use trustformers_core::{
    errors::Result,
    layers::{Embedding, LayerNorm, Linear},
    quantum::{QuantumManager, QuantumNeuralLayer},
    tensor::Tensor,
    traits::Layer,
};

use crate::quantum_classical_hybrids::{
    config::{QuantumClassicalConfig, QuantumHybridArchitecture},
    quantum_attention::QuantumAttentionLayer,
    quantum_cnn::QuantumConvolutionalNN,
    quantum_embedding::QuantumEmbeddingModel,
    quantum_gnn::QuantumGraphNeuralNetwork,
    quantum_optimizer::QuantumOptimizer,
    quantum_rnn::QuantumRecurrentNN,
    quantum_training::QuantumTrainingManager,
    quantum_transformer::QuantumTransformer,
};

/// Quantum-classical hybrid model output
#[derive(Debug, Clone)]
pub struct QuantumClassicalModelOutput {
    /// Hidden states from the model
    pub hidden_states: Tensor,
    /// Quantum measurements
    pub quantum_measurements: Option<Tensor>,
    /// Classical activations
    pub classical_activations: Option<Tensor>,
    /// Quantum attention weights
    pub quantum_attention_weights: Option<Tensor>,
    /// Quantum fidelity scores
    pub quantum_fidelity_scores: Option<Tensor>,
    /// Quantum entanglement measures
    pub quantum_entanglement_measures: Option<Tensor>,
    /// Quantum error mitigation results
    pub quantum_error_mitigation: Option<Tensor>,
}

/// Core quantum-classical hybrid model
#[derive(Debug)]
pub struct QuantumClassicalModel {
    /// Configuration
    pub config: QuantumClassicalConfig,
    /// Token embeddings
    pub embeddings: Embedding,
    /// Classical layers
    pub classical_layers: Vec<Linear>,
    /// Quantum layers
    pub quantum_layers: Vec<QuantumNeuralLayer>,
    /// Quantum manager
    pub quantum_manager: QuantumManager,
    /// Architecture-specific model
    pub architecture: QuantumHybridArchitectureModel,
    /// Layer normalization
    pub layer_norm: LayerNorm,
    /// Output projection
    pub output_projection: Linear,
    /// Quantum optimizer
    pub quantum_optimizer: QuantumOptimizer,
    /// Training manager
    pub training_manager: QuantumTrainingManager,
}

/// Architecture-specific model implementations
#[derive(Debug)]
pub enum QuantumHybridArchitectureModel {
    /// Quantum transformer
    QuantumTransformer(QuantumTransformer),
    /// Quantum graph neural network
    QuantumGraphNeuralNetwork(QuantumGraphNeuralNetwork),
    /// Quantum convolutional neural network
    QuantumConvolutionalNN(QuantumConvolutionalNN),
    /// Quantum recurrent neural network
    QuantumRecurrentNN(QuantumRecurrentNN),
    /// Quantum attention
    QuantumAttention(QuantumAttentionLayer),
    /// Quantum embedding
    QuantumEmbedding(QuantumEmbeddingModel),
    /// Variational quantum circuit
    VariationalQuantumCircuit(QuantumNeuralLayer),
    /// Quantum approximate optimization
    QuantumApproximateOptimization(QuantumOptimizer),
}

impl QuantumClassicalModel {
    /// Create a new quantum-classical hybrid model
    pub fn new(config: QuantumClassicalConfig) -> Result<Self> {
        let embeddings = Embedding::new(config.vocab_size, config.d_model, None)?;
        let layer_norm = LayerNorm::new(vec![config.d_model], 1e-12)?;
        let output_projection = Linear::new(config.d_model, config.vocab_size, config.use_bias);

        // Create classical layers
        let mut classical_layers = Vec::new();
        for _ in 0..config.n_classical_layers {
            classical_layers.push(Linear::new(config.d_model, config.d_model, config.use_bias));
        }

        // Create quantum layers
        let mut quantum_layers = Vec::new();
        for _ in 0..config.n_quantum_layers {
            let ansatz = config.quantum_ansatz.clone().into();
            let parameters = vec![0.1; config.get_quantum_parameters_count()];
            quantum_layers.push(QuantumNeuralLayer::new(
                config.num_qubits,
                ansatz,
                &parameters,
            )?);
        }

        // Create quantum manager
        let quantum_manager = QuantumManager::simulator(config.num_qubits);

        // Create quantum optimizer
        let quantum_optimizer = QuantumOptimizer::new(&config)?;

        // Create training manager
        let training_manager = QuantumTrainingManager::new(&config)?;

