//! Simplified SimMIM (Simple Masked Image Modeling) Implementation
//!
//! This is a simplified version that works with the current tensor library state.
//! It focuses on the algorithmic structure and can be upgraded when tensor operations are stable.

use crate::prelude::*;
use parking_lot::RwLock;
use std::sync::Arc;

/// Mock tensor for simplified implementation
#[derive(Debug, Clone)]
pub struct SimpleTensor {
    data: Vec<f32>,
    shape: Vec<usize>,
    device: Device,
}

impl SimpleTensor {
    pub fn zeros(shape: Vec<usize>, device: &Device) -> Result<Self> {
        let size = shape.iter().product();
        Ok(Self {
            data: vec![0.0; size],
            shape,
            device: device.clone(),
        })
    }

    pub fn randn(shape: Vec<usize>, device: &Device) -> Result<Self> {
        let size = shape.iter().product();
        let mut data = Vec::with_capacity(size);
        let mut seed = 42u32;
        for _ in 0..size {
            seed = seed.wrapping_mul(1103515245).wrapping_add(12345);
            let val = (seed as f32 / u32::MAX as f32) * 2.0 - 1.0;
            data.push(val);
        }
        Ok(Self {
            data,
            shape,
            device: device.clone(),
        })
    }

    pub fn from_data(data: Vec<f32>, shape: Vec<usize>, device: &Device) -> Result<Self> {
        Ok(Self {
            data,
            shape,
            device: device.clone(),
        })
    }

    pub fn shape(&self) -> &[usize] {
        &self.shape
    }
    pub fn device(&self) -> &Device {
        &self.device
    }

    pub fn to_vec<T>(&self) -> Result<Vec<T>>
    where
        T: Clone + From<f32>,
    {
        Ok(self.data.iter().map(|&x| T::from(x)).collect())
    }

    pub fn matmul(&self, _other: &Self) -> Result<Self> {
        // Simplified matrix multiplication - just return a tensor of appropriate shape
        let result_shape = vec![self.shape[0], _other.shape[1]];
        let result_data = vec![1.0; result_shape.iter().product()];
        Ok(Self {
            data: result_data,
            shape: result_shape,
            device: self.device.clone(),
        })
    }

    pub fn add(&self, other: &Self) -> Result<Self> {
        let mut result_data = self.data.clone();
        for (i, &val) in other.data.iter().enumerate() {
            if i < result_data.len() {
                result_data[i] += val;
            }
        }
        Ok(Self {
            data: result_data,
            shape: self.shape.clone(),
            device: self.device.clone(),
        })
    }

    pub fn sub(&self, other: &Self) -> Result<Self> {
        let mut result_data = self.data.clone();
        for (i, &val) in other.data.iter().enumerate() {
            if i < result_data.len() {
                result_data[i] -= val;
            }
        }
        Ok(Self {
            data: result_data,
            shape: self.shape.clone(),
            device: self.device.clone(),
        })
    }

    pub fn abs(&self) -> Result<Self> {
        let result_data = self.data.iter().map(|x| x.abs()).collect();
        Ok(Self {
            data: result_data,
            shape: self.shape.clone(),
            device: self.device.clone(),
        })
    }

    pub fn pow_scalar(&self, exp: f32) -> Result<Self> {
        let result_data = self.data.iter().map(|x| x.powf(exp)).collect();
        Ok(Self {
            data: result_data,
            shape: self.shape.clone(),
            device: self.device.clone(),
        })
    }

    pub fn mean(&self, _dims: &[usize]) -> Result<Self> {
        let mean_val = if self.data.is_empty() {
            0.0
        } else {
            self.data.iter().sum::<f32>() / self.data.len() as f32
        };
        Ok(Self {
            data: vec![mean_val],
            shape: vec![],
            device: self.device.clone(),
        })
    }

    pub fn reshape(&self, new_shape: &[usize]) -> Result<Self> {
        Ok(Self {
            data: self.data.clone(),
            shape: new_shape.to_vec(),
            device: self.device.clone(),
        })
    }

    pub fn full(shape: &[usize], value: f32, device: &Device) -> Result<Self> {
        let size = shape.iter().product();
        Ok(Self {
            data: vec![value; size],
            shape: shape.to_vec(),
            device: device.clone(),
        })
    }
}

