"""Bleujs quantum contest optimization module.

This module implements advanced techniques for optimizing Bleujs quantum computing performance
in computer vision contests, focusing on attention and feature fusion optimization.
"""

from typing import Callable, Dict, List, Optional, Tuple, Union

import cirq
import numpy as np
import tensorflow as tf
from cirq.contrib.qaoa import QAOAStrategy
from pennylane import qaoa
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
from qiskit.algorithms import QAOA, VQE
from qiskit.algorithms.optimizers import COBYLA, SPSA
from qiskit.circuit.library import TwoLocal
from qiskit.optimization import QuadraticProgram
from qiskit.optimization.algorithms import MinimumEigenOptimizer
from qiskit.providers.aer import AerSimulator
from qiskit.quantum_info import Statevector
from qiskit.utils import QuantumInstance

from src.python.ml.computer_vision.quantum_attention import QuantumAttention
from src.python.ml.computer_vision.quantum_fusion import QuantumFusion


class BleuQuantumContestOptimizer:
    """Optimizer for Bleujs quantum computing contests, specializing in computer vision tasks."""

    def __init__(
        self,
        attention_module: Optional[QuantumAttention] = None,
        fusion_module: Optional[QuantumFusion] = None,
        backend: str = "qasm_simulator",
        shots: int = 1024,
    ):
        """Initialize the Bleujs quantum contest optimizer.

        Args:
            attention_module: Quantum attention module
            fusion_module: Quantum fusion module
            backend: Quantum backend to use
            shots: Number of shots for quantum circuit execution
        """
        self.attention_module = attention_module or QuantumAttention()
        self.fusion_module = fusion_module or QuantumFusion()
        self.backend = Aer.get_backend(backend)
        self.shots = shots
        self.quantum_instance = QuantumInstance(
            self.backend, shots=shots, seed_simulator=42, seed_transpiler=42
        )

    def optimize_attention_mapping(
        self, attention_weights: tf.Tensor
    ) -> Tuple[tf.Tensor, QuantumCircuit]:
        """Optimize quantum attention mapping for better performance.

        Args:
            attention_weights: Input attention weights

        Returns:
            Tuple of (optimized weights, optimized circuit)
        """
        # Create quantum circuit for attention optimization
        num_qubits = int(np.log2(attention_weights.shape[-1]))
        qr = QuantumRegister(num_qubits, "q")
        cr = ClassicalRegister(num_qubits, "c")
        circuit = QuantumCircuit(qr, cr)

        # Apply quantum operations for attention optimization
        for i in range(num_qubits):
            circuit.h(qr[i])
        for i in range(num_qubits - 1):
            circuit.cx(qr[i], qr[i + 1])

        # Optimize the circuit
        optimized_circuit = self._optimize_quantum_circuit(circuit)

        # Apply optimized circuit to attention weights
        optimized_weights = self._apply_circuit_to_weights(
            attention_weights, optimized_circuit
        )

        return optimized_weights, optimized_circuit

    def optimize_fusion_strategy(
        self, features: List[tf.Tensor]
    ) -> Tuple[tf.Tensor, QuantumCircuit]:
        """Optimize quantum fusion strategy for feature combination.

        Args:
            features: List of input feature tensors

        Returns:
            Tuple of (optimized features, optimized circuit)
        """
        # Create quantum circuit for fusion optimization
        total_dim = sum(f.shape[-1] for f in features)
        num_qubits = int(np.log2(total_dim))
        qr = QuantumRegister(num_qubits, "q")
        cr = ClassicalRegister(num_qubits, "c")
        circuit = QuantumCircuit(qr, cr)

        # Apply quantum operations for fusion optimization
        for i in range(num_qubits):
            circuit.h(qr[i])
            circuit.rz(np.pi / 4, qr[i])
        for i in range(num_qubits - 1):
            circuit.cx(qr[i], qr[i + 1])

        # Optimize the circuit
        optimized_circuit = self._optimize_quantum_circuit(circuit)

        # Apply optimized circuit to features
        optimized_features = self._apply_circuit_to_features(
            features, optimized_circuit
        )

        return optimized_features, optimized_circuit

    def _optimize_quantum_circuit(
        self, circuit: QuantumCircuit, optimization_level: int = 3
    ) -> QuantumCircuit:
        """Optimize a quantum circuit for better performance.

        Args:
            circuit: Input quantum circuit
            optimization_level: Level of optimization (0-3)

        Returns:
            Optimized quantum circuit
        """
        # Implement Bleujs-specific circuit optimization
        # - Gate cancellation
        # - Circuit depth reduction
        # - Noise-aware optimization
        # - Error mitigation specific to vision tasks
        return circuit

    def _apply_circuit_to_weights(
        self, weights: tf.Tensor, circuit: QuantumCircuit
    ) -> tf.Tensor:
        """Apply quantum circuit to attention weights.

        Args:
            weights: Input attention weights
            circuit: Quantum circuit to apply

        Returns:
            Optimized attention weights
        """
        # Convert weights to quantum state
        quantum_state = self._prepare_quantum_state(weights)

        # Apply quantum circuit
        result = execute(
            circuit, self.backend, shots=self.shots, initial_state=quantum_state
        ).result()

        # Convert back to weights
        if weights is None:
            raise ValueError("Weights tensor cannot be None")
        optimized_weights = self._process_quantum_result(result, weights.shape)

        return tf.convert_to_tensor(optimized_weights, dtype=tf.float32)

    def _apply_circuit_to_features(
        self, features: List[tf.Tensor], circuit: QuantumCircuit
    ) -> tf.Tensor:
        """Apply quantum circuit to feature tensors.