        // Create architecture-specific model
        let architecture =
            match config.architecture {
                QuantumHybridArchitecture::QuantumTransformer => {
                    QuantumHybridArchitectureModel::QuantumTransformer(QuantumTransformer::new(
                        &config,
                    )?)
                },
                QuantumHybridArchitecture::QuantumGraphNeuralNetwork => {
                    QuantumHybridArchitectureModel::QuantumGraphNeuralNetwork(
                        QuantumGraphNeuralNetwork::new(&config)?,
                    )
                },
                QuantumHybridArchitecture::QuantumConvolutionalNN => {
                    QuantumHybridArchitectureModel::QuantumConvolutionalNN(
                        QuantumConvolutionalNN::new(&config)?,
                    )
                },
                QuantumHybridArchitecture::QuantumRecurrentNN => {
                    QuantumHybridArchitectureModel::QuantumRecurrentNN(QuantumRecurrentNN::new(
                        &config,
                    )?)
                },
                QuantumHybridArchitecture::QuantumAttention => {
                    QuantumHybridArchitectureModel::QuantumAttention(QuantumAttentionLayer::new(
                        &config,
                    )?)
                },
                QuantumHybridArchitecture::QuantumEmbedding => {
                    QuantumHybridArchitectureModel::QuantumEmbedding(QuantumEmbeddingModel::new(
                        &config,
                    )?)
                },
                QuantumHybridArchitecture::VariationalQuantumCircuit => {
                    let ansatz = config.quantum_ansatz.clone().into();
                    let parameters = vec![0.1; config.get_quantum_parameters_count()];
                    QuantumHybridArchitectureModel::VariationalQuantumCircuit(
                        QuantumNeuralLayer::new(config.num_qubits, ansatz, &parameters)?,
                    )
                },
                QuantumHybridArchitecture::QuantumApproximateOptimization => {
                    QuantumHybridArchitectureModel::QuantumApproximateOptimization(
                        QuantumOptimizer::new(&config)?,
                    )
                },
            };

        Ok(Self {
            config,
            embeddings,
            classical_layers,
            quantum_layers,
            quantum_manager,
            architecture,
            layer_norm,
            output_projection,
            quantum_optimizer,
            training_manager,
        })
    }

    /// Forward pass through the model
    pub fn forward(&mut self, input_ids: &Tensor) -> Result<QuantumClassicalModelOutput> {
        // Convert tensor to Vec<u32> for embeddings
        let input_ids_vec: Vec<u32> =
            input_ids.to_vec_f32()?.into_iter().map(|x| x as u32).collect();

        // Get embeddings
        let embeddings = self.embeddings.forward(input_ids_vec)?;
        let mut hidden_states = embeddings;

        // Process through classical layers
        for layer in &self.classical_layers {
            hidden_states = layer.forward(hidden_states)?;
            hidden_states = hidden_states.tanh()?; // Activation
        }

        // Process through architecture-specific model
        let output = match &mut self.architecture {
            QuantumHybridArchitectureModel::QuantumTransformer(model) => {
                model.forward(&hidden_states.clone())?
            },
            QuantumHybridArchitectureModel::QuantumGraphNeuralNetwork(model) => {
                model.forward(&hidden_states.clone())?
            },
            QuantumHybridArchitectureModel::QuantumConvolutionalNN(model) => {
                model.forward(&hidden_states.clone())?
            },
            QuantumHybridArchitectureModel::QuantumRecurrentNN(model) => {
                model.forward(&hidden_states.clone())?
            },
            QuantumHybridArchitectureModel::QuantumAttention(model) => {
                model.forward(&hidden_states.clone())?
            },
            QuantumHybridArchitectureModel::QuantumEmbedding(model) => {
                model.forward(&hidden_states.clone())?
            },
            QuantumHybridArchitectureModel::VariationalQuantumCircuit(model) => {
                let quantum_output = model.forward(&hidden_states)?;
                QuantumClassicalModelOutput {
                    hidden_states: quantum_output,
                    quantum_measurements: None,
                    classical_activations: Some(hidden_states),
                    quantum_attention_weights: None,
                    quantum_fidelity_scores: None,
                    quantum_entanglement_measures: None,
                    quantum_error_mitigation: None,
                }
            },
            QuantumHybridArchitectureModel::QuantumApproximateOptimization(optimizer) => {
                let optimized_output = optimizer.optimize(&hidden_states)?;
                QuantumClassicalModelOutput {
                    hidden_states: optimized_output,
                    quantum_measurements: None,
                    classical_activations: Some(hidden_states),
                    quantum_attention_weights: None,
                    quantum_fidelity_scores: None,
                    quantum_entanglement_measures: None,
                    quantum_error_mitigation: None,
                }
            },
        };

        Ok(output)
    }

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

    /// Get architecture type
    pub fn architecture(&self) -> &QuantumHybridArchitecture {
        &self.config.architecture
    }

    /// Update quantum parameters
    pub fn update_quantum_parameters(&mut self, gradients: &[f64]) -> Result<()> {
        for (layer, grad_slice) in self
            .quantum_layers
            .iter_mut()
            .zip(gradients.chunks(self.config.get_quantum_parameters_count()))
        {
            layer.update_parameters(grad_slice, self.config.quantum_learning_rate);
        }
        Ok(())
    }

