inferno-ai 0.10.3

#![allow(dead_code, unused_imports, unused_variables)]
use crate::{InfernoError, backends::InferenceParams};
use anyhow::Result;
use base64::{Engine as _, engine::general_purpose};
use chrono::{DateTime, Utc};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::path::{Path, PathBuf};
use std::sync::Arc;
use tokio::sync::{Mutex, RwLock};
use tracing::info;

/// Multi-modal inference configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MultiModalConfig {
    /// Maximum file size for uploads (in bytes)
    pub max_file_size_bytes: u64,
    /// Supported image formats
    pub supported_image_formats: Vec<String>,
    /// Supported audio formats
    pub supported_audio_formats: Vec<String>,
    /// Supported video formats
    pub supported_video_formats: Vec<String>,
    /// Maximum image resolution (width x height)
    pub max_image_resolution: (u32, u32),
    /// Maximum audio duration (in seconds)
    pub max_audio_duration_seconds: u32,
    /// Enable GPU acceleration for media processing
    pub gpu_acceleration_enabled: bool,
    /// Temporary storage directory for processed media
    pub temp_storage_dir: PathBuf,
    /// Enable caching of processed media
    pub enable_media_cache: bool,
    /// Cache expiration time (in hours)
    pub cache_expiration_hours: u32,
}

impl Default for MultiModalConfig {
    fn default() -> Self {
        Self {
            max_file_size_bytes: 100 * 1024 * 1024, // 100MB
            supported_image_formats: vec![
                "jpg".to_string(),
                "jpeg".to_string(),
                "png".to_string(),
                "bmp".to_string(),
                "gif".to_string(),
                "webp".to_string(),
                "tiff".to_string(),
            ],
            supported_audio_formats: vec![
                "wav".to_string(),
                "mp3".to_string(),
                "flac".to_string(),
                "ogg".to_string(),
                "m4a".to_string(),
                "aac".to_string(),
            ],
            supported_video_formats: vec![
                "mp4".to_string(),
                "avi".to_string(),
                "mov".to_string(),
                "mkv".to_string(),
                "webm".to_string(),
            ],
            max_image_resolution: (4096, 4096),
            max_audio_duration_seconds: 3600, // 1 hour
            gpu_acceleration_enabled: true,
            temp_storage_dir: PathBuf::from("./temp/multimodal"),
            enable_media_cache: true,
            cache_expiration_hours: 24,
        }
    }
}

/// Media input types supported by the system
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum MediaInput {
    /// Text input with optional metadata
    Text {
        content: String,
        metadata: Option<HashMap<String, String>>,
    },
    /// Image input with various formats supported
    Image {
        data: Vec<u8>,
        format: ImageFormat,
        metadata: Option<ImageMetadata>,
    },
    /// Audio input with various formats supported
    Audio {
        data: Vec<u8>,
        format: AudioFormat,
        metadata: Option<AudioMetadata>,
    },
    /// Video input with frame extraction capabilities
    Video {
        data: Vec<u8>,
        format: VideoFormat,
        metadata: Option<VideoMetadata>,
    },
    /// Combined multi-modal input
    MultiModal {
        text: Option<String>,
        images: Vec<MediaInput>,
        audio: Vec<MediaInput>,
        video: Vec<MediaInput>,
        metadata: Option<HashMap<String, String>>,
    },
}

/// Supported image formats
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum ImageFormat {
    JPEG,
    PNG,
    BMP,
    GIF,
    WebP,
    TIFF,
    Unknown(String),
}

impl From<&str> for ImageFormat {
    fn from(s: &str) -> Self {
        match s.to_lowercase().as_str() {
            "jpg" | "jpeg" => ImageFormat::JPEG,
            "png" => ImageFormat::PNG,
            "bmp" => ImageFormat::BMP,
            "gif" => ImageFormat::GIF,
            "webp" => ImageFormat::WebP,
            "tiff" | "tif" => ImageFormat::TIFF,
            _ => ImageFormat::Unknown(s.to_string()),
        }
    }
}

/// Supported audio formats
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum AudioFormat {
    WAV,
    MP3,
    FLAC,
    OGG,
    M4A,
    AAC,
    Unknown(String),
}

impl From<&str> for AudioFormat {
    fn from(s: &str) -> Self {
        match s.to_lowercase().as_str() {
            "wav" => AudioFormat::WAV,
            "mp3" => AudioFormat::MP3,
            "flac" => AudioFormat::FLAC,
            "ogg" => AudioFormat::OGG,
            "m4a" => AudioFormat::M4A,
            "aac" => AudioFormat::AAC,
            _ => AudioFormat::Unknown(s.to_string()),
        }
    }
}

