ocr 0.1.2

//! Data loading and preprocessing for training

use crate::core::geometry::TBox;
use crate::core::image::OcrImage;
use anyhow::Result;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::path::Path;
use tokio::fs;

/// Training dataset entry
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrainingSample {
    /// Image data
    pub image: OcrImage,
    /// Ground truth text
    pub text: String,
    /// Bounding boxes for text regions
    pub bounding_boxes: Vec<TBox>,
    /// Language of the text
    pub language: Option<String>,
    /// Additional metadata
    pub metadata: HashMap<String, String>,
}

/// Dataset configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DatasetConfig {
    /// Path to dataset directory
    pub dataset_path: String,
    /// Dataset format (synthetic, real, mixed)
    pub format: DatasetFormat,
    /// Training split ratio (0.0 to 1.0)
    pub train_split: f32,
    /// Validation split ratio (0.0 to 1.0)
    pub val_split: f32,
    /// Test split ratio (0.0 to 1.0)
    pub test_split: f32,
    /// Maximum number of samples to load
    pub max_samples: Option<usize>,
    /// Whether to shuffle the dataset
    pub shuffle: bool,
    /// Random seed for reproducibility
    pub seed: Option<u64>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum DatasetFormat {
    /// Synthetic dataset with generated text
    Synthetic,
    /// Real-world dataset with actual images
    Real,
    /// Mixed dataset combining both
    Mixed,
    /// Custom format with specific loader
    Custom(String),
}

impl DatasetFormat {
    pub fn from_string(s: &str) -> Self {
        match s.to_lowercase().as_str() {
            "synthetic" => DatasetFormat::Synthetic,
            "real" => DatasetFormat::Real,
            "mixed" => DatasetFormat::Mixed,
            _ => DatasetFormat::Custom(s.to_string()),
        }
    }
}

/// Dataset splits
#[derive(Debug, Clone)]
pub struct DatasetSplits {
    pub train: Vec<TrainingSample>,
    pub validation: Vec<TrainingSample>,
    pub test: Vec<TrainingSample>,
}

/// Data loader for training datasets
pub struct DataLoader {
    config: DatasetConfig,
    splits: Option<DatasetSplits>,
}

impl DataLoader {
    pub fn new(config: DatasetConfig) -> Self {
        Self {
            config,
            splits: None,
        }
    }

    /// Load dataset from disk
    pub async fn load_dataset(&mut self) -> Result<()> {
        tracing::info!("Loading dataset from: {}", self.config.dataset_path);

        match self.config.format {
            DatasetFormat::Synthetic => self.load_synthetic_dataset().await,
            DatasetFormat::Real => self.load_real_dataset().await,
            DatasetFormat::Mixed => self.load_mixed_dataset().await,
            DatasetFormat::Custom(ref loader_name) => {
                let loader_name = loader_name.clone();
                self.load_custom_dataset(&loader_name).await
            }
        }
    }

    /// Get dataset splits
    pub fn get_splits(&self) -> Option<&DatasetSplits> {
        self.splits.as_ref()
    }

    /// Get training samples
    pub fn get_train_samples(&self) -> Option<&[TrainingSample]> {
        self.splits.as_ref().map(|s| s.train.as_slice())
    }

    /// Get validation samples
    pub fn get_val_samples(&self) -> Option<&[TrainingSample]> {
        self.splits.as_ref().map(|s| s.validation.as_slice())
    }

    /// Get test samples
    pub fn get_test_samples(&self) -> Option<&[TrainingSample]> {
        self.splits.as_ref().map(|s| s.test.as_slice())
    }

    async fn load_synthetic_dataset(&mut self) -> Result<()> {
        tracing::info!("Loading synthetic dataset");

        // For now, create dummy data
        // In a real implementation, this would load from synthetic data generators
        let samples = self.generate_synthetic_samples(1000).await?;
        self.splits = Some(self.split_dataset(samples));

