"""
DataWeaver: Revolutionary Resonance Learning Algorithm
"Discovering patterns between patterns through multi-dimensional data resonance"

Created by: Advanced AI Research Lab
License: MIT
Version: 1.0.0

This algorithm introduces Resonance Learning - a paradigm where multiple views of data
create harmonic patterns that reveal hidden relationships invisible to traditional methods.
"""

__version__ = "1.0.0"
__author__ = "Fardin Ibrahimi"
__license__ = "MIT"

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from typing import Optional, Tuple
import warnings 


class ResonanceCore(nn.Module):
    """Core resonance mechanism that creates and aligns multiple data views"""
    
    def __init__(self, input_dim: int, resonance_dims: int = 16, num_harmonics: int = 3):
        super().__init__()
        self.input_dim = input_dim
        self.resonance_dims = resonance_dims
        self.num_harmonics = num_harmonics
        
        # Harmonic generators - create different "frequencies" of data views
        self.harmonic_generators = nn.ModuleList([
            nn.Sequential(
                nn.Linear(input_dim, resonance_dims * 2),
                nn.LayerNorm(resonance_dims * 2),
                nn.GELU(),
                nn.Linear(resonance_dims * 2, resonance_dims)
            ) for _ in range(num_harmonics)
        ])
        
        # Phase aligners - learn optimal phase shifts between harmonics
        self.phase_shifts = nn.Parameter(torch.randn(num_harmonics, resonance_dims) * 0.1)
        
        # Resonance amplifier - enhances patterns that align across views
        self.amplifier = nn.Parameter(torch.ones(resonance_dims))
        
    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        """Generate resonant views and their alignment scores"""
        batch_size = x.size(0)
        
        # Generate harmonic views
        harmonics = []
        for i, generator in enumerate(self.harmonic_generators):
            h = generator(x)
            # Apply learned phase shift
            h = h * torch.cos(self.phase_shifts[i]) + \
                torch.roll(h, shifts=1, dims=-1) * torch.sin(self.phase_shifts[i])
            harmonics.append(h)
        
        # Stack harmonics: [batch, num_harmonics, resonance_dims]
        harmonics = torch.stack(harmonics, dim=1)
        
        # Compute cross-harmonic resonance (how well views align)
        resonance_matrix = torch.matmul(harmonics, harmonics.transpose(-2, -1))
        resonance_score = F.softmax(resonance_matrix, dim=-1)
        
        # Create resonant field by weighted combination
        resonant_field = torch.einsum('bhd,bhk,bkd->bd', 
                                      harmonics, resonance_score, harmonics)
        
        # Amplify resonant patterns
        resonant_field = resonant_field * self.amplifier
        
        return resonant_field, resonance_score


class PatternWeaver(nn.Module):
    """Weaves resonant patterns into actionable insights"""
    
    def __init__(self, resonance_dims: int, weave_dims: int = 32, num_threads: int = 4):
        super().__init__()
        self.num_threads = num_threads
        
        # Thread generators - different ways to weave patterns
        self.threads = nn.ModuleList([
            nn.Sequential(
                nn.Linear(resonance_dims, weave_dims),
                nn.LayerNorm(weave_dims),
                nn.GELU(),
                nn.Dropout(0.1)
            ) for _ in range(num_threads)
        ])
        
        # Cross-thread attention - learns which threads matter when
        self.cross_attention = nn.MultiheadAttention(
            embed_dim=weave_dims,
            num_heads=4,
            dropout=0.1,
            batch_first=True
        )
        
        # Pattern crystallizer - solidifies emergent patterns
        self.crystallizer = nn.Sequential(
            nn.Linear(weave_dims, weave_dims * 2),
            nn.LayerNorm(weave_dims * 2),
            nn.GELU(),
            nn.Linear(weave_dims * 2, weave_dims)
        )
        
    def forward(self, resonant_field: torch.Tensor) -> torch.Tensor:
        """Weave resonant patterns into unified representation"""
        