/// Supported video formats
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum VideoFormat {
    MP4,
    AVI,
    MOV,
    MKV,
    WebM,
    Unknown(String),
}

impl From<&str> for VideoFormat {
    fn from(s: &str) -> Self {
        match s.to_lowercase().as_str() {
            "mp4" => VideoFormat::MP4,
            "avi" => VideoFormat::AVI,
            "mov" => VideoFormat::MOV,
            "mkv" => VideoFormat::MKV,
            "webm" => VideoFormat::WebM,
            _ => VideoFormat::Unknown(s.to_string()),
        }
    }
}

/// Image metadata extracted during processing
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ImageMetadata {
    pub width: u32,
    pub height: u32,
    pub channels: u8,
    pub color_space: String,
    pub file_size_bytes: u64,
    pub creation_time: Option<DateTime<Utc>>,
    pub camera_info: Option<CameraInfo>,
}

/// Camera information from EXIF data
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CameraInfo {
    pub make: Option<String>,
    pub model: Option<String>,
    pub focal_length: Option<f32>,
    pub aperture: Option<f32>,
    pub iso: Option<u32>,
    pub exposure_time: Option<f32>,
}

/// Audio metadata extracted during processing
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AudioMetadata {
    pub duration_seconds: f64,
    pub sample_rate: u32,
    pub channels: u8,
    pub bit_depth: Option<u8>,
    pub bitrate: Option<u32>,
    pub file_size_bytes: u64,
    pub codec: Option<String>,
}

/// Video metadata extracted during processing
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VideoMetadata {
    pub duration_seconds: f64,
    pub width: u32,
    pub height: u32,
    pub frame_rate: f32,
    pub total_frames: u64,
    pub video_codec: Option<String>,
    pub audio_codec: Option<String>,
    pub file_size_bytes: u64,
}

/// Multi-modal processing result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MultiModalResult {
    pub id: String,
    pub input_summary: String,
    pub processed_components: Vec<ProcessedComponent>,
    pub inference_result: String,
    pub confidence_scores: Option<HashMap<String, f32>>,
    pub processing_time_ms: u64,
    pub model_used: String,
    pub created_at: DateTime<Utc>,
}

/// Individual processed component result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProcessedComponent {
    pub component_type: String,
    pub description: String,
    pub extracted_features: Option<HashMap<String, serde_json::Value>>,
    pub processing_time_ms: u64,
}

/// Multi-modal model capabilities
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelCapabilities {
    pub supports_text: bool,
    pub supports_images: bool,
    pub supports_audio: bool,
    pub supports_video: bool,
    pub max_context_length: Option<u32>,
    pub supported_languages: Vec<String>,
    pub vision_features: Option<VisionFeatures>,
    pub audio_features: Option<AudioFeatures>,
}

/// Vision-specific model capabilities
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VisionFeatures {
    pub object_detection: bool,
    pub ocr: bool,
    pub scene_understanding: bool,
    pub face_recognition: bool,
    pub image_generation: bool,
    pub max_image_size: (u32, u32),
}

/// Audio-specific model capabilities
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AudioFeatures {
    pub speech_to_text: bool,
    pub audio_classification: bool,
    pub music_analysis: bool,
    pub voice_synthesis: bool,
    pub noise_reduction: bool,
    pub max_audio_length_seconds: u32,
}

/// Main multi-modal processor
pub struct MultiModalProcessor {
    config: MultiModalConfig,
    model_capabilities: Arc<RwLock<HashMap<String, ModelCapabilities>>>,
    media_cache: Arc<RwLock<HashMap<String, ProcessedMedia>>>,
    active_sessions: Arc<Mutex<HashMap<String, ProcessingSession>>>,
}

/// Cached processed media
#[derive(Debug, Clone)]
struct ProcessedMedia {
    pub data: Vec<u8>,
    pub metadata: serde_json::Value,
    pub created_at: DateTime<Utc>,
    pub expires_at: DateTime<Utc>,
}

