aethershell 0.3.1

use crate::ai_api::models::*;
use anyhow::Result;
use serde::{Deserialize, Serialize};
use sha2::{Digest, Sha256};
use std::collections::HashMap;
use std::fs;
use std::path::Path;

/// Model format converter for converting between different AI model formats
pub struct ModelConverter {
    conversion_rules: HashMap<(ModelFormat, ModelFormat), ConversionStrategy>,
}

#[derive(Debug, Clone)]
pub enum ConversionStrategy {
    Direct,
    ThroughIntermediate(ModelFormat),
    ExternalTool(String),
    Unsupported,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ConversionRequest {
    pub source_path: String,
    pub source_format: ModelFormat,
    pub target_format: ModelFormat,
    pub target_path: String,
    pub preserve_metadata: bool,
    pub compression_level: Option<u8>,
    pub quantization: Option<QuantizationType>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum QuantizationType {
    F16,
    Q4_0,
    Q4_1,
    Q5_0,
    Q5_1,
    Q8_0,
    Q8_1,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ConversionResult {
    pub success: bool,
    pub target_path: String,
    pub target_size: u64,
    pub checksum: String,
    pub metadata: LocalModelMetadata,
    pub conversion_time_ms: u64,
    pub warnings: Vec<String>,
}

impl ModelConverter {
    pub fn new() -> Self {
        let mut conversion_rules = HashMap::new();

        // Define conversion rules
        // Direct conversions
        conversion_rules.insert(
            (ModelFormat::PyTorch, ModelFormat::SafeTensors),
            ConversionStrategy::Direct,
        );
        conversion_rules.insert(
            (ModelFormat::SafeTensors, ModelFormat::PyTorch),
            ConversionStrategy::Direct,
        );
        conversion_rules.insert(
            (ModelFormat::GGUF, ModelFormat::GGUF),
            ConversionStrategy::Direct,
        );

        // Through intermediate formats
        conversion_rules.insert(
            (ModelFormat::PyTorch, ModelFormat::GGUF),
            ConversionStrategy::ThroughIntermediate(ModelFormat::SafeTensors),
        );
        conversion_rules.insert(
            (ModelFormat::Huggingface, ModelFormat::GGUF),
            ConversionStrategy::ThroughIntermediate(ModelFormat::SafeTensors),
        );

        // External tool conversions
        conversion_rules.insert(
            (ModelFormat::PyTorch, ModelFormat::ONNX),
            ConversionStrategy::ExternalTool("torch2onnx".to_string()),
        );
        conversion_rules.insert(
            (ModelFormat::TensorFlow, ModelFormat::ONNX),
            ConversionStrategy::ExternalTool("tf2onnx".to_string()),
        );

        Self { conversion_rules }
    }

    /// Check if conversion between formats is supported
    pub fn can_convert(&self, source: &ModelFormat, target: &ModelFormat) -> bool {
        if source == target {
            return true;
        }
        self.conversion_rules
            .get(&(source.clone(), target.clone()))
            .map(|s| !matches!(s, ConversionStrategy::Unsupported))
            .unwrap_or(false)
    }

    /// Get the conversion strategy for a format pair
    pub fn get_strategy(
        &self,
        source: &ModelFormat,
        target: &ModelFormat,
    ) -> Option<&ConversionStrategy> {
        self.conversion_rules.get(&(source.clone(), target.clone()))
    }

    /// Convert a model from one format to another
    pub async fn convert_model(&self, request: ConversionRequest) -> Result<ConversionResult> {
        let _start_time = std::time::Instant::now();
        let _warnings: Vec<String> = Vec::new();

        // Validate source file exists
        if !Path::new(&request.source_path).exists() {
            return Err(anyhow::anyhow!(
                "Source file does not exist: {}",
                request.source_path
            ));
        }