        Ok(())
    }

    async fn load_real_dataset(&mut self) -> Result<()> {
        tracing::info!("Loading real dataset");

        let dataset_path = Path::new(&self.config.dataset_path);
        if !dataset_path.exists() {
            return Err(anyhow::anyhow!(
                "Dataset path does not exist: {}",
                self.config.dataset_path
            ));
        }

        // Look for common dataset formats
        if dataset_path.join("annotations.json").exists() {
            self.load_json_dataset(dataset_path).await
        } else if dataset_path.join("labels.txt").exists() {
            self.load_text_dataset(dataset_path).await
        } else {
            Err(anyhow::anyhow!(
                "Unknown dataset format in: {}",
                self.config.dataset_path
            ))
        }
    }

    async fn load_mixed_dataset(&mut self) -> Result<()> {
        tracing::info!("Loading mixed dataset");

        // Load both synthetic and real data
        let mut synthetic_loader = DataLoader::new(DatasetConfig {
            dataset_path: self.config.dataset_path.clone(),
            format: DatasetFormat::Synthetic,
            train_split: 0.5,
            val_split: 0.25,
            test_split: 0.25,
            max_samples: self.config.max_samples.map(|n| n / 2),
            shuffle: self.config.shuffle,
            seed: self.config.seed,
        });

        let mut real_loader = DataLoader::new(DatasetConfig {
            dataset_path: self.config.dataset_path.clone(),
            format: DatasetFormat::Real,
            train_split: 0.5,
            val_split: 0.25,
            test_split: 0.25,
            max_samples: self.config.max_samples.map(|n| n / 2),
            shuffle: self.config.shuffle,
            seed: self.config.seed,
        });

        synthetic_loader.load_synthetic_dataset().await?;
        real_loader.load_real_dataset().await?;

        // Combine datasets
        let synthetic_splits = synthetic_loader.splits.unwrap();
        let real_splits = real_loader.splits.unwrap();

        self.splits = Some(DatasetSplits {
            train: [synthetic_splits.train, real_splits.train].concat(),
            validation: [synthetic_splits.validation, real_splits.validation].concat(),
            test: [synthetic_splits.test, real_splits.test].concat(),
        });

        Ok(())
    }

    async fn load_custom_dataset(&mut self, _loader_name: &str) -> Result<()> {
        tracing::info!("Loading custom dataset");

        // Placeholder for custom dataset loaders
        Err(anyhow::anyhow!(
            "Custom dataset loaders not implemented yet"
        ))
    }

    async fn load_json_dataset(&self, dataset_path: &Path) -> Result<()> {
        let annotations_path = dataset_path.join("annotations.json");
        let annotations_data = fs::read_to_string(annotations_path).await?;
        let annotations: serde_json::Value = serde_json::from_str(&annotations_data)?;

        // Parse JSON annotations and load images
        // This is a placeholder implementation
        tracing::info!(
            "Loaded JSON annotations with {} entries",
            annotations.as_object().unwrap().len()
        );

        Ok(())
    }

    async fn load_text_dataset(&self, dataset_path: &Path) -> Result<()> {
        let labels_path = dataset_path.join("labels.txt");
        let labels_data = fs::read_to_string(labels_path).await?;

        // Parse text labels and load images
        // This is a placeholder implementation
        let lines: Vec<&str> = labels_data.lines().collect();
        tracing::info!("Loaded text labels with {} entries", lines.len());

        Ok(())
    }

    async fn generate_synthetic_samples(&self, count: usize) -> Result<Vec<TrainingSample>> {
        let mut samples = Vec::with_capacity(count);

        for i in 0..count {
            // Generate synthetic image and text
            let image = self.generate_synthetic_image(i).await?;
            let text = self.generate_synthetic_text(i).await?;
            let bounding_boxes = self.generate_synthetic_boxes(&image, &text).await?;

            samples.push(TrainingSample {
                image,
                text,
                bounding_boxes,
                language: Some("en".to_string()),
                metadata: HashMap::new(),
            });
        }

        Ok(samples)
    }

    async fn generate_synthetic_image(&self, _index: usize) -> Result<OcrImage> {
        // Generate a simple synthetic image
        // In a real implementation, this would use text rendering libraries
        use image::{ImageBuffer, Rgb, RgbImage};

        let width = 200;
        let height = 50;
        let mut img: RgbImage = ImageBuffer::new(width, height);