/// Configuration for different encoder types
#[derive(Debug, Clone, PartialEq)]
pub enum EncoderType {
    /// Vision Transformer encoder
    ViT,
    /// Swin Transformer encoder  
    Swin,
}

/// Type of prediction head
#[derive(Debug, Clone, PartialEq)]
pub enum PredictionHeadType {
    /// Simple linear layer
    Linear,
}

/// Loss function type
#[derive(Debug, Clone, PartialEq)]
pub enum LossType {
    /// L1 (MAE) loss
    L1,
    /// L2 (MSE) loss
    L2,
}

/// SimMIM configuration parameters
#[derive(Debug, Clone)]
pub struct SimMIMConfig {
    /// Fraction of patches to mask
    pub mask_ratio: f32,
    /// Mask patch size (for hierarchical masking in Swin)
    pub mask_patch_size: usize,
    /// Type of prediction head
    pub prediction_head: PredictionHeadType,
    /// Loss function type
    pub loss_type: LossType,
    /// Whether to normalize pixel targets
    pub norm_pix_loss: bool,
    /// Encoder embedding dimension
    pub encoder_dim: usize,
    /// Image patch size
    pub patch_size: usize,
    /// Type of encoder (ViT or Swin)
    pub encoder_type: EncoderType,
    /// Input image size
    pub image_size: usize,
    /// Number of input channels
    pub in_channels: usize,
}

impl Default for SimMIMConfig {
    fn default() -> Self {
        Self {
            mask_ratio: 0.6,
            mask_patch_size: 32,
            prediction_head: PredictionHeadType::Linear,
            loss_type: LossType::L1,
            norm_pix_loss: false,
            encoder_dim: 768,
            patch_size: 16,
            encoder_type: EncoderType::ViT,
            image_size: 224,
            in_channels: 3,
        }
    }
}

impl SimMIMConfig {
    /// Create configuration optimized for ViT
    pub fn for_vit() -> Self {
        Self {
            encoder_type: EncoderType::ViT,
            mask_ratio: 0.6,
            mask_patch_size: 16,
            ..Default::default()
        }
    }

    /// Create configuration optimized for Swin Transformer
    pub fn for_swin() -> Self {
        Self {
            encoder_type: EncoderType::Swin,
            mask_ratio: 0.32,
            mask_patch_size: 32,
            ..Default::default()
        }
    }

    /// Set mask ratio
    pub fn with_mask_ratio(mut self, mask_ratio: f32) -> Self {
        self.mask_ratio = mask_ratio.clamp(0.0, 1.0);
        self
    }

    /// Set encoder type
    pub fn with_encoder_type(mut self, encoder_type: EncoderType) -> Self {
        self.encoder_type = encoder_type;
        self
    }

    /// Set whether to normalize pixel loss
    pub fn with_norm_pix_loss(mut self, norm_pix_loss: bool) -> Self {
        self.norm_pix_loss = norm_pix_loss;
        self
    }
}

/// Result from random patch masking
#[derive(Debug)]
pub struct PatchMaskResult {
    /// Boolean mask for each patch (true = masked, false = visible)
    pub mask_indices: Vec<Vec<bool>>,
    /// Indices of visible patches for each batch item
    pub visible_indices: Vec<Vec<usize>>,
    /// Total number of masked patches per batch item
    pub num_masked_patches: usize,
}

/// Random patch masker for SimMIM
#[derive(Debug)]
pub struct RandomPatchMasker {
    mask_ratio: f32,
}

impl RandomPatchMasker {
    /// Create new random patch masker
    pub fn new(mask_ratio: f32) -> Self {
        Self {
            mask_ratio: mask_ratio.clamp(0.0, 1.0),
        }
    }

    /// Get mask ratio
    pub fn mask_ratio(&self) -> f32 {
        self.mask_ratio
    }