        // Get conversion strategy
        let strategy = self
            .conversion_rules
            .get(&(request.source_format.clone(), request.target_format.clone()))
            .ok_or_else(|| {
                anyhow::anyhow!(
                    "No conversion strategy available from {:?} to {:?}",
                    request.source_format,
                    request.target_format
                )
            })?;

        match strategy {
            ConversionStrategy::Direct => self.direct_conversion(&request).await,
            ConversionStrategy::ThroughIntermediate(intermediate) => {
                self.intermediate_conversion(&request, intermediate.clone())
                    .await
            }
            ConversionStrategy::ExternalTool(tool) => {
                self.external_tool_conversion(&request, &tool).await
            }
            ConversionStrategy::Unsupported => Err(anyhow::anyhow!(
                "Conversion from {:?} to {:?} is not supported",
                request.source_format,
                request.target_format
            )),
        }
    }

    /// Direct format conversion (e.g., PyTorch to SafeTensors)
    async fn direct_conversion(&self, request: &ConversionRequest) -> Result<ConversionResult> {
        match (&request.source_format, &request.target_format) {
            (ModelFormat::PyTorch, ModelFormat::SafeTensors) => {
                self.pytorch_to_safetensors(request).await
            }
            (ModelFormat::SafeTensors, ModelFormat::PyTorch) => {
                self.safetensors_to_pytorch(request).await
            }
            (ModelFormat::GGUF, ModelFormat::GGUF) => {
                // Handle GGUF quantization
                self.gguf_quantize(request).await
            }
            _ => Err(anyhow::anyhow!("Direct conversion not implemented")),
        }
    }

    /// Conversion through intermediate format
    async fn intermediate_conversion(
        &self,
        request: &ConversionRequest,
        intermediate: ModelFormat,
    ) -> Result<ConversionResult> {
        // First convert to intermediate format
        let temp_path = format!("{}.intermediate", request.target_path);
        let intermediate_request = ConversionRequest {
            source_path: request.source_path.clone(),
            source_format: request.source_format.clone(),
            target_format: intermediate.clone(),
            target_path: temp_path.clone(),
            preserve_metadata: request.preserve_metadata,
            compression_level: request.compression_level,
            quantization: request.quantization.clone(),
        };

        let _intermediate_result = self.direct_conversion(&intermediate_request).await?;

        // Then convert from intermediate to target
        let final_request = ConversionRequest {
            source_path: temp_path.clone(),
            source_format: intermediate,
            target_format: request.target_format.clone(),
            target_path: request.target_path.clone(),
            preserve_metadata: request.preserve_metadata,
            compression_level: request.compression_level,
            quantization: request.quantization.clone(),
        };

        let final_result = self.direct_conversion(&final_request).await?;

        // Clean up intermediate file
        if let Err(_) = fs::remove_file(&temp_path) {
            // Log warning but don't fail
        }

        Ok(final_result)
    }

    /// Conversion using external tools
    async fn external_tool_conversion(
        &self,
        request: &ConversionRequest,
        tool: &str,
    ) -> Result<ConversionResult> {
        match tool {
            "torch2onnx" => self.pytorch_to_onnx(request).await,
            "tf2onnx" => self.tensorflow_to_onnx(request).await,
            _ => Err(anyhow::anyhow!("External tool {} not implemented", tool)),
        }
    }

    /// Convert PyTorch model to SafeTensors
    async fn pytorch_to_safetensors(
        &self,
        request: &ConversionRequest,
    ) -> Result<ConversionResult> {
        // This would use the safetensors library to convert PyTorch tensors
        // For now, we'll simulate the conversion

        let _source_size = fs::metadata(&request.source_path)?.len();

        // Simulate conversion by copying file (in real implementation, this would use safetensors)
        fs::copy(&request.source_path, &request.target_path)?;

        let target_size = fs::metadata(&request.target_path)?.len();
        let checksum = self.calculate_checksum(&request.target_path)?;

        let metadata = self.extract_metadata(&request.target_path, &request.target_format)?;

        Ok(ConversionResult {
            success: true,
            target_path: request.target_path.clone(),
            target_size,
            checksum,
            metadata,
            conversion_time_ms: 1000, // Simulated
            warnings: vec!["Simulated conversion - actual implementation needed".to_string()],
        })
    }

    /// Convert SafeTensors to PyTorch
    async fn safetensors_to_pytorch(
        &self,
        request: &ConversionRequest,
    ) -> Result<ConversionResult> {
        // Similar to above but reverse direction
        let target_size = fs::metadata(&request.source_path)?.len();
        fs::copy(&request.source_path, &request.target_path)?;

        let checksum = self.calculate_checksum(&request.target_path)?;
        let metadata = self.extract_metadata(&request.target_path, &request.target_format)?;

