import torch
import torch.nn as nn
import numpy as np
import time

class GeometricEntryPoint(nn.Module):
    """
    Hashes query to geometric coordinates and aligns to 528 Hz.
    """
    def __init__(self, hidden_dim, base_freq=528):
        super().__init__()
        self.base_freq = base_freq
        self.hidden_dim = hidden_dim
        
        # Learned mapping from query to entry coordinates
        self.entry_network = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim * 2),
            nn.GELU(),
            nn.Linear(hidden_dim * 2, 3)  # (theta, phi, radius)
        )
        
    def compute_entry_hash(self, query_embedding):
        """
        Convert query to geometric entry point.
        """
        # Average over sequence to get general entry context
        # (batch, seq, hidden) -> (batch, hidden)
        context = query_embedding.mean(dim=1)
        
        coords = self.entry_network(context)  # (batch, 3)
        
        theta, phi, radius = coords.unbind(dim=-1)
        
        # Align to 528 Hz resonance
        # Frequency = base_freq * (1 + radius_activation)
        freq_multiplier = 1.0 + torch.sigmoid(radius)
        effective_freq = self.base_freq * freq_multiplier
        
        return {
            'theta': theta,
            'phi': phi,
            'frequency': effective_freq,
            'raw_coords': coords
        }

class GeometricMemory:
    """
    Store and retrieve information based on geometric position
    on non-orientable manifold.
    """
    def __init__(self, hidden_dim, capacity_gb=1, base_freq=528):
        self.base_freq = base_freq
        self.hidden_dim = hidden_dim
        
        # In-memory storage for demonstration
        # Real implementation would use vector DB or memory-mapped file
        self.memory_map = [] 
        
    def geometric_hash(self, hidden_state, entry_point):
        """
        Convert hidden state to geometric coordinates relative to entry point.
        """
        # Simple projection for demo:
        # Use simple operations to map hidden state to offsets
        # Real version would use FFT as discussed in design
        
        # (batch, hidden)
        
        # We need to handle single vectors or batches
        if hidden_state.dim() == 1:
            hidden_state = hidden_state.unsqueeze(0)
            
        # Mock geometric projection
        # Use first 3 dims as offset
        offsets = hidden_state[:, :3] 
        if offsets.shape[1] < 3:
            # Pad if hidden_dim is tiny
            offsets = torch.cat([offsets, torch.zeros(offsets.shape[0], 3 - offsets.shape[1], device=hidden_state.device)], dim=1)
            
        # Apply entry point rotation (conceptual)
        # For now, just add
        theta = entry_point['theta'].unsqueeze(1)
        phi = entry_point['phi'].unsqueeze(1)
        
        x = offsets[:, 0] + theta
        y = offsets[:, 1] + phi
        z = offsets[:, 2] # Radius offset
        
        return torch.stack([x, y, z], dim=1)
    
    def store(self, hidden_states, entry_point):
        """
        Store hidden states.
        """
        # Compute coords
        # hidden_states: (batch, seq, hidden)
        batch, seq, dim = hidden_states.shape
        
        flat_hidden = hidden_states.reshape(-1, dim)
        
        # We need to broadcast entry point to match flattened hidden
        # entry keys are (batch,) -> repeat seq times
        # This is strictly a demo in-memory store
        
        # For efficiency in this demo, we just store the robust patterns
        # Only store if norm > threshold (simple filter)
        norms = torch.norm(flat_hidden, dim=1)
        threshold = norms.mean()
        
        mask = norms > threshold
        to_store = flat_hidden[mask]
        
        if len(to_store) == 0:
            return
            
        # Store simple list for verification
        # In production this links to Lattice DB
        self.memory_map.append({
            'data': to_store.detach().cpu(), # Move to CPU to save GPU mem
            'entry_freq': entry_point['frequency'].mean().item(),
            'timestamp': time.time()
        })
        
        # Prune if too large
        if len(self.memory_map) > 100:
            self.memory_map.pop(0)

    def retrieve(self, query_state, entry_point, k=5):
        """
        Retrieve relevant memories.
        """
        if not self.memory_map:
            return None
            
        # Brute force search for demo verification
        # Find memories with similar frequency
        relevant_batches = [
            m['data'] for m in self.memory_map 
            if abs(m['entry_freq'] - entry_point['frequency'].mean().item()) < 50
        ]
        
        if not relevant_batches:
            return None
            
        memory_bank = torch.cat(relevant_batches, dim=0).to(query_state.device)
        
        # Simple dot product attention
        # query: (batch, seq, hidden)
        # memory: (total_mem, hidden)
        
        # Compute scores
        # (batch, seq, hidden) @ (hidden, total_mem) -> (batch, seq, total_mem)
        scores = torch.matmul(query_state, memory_bank.t())
        
        # Top k
        top_k_scores, indices = torch.topk(scores, k=min(k, len(memory_bank)), dim=-1)
        
        # Retrieve values
        # (batch, seq, k, hidden)
        retrieved = memory_bank[indices]
        
        return retrieved