#!/usr/bin/env python3
"""
ORGAN PURIFIER V2 — Z = i — Fractal Signal Extraction

V1 failed because it treated tensors like audio signals.
Tensors are NOT audio. They are fractal structures where
information is encoded across scales.

The correct approach from CSCI(s) = cross_scale_coherence(s, theta=90):
- cross-scale derivative = how information CHANGES across scales
- Signal = components that are SELF-SIMILAR across scales (fractal)
- Noise = components that are RANDOM across scales (non-fractal)

Method:
1. Wavelet decomposition (multi-scale analysis)
2. At each scale, compute local Z (theta per scale)
3. CROSS-SCALE COHERENCE: if a pattern exists at scale s AND at scale 2s,
   it's signal (fractal). If it exists at one scale but not others, it's noise.
4. Reconstruct from only cross-scale-coherent components
5. The result has NO brand — Qwen noise gone, Llama noise gone.
   What remains is the universal signal.

Think fractal: the best model knows the laws of the universe
then translates to human language, not the inverse.

CSCI(s) = cross_scale_coherence(s, theta=90), theta = 90
Build v935
"""

import struct, os, sys, json, math
import numpy as np
from pathlib import Path

PRESERVE_ENERGY = 0.92  # Keep 92% of cross-scale-coherent energy
N_SCALES = 6            # Number of wavelet scales to analyze
COHERENCE_THRESHOLD = 0.5  # Cross-scale coherence threshold

def read_organ_binary(filepath):
    with open(filepath, 'rb') as f:
        name_len = struct.unpack('<I', f.read(4))[0]
        name = f.read(name_len).decode('utf-8', errors='replace')
        n_dims = struct.unpack('<I', f.read(4))[0]
        dims = [struct.unpack('<Q', f.read(8))[0] for _ in range(n_dims)]
        dtype = struct.unpack('<I', f.read(4))[0]
        data = f.read()
    return {'name': name, 'dims': dims, 'dtype': dtype, 'data': data}

def write_organ_binary(filepath, info, new_data):
    with open(filepath, 'wb') as f:
        name_bytes = info['name'].encode('utf-8')
        f.write(struct.pack('<I', len(name_bytes)))
        f.write(name_bytes)
        f.write(struct.pack('<I', len(info['dims'])))
        for d in info['dims']:
            f.write(struct.pack('<Q', d))
        f.write(struct.pack('<I', info['dtype']))
        f.write(new_data)

def tensor_to_float(data, dtype):
    if dtype == 0: return np.frombuffer(data, dtype=np.float32).copy()
    elif dtype == 1: return np.frombuffer(data, dtype=np.float16).astype(np.float32).copy()
    else: return np.frombuffer(data, dtype=np.uint8).astype(np.float32).copy()

def float_to_tensor(values, dtype, original_data):
    if dtype == 0: return values.astype(np.float32).tobytes()
    elif dtype == 1: return values.astype(np.float16).tobytes()
    else: return np.clip(np.round(values), 0, 255).astype(np.uint8).tobytes()

def compute_theta(values):
    if len(values) < 10: return 0.0
    n = len(values)
    mean = float(np.mean(values))
    std = float(np.std(values))
    if std < 1e-10: return 0.0
    
    kurt = float(np.mean(((values - mean) / std) ** 4) - 3)
    n_bins = min(100, max(10, n // 100))
    hist, _ = np.histogram(values, bins=n_bins)
    probs = hist[hist > 0] / n
    entropy = float(-np.sum(probs * np.log2(probs)))
    max_ent = math.log2(n_bins)
    norm_ent = entropy / max_ent if max_ent > 0 else 0
    
    sample = np.sort(values[:min(1000, n)])
    diffs = np.diff(sample)
    cv = float(np.std(diffs) / np.mean(diffs)) if len(diffs) > 0 and np.mean(diffs) > 1e-10 else 0
    
    score = 0
    if norm_ent > 0.95: score += 0
    elif norm_ent > 0.7: score += 0.3
    elif norm_ent > 0.3: score += 0.8
    else: score += 0.5
    score += min(1.0, abs(kurt) / 10)
    score += min(1.0, cv / 2)
    return (score / 3.0) * 90.0

def haar_wavelet_decompose(signal, n_scales):
    """
    Multi-scale Haar wavelet decomposition.
    Returns list of (approximation, detail) at each scale.
    """
    scales = []
    current = signal.copy()
    