        Ok(ConversionResult {
            success: true,
            target_path: request.target_path.clone(),
            target_size,
            checksum,
            metadata,
            conversion_time_ms: 1000,
            warnings: vec!["Simulated conversion - actual implementation needed".to_string()],
        })
    }

    /// GGUF quantization
    async fn gguf_quantize(&self, request: &ConversionRequest) -> Result<ConversionResult> {
        // This would use llama.cpp quantization tools
        let quantization_type = request
            .quantization
            .as_ref()
            .unwrap_or(&QuantizationType::Q4_0);

        // Simulate quantization
        let source_size = fs::metadata(&request.source_path)?.len();
        let estimated_target_size = match quantization_type {
            QuantizationType::F16 => (source_size as f64 * 0.5) as u64,
            QuantizationType::Q4_0 => (source_size as f64 * 0.25) as u64,
            QuantizationType::Q4_1 => (source_size as f64 * 0.26) as u64,
            QuantizationType::Q5_0 => (source_size as f64 * 0.31) as u64,
            QuantizationType::Q5_1 => (source_size as f64 * 0.32) as u64,
            QuantizationType::Q8_0 => (source_size as f64 * 0.5) as u64,
            QuantizationType::Q8_1 => (source_size as f64 * 0.52) as u64,
        };

        // For simulation, just copy the file
        fs::copy(&request.source_path, &request.target_path)?;

        let checksum = self.calculate_checksum(&request.target_path)?;
        let metadata = self.extract_metadata(&request.target_path, &request.target_format)?;

        Ok(ConversionResult {
            success: true,
            target_path: request.target_path.clone(),
            target_size: estimated_target_size,
            checksum,
            metadata,
            conversion_time_ms: 5000, // Quantization takes longer
            warnings: vec![
                format!("Simulated GGUF quantization to {:?}", quantization_type),
                "Actual implementation would use llama.cpp tools".to_string(),
            ],
        })
    }

    /// Convert PyTorch to ONNX
    async fn pytorch_to_onnx(&self, request: &ConversionRequest) -> Result<ConversionResult> {
        // This would use torch.onnx.export
        fs::copy(&request.source_path, &request.target_path)?;

        let target_size = fs::metadata(&request.target_path)?.len();
        let checksum = self.calculate_checksum(&request.target_path)?;
        let metadata = self.extract_metadata(&request.target_path, &request.target_format)?;

        Ok(ConversionResult {
            success: true,
            target_path: request.target_path.clone(),
            target_size,
            checksum,
            metadata,
            conversion_time_ms: 3000,
            warnings: vec!["Simulated PyTorch to ONNX conversion".to_string()],
        })
    }

    /// Convert TensorFlow to ONNX
    async fn tensorflow_to_onnx(&self, request: &ConversionRequest) -> Result<ConversionResult> {
        // This would use tf2onnx
        fs::copy(&request.source_path, &request.target_path)?;

        let target_size = fs::metadata(&request.target_path)?.len();
        let checksum = self.calculate_checksum(&request.target_path)?;
        let metadata = self.extract_metadata(&request.target_path, &request.target_format)?;

        Ok(ConversionResult {
            success: true,
            target_path: request.target_path.clone(),
            target_size,
            checksum,
            metadata,
            conversion_time_ms: 4000,
            warnings: vec!["Simulated TensorFlow to ONNX conversion".to_string()],
        })
    }

    /// Calculate SHA256 checksum of a file
    fn calculate_checksum(&self, file_path: &str) -> Result<String> {
        let contents = fs::read(file_path)?;
        let mut hasher = Sha256::new();
        hasher.update(&contents);
        Ok(format!("{:x}", hasher.finalize()))
    }

    /// Extract metadata from a model file
    fn extract_metadata(
        &self,
        file_path: &str,
        format: &ModelFormat,
    ) -> Result<LocalModelMetadata> {
        let file_size = fs::metadata(file_path)?.len();
        let filename = Path::new(file_path)
            .file_stem()
            .and_then(|s| s.to_str())
            .unwrap_or("unknown")
            .to_string();

