"""
Implementation of hybrid quantum-classical computing for Neurenix.
"""

from typing import List, Dict, Optional, Union, Any, Tuple, Callable
import numpy as np

from neurenix.device import Device, DeviceType
from neurenix.tensor import Tensor
from neurenix.nn import Module
from neurenix.quantum.circuit import QuantumCircuit, ParameterizedCircuit

class QuantumTensor(Tensor):
    """
    Tensor representation of quantum states.
    """
    
    def __init__(self, data, circuit: Optional[QuantumCircuit] = None, device: Optional[Device] = None):
        """
        Initialize a quantum tensor.
        
        Args:
            data: Tensor data
            circuit: Quantum circuit that generated the data
            device: Device to store the tensor on
        """
        super().__init__(data, device=device)
        self.circuit = circuit
        
    @classmethod
    def from_circuit(cls, circuit: QuantumCircuit) -> 'QuantumTensor':
        """
        Create a quantum tensor from a quantum circuit.
        
        Args:
            circuit: Quantum circuit
            
        Returns:
            Quantum tensor
        """
        state_vector = circuit.to_matrix() @ np.array([1] + [0] * (2**circuit.num_qubits - 1))
        
        return cls(state_vector, circuit=circuit, device=circuit.device)
        
    def to_circuit(self) -> QuantumCircuit:
        """
        Convert the quantum tensor back to a quantum circuit.
        
        Returns:
            Quantum circuit
        """
        if self.circuit is not None:
            return self.circuit
            
        from neurenix.quantum.utils import tensor_to_circuit
        return tensor_to_circuit(self)
        
    def measure(self, shots: int = 1024) -> Dict[str, int]:
        """
        Measure the quantum state.
        
        Args:
            shots: Number of measurement shots
            
        Returns:
            Measurement results
        """
        if self.circuit is not None:
            return self.circuit.run(shots=shots)
            
        circuit = self.to_circuit()
        return circuit.run(shots=shots)


class QuantumLayer(Module):
    """
    Neural network layer that applies a quantum circuit.
    """
    
    def __init__(self, 
                 circuit_template: Union[ParameterizedCircuit, Callable[[int], ParameterizedCircuit]],
                 num_qubits: int,
                 input_size: int,
                 output_size: int,
                 device: Optional[Device] = None):
        """
        Initialize a quantum layer.
        
        Args:
            circuit_template: Parameterized quantum circuit or function to create one
            num_qubits: Number of qubits in the circuit
            input_size: Size of the input tensor
            output_size: Size of the output tensor
            device: Device to run the layer on
        """
        super().__init__()
        self.num_qubits = num_qubits
        self.input_size = input_size
        self.output_size = output_size
        self.device = device or Device(DeviceType.QUANTUM)
        
        if callable(circuit_template):
            self.circuit = circuit_template(num_qubits)
        else:
            self.circuit = circuit_template
            
        self.param_values = {param: np.random.randn() for param in self.circuit.parameters}
        
    def forward(self, x: Tensor) -> Tensor:
        """
        Forward pass through the quantum layer.
        
        Args:
            x: Input tensor
            
        Returns:
            Output tensor
        """
        input_data = x.numpy()
        
        param_values = {}
        for i, param in enumerate(self.circuit.parameters):
            if i < self.input_size:
                param_values[param] = input_data[i]
            else:
                param_values[param] = self.param_values[param]
                
        bound_circuit = self.circuit.bind_parameters(param_values)
        
        state_vector = bound_circuit.to_matrix() @ np.array([1] + [0] * (2**self.num_qubits - 1))
        
        output_data = np.zeros(self.output_size)
        for i in range(min(self.output_size, len(state_vector))):
            output_data[i] = np.abs(state_vector[i])**2
            
        return Tensor(output_data, device=self.device)
        
    def parameters(self) -> Dict[str, float]:
        """Get the parameters of the layer."""
        return self.param_values


class HybridModel(Module):
    """
    Hybrid quantum-classical neural network model.
    """
    
    def __init__(self, device: Optional[Device] = None):
        """
        Initialize a hybrid model.
        
        Args:
            device: Device to run the model on
        """
        super().__init__()
        self.device = device
        self.classical_layers = []
        self.quantum_layers = []
        self.layers = []
        
    def add_classical_layer(self, layer: Module) -> 'HybridModel':
        """
        Add a classical layer to the model.
        
        Args:
            layer: Classical neural network layer
            
        Returns:
            Self
        """
        self.classical_layers.append(layer)
        self.layers.append(('classical', len(self.classical_layers) - 1))
        return self
        
    def add_quantum_layer(self, layer: QuantumLayer) -> 'HybridModel':
        """
        Add a quantum layer to the model.
        
        Args:
            layer: Quantum layer
            
        Returns:
            Self
        """
        self.quantum_layers.append(layer)
        self.layers.append(('quantum', len(self.quantum_layers) - 1))
        return self
        
    def forward(self, x: Tensor) -> Tensor:
        """
        Forward pass through the hybrid model.
        
        Args:
            x: Input tensor
            
        Returns:
            Output tensor
        """
        output = x
        
        for layer_type, layer_idx in self.layers:
            if layer_type == 'classical':
                output = self.classical_layers[layer_idx](output)
            else:
                output = self.quantum_layers[layer_idx](output)
                
        return output