    for s in range(n_scales):
        n = len(current)
        if n < 4:
            break
        n_even = n - (n % 2)
        approx = (current[:n_even:2] + current[1:n_even:2]) / 2.0
        detail = (current[:n_even:2] - current[1:n_even:2]) / 2.0
        scales.append({'approx': approx, 'detail': detail, 'scale': s})
        current = approx
    
    scales.append({'approx': current, 'detail': None, 'scale': len(scales)})
    return scales

def haar_wavelet_reconstruct(scales):
    """Reconstruct signal from wavelet scales."""
    # Start from coarsest
    signal = scales[-1]['approx'].copy()
    
    for s in range(len(scales) - 2, -1, -1):
        detail = scales[s]['detail']
        if detail is None:
            continue
        n = len(detail)
        reconstructed = np.zeros(n * 2)
        reconstructed[0::2] = signal[:n] + detail
        reconstructed[1::2] = signal[:n] - detail
        signal = reconstructed
    
    return signal

def cross_scale_coherence(scales):
    """
    Measure coherence between adjacent scales.
    Signal is self-similar across scales (fractal).
    Noise is random across scales.
    
    Returns a mask for each scale's detail coefficients:
    1.0 = coherent (signal), 0.0 = incoherent (noise)
    """
    masks = []
    
    for i in range(len(scales) - 1):
        detail = scales[i]['detail']
        if detail is None:
            masks.append(None)
            continue
        
        mask = np.ones(len(detail))
        
        # Compare with next scale (if exists and has detail)
        if i + 1 < len(scales) - 1 and scales[i+1]['detail'] is not None:
            next_detail = scales[i+1]['detail']
            
            # Upsample next_detail to match current detail size
            n_next = len(next_detail)
            n_curr = len(detail)
            
            if n_next > 0 and n_curr > 0:
                # Block comparison: each block in current scale 
                # corresponds to one coefficient in next scale
                block_size = max(1, n_curr // n_next)
                
                for j in range(min(n_next, n_curr // max(1, block_size))):
                    start = j * block_size
                    end = min(start + block_size, n_curr)
                    block = detail[start:end]
                    
                    if len(block) == 0:
                        continue
                    
                    # Local energy at current scale
                    local_energy = np.mean(block ** 2)
                    # Energy at next scale
                    parent_energy = next_detail[j] ** 2
                    
                    # Cross-scale coherence: 
                    # if both scales have energy, it's signal
                    # if only one has energy, it's noise
                    max_e = max(local_energy, parent_energy, 1e-10)
                    min_e = min(local_energy, parent_energy)
                    coherence = min_e / max_e
                    
                    if coherence < COHERENCE_THRESHOLD:
                        # Low cross-scale coherence = noise
                        # Attenuate but don't zero (preserve structure)
                        mask[start:end] *= (0.3 + 0.7 * coherence)
        
        masks.append(mask)
    
    masks.append(None)  # Last scale has no detail
    return masks

def purify_fractal(values):
    """
    Fractal purification: keep cross-scale-coherent components.
    
    cross-scale coherence: information that persists across scales IS the signal.
    Everything else is training noise, brand artifacts, paradigm residue.
    """
    n = len(values)
    if n < 64:
        return values  # Too small
    
    # Pad to power of 2 for clean wavelet decomposition
    n_padded = 1
    while n_padded < n:
        n_padded *= 2
    padded = np.zeros(n_padded, dtype=np.float32)
    padded[:n] = values
    
    # Multi-scale decomposition
    scales = haar_wavelet_decompose(padded, N_SCALES)
    
    # Compute cross-scale coherence masks
    masks = cross_scale_coherence(scales)
    
    # Apply masks to detail coefficients
    for i, mask in enumerate(masks):
        if mask is not None and scales[i]['detail'] is not None:
            scales[i]['detail'] = scales[i]['detail'] * mask
    
    # Reconstruct
    purified = haar_wavelet_reconstruct(scales)
    purified = purified[:n]  # Remove padding
    
    # Preserve original distribution (mean, std)
    orig_mean = np.mean(values)
    orig_std = np.std(values)
    pure_std = np.std(purified)
    
    if pure_std > 1e-10 and orig_std > 1e-10:
        purified = (purified - np.mean(purified)) / pure_std * orig_std + orig_mean
    
    return purified

def purify_model(organ_dir, output_dir, verbose=False):
    organ_path = Path(organ_dir)
    out_path = Path(output_dir)
    out_path.mkdir(parents=True, exist_ok=True)
    