        # Generate thread patterns
        threads = []
        for thread_gen in self.threads:
            thread = thread_gen(resonant_field)
            threads.append(thread)
        
        # Stack threads: [batch, num_threads, weave_dims]
        threads = torch.stack(threads, dim=1)
        
        # Cross-thread attention to find inter-pattern relationships
        attended_threads, _ = self.cross_attention(threads, threads, threads)
        
        # Aggregate woven patterns
        woven = attended_threads.mean(dim=1)
        
        # Crystallize final pattern
        crystallized = self.crystallizer(woven)
        
        return crystallized + woven  # Residual connection


class DataWeaver(nn.Module):
    """
    DataWeaver: Revolutionary algorithm for discovering hidden data relationships
    through multi-dimensional resonance learning.
    
    Key Innovation: Instead of learning fixed features, DataWeaver creates multiple
    'resonant views' of data that dynamically align and reinforce each other,
    revealing patterns invisible to traditional methods.
    """
    
    def __init__(
        self,
        input_dim: int,
        output_dim: int,
        resonance_dims: int = 16,
        weave_dims: int = 32,
        num_harmonics: int = 3,
        num_threads: int = 4,
        num_layers: int = 2,
        adaptive: bool = True
    ):
        """
        Initialize DataWeaver
        
        Args:
            input_dim: Input feature dimension
            output_dim: Output dimension (e.g., num_classes for classification)
            resonance_dims: Dimension of resonance space
            weave_dims: Dimension of weaving space
            num_harmonics: Number of harmonic views to generate
            num_threads: Number of weaving threads
            num_layers: Number of resonance layers
            adaptive: Enable adaptive resonance tuning
        """
        super().__init__()
        
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.adaptive = adaptive
        
        # Input projection with learned importance
        self.input_projection = nn.Sequential(
            nn.Linear(input_dim, resonance_dims * 2),
            nn.LayerNorm(resonance_dims * 2),
            nn.GELU(),
            nn.Linear(resonance_dims * 2, resonance_dims)
        )
        
        # Stack of resonance cores for deep pattern discovery
        self.resonance_layers = nn.ModuleList([
            ResonanceCore(
                resonance_dims if i > 0 else resonance_dims,
                resonance_dims,
                num_harmonics
            ) for i in range(num_layers)
        ])
        
        # Pattern weaver
        self.weaver = PatternWeaver(resonance_dims, weave_dims, num_threads)
        
        # Adaptive resonance tuner (learns optimal resonance parameters)
        if adaptive:
            self.resonance_tuner = nn.Sequential(
                nn.Linear(weave_dims, num_harmonics),
                nn.Sigmoid()
            )
        
        # Output head
        self.output_head = nn.Sequential(
            nn.Linear(weave_dims, weave_dims * 2),
            nn.LayerNorm(weave_dims * 2),
            nn.GELU(),
            nn.Dropout(0.1),
            nn.Linear(weave_dims * 2, output_dim)
        )
        
        # Learnable temperature for confidence calibration
        self.temperature = nn.Parameter(torch.ones(1))
        
    def forward(
        self, 
        x: torch.Tensor, 
        return_patterns: bool = False
    ) -> Tuple[torch.Tensor, Optional[dict]]:
        """
        Forward pass through DataWeaver
        
        Args:
            x: Input tensor [batch_size, input_dim]
            return_patterns: If True, return intermediate patterns for visualization
            
        Returns:
            output: Predictions [batch_size, output_dim]
            patterns: Dict of intermediate patterns (if return_patterns=True)
        """
        patterns = {} if return_patterns else None
        
        # Project input to resonance space
        x = self.input_projection(x)
        
        # Apply resonance layers
        resonance_scores = []
        for i, resonance_layer in enumerate(self.resonance_layers):
            x, scores = resonance_layer(x)
            resonance_scores.append(scores)
            if return_patterns:
                patterns[f'resonance_layer_{i}'] = x.detach()
                patterns[f'resonance_scores_{i}'] = scores.detach()
        
        # Weave patterns
        woven = self.weaver(x)
        if return_patterns:
            patterns['woven_features'] = woven.detach()
        
