#!/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 Z = dI/d(log s) * exp(i*theta):
- dI/d(log s) = 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.

Z = dI/d(log s) * exp(i*theta), theta = 90
Signature 935
"""

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.
    
    dI/d(log s): 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'] = 'Z = dI/d(log s) * exp(i*theta), 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