/// Active processing session
#[derive(Debug, Clone)]
pub struct ProcessingSession {
    pub id: String,
    pub model_id: String,
    pub status: ProcessingStatus,
    pub progress: f32,
    pub created_at: DateTime<Utc>,
    pub updated_at: DateTime<Utc>,
}

/// Processing status
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ProcessingStatus {
    Pending,
    Processing,
    Completed,
    Failed,
    Cancelled,
}

impl MultiModalProcessor {
    /// Create a new multi-modal processor
    pub fn new(config: MultiModalConfig) -> Self {
        Self {
            config,
            model_capabilities: Arc::new(RwLock::new(HashMap::new())),
            media_cache: Arc::new(RwLock::new(HashMap::new())),
            active_sessions: Arc::new(Mutex::new(HashMap::new())),
        }
    }

    /// Initialize the processor
    pub async fn initialize(&self) -> Result<()> {
        info!("Initializing multi-modal processor");

        // Create temporary storage directory
        tokio::fs::create_dir_all(&self.config.temp_storage_dir).await?;

        // Load model capabilities
        self.load_model_capabilities().await?;

        // Clean up expired cache entries
        self.cleanup_expired_cache().await?;

        Ok(())
    }

    /// Register model capabilities
    pub async fn register_model_capabilities(
        &self,
        model_id: String,
        capabilities: ModelCapabilities,
    ) -> Result<()> {
        let mut caps = self.model_capabilities.write().await;
        caps.insert(model_id.clone(), capabilities);
        info!("Registered capabilities for model: {}", model_id);
        Ok(())
    }

    /// Process multi-modal input
    pub async fn process_input(
        &self,
        model_id: &str,
        input: MediaInput,
        params: InferenceParams,
    ) -> Result<MultiModalResult> {
        let session_id = uuid::Uuid::new_v4().to_string();
        let start_time = std::time::Instant::now();

        // Create processing session
        let session = ProcessingSession {
            id: session_id.clone(),
            model_id: model_id.to_string(),
            status: ProcessingStatus::Pending,
            progress: 0.0,
            created_at: Utc::now(),
            updated_at: Utc::now(),
        };

        {
            let mut sessions = self.active_sessions.lock().await;
            sessions.insert(session_id.clone(), session);
        }

        // Validate model capabilities
        let capabilities = self.get_model_capabilities(model_id).await?;
        self.validate_input_compatibility(&input, &capabilities)?;

        // Update session status
        self.update_session_status(&session_id, ProcessingStatus::Processing, 10.0)
            .await?;

        // Process input components
        let processed_components = self.process_media_components(&input).await?;
        self.update_session_status(&session_id, ProcessingStatus::Processing, 50.0)
            .await?;

        // Perform inference
        let inference_result = self
            .perform_multimodal_inference(model_id, &input, &processed_components, params)
            .await?;
        self.update_session_status(&session_id, ProcessingStatus::Processing, 90.0)
            .await?;

        // Create result
        let result = MultiModalResult {
            id: session_id.clone(),
            input_summary: self.create_input_summary(&input),
            processed_components,
            inference_result,
            confidence_scores: None, // Would be populated by actual model
            processing_time_ms: start_time.elapsed().as_millis() as u64,
            model_used: model_id.to_string(),
            created_at: Utc::now(),
        };

        // Complete session
        self.update_session_status(&session_id, ProcessingStatus::Completed, 100.0)
            .await?;

        // Clean up session after delay
        let sessions_clone = Arc::clone(&self.active_sessions);
        let session_id_clone = session_id.clone();
        tokio::spawn(async move {
            tokio::time::sleep(tokio::time::Duration::from_secs(300)).await; // 5 minutes
            let mut sessions = sessions_clone.lock().await;
            sessions.remove(&session_id_clone);
        });

        Ok(result)
    }

    /// Process media from file path
    pub async fn process_file(
        &self,
        model_id: &str,
        file_path: &Path,
        text_prompt: Option<String>,
        params: InferenceParams,
    ) -> Result<MultiModalResult> {
        // Read file
        let file_data = tokio::fs::read(file_path).await?;
        let file_extension = file_path
            .extension()
            .and_then(|ext| ext.to_str())
            .unwrap_or("");

        // Determine media type and create input
        let input = self.create_media_input_from_file(file_data, file_extension, text_prompt)?;

        // Process the input
        self.process_input(model_id, input, params).await
    }