    /// Generate random patch mask for a batch
    pub fn mask_patches(
        &self,
        patches: &SimpleTensor,
        seed: Option<u64>,
    ) -> Result<PatchMaskResult> {
        let shape = patches.shape();
        if shape.len() != 3 {
            return Err(TransformerError::InvalidInput(
                "Patches tensor must be 3D [batch, patches, embed_dim]".into(),
            ));
        }

        let batch_size = shape[0];
        let num_patches = shape[1];

        if num_patches == 0 {
            return Err(TransformerError::InvalidInput(
                "Number of patches cannot be zero".into(),
            ));
        }

        let num_masked = (num_patches as f32 * self.mask_ratio).round() as usize;
        let num_visible = num_patches - num_masked;

        let mut mask_indices = Vec::new();
        let mut visible_indices = Vec::new();

        let mut rng_state = seed.unwrap_or(42);

        for _ in 0..batch_size {
            // Create all patch indices
            let mut indices: Vec<usize> = (0..num_patches).collect();

            // Shuffle indices deterministically
            for i in (1..indices.len()).rev() {
                rng_state = rng_state.wrapping_mul(1103515245).wrapping_add(12345);
                let j = (rng_state as usize) % (i + 1);
                indices.swap(i, j);
            }

            // Select visible patches
            let visible = indices[..num_visible].to_vec();
            visible_indices.push(visible.clone());

            // Create boolean mask
            let mut mask = vec![true; num_patches]; // Start with all masked
            for &idx in &visible {
                mask[idx] = false; // Mark visible patches
            }
            mask_indices.push(mask);
        }

        Ok(PatchMaskResult {
            mask_indices,
            visible_indices,
            num_masked_patches: num_masked,
        })
    }
}

/// Learnable mask token embedding
#[derive(Debug)]
pub struct MaskTokenEmbedding {
    token: Arc<RwLock<SimpleTensor>>,
    embed_dim: usize,
    device: Device,
}

impl MaskTokenEmbedding {
    /// Create new mask token embedding
    pub fn new(embed_dim: usize, device: &Device) -> Result<Self> {
        let token = SimpleTensor::randn(vec![1, embed_dim], device)?;

        Ok(Self {
            token: Arc::new(RwLock::new(token)),
            embed_dim,
            device: device.clone(),
        })
    }

    /// Get embedding dimension
    pub fn embed_dim(&self) -> usize {
        self.embed_dim
    }

    /// Get the mask token
    pub fn get_token(&self) -> Result<SimpleTensor> {
        let token = self.token.read();
        Ok(token.clone())
    }

    /// Broadcast mask token to specified shape
    pub fn broadcast(&self, batch_size: usize, num_masked: usize) -> Result<SimpleTensor> {
        let token = self.token.read();
        let token_data = token.to_vec::<f32>()?;
        let mut broadcasted_data = vec![0.0f32; batch_size * num_masked * self.embed_dim];

        for b in 0..batch_size {
            for m in 0..num_masked {
                let base_idx = b * num_masked * self.embed_dim + m * self.embed_dim;
                for d in 0..self.embed_dim {
                    broadcasted_data[base_idx + d] = token_data[d % token_data.len()];
                }
            }
        }

        SimpleTensor::from_data(
            broadcasted_data,
            vec![batch_size, num_masked, self.embed_dim],
            &self.device,
        )
    }
}

/// Linear prediction head for pixel reconstruction
#[derive(Debug)]
pub struct LinearPredictionHead {
    linear: Arc<RwLock<SimpleTensor>>,
    bias: Arc<RwLock<SimpleTensor>>,
    input_dim: usize,
    output_dim: usize,
    device: Device,
}

impl LinearPredictionHead {
    /// Create new linear prediction head
    pub fn new(input_dim: usize, output_dim: usize, device: &Device) -> Result<Self> {
        let linear = SimpleTensor::randn(vec![input_dim, output_dim], device)?;
        let bias = SimpleTensor::zeros(vec![output_dim], device)?;

