import numpy as np
try:
    from datasets import Dataset
except ImportError:
    class Dataset:
        @staticmethod
        def from_list(data_list): return data_list
        @staticmethod
        def from_dict(data_dict): return data_dict

from sovereign_shm_bridge import SovereignSHMBridge

class SovereignTopologicalSieve:
    """
    Kaiser-Squires E/B Mode Sieve for Dataset Filtering.
    Uses the WGPU/Rust engine via SHM to identify the 'Invariant Backbone'.
    """
    def __init__(self, bridge: SovereignSHMBridge = None, threshold: float = 0.5):
        self.bridge = bridge or SovereignSHMBridge()
        self.threshold = threshold

    def _get_collapsed_verdict(self, data_chunk: bytes) -> bool:
        """
        1D Collapse: Converts high-dimensional E-mode map to a scalar verdict.
        Follows the DefaultCollapser logic:
        bow_wave_intensity → collision_val → net_value
        """
        self.bridge.write_dataset_chunk(data_chunk)
        # In a real sync, we'd trigger the internal_module_api socket here
        e_mode_map = self.bridge.read_signal_map()
        
        if len(e_mode_map) == 0:
            return False

        # 1D Collapse Calculation
        bow_wave_intensity = np.mean(e_mode_map)
        collision_val = np.round(bow_wave_intensity * 10000)
        net_value = collision_val / 10000.0
        
        # print(f"  [DEBUG] Collapse: Intensity={bow_wave_intensity:.4f}, NetValue={net_value:.4f}")
        
        # Judge (PAUSE) mechanism
        return net_value > self.threshold

    def filter_text_dataset(self, dataset: Dataset, text_column: str = "text") -> Dataset:
        """
        Applies the sieve to a standard datasets.Dataset.
        Uses the 1D Collapse verdict (Judge PAUSE) to gate entire chunks.
        """
        filtered_records = []
        for record in dataset:
            raw_text = record[text_column]
            raw_bytes = raw_text.encode("utf-8")
            
            # Apply 1D Collapse Verdict
            if self._get_collapsed_verdict(raw_bytes):
                filtered_records.append(record)
            # else: Skip high-entropy noise (Judge PAUSE)
                
        return Dataset.from_list(filtered_records)