    /// Process media from base64 encoded data
    pub async fn process_base64(
        &self,
        model_id: &str,
        base64_data: &str,
        media_type: &str,
        text_prompt: Option<String>,
        params: InferenceParams,
    ) -> Result<MultiModalResult> {
        // Decode base64 data
        let decoded_data = general_purpose::STANDARD
            .decode(base64_data)
            .map_err(|e| InfernoError::InvalidArgument(format!("Invalid base64 data: {}", e)))?;

        // Create media input
        let input = self.create_media_input_from_file(decoded_data, media_type, text_prompt)?;

        // Process the input
        self.process_input(model_id, input, params).await
    }

    /// Get processing session status
    pub async fn get_session_status(&self, session_id: &str) -> Result<Option<ProcessingSession>> {
        let sessions = self.active_sessions.lock().await;
        Ok(sessions.get(session_id).cloned())
    }

    /// List active processing sessions
    pub async fn list_active_sessions(&self) -> Result<Vec<ProcessingSession>> {
        let sessions = self.active_sessions.lock().await;
        Ok(sessions.values().cloned().collect())
    }

    /// Cancel processing session
    pub async fn cancel_session(&self, session_id: &str) -> Result<()> {
        self.update_session_status(session_id, ProcessingStatus::Cancelled, 0.0)
            .await?;
        Ok(())
    }

    /// Get supported media formats
    pub fn get_supported_formats(&self) -> HashMap<String, Vec<String>> {
        let mut formats = HashMap::new();
        formats.insert(
            "image".to_string(),
            self.config.supported_image_formats.clone(),
        );
        formats.insert(
            "audio".to_string(),
            self.config.supported_audio_formats.clone(),
        );
        formats.insert(
            "video".to_string(),
            self.config.supported_video_formats.clone(),
        );
        formats
    }

    // Private helper methods

    async fn load_model_capabilities(&self) -> Result<()> {
        // Load capabilities from configuration or model registry
        // This is a mock implementation
        let mut caps = self.model_capabilities.write().await;

        // Example multi-modal model capabilities
        caps.insert(
            "gpt-4-vision".to_string(),
            ModelCapabilities {
                supports_text: true,
                supports_images: true,
                supports_audio: false,
                supports_video: false,
                max_context_length: Some(8000),
                supported_languages: vec!["en".to_string(), "es".to_string(), "fr".to_string()],
                vision_features: Some(VisionFeatures {
                    object_detection: true,
                    ocr: true,
                    scene_understanding: true,
                    face_recognition: false,
                    image_generation: false,
                    max_image_size: (2048, 2048),
                }),
                audio_features: None,
            },
        );

        caps.insert(
            "whisper-large".to_string(),
            ModelCapabilities {
                supports_text: true,
                supports_images: false,
                supports_audio: true,
                supports_video: false,
                max_context_length: None,
                supported_languages: vec![
                    "en".to_string(),
                    "es".to_string(),
                    "fr".to_string(),
                    "de".to_string(),
                ],
                vision_features: None,
                audio_features: Some(AudioFeatures {
                    speech_to_text: true,
                    audio_classification: false,
                    music_analysis: false,
                    voice_synthesis: false,
                    noise_reduction: true,
                    max_audio_length_seconds: 3600,
                }),
            },
        );

        Ok(())
    }

    async fn cleanup_expired_cache(&self) -> Result<()> {
        let mut cache = self.media_cache.write().await;
        let now = Utc::now();
        cache.retain(|_, media| media.expires_at > now);
        Ok(())
    }

    async fn get_model_capabilities(&self, model_id: &str) -> Result<ModelCapabilities> {
        let caps = self.model_capabilities.read().await;
        caps.get(model_id).cloned().ok_or_else(|| {
            InfernoError::ModelNotFound(format!("Model capabilities not found: {}", model_id))
                .into()
        })
    }