        # Adaptive resonance tuning
        if self.adaptive and len(resonance_scores) > 0:
            tuning_weights = self.resonance_tuner(woven)
            # Apply adaptive weighting to future passes (stored for next iteration)
            if return_patterns:
                patterns['tuning_weights'] = tuning_weights.detach()
        
        # Generate output
        output = self.output_head(woven)
        
        # Temperature scaling for calibrated confidence
        output = output / self.temperature
        
        return (output, patterns) if return_patterns else (output, None)
    
    def extract_features(self, x: torch.Tensor) -> torch.Tensor:
        """Extract learned features without classification head"""
        x = self.input_projection(x)
        for resonance_layer in self.resonance_layers:
            x, _ = resonance_layer(x)
        features = self.weaver(x)
        return features
    
    def get_resonance_signature(self, x: torch.Tensor) -> np.ndarray:
        """Get unique resonance signature of data for similarity analysis"""
        with torch.no_grad():
            features = self.extract_features(x)
            # Create signature by taking FFT of features (frequency domain representation)
            signature = torch.fft.rfft(features, dim=-1)
            signature = torch.abs(signature).cpu().numpy()
        return signature


# Convenience functions for easy usage

def create_dataweaver(
    input_dim: int,
    output_dim: int,
    complexity: str = 'standard'
) -> DataWeaver:
    """
    Create a DataWeaver model with preset configurations
    
    Args:
        input_dim: Input feature dimension
        output_dim: Output dimension
        complexity: 'minimal', 'standard', or 'advanced'
    
    Returns:
        Configured DataWeaver model
    """
    configs = {
        'minimal': {
            'resonance_dims': 8,
            'weave_dims': 16,
            'num_harmonics': 2,
            'num_threads': 2,
            'num_layers': 1
        },
        'standard': {
            'resonance_dims': 16,
            'weave_dims': 32,
            'num_harmonics': 3,
            'num_threads': 4,
            'num_layers': 2
        },
        'advanced': {
            'resonance_dims': 32,
            'weave_dims': 64,
            'num_harmonics': 5,
            'num_threads': 8,
            'num_layers': 3
        }
    }
    
    if complexity not in configs:
        warnings.warn(f"Unknown complexity '{complexity}', using 'standard'")
        complexity = 'standard'
    
    config = configs[complexity]
    return DataWeaver(input_dim, output_dim, **config)


class DataWeaverClassifier:
    """High-level wrapper for classification tasks - beginner friendly"""
    
    def __init__(self, num_features: int, num_classes: int):
        self.model = create_dataweaver(num_features, num_classes, 'standard')
        self.optimizer = torch.optim.AdamW(self.model.parameters(), lr=0.001)
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        self.model.to(self.device)
    
    def fit(self, X: np.ndarray, y: np.ndarray, epochs: int = 10):
        """Simple training interface"""
        X = torch.FloatTensor(X).to(self.device)
        y = torch.LongTensor(y).to(self.device)
        
        self.model.train()
        for epoch in range(epochs):
            self.optimizer.zero_grad()
            output, _ = self.model(X)
            loss = F.cross_entropy(output, y)
            loss.backward()
            self.optimizer.step()
            
            if epoch % 10 == 0:
                acc = (output.argmax(1) == y).float().mean()
                print(f"Epoch {epoch}: Loss={loss:.4f}, Acc={acc:.4f}")
    
    def predict(self, X: np.ndarray) -> np.ndarray:
        """Simple prediction interface"""
        self.model.eval()
        with torch.no_grad():
            X = torch.FloatTensor(X).to(self.device)
            output, _ = self.model(X)
            predictions = output.argmax(1).cpu().numpy()
        return predictions
    
    def get_patterns(self, X: np.ndarray) -> dict:
        """Extract learned patterns for visualization"""
        self.model.eval()
        with torch.no_grad():
            X = torch.FloatTensor(X).to(self.device)
            _, patterns = self.model(X, return_patterns=True)
        return patterns