        // Fill with white background
        for pixel in img.pixels_mut() {
            *pixel = Rgb([255, 255, 255]);
        }

        // Add some random noise/text
        for y in 10..40 {
            for x in 10..190 {
                if (x + y) % 3 == 0 {
                    img.put_pixel(x, y, Rgb([0, 0, 0]));
                }
            }
        }

        Ok(OcrImage::new(img.into(), 300))
    }

    async fn generate_synthetic_text(&self, index: usize) -> Result<String> {
        // Generate synthetic text
        let texts = vec![
            "Hello World",
            "Sample Text",
            "OCR Training",
            "Neural Network",
            "Machine Learning",
            "Computer Vision",
            "Text Recognition",
            "Image Processing",
        ];

        Ok(texts[index % texts.len()].to_string())
    }

    async fn generate_synthetic_boxes(&self, _image: &OcrImage, _text: &str) -> Result<Vec<TBox>> {
        // Generate bounding boxes for text regions
        // This is a placeholder implementation
        Ok(vec![TBox::new(10, 10, 190, 40)])
    }

    fn split_dataset(&self, mut samples: Vec<TrainingSample>) -> DatasetSplits {
        if self.config.shuffle {
            use rand::seq::SliceRandom;
            use rand::SeedableRng;
            let mut rng = if let Some(seed) = self.config.seed {
                rand::rngs::StdRng::seed_from_u64(seed)
            } else {
                rand::rngs::StdRng::from_entropy()
            };
            samples.shuffle(&mut rng);
        }

        let total = samples.len();
        let train_end = (total as f32 * self.config.train_split) as usize;
        let val_end = train_end + (total as f32 * self.config.val_split) as usize;

        DatasetSplits {
            train: samples[..train_end].to_vec(),
            validation: samples[train_end..val_end].to_vec(),
            test: samples[val_end..].to_vec(),
        }
    }
}

/// Batch of training samples
#[derive(Debug, Clone)]
pub struct TrainingBatch {
    pub images: Vec<OcrImage>,
    pub texts: Vec<String>,
    pub bounding_boxes: Vec<Vec<TBox>>,
    pub languages: Vec<Option<String>>,
    pub metadata: Vec<HashMap<String, String>>,
}

impl TrainingBatch {
    pub fn new(batch_size: usize) -> Self {
        Self {
            images: Vec::with_capacity(batch_size),
            texts: Vec::with_capacity(batch_size),
            bounding_boxes: Vec::with_capacity(batch_size),
            languages: Vec::with_capacity(batch_size),
            metadata: Vec::with_capacity(batch_size),
        }
    }

    pub fn add_sample(&mut self, sample: TrainingSample) {
        self.images.push(sample.image);
        self.texts.push(sample.text);
        self.bounding_boxes.push(sample.bounding_boxes);
        self.languages.push(sample.language);
        self.metadata.push(sample.metadata);
    }

    pub fn len(&self) -> usize {
        self.images.len()
    }

    pub fn is_empty(&self) -> bool {
        self.images.is_empty()
    }
}

/// Iterator for batching training samples
pub struct BatchIterator {
    samples: Vec<TrainingSample>,
    batch_size: usize,
    current_index: usize,
}

impl BatchIterator {
    pub fn new(samples: Vec<TrainingSample>, batch_size: usize) -> Self {
        Self {
            samples,
            batch_size,
            current_index: 0,
        }
    }
}

impl Iterator for BatchIterator {
    type Item = TrainingBatch;

    fn next(&mut self) -> Option<Self::Item> {
        if self.current_index >= self.samples.len() {
            return None;
        }

        let end_index = (self.current_index + self.batch_size).min(self.samples.len());
        let mut batch = TrainingBatch::new(self.batch_size);

        for sample in &self.samples[self.current_index..end_index] {
            batch.add_sample(sample.clone());
        }

        self.current_index = end_index;
        Some(batch)
    }
}