    fn validate_input_compatibility(
        &self,
        input: &MediaInput,
        capabilities: &ModelCapabilities,
    ) -> Result<()> {
        match input {
            MediaInput::Text { .. } => {
                if !capabilities.supports_text {
                    return Err(InfernoError::InvalidArgument(
                        "Model does not support text input".to_string(),
                    )
                    .into());
                }
            }
            MediaInput::Image { .. } => {
                if !capabilities.supports_images {
                    return Err(InfernoError::InvalidArgument(
                        "Model does not support image input".to_string(),
                    )
                    .into());
                }
            }
            MediaInput::Audio { .. } => {
                if !capabilities.supports_audio {
                    return Err(InfernoError::InvalidArgument(
                        "Model does not support audio input".to_string(),
                    )
                    .into());
                }
            }
            MediaInput::Video { .. } => {
                if !capabilities.supports_video {
                    return Err(InfernoError::InvalidArgument(
                        "Model does not support video input".to_string(),
                    )
                    .into());
                }
            }
            MediaInput::MultiModal {
                text,
                images,
                audio,
                video,
                ..
            } => {
                if text.is_some() && !capabilities.supports_text {
                    return Err(InfernoError::InvalidArgument(
                        "Model does not support text in multi-modal input".to_string(),
                    )
                    .into());
                }
                if !images.is_empty() && !capabilities.supports_images {
                    return Err(InfernoError::InvalidArgument(
                        "Model does not support images in multi-modal input".to_string(),
                    )
                    .into());
                }
                if !audio.is_empty() && !capabilities.supports_audio {
                    return Err(InfernoError::InvalidArgument(
                        "Model does not support audio in multi-modal input".to_string(),
                    )
                    .into());
                }
                if !video.is_empty() && !capabilities.supports_video {
                    return Err(InfernoError::InvalidArgument(
                        "Model does not support video in multi-modal input".to_string(),
                    )
                    .into());
                }
            }
        }
        Ok(())
    }

    async fn process_media_components(
        &self,
        input: &MediaInput,
    ) -> Result<Vec<ProcessedComponent>> {
        let mut components = Vec::new();

        match input {
            MediaInput::Text { content, .. } => {
                components.push(ProcessedComponent {
                    component_type: "text".to_string(),
                    description: format!("Text input ({} characters)", content.len()),
                    extracted_features: None,
                    processing_time_ms: 1,
                });
            }
            MediaInput::Image {
                data,
                format,
                metadata,
            } => {
                let start = std::time::Instant::now();
                let description = format!("Image input ({:?}, {} bytes)", format, data.len());

                // Mock image processing
                let mut features = HashMap::new();
                features.insert("format".to_string(), serde_json::json!(format));
                features.insert("size_bytes".to_string(), serde_json::json!(data.len()));

                if let Some(meta) = metadata {
                    features.insert("width".to_string(), serde_json::json!(meta.width));
                    features.insert("height".to_string(), serde_json::json!(meta.height));
                    features.insert("channels".to_string(), serde_json::json!(meta.channels));
                }

                components.push(ProcessedComponent {
                    component_type: "image".to_string(),
                    description,
                    extracted_features: Some(features),
                    processing_time_ms: start.elapsed().as_millis() as u64,
                });
            }
            MediaInput::Audio {
                data,
                format,
                metadata,
            } => {
                let start = std::time::Instant::now();
                let description = format!("Audio input ({:?}, {} bytes)", format, data.len());

                // Mock audio processing
                let mut features = HashMap::new();
                features.insert("format".to_string(), serde_json::json!(format));
                features.insert("size_bytes".to_string(), serde_json::json!(data.len()));

                if let Some(meta) = metadata {
                    features.insert(
                        "duration_seconds".to_string(),
                        serde_json::json!(meta.duration_seconds),
                    );
                    features.insert(
                        "sample_rate".to_string(),
                        serde_json::json!(meta.sample_rate),
                    );
                    features.insert("channels".to_string(), serde_json::json!(meta.channels));
                }

                components.push(ProcessedComponent {
                    component_type: "audio".to_string(),
                    description,
                    extracted_features: Some(features),
                    processing_time_ms: start.elapsed().as_millis() as u64,
                });
            }
            MediaInput::Video {
                data,
                format,
                metadata,
            } => {
                let start = std::time::Instant::now();
                let description = format!("Video input ({:?}, {} bytes)", format, data.len());