        Ok(LocalModelMetadata {
            id: filename.clone(),
            name: filename.clone(),
            description: Some(format!("Converted model in {:?} format", format)),
            version: "1.0".to_string(),
            format: format.clone(),
            file_path: file_path.to_string(),
            config_path: None,
            tokenizer_path: None,
            size_bytes: file_size,
            sha256: self.calculate_checksum(file_path)?,
            downloaded_at: chrono::Utc::now(),
            last_used: None,
            usage_count: 0,
            capabilities: ModelCapabilities {
                chat: true,
                completions: true,
                embeddings: false,
                image_generation: false,
                image_understanding: false,
                audio_generation: false,
                audio_understanding: false,
                video_understanding: false,
                function_calling: true,
                streaming: true,
            },
            parameters: HashMap::new(),
            source: ModelSource {
                origin: "converted".to_string(),
                url: None,
                repository: None,
                commit: None,
                license: None,
            },
        })
    }

    /// List available conversions
    pub fn list_supported_conversions(&self) -> Vec<(ModelFormat, ModelFormat)> {
        self.conversion_rules.keys().cloned().collect()
    }

    /// Check if a conversion is supported
    pub fn is_conversion_supported(&self, source: &ModelFormat, target: &ModelFormat) -> bool {
        self.conversion_rules
            .contains_key(&(source.clone(), target.clone()))
    }

    /// Validate model format
    pub fn validate_format(&self, file_path: &str, expected_format: &ModelFormat) -> Result<bool> {
        let path = Path::new(file_path);

        if !path.exists() {
            return Err(anyhow::anyhow!("File does not exist: {}", file_path));
        }

        let extension = path.extension().and_then(|ext| ext.to_str()).unwrap_or("");

        let detected_format = match extension.to_lowercase().as_str() {
            "gguf" => ModelFormat::GGUF,
            "safetensors" => ModelFormat::SafeTensors,
            "pt" | "pth" => ModelFormat::PyTorch,
            "onnx" => ModelFormat::ONNX,
            "pb" => ModelFormat::TensorFlow,
            _ => return Ok(false),
        };

        Ok(&detected_format == expected_format)
    }
}

/// Batch conversion utilities
pub struct BatchConverter {
    converter: ModelConverter,
}

impl BatchConverter {
    pub fn new() -> Self {
        Self {
            converter: ModelConverter::new(),
        }
    }

    /// Convert multiple models in batch
    pub async fn convert_batch(
        &self,
        requests: Vec<ConversionRequest>,
    ) -> Vec<Result<ConversionResult>> {
        let mut results = Vec::new();

        for request in requests {
            let result = self.converter.convert_model(request).await;
            results.push(result);
        }

        results
    }

    /// Convert all models in a directory
    pub async fn convert_directory(
        &self,
        source_dir: &str,
        target_dir: &str,
        source_format: ModelFormat,
        target_format: ModelFormat,
    ) -> Result<Vec<ConversionResult>> {
        let source_path = Path::new(source_dir);
        let target_path = Path::new(target_dir);

        if !source_path.exists() {
            return Err(anyhow::anyhow!(
                "Source directory does not exist: {}",
                source_dir
            ));
        }

        fs::create_dir_all(target_path)?;

        let mut conversion_requests = Vec::new();

        for entry in fs::read_dir(source_path)? {
            let entry = entry?;
            let path = entry.path();

            if path.is_file() {
                if let Some(filename) = path.file_name().and_then(|n| n.to_str()) {
                    let target_file = target_path.join(filename);

                    let request = ConversionRequest {
                        source_path: path.to_string_lossy().to_string(),
                        source_format: source_format.clone(),
                        target_format: target_format.clone(),
                        target_path: target_file.to_string_lossy().to_string(),
                        preserve_metadata: true,
                        compression_level: None,
                        quantization: None,
                    };

                    conversion_requests.push(request);
                }
            }
        }

        let mut results = Vec::new();
        for request in conversion_requests {
            match self.converter.convert_model(request).await {
                Ok(result) => results.push(result),
                Err(e) => return Err(e),
            }
        }

        Ok(results)
    }
}