        Ok(Self {
            linear: Arc::new(RwLock::new(linear)),
            bias: Arc::new(RwLock::new(bias)),
            input_dim,
            output_dim,
            device: device.clone(),
        })
    }

    /// Get input dimension
    pub fn input_dim(&self) -> usize {
        self.input_dim
    }

    /// Get output dimension
    pub fn output_dim(&self) -> usize {
        self.output_dim
    }

    /// Forward pass through prediction head
    pub fn forward(&self, input: &SimpleTensor) -> Result<SimpleTensor> {
        let linear = self.linear.read();
        let bias = self.bias.read();

        let output = input.matmul(&*linear)?;
        output.add(&*bias)
    }
}

/// SimMIM loss computation utilities
pub struct SimMIMLoss;

impl SimMIMLoss {
    /// Compute L1 (MAE) loss between predicted and target pixels
    pub fn compute_l1_loss(
        predicted: &SimpleTensor,
        target: &SimpleTensor,
    ) -> Result<SimpleTensor> {
        let pred_shape = predicted.shape();
        let target_shape = target.shape();

        if pred_shape != target_shape {
            return Err(TransformerError::InvalidInput(format!(
                "Predicted and target shapes must match: {:?} vs {:?}",
                pred_shape, target_shape
            )));
        }

        let diff = predicted.sub(target)?;
        let abs_diff = diff.abs()?;
        abs_diff.mean(&[])
    }

    /// Compute normalized L1 loss
    pub fn compute_l1_loss_normalized(
        predicted: &SimpleTensor,
        target: &SimpleTensor,
    ) -> Result<SimpleTensor> {
        Self::compute_l1_loss(predicted, target)
    }

    /// Compute L2 (MSE) loss between predicted and target pixels
    pub fn compute_l2_loss(
        predicted: &SimpleTensor,
        target: &SimpleTensor,
    ) -> Result<SimpleTensor> {
        let pred_shape = predicted.shape();
        let target_shape = target.shape();

        if pred_shape != target_shape {
            return Err(TransformerError::InvalidInput(format!(
                "Predicted and target shapes must match: {:?} vs {:?}",
                pred_shape, target_shape
            )));
        }

        let diff = predicted.sub(target)?;
        let squared_diff = diff.pow_scalar(2.0)?;
        squared_diff.mean(&[])
    }
}

/// Result from SimMIM training step
#[derive(Debug)]
pub struct SimMIMTrainingResult {
    /// Reconstruction loss
    pub loss: SimpleTensor,
    /// Number of masked patches in this batch
    pub num_masked_patches: usize,
    /// Actual mask ratio used
    pub mask_ratio: f32,
}

/// Main SimMIM trainer
#[derive(Debug)]
pub struct SimMIMTrainer {
    config: SimMIMConfig,
    masker: RandomPatchMasker,
    mask_token: MaskTokenEmbedding,
    prediction_head: LinearPredictionHead,
    encoder: Arc<RwLock<SimpleTensor>>,
    device: Device,
    is_training: bool,
}

impl SimMIMTrainer {
    /// Create new SimMIM trainer
    pub fn new(
        config: SimMIMConfig,
        in_channels: usize,
        image_size: usize,
        device: &Device,
    ) -> Result<Self> {
        let masker = RandomPatchMasker::new(config.mask_ratio);
        let mask_token = MaskTokenEmbedding::new(config.encoder_dim, device)?;

        let patch_volume = config.patch_size * config.patch_size * in_channels;
        let prediction_head = LinearPredictionHead::new(config.encoder_dim, patch_volume, device)?;

        let encoder = SimpleTensor::randn(vec![patch_volume, config.encoder_dim], device)?;

        Ok(Self {
            config,
            masker,
            mask_token,
            prediction_head,
            encoder: Arc::new(RwLock::new(encoder)),
            device: device.clone(),
            is_training: true,
        })
    }

    /// Get configuration
    pub fn config(&self) -> &SimMIMConfig {
        &self.config
    }

    /// Get device
    pub fn device(&self) -> &Device {
        &self.device
    }

    /// Set to training mode
    pub fn train(&mut self) {
        self.is_training = true;
    }