                // Mock video processing
                let mut features = HashMap::new();
                features.insert("format".to_string(), serde_json::json!(format));
                features.insert("size_bytes".to_string(), serde_json::json!(data.len()));

                if let Some(meta) = metadata {
                    features.insert(
                        "duration_seconds".to_string(),
                        serde_json::json!(meta.duration_seconds),
                    );
                    features.insert("width".to_string(), serde_json::json!(meta.width));
                    features.insert("height".to_string(), serde_json::json!(meta.height));
                    features.insert("frame_rate".to_string(), serde_json::json!(meta.frame_rate));
                }

                components.push(ProcessedComponent {
                    component_type: "video".to_string(),
                    description,
                    extracted_features: Some(features),
                    processing_time_ms: start.elapsed().as_millis() as u64,
                });
            }
            MediaInput::MultiModal {
                text,
                images,
                audio,
                video,
                ..
            } => {
                if let Some(text_content) = text {
                    components.push(ProcessedComponent {
                        component_type: "text".to_string(),
                        description: format!("Text input ({} characters)", text_content.len()),
                        extracted_features: None,
                        processing_time_ms: 1,
                    });
                }

                for (i, img) in images.iter().enumerate() {
                    if let MediaInput::Image {
                        data,
                        format,
                        metadata,
                    } = img
                    {
                        let start = std::time::Instant::now();
                        let description =
                            format!("Image {} ({:?}, {} bytes)", i + 1, format, data.len());

                        let mut features = std::collections::HashMap::new();
                        features.insert("format".to_string(), serde_json::json!(format));
                        features.insert("size_bytes".to_string(), serde_json::json!(data.len()));

                        if let Some(meta) = metadata {
                            features.insert("width".to_string(), serde_json::json!(meta.width));
                            features.insert("height".to_string(), serde_json::json!(meta.height));
                            features
                                .insert("channels".to_string(), serde_json::json!(meta.channels));
                        }

                        components.push(ProcessedComponent {
                            component_type: "image".to_string(),
                            description,
                            extracted_features: Some(features),
                            processing_time_ms: start.elapsed().as_millis() as u64,
                        });
                    }
                }

                for (i, aud) in audio.iter().enumerate() {
                    if let MediaInput::Audio {
                        data,
                        format,
                        metadata,
                    } = aud
                    {
                        let start = std::time::Instant::now();
                        let description =
                            format!("Audio {} ({:?}, {} bytes)", i + 1, format, data.len());

                        let mut features = std::collections::HashMap::new();
                        features.insert("format".to_string(), serde_json::json!(format));
                        features.insert("size_bytes".to_string(), serde_json::json!(data.len()));

                        if let Some(meta) = metadata {
                            features.insert(
                                "duration_seconds".to_string(),
                                serde_json::json!(meta.duration_seconds),
                            );
                            features.insert(
                                "sample_rate".to_string(),
                                serde_json::json!(meta.sample_rate),
                            );
                            features
                                .insert("channels".to_string(), serde_json::json!(meta.channels));
                        }

                        components.push(ProcessedComponent {
                            component_type: "audio".to_string(),
                            description,
                            extracted_features: Some(features),
                            processing_time_ms: start.elapsed().as_millis() as u64,
                        });
                    }
                }

                for (i, vid) in video.iter().enumerate() {
                    if let MediaInput::Video {
                        data,
                        format,
                        metadata,
                    } = vid
                    {
                        let start = std::time::Instant::now();
                        let description =
                            format!("Video {} ({:?}, {} bytes)", i + 1, format, data.len());

                        let mut features = std::collections::HashMap::new();
                        features.insert("format".to_string(), serde_json::json!(format));
                        features.insert("size_bytes".to_string(), serde_json::json!(data.len()));

                        if let Some(meta) = metadata {
                            features.insert(
                                "duration_seconds".to_string(),
                                serde_json::json!(meta.duration_seconds),
                            );
                            features.insert("width".to_string(), serde_json::json!(meta.width));
                            features.insert("height".to_string(), serde_json::json!(meta.height));
                            features.insert(
                                "frame_rate".to_string(),
                                serde_json::json!(meta.frame_rate),
                            );
                        }

                        components.push(ProcessedComponent {
                            component_type: "video".to_string(),
                            description,
                            extracted_features: Some(features),
                            processing_time_ms: start.elapsed().as_millis() as u64,
                        });
                    }
                }
            }
        }

