from typing import List, Dict, Any
from scode.heuristic_layer.surface_code_object import SurfaceCodeObject
import networkx as nx
import numpy as np

class MultiPatchMapper:
    def __init__(self, config: Dict[str, Any], hardware_graph: Dict[str, Any]):
        self.config = config
        self.hardware_graph = hardware_graph

    def map_patches(self, surface_code_objects: List[SurfaceCodeObject], mapping_constraints: Dict[str, Any]) -> Dict[str, Any]:
        # Config-driven patch placement optimization
        layout_type = mapping_constraints.get('layout_type', 'adjacent')
        min_distance = mapping_constraints.get('min_distance_between_patches', 1)
        patch_shapes = mapping_constraints.get('patch_shapes', ['rectangular'] * len(surface_code_objects))
        multi_patch_layout = {}
        resource_allocation = {}
        used_qubits = set()
        # Place patches according to layout_type and constraints
        for i, patch in enumerate(surface_code_objects):
            offset = i * (max(q['x'] for q in patch.qubit_layout.values()) + min_distance)
            patch_layout = {}
            for q, pos in patch.qubit_layout.items():
                # Offset patch positions to avoid overlap
                new_x = pos['x'] + offset if layout_type == 'adjacent' else pos['x']
                new_y = pos['y']
                patch_layout[q] = {'x': new_x, 'y': new_y}
                resource_allocation[(i, q)] = i
                used_qubits.add((new_x, new_y))
            multi_patch_layout[i] = {'layout': patch_layout, 'shape': patch_shapes[i]}
        inter_patch_connectivity = self._compute_inter_patch_connectivity(multi_patch_layout, min_distance)
        optimization_metrics = self._compute_optimization_metrics(multi_patch_layout, inter_patch_connectivity)
        return {
            'multi_patch_layout': multi_patch_layout,
            'inter_patch_connectivity': inter_patch_connectivity,
            'resource_allocation': resource_allocation,
            'optimization_metrics': optimization_metrics
        }

    def _compute_inter_patch_connectivity(self, multi_patch_layout: Dict[int, Any], min_distance: int) -> Dict[str, Any]:
        # Compute inter-patch connectivity based on minimum distance
        connectivity = {}
        patch_positions = {i: [tuple(pos.values()) for pos in patch['layout'].values()] for i, patch in multi_patch_layout.items()}
        for i in patch_positions:
            for j in patch_positions:
                if i < j:
                    min_dist = np.min([np.linalg.norm(np.array(p1) - np.array(p2)) for p1 in patch_positions[i] for p2 in patch_positions[j]])
                    if min_dist <= min_distance:
                        connectivity[(i, j)] = {'distance': min_dist}
        return connectivity

    def _compute_optimization_metrics(self, multi_patch_layout: Dict[int, Any], inter_patch_connectivity: Dict[str, Any]) -> Dict[str, Any]:
        # Compute metrics such as total swaps, average error rate, etc.
        total_swaps = 0  # Should be updated if swaps are tracked
        avg_error_rate = 0.0  # Should be computed from hardware graph and patch layouts
        return {'total_swaps': total_swaps, 'avg_error_rate': avg_error_rate} 