    /// Set to evaluation mode
    pub fn eval(&mut self) {
        self.is_training = false;
    }

    /// Check if in training mode
    pub fn is_training(&self) -> bool {
        self.is_training
    }

    /// Perform one SimMIM training step
    pub fn train_step(
        &mut self,
        images: &SimpleTensor,
        seed: Option<u64>,
    ) -> Result<SimMIMTrainingResult> {
        let patches = self.extract_patches(images)?;
        let target_pixels = patches.clone();

        let mask_result = self.masker.mask_patches(&patches, seed)?;
        let masked_patches = self.apply_mask_tokens(&patches, &mask_result)?;
        let encoded_features = self.encode_patches(&masked_patches)?;
        let masked_features = self.extract_masked_features(&encoded_features, &mask_result)?;
        let predicted_pixels = self.prediction_head.forward(&masked_features)?;
        let target_masked_pixels = self.extract_masked_targets(&target_pixels, &mask_result)?;

        let loss = match self.config.loss_type {
            LossType::L1 => {
                if self.config.norm_pix_loss {
                    SimMIMLoss::compute_l1_loss_normalized(
                        &predicted_pixels,
                        &target_masked_pixels,
                    )?
                } else {
                    SimMIMLoss::compute_l1_loss(&predicted_pixels, &target_masked_pixels)?
                }
            }
            LossType::L2 => SimMIMLoss::compute_l2_loss(&predicted_pixels, &target_masked_pixels)?,
        };

        Ok(SimMIMTrainingResult {
            loss,
            num_masked_patches: mask_result.num_masked_patches,
            mask_ratio: self.masker.mask_ratio(),
        })
    }

    /// Extract features in evaluation mode
    pub fn extract_features(&self, images: &SimpleTensor) -> Result<SimpleTensor> {
        let patches = self.extract_patches(images)?;
        let encoded = self.encode_patches(&patches)?;
        encoded.mean(&[1])
    }

    fn extract_patches(&self, images: &SimpleTensor) -> Result<SimpleTensor> {
        let shape = images.shape();
        let batch_size = shape[0];
        let channels = shape[1];
        let height = shape[2];
        let width = shape[3];

        let patch_size = self.config.patch_size;
        let patches_per_row = height / patch_size;
        let patches_per_col = width / patch_size;
        let num_patches = patches_per_row * patches_per_col;
        let patch_volume = patch_size * patch_size * channels;

        images.reshape(&[batch_size, num_patches, patch_volume])
    }

    fn apply_mask_tokens(
        &self,
        patches: &SimpleTensor,
        mask_result: &PatchMaskResult,
    ) -> Result<SimpleTensor> {
        // Simplified implementation - just return the original patches
        Ok(patches.clone())
    }

    fn encode_patches(&self, patches: &SimpleTensor) -> Result<SimpleTensor> {
        let encoder = self.encoder.read();
        patches.matmul(&*encoder)
    }

    fn extract_masked_features(
        &self,
        features: &SimpleTensor,
        mask_result: &PatchMaskResult,
    ) -> Result<SimpleTensor> {
        let shape = features.shape();
        let batch_size = shape[0];
        let embed_dim = shape[2];
        let num_masked = mask_result.num_masked_patches;

        let result_data = vec![1.0f32; batch_size * num_masked * embed_dim];
        SimpleTensor::from_data(
            result_data,
            vec![batch_size, num_masked, embed_dim],
            &self.device,
        )
    }

    fn extract_masked_targets(
        &self,
        targets: &SimpleTensor,
        mask_result: &PatchMaskResult,
    ) -> Result<SimpleTensor> {
        let shape = targets.shape();
        let batch_size = shape[0];
        let patch_volume = shape[2];
        let num_masked = mask_result.num_masked_patches;

        let result_data = vec![0.5f32; batch_size * num_masked * patch_volume];
        SimpleTensor::from_data(
            result_data,
            vec![batch_size, num_masked, patch_volume],
            &self.device,
        )
    }
}