        Ok(components)
    }

    async fn perform_multimodal_inference(
        &self,
        model_id: &str,
        input: &MediaInput,
        _components: &[ProcessedComponent],
        _params: InferenceParams,
    ) -> Result<String> {
        // Mock inference implementation
        // In a real implementation, this would call the actual model

        let result = match input {
            MediaInput::Text { content, .. } => {
                format!(
                    "Text analysis result for: {}",
                    content.chars().take(50).collect::<String>()
                )
            }
            MediaInput::Image { format, .. } => {
                format!(
                    "Image analysis result: Detected objects in {:?} image - cars, buildings, people",
                    format
                )
            }
            MediaInput::Audio { format, .. } => {
                format!(
                    "Audio analysis result: Transcribed speech from {:?} audio - 'Hello, this is a test recording'",
                    format
                )
            }
            MediaInput::Video { format, .. } => {
                format!(
                    "Video analysis result: Scene analysis of {:?} video - outdoor scene with moving objects",
                    format
                )
            }
            MediaInput::MultiModal {
                text,
                images,
                audio,
                video,
                ..
            } => {
                let mut parts = Vec::new();

                if text.is_some() {
                    parts.push("text analysis".to_string());
                }
                if !images.is_empty() {
                    parts.push(format!("{} image(s) analyzed", images.len()));
                }
                if !audio.is_empty() {
                    parts.push(format!("{} audio file(s) processed", audio.len()));
                }
                if !video.is_empty() {
                    parts.push(format!("{} video file(s) analyzed", video.len()));
                }

                format!("Multi-modal analysis combining: {}", parts.join(", "))
            }
        };

        info!("Performed inference with model: {}", model_id);
        Ok(result)
    }

    async fn update_session_status(
        &self,
        session_id: &str,
        status: ProcessingStatus,
        progress: f32,
    ) -> Result<()> {
        let mut sessions = self.active_sessions.lock().await;
        if let Some(session) = sessions.get_mut(session_id) {
            session.status = status;
            session.progress = progress;
            session.updated_at = Utc::now();
        }
        Ok(())
    }

    fn create_input_summary(&self, input: &MediaInput) -> String {
        match input {
            MediaInput::Text { content, .. } => {
                format!("Text input ({} chars)", content.len())
            }
            MediaInput::Image { format, .. } => {
                format!("Image input ({:?})", format)
            }
            MediaInput::Audio { format, .. } => {
                format!("Audio input ({:?})", format)
            }
            MediaInput::Video { format, .. } => {
                format!("Video input ({:?})", format)
            }
            MediaInput::MultiModal {
                text,
                images,
                audio,
                video,
                ..
            } => {
                let mut parts = Vec::new();
                if text.is_some() {
                    parts.push("text".to_string());
                }
                if !images.is_empty() {
                    parts.push(format!("{} images", images.len()));
                }
                if !audio.is_empty() {
                    parts.push(format!("{} audio", audio.len()));
                }
                if !video.is_empty() {
                    parts.push(format!("{} videos", video.len()));
                }
                format!("Multi-modal input: {}", parts.join(", "))
            }
        }
    }

    fn create_media_input_from_file(
        &self,
        data: Vec<u8>,
        file_extension: &str,
        text_prompt: Option<String>,
    ) -> Result<MediaInput> {
        // Check file size
        if data.len() as u64 > self.config.max_file_size_bytes {
            return Err(InfernoError::InvalidArgument(format!(
                "File size exceeds maximum allowed: {} bytes",
                self.config.max_file_size_bytes
            ))
            .into());
        }

        // Determine media type based on extension
        if self
            .config
            .supported_image_formats
            .contains(&file_extension.to_lowercase())
        {
            let format = ImageFormat::from(file_extension);
            let metadata = self.extract_image_metadata(&data, &format)?;

            let input = MediaInput::Image {
                data,
                format,
                metadata: Some(metadata),
            };

            // If there's a text prompt, create multi-modal input
            if let Some(prompt) = text_prompt {
                Ok(MediaInput::MultiModal {
                    text: Some(prompt),
                    images: vec![input],
                    audio: vec![],
                    video: vec![],
                    metadata: None,
                })
            } else {
                Ok(input)
            }
        } else if self
            .config
            .supported_audio_formats
            .contains(&file_extension.to_lowercase())
        {
            let format = AudioFormat::from(file_extension);
            let metadata = self.extract_audio_metadata(&data, &format)?;

            let input = MediaInput::Audio {
                data,
                format,
                metadata: Some(metadata),
            };

            if let Some(prompt) = text_prompt {
                Ok(MediaInput::MultiModal {
                    text: Some(prompt),
                    images: vec![],
                    audio: vec![input],
                    video: vec![],
                    metadata: None,
                })
            } else {
                Ok(input)
            }
        } else if self
            .config
            .supported_video_formats
            .contains(&file_extension.to_lowercase())
        {
            let format = VideoFormat::from(file_extension);
            let metadata = self.extract_video_metadata(&data, &format)?;

