_shapes: vec![],
            output_shapes: vec![],
            metadata: {
                let mut map = HashMap::new();
                map.insert("architecture".to_string(), "Unknown".to_string());
                map.insert("total_parameters".to_string(), "0".to_string());
                map
            },
        })
    }

    /// Convert PyTorch layers to Torsh layers
    fn convert_pytorch_layers(&self, _metadata: &ModelMetadata) -> Result<Vec<LayerInfo>> {
        // This would parse PyTorch layer definitions and convert them
        Ok(vec![])
    }

    /// Convert TensorFlow layers to Torsh layers
    fn convert_tensorflow_layers(&self, _metadata: &ModelMetadata) -> Result<Vec<LayerInfo>> {
        Ok(vec![])
    }

    /// Convert ONNX operators to Torsh layers
    fn convert_onnx_layers(&self, _metadata: &ModelMetadata) -> Result<Vec<LayerInfo>> {
        Ok(vec![])
    }

    /// Convert PyTorch parameters to Torsh tensors
    fn convert_pytorch_parameters(
        &self,
        _metadata: &ModelMetadata,
    ) -> Result<HashMap<String, Tensor>> {
        Ok(HashMap::new())
    }

    /// Convert TensorFlow variables to Torsh tensors
    fn convert_tensorflow_parameters(
        &self,
        _metadata: &ModelMetadata,
    ) -> Result<HashMap<String, Tensor>> {
        Ok(HashMap::new())
    }

    /// Convert ONNX initializers to Torsh tensors
    fn convert_onnx_parameters(
        &self,
        _metadata: &ModelMetadata,
    ) -> Result<HashMap<String, Tensor>> {
        Ok(HashMap::new())
    }
}

/// Migration utilities for updating existing Torsh models
pub struct MigrationHelper {
    from_version: String,
    to_version: String,
}

impl MigrationHelper {
    /// Create a new migration helper
    pub fn new(from_version: String, to_version: String) -> Self {
        Self {
            from_version,
            to_version,
        }
    }

    /// Migrate a model from one version to another
    pub fn migrate<M: Module>(&self, model: &M) -> Result<MigratedModel> {
        // Version-specific migration logic
        match (self.from_version.as_str(), self.to_version.as_str()) {
            ("0.1.0", "0.2.0") => self.migrate_0_1_to_0_2(model),
            _ => Err(TorshError::InvalidArgument(format!(
                "Migration from {} to {} not supported",
                self.from_version, self.to_version
            ))),
        }
    }

    /// Migrate from version 0.1.0 to 0.2.0
    fn migrate_0_1_to_0_2<M: Module>(&self, _model: &M) -> Result<MigratedModel> {
        // Specific migration logic for this version upgrade
        Ok(MigratedModel {
            migration_log: vec!["Migrated from 0.1.0 to 0.2.0".to_string()],
        })
    }
}

/// Result of model migration
#[derive(Debug)]
pub struct MigratedModel {
    /// Migration log
    pub migration_log: Vec<String>,
}

/// PyTorch compatibility utilities
pub mod pytorch_compat {
    use super::*;

    /// Convert PyTorch state dict to Torsh parameters
    pub fn convert_state_dict(_state_dict_path: &Path) -> Result<HashMap<String, Tensor>> {
        // This would parse PyTorch .pth files and extract tensors
        // For now, return empty map
        Ok(HashMap::new())
    }

    /// Generate PyTorch-compatible layer names
    pub fn generate_pytorch_names<M: Module>(model: &M) -> HashMap<String, String> {
        // Generate names compatible with PyTorch conventions
        let mut names = HashMap::new();
        let params = model.parameters();

        for (i, (original_name, _)) in params.iter().enumerate() {
            let pytorch_name = format!("layer_{}.weight", i);
            names.insert(original_name.clone(), pytorch_name);
        }

        names
    }

    /// Create mapping between PyTorch and Torsh layer types
    pub fn layer_type_mapping() -> HashMap<String, String> {
        let mut mapping = HashMap::new();

        // Common layer mappings
        mapping.insert(
            "torch.nn.Linear".to_string(),
            "torsh_nn::layers::Linear".to_string(),
        );
        mapping.insert(
            "torch.nn.Conv2d".to_string(),
            "torsh_nn::layers::Conv2d".to_string(),
        );
        mapping.insert(
            "torch.nn.BatchNorm2d".to_string(),
            "torsh_nn::layers::BatchNorm2d".to_string(),
        );
        mapping.insert(
            "torch.nn.ReLU".to_string(),
            "torsh_nn::layers::ReLU".to_string(),
        );
        mapping.insert(
            "torch.nn.Dropout".to_string(),
            "torsh_nn::layers::Dropout".to_string(),
        );

        mapping
    }
}

/// TensorFlow compatibility utilities
pub mod tensorflow_compat {
    use super::*;

    /// Convert TensorFlow SavedModel to Torsh
    pub fn convert_saved_model(_model_path: &Path) -> Result<ConvertedModel> {
        // Parse TensorFlow SavedModel format
        let metadata = ModelMetadata {
            framework: "TensorFlow".to_string(),
            ..Default::default()
        };

        Ok(ConvertedModel {
            layers: vec![],
            parameters: HashMap::new(),
            metadata,
            conversion_log: vec!["Converted TensorFlow SavedModel".to_string()],
        })
    }

    /// Map TensorFlow operations to Torsh layers
    pub fn operation_mapping() -> HashMap<String, String> {
        let mut mapping = HashMap::new();

        mapping.insert("MatMul".to_string(), "Linear".to_string());
        mapping.insert("Conv2D".to_string(), "Conv2d".to_string());
        mapping.insert("BatchNorm".to_string(), "BatchNorm2d".to_string());
        mapping.insert("Relu".to_string(), "ReLU".to_string());

        mapping
    }
}

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

    #[test]
    fn test_conversion_config_default() {
        let config = ConversionConfig::default();
        assert_eq!(config.source, FrameworkSource::PyTorch);
        assert!(config.preserve_names);
        assert!(config.strict_types);
    }

    #[test]
    fn test_model_converter_creation() {
        let converter = ModelConverter::from_pytorch();
        assert_eq!(converter.config.source, FrameworkSource::PyTorch);

        let converter = ModelConverter::from_tensorflow();
        assert_eq!(converter.config.source, FrameworkSource::TensorFlow);
    }

    #[test]
    fn test_migration_helper() {
        let helper = MigrationHelper::new("0.1.0".to_string(), "0.2.0".to_string());
        assert_eq!(helper.from_version, "0.1.0");
        assert_eq!(helper.to_version, "0.2.0");
    }

    #[test]
    fn test_pytorch_layer_mapping() {
        let mapping = pytorch_compat::layer_type_mapping();
        assert_eq!(
            mapping.get("torch.nn.Linear").unwrap(),
            "torsh_nn::layers::Linear"
        );
        assert_eq!(
            mapping.get("torch.nn.Conv2d").unwrap(),
            "torsh_nn::layers::Conv2d"
        );
    }
}