    /// Get quantum advantage estimate
    pub fn get_quantum_advantage(&self) -> f64 {
        self.config.get_quantum_advantage_factor()
    }

    /// Get quantum fidelity
    pub fn get_quantum_fidelity(&self) -> f64 {
        // Simplified fidelity calculation
        1.0 - self.config.quantum_noise_variance
    }

    /// Get parameter count
    pub fn parameter_count(&self) -> usize {
        let classical_params = self.embeddings.parameter_count()
            + self.classical_layers.iter().map(|l| l.parameter_count()).sum::<usize>()
            + self.layer_norm.parameter_count()
            + self.output_projection.parameter_count();

        let quantum_params = self.quantum_layers.len() * self.config.get_quantum_parameters_count();

        classical_params + quantum_params
    }

    /// Get memory usage in MB
    pub fn memory_usage(&self) -> f32 {
        let classical_memory = self.parameter_count() as f32 * 4.0 / 1_000_000.0;
        let quantum_memory = self.config.get_quantum_dimension() as f32 * 8.0 / 1_000_000.0; // Complex numbers
        classical_memory + quantum_memory
    }
}

/// Quantum-classical model for causal language modeling
#[derive(Debug)]
pub struct QuantumClassicalModelForCausalLM {
    /// Base model
    pub model: QuantumClassicalModel,
    /// Language modeling head
    pub lm_head: Linear,
}

impl QuantumClassicalModelForCausalLM {
    /// Create a new causal language modeling model
    pub fn new(config: QuantumClassicalConfig) -> Result<Self> {
        let model = QuantumClassicalModel::new(config.clone())?;
        let lm_head = Linear::new(config.d_model, config.vocab_size, false);

        Ok(Self { model, lm_head })
    }

    /// Forward pass for causal language modeling
    pub fn forward(&mut self, input_ids: &Tensor) -> Result<Tensor> {
        let output = self.model.forward(input_ids)?;
        let logits = self.lm_head.forward(output.hidden_states)?;
        Ok(logits)
    }

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

    /// Update quantum parameters
    pub fn update_quantum_parameters(&mut self, gradients: &[f64]) -> Result<()> {
        self.model.update_quantum_parameters(gradients)
    }
}

/// Quantum-classical model for sequence classification
#[derive(Debug)]
pub struct QuantumClassicalModelForSequenceClassification {
    /// Base model
    pub model: QuantumClassicalModel,
    /// Classification head
    pub classifier: Linear,
    /// Number of labels
    pub num_labels: usize,
}

impl QuantumClassicalModelForSequenceClassification {
    /// Create a new sequence classification model
    pub fn new(config: QuantumClassicalConfig, num_labels: usize) -> Result<Self> {
        let model = QuantumClassicalModel::new(config.clone())?;
        let classifier = Linear::new(config.d_model, num_labels, true);

        Ok(Self {
            model,
            classifier,
            num_labels,
        })
    }

    /// Forward pass for sequence classification
    pub fn forward(&mut self, input_ids: &Tensor) -> Result<Tensor> {
        let output = self.model.forward(input_ids)?;

        // Take the mean of the sequence for classification
        let pooled = output.hidden_states.mean_axes(&[1])?;
        let logits = self.classifier.forward(pooled)?;

        Ok(logits)
    }

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

    /// Update quantum parameters
    pub fn update_quantum_parameters(&mut self, gradients: &[f64]) -> Result<()> {
        self.model.update_quantum_parameters(gradients)
    }
}

/// Quantum-classical model statistics
#[derive(Debug, Clone)]
pub struct QuantumClassicalModelStats {
    /// Total parameters
    pub total_parameters: usize,
    /// Classical parameters
    pub classical_parameters: usize,
    /// Quantum parameters
    pub quantum_parameters: usize,
    /// Memory usage in MB
    pub memory_usage_mb: f32,
    /// Quantum advantage factor
    pub quantum_advantage_factor: f64,
    /// Quantum fidelity
    pub quantum_fidelity: f64,
    /// Architecture type
    pub architecture_type: QuantumHybridArchitecture,
}

impl QuantumClassicalModel {
    /// Get model statistics
    pub fn get_stats(&self) -> QuantumClassicalModelStats {
        let total_params = self.parameter_count();
        let quantum_params = self.quantum_layers.len() * self.config.get_quantum_parameters_count();
        let classical_params = total_params - quantum_params;

        QuantumClassicalModelStats {
            total_parameters: total_params,
            classical_parameters: classical_params,
            quantum_parameters: quantum_params,
            memory_usage_mb: self.memory_usage(),
            quantum_advantage_factor: self.get_quantum_advantage(),
            quantum_fidelity: self.get_quantum_fidelity(),
            architecture_type: self.config.architecture.clone(),
        }
    }
}