            let input = MediaInput::Video {
                data,
                format,
                metadata: Some(metadata),
            };

            if let Some(prompt) = text_prompt {
                Ok(MediaInput::MultiModal {
                    text: Some(prompt),
                    images: vec![],
                    audio: vec![],
                    video: vec![input],
                    metadata: None,
                })
            } else {
                Ok(input)
            }
        } else {
            Err(InfernoError::UnsupportedFormat(format!(
                "Unsupported file format: {}",
                file_extension
            ))
            .into())
        }
    }

    fn extract_image_metadata(&self, data: &[u8], _format: &ImageFormat) -> Result<ImageMetadata> {
        // Mock metadata extraction
        // In a real implementation, this would use image processing libraries
        Ok(ImageMetadata {
            width: 1920,
            height: 1080,
            channels: 3,
            color_space: "RGB".to_string(),
            file_size_bytes: data.len() as u64,
            creation_time: Some(Utc::now()),
            camera_info: None,
        })
    }

    fn extract_audio_metadata(&self, data: &[u8], format: &AudioFormat) -> Result<AudioMetadata> {
        // Mock metadata extraction
        Ok(AudioMetadata {
            duration_seconds: 120.0,
            sample_rate: 44100,
            channels: 2,
            bit_depth: Some(16),
            bitrate: Some(128),
            file_size_bytes: data.len() as u64,
            codec: Some(format!("{:?}", format)),
        })
    }

    fn extract_video_metadata(&self, data: &[u8], _format: &VideoFormat) -> Result<VideoMetadata> {
        // Mock metadata extraction
        Ok(VideoMetadata {
            duration_seconds: 300.0,
            width: 1920,
            height: 1080,
            frame_rate: 30.0,
            total_frames: 9000,
            video_codec: Some("H.264".to_string()),
            audio_codec: Some("AAC".to_string()),
            file_size_bytes: data.len() as u64,
        })
    }
}

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

    #[tokio::test]
    async fn test_multimodal_processor_initialization() {
        let config = MultiModalConfig::default();
        let processor = MultiModalProcessor::new(config);

        let result = processor.initialize().await;
        assert!(result.is_ok());
    }

    #[tokio::test]
    async fn test_text_input_processing() {
        let config = MultiModalConfig::default();
        let processor = MultiModalProcessor::new(config);
        processor.initialize().await.unwrap();

        // Register mock model capabilities
        processor
            .register_model_capabilities(
                "test-model".to_string(),
                ModelCapabilities {
                    supports_text: true,
                    supports_images: false,
                    supports_audio: false,
                    supports_video: false,
                    max_context_length: Some(1000),
                    supported_languages: vec!["en".to_string()],
                    vision_features: None,
                    audio_features: None,
                },
            )
            .await
            .unwrap();

        let input = MediaInput::Text {
            content: "Test text input".to_string(),
            metadata: None,
        };

        let params = InferenceParams {
            max_tokens: 100,
            temperature: 0.7,
            top_p: 0.9,
            top_k: 40,
            stream: false,
            stop_sequences: vec![],
            seed: None,
        };

        let result = processor.process_input("test-model", input, params).await;
        assert!(result.is_ok());

        let result = result.unwrap();
        assert!(result.inference_result.contains("Text analysis"));
        assert_eq!(result.processed_components.len(), 1);
        assert_eq!(result.processed_components[0].component_type, "text");
    }

    #[test]
    fn test_format_detection() {
        assert_eq!(ImageFormat::from("jpg"), ImageFormat::JPEG);
        assert_eq!(ImageFormat::from("PNG"), ImageFormat::PNG);
        assert_eq!(AudioFormat::from("mp3"), AudioFormat::MP3);
        assert_eq!(VideoFormat::from("mp4"), VideoFormat::MP4);
    }

    #[test]
    fn test_supported_formats() {
        let config = MultiModalConfig::default();
        let processor = MultiModalProcessor::new(config);

        let formats = processor.get_supported_formats();
        assert!(formats.contains_key("image"));
        assert!(formats.contains_key("audio"));
        assert!(formats.contains_key("video"));

        assert!(formats["image"].contains(&"jpg".to_string()));
        assert!(formats["audio"].contains(&"mp3".to_string()));
        assert!(formats["video"].contains(&"mp4".to_string()));
    }
}