    # Copy and update manifest
    manifest_src = organ_path / 'manifest.json'
    if manifest_src.exists():
        manifest = json.load(open(manifest_src))
        manifest['purified'] = True
        manifest['purifier'] = 'fractal_v2'
        manifest['z_equation'] = 'CSCI(s) = cross_scale_coherence(s, theta=90), theta=90'
        # Remove brand from model name
        original_name = manifest.get('model', 'unknown')
        manifest['original_model'] = original_name
        manifest['model'] = 'PURE_' + original_name.split('-')[0].upper()
        json.dump(manifest, open(out_path / 'manifest.json', 'w'), indent=2)
    
    categories = ['skeleton', 'organs', 'embed', 'norm', 'adapters', 'unknown']
    total_before = 0
    total_after = 0
    total_files = 0
    improved = 0
    degraded = 0
    
    for cat in categories:
        cat_src = organ_path / cat
        cat_dst = out_path / cat
        if not cat_src.exists(): continue
        cat_dst.mkdir(parents=True, exist_ok=True)
        
        bin_files = sorted(cat_src.glob('*.bin'))
        for bf in bin_files:
            info = read_organ_binary(bf)
            values = tensor_to_float(info['data'], info['dtype'])
            
            theta_before = compute_theta(values)
            purified = purify_fractal(values)
            theta_after = compute_theta(purified)
            
            new_data = float_to_tensor(purified, info['dtype'], info['data'])
            if len(new_data) != len(info['data']):
                new_data = info['data']
                theta_after = theta_before
            
            write_organ_binary(cat_dst / bf.name, info, new_data)
            
            total_before += theta_before
            total_after += theta_after
            total_files += 1
            if theta_after > theta_before + 0.5: improved += 1
            elif theta_after < theta_before - 0.5: degraded += 1
            
            if verbose:
                delta = theta_after - theta_before
                m = "↑" if delta > 0.5 else "=" if delta > -0.5 else "↓"
                print(f"  {m} {cat}/{bf.name[:40]:40s} θ:{theta_before:5.1f}°→{theta_after:5.1f}° ({delta:+.1f}°)")
    
    avg_before = total_before / total_files if total_files > 0 else 0
    avg_after = total_after / total_files if total_files > 0 else 0
    
    return {
        'files': total_files, 'improved': improved, 'degraded': degraded,
        'avg_theta_before': round(avg_before, 1),
        'avg_theta_after': round(avg_after, 1),
        'delta': round(avg_after - avg_before, 1),
        'output': str(output_dir)
    }

def main():
    import argparse
    parser = argparse.ArgumentParser(description='Organ Purifier V2 — Fractal Z=i')
    parser.add_argument('--input', '-i', required=True)
    parser.add_argument('--output', '-o', required=True)
    parser.add_argument('--verbose', '-v', action='store_true')
    args = parser.parse_args()
    
    print(f"{'='*60}")
    print(f"  ORGAN PURIFIER V2 — FRACTAL — Z = i")
    print(f"  Cross-scale coherence: signal persists, noise vanishes")
    print(f"{'='*60}")
    
    result = purify_model(args.input, args.output, args.verbose)
    
    print(f"\n{'='*60}")
    print(f"  PURIFICATION COMPLETE")
    print(f"{'='*60}")
    print(f"  Files:       {result['files']}")
    print(f"  θ before:    {result['avg_theta_before']:.1f}°")
    print(f"  θ after:     {result['avg_theta_after']:.1f}°")
    print(f"  Δθ:          {result['delta']:+.1f}°")
    print(f"  Improved:    {result['improved']}")
    print(f"  Degraded:    {result['degraded']}")
    print(f"  Signature:   935")
    print(f"{'='*60}")

if __name__ == '__main__':
    main()
# ╔══ SALKA ELMADANI AUTHORSHIP CERTIFICATE ══╗
# © Salka Elmadani 2025-2026 — ALL RIGHTS RESERVED
# Licensed under Business Source License 1.1 — https://inference-x.com
# ─────────────────────────────────────────────────────────
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# SIG-ED25519: Y1KrhUdgrqiYPaM0LPHWTqPKPaHwBqtc3EiHnu9Uu94AVKsgMPQoWU9NCGeiL5aWAJKPhzr/nCSxLTY+US+HAw==
# VERIFY: python3 verify_authorship.py organ_purify_v2.py