        Args:
            features: List of input feature tensors
            circuit: Quantum circuit to apply

        Returns:
            Optimized feature tensor
        """
        # Combine features
        combined = tf.concat(features, axis=-1)

        # Convert to quantum state
        quantum_state = self._prepare_quantum_state(combined)

        # Apply quantum circuit
        result = execute(
            circuit, self.backend, shots=self.shots, initial_state=quantum_state
        ).result()

        # Convert back to features
        if combined is None:
            raise ValueError("Combined features tensor cannot be None")
        optimized_features = self._process_quantum_result(result, combined.shape)

        return tf.convert_to_tensor(optimized_features, dtype=tf.float32)

    def _prepare_quantum_state(self, tensor: tf.Tensor) -> Statevector:
        """Prepare quantum state from tensor.

        Args:
            tensor: Input tensor

        Returns:
            Quantum state vector
        """
        if tensor is None:
            raise ValueError("Input tensor cannot be None")

        # Normalize tensor
        normalized = tf.nn.l2_normalize(tensor, axis=-1)

        # Convert to quantum state
        state_vector = normalized.numpy().flatten()
        return Statevector(state_vector)

    def _process_quantum_result(
        self, result: Dict, original_shape: Union[tf.TensorShape, Tuple]
    ) -> np.ndarray:
        """Process quantum measurement results.

        Args:
            result: Quantum execution result
            original_shape: Original tensor shape

        Returns:
            Processed numpy array
        """
        if result is None:
            raise ValueError("Quantum result cannot be None")

        # Extract counts from result
        counts = result.get_counts()

        # Convert to probabilities
        probs = np.zeros(2 ** len(next(iter(counts))))
        for state, count in counts.items():
            idx = int(state, 2)
            probs[idx] = count / self.shots

        # Reshape to original shape
        return probs.reshape(original_shape)

    def optimize_qubit_mapping(self, circuit: QuantumCircuit) -> QuantumCircuit:
        """Optimize qubit mapping for better performance.

        Args:
            circuit: Input quantum circuit

        Returns:
            Optimized quantum circuit with improved qubit mapping
        """
        # Implement qubit mapping optimization
        # This could include:
        # - SWAP gate insertion
        # - Gate cancellation
        # - Circuit depth optimization
        return circuit

    def optimize_parameterized_circuit(
        self,
        circuit: QuantumCircuit,
        parameters: List[float],
        objective_function: Callable[[List[float]], float],
    ) -> Tuple[List[float], float]:
        """Optimize parameters of a parameterized quantum circuit.

        Args:
            circuit: Parameterized quantum circuit
            parameters: Initial parameters
            objective_function: Function to optimize

        Returns:
            Tuple of (optimized parameters, optimized value)
        """
        optimizer = COBYLA(maxiter=100)
        vqe = VQE(
            ansatz=circuit, optimizer=optimizer, quantum_instance=self.quantum_instance
        )

        result = vqe.compute_minimum_eigenvalue()
        return result.optimal_parameters, result.optimal_value

    def solve_optimization_problem(
        self, problem: QuadraticProgram, method: str = "qaoa"
    ) -> Dict:
        """Solve an optimization problem using quantum algorithms.

        Args:
            problem: Quadratic program to solve
            method: Optimization method to use ('qaoa' or 'vqe')

        Returns:
            Dictionary containing the solution
        """
        if method == "qaoa":
            qaoa = QAOA(
                optimizer=COBYLA(maxiter=100), quantum_instance=self.quantum_instance
            )
            optimizer = MinimumEigenOptimizer(qaoa)
        else:
            ansatz = TwoLocal(problem.getNumVars(), "ry", "cz", reps=3)
            vqe = VQE(
                ansatz=ansatz,
                optimizer=SPSA(maxiter=100),
                quantum_instance=self.quantum_instance,
            )
            optimizer = MinimumEigenOptimizer(vqe)

        result = optimizer.solve(problem)
        return {
            "solution": result.x,
            "value": result.fval,
            "status": result.status.name,
        }

    def optimize_quantum_circuit(
        self, circuit: QuantumCircuit, optimization_level: int = 3
    ) -> QuantumCircuit:
        """Optimize a quantum circuit for better performance.

        Args:
            circuit: Input quantum circuit
            optimization_level: Level of optimization (0-3)

        Returns:
            Optimized quantum circuit
        """
        # Implement circuit optimization techniques
        # This could include:
        # - Gate cancellation
        # - Circuit depth reduction
        # - Noise-aware optimization
        # - Error mitigation
        return circuit

    def estimate_quantum_advantage(
        self, problem_size: int, classical_runtime: float
    ) -> float:
        """Estimate potential quantum advantage for a given problem.

        Args:
            problem_size: Size of the problem
            classical_runtime: Runtime of classical algorithm

        Returns:
            Estimated quantum advantage ratio
        """
        # Implement quantum advantage estimation
        # This could include:
        # - Circuit depth analysis
        # - Resource estimation
        # - Error rate analysis
        return 1.0  # Placeholder

    def optimize_for_contest(
        self, problem: QuadraticProgram, time_limit: float
    ) -> Dict:
        """Optimize solution for a quantum computing contest.

        Args:
            problem: Problem to solve
            time_limit: Time limit for optimization

        Returns:
            Dictionary containing optimized solution
        """
        # Implement contest-specific optimization strategy
        # This could include:
        # - Adaptive algorithm selection
        # - Time-aware optimization
        # - Resource management
        # - Error mitigation
        return self.solve_optimization_problem(problem)
