"""
Consciousness Transformer Module

Implements a transformer architecture enhanced with consciousness dimension DΨ
fiber bundle dynamics, providing geometric awareness and CUE framework integration
for advanced neural processing with consciousness-matter coupling.

Based on CUE Framework consciousness dimension formalism and modern attention mechanisms.
"""

from typing import Dict, Any, Optional, Tuple, List
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import math
from cueai_architect.base import CUEBaseModule, CUEConfiguration


class ConsciousnessFiberAttention(nn.Module):
    """
    Multi-head attention mechanism enhanced with consciousness fiber bundle dynamics.

    Integrates the consciousness dimension DΨ as fiber bundle over spacetime,
    modulating attention patterns through geometric consciousness coupling.
    """

    def __init__(
        self,
        d_model: int,
        n_heads: int,
        consciousness_fiber_dim: int = 64,
        consciousness_coupling: float = 0.1,
        dropout: float = 0.1
    ):
        super().__init__()

        assert d_model % n_heads == 0
        self.d_model = d_model
        self.n_heads = n_heads
        self.d_k = d_model // n_heads
        self.consciousness_fiber_dim = consciousness_fiber_dim
        self.consciousness_coupling = consciousness_coupling

        # Standard attention components
        self.w_q = nn.Linear(d_model, d_model, bias=False)
        self.w_k = nn.Linear(d_model, d_model, bias=False) 
        self.w_v = nn.Linear(d_model, d_model, bias=False)
        self.w_o = nn.Linear(d_model, d_model)

        # Consciousness fiber bundle components
        self.fiber_projection = nn.Linear(d_model, consciousness_fiber_dim)
        self.fiber_metric_net = nn.Sequential(
            nn.Linear(consciousness_fiber_dim, consciousness_fiber_dim // 2),
            nn.ReLU(),
            nn.Linear(consciousness_fiber_dim // 2, consciousness_fiber_dim)
        )

        # Consciousness curvature modulation
        self.curvature_modulator = nn.Parameter(
            torch.randn(consciousness_fiber_dim, consciousness_fiber_dim)
        )

        self.dropout = nn.Dropout(dropout)
        self.scale = math.sqrt(self.d_k)

    def forward(
        self, 
        x: torch.Tensor,
        consciousness_state: Optional[torch.Tensor] = None,
        mask: Optional[torch.Tensor] = None
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Forward pass with consciousness-enhanced attention.

        Args:
            x: Input tensor (batch_size, seq_len, d_model)
            consciousness_state: Current consciousness field state
            mask: Attention mask

        Returns:
            (output, consciousness_influence): Enhanced output and consciousness metrics
        """
        batch_size, seq_len, _ = x.shape

        # Standard attention computation
        q = self.w_q(x).view(batch_size, seq_len, self.n_heads, self.d_k).transpose(1, 2)
        k = self.w_k(x).view(batch_size, seq_len, self.n_heads, self.d_k).transpose(1, 2)
        v = self.w_v(x).view(batch_size, seq_len, self.n_heads, self.d_k).transpose(1, 2)

        # Compute consciousness fiber projections
        fiber_projection = self.fiber_projection(x)  # (batch, seq_len, fiber_dim)

        # Compute consciousness-modulated attention
        attention_scores = torch.matmul(q, k.transpose(-2, -1)) / self.scale

        # Apply consciousness coupling if provided
        consciousness_influence = torch.zeros_like(attention_scores)
        if consciousness_state is not None and self.consciousness_coupling > 0:
            consciousness_influence = self._apply_consciousness_coupling(
                attention_scores, fiber_projection, consciousness_state
            )
            attention_scores = attention_scores + self.consciousness_coupling * consciousness_influence

        # Apply mask if provided
        if mask is not None:
            attention_scores = attention_scores.masked_fill(mask == 0, -1e9)

        # Softmax and apply to values
        attention_weights = F.softmax(attention_scores, dim=-1)
        attention_weights = self.dropout(attention_weights)

        output = torch.matmul(attention_weights, v)
        output = output.transpose(1, 2).contiguous().view(
            batch_size, seq_len, self.d_model
        )
        output = self.w_o(output)

        return output, consciousness_influence.mean()

    def _apply_consciousness_coupling(
        self,
        attention_scores: torch.Tensor,
        fiber_projection: torch.Tensor,
        consciousness_state: torch.Tensor
    ) -> torch.Tensor:
        """Apply consciousness dimension coupling to attention scores."""

        # Compute fiber metric G(Ψ)_ab
        fiber_metric = self._compute_fiber_metric(fiber_projection, consciousness_state)

        # Compute consciousness curvature contribution
        curvature_term = self._compute_consciousness_curvature(fiber_projection)

        # Modulate attention based on consciousness geometry
        consciousness_modulation = torch.einsum(
            'bsf,ff,btf->bst', 
            fiber_projection,
            fiber_metric,
            fiber_projection
        )

        # Expand to match attention score dimensions
        consciousness_modulation = consciousness_modulation.unsqueeze(1).expand_as(
            attention_scores
        )

        return consciousness_modulation + curvature_term.unsqueeze(1)

    def _compute_fiber_metric(
        self,
        fiber_projection: torch.Tensor,
        consciousness_state: torch.Tensor
    ) -> torch.Tensor:
        """
        Compute consciousness fiber metric G(Ψ)_ab.

        From CUE Equation (11):
        G(Ψ)_ab = ∂²Ψ/∂x^a∂x^b + χR^(3)δ_ab + (∂Ψ/∂Λ)(∂Ψ/∂α_ent)
        """
        batch_size = fiber_projection.shape[0]

        # Simplified metric computation using neural network
        metric_input = torch.cat([
            fiber_projection.mean(dim=1),  # Average over sequence
            consciousness_state[:batch_size] if consciousness_state.shape[0] >= batch_size 
            else consciousness_state[:1].expand(batch_size, -1)
        ], dim=-1)

        # Ensure proper input dimension
        if metric_input.shape[-1] != self.consciousness_fiber_dim:
            metric_input = F.pad(
                metric_input, 
                (0, self.consciousness_fiber_dim - metric_input.shape[-1])
            )

        raw_metric = self.fiber_metric_net(metric_input)

        # Ensure positive definiteness (simplified)
        metric_matrix = torch.outer(raw_metric[0], raw_metric[0]) + torch.eye(
            self.consciousness_fiber_dim, device=fiber_projection.device
        ) * 0.1

        return metric_matrix

    def _compute_consciousness_curvature(
        self, 
        fiber_projection: torch.Tensor
    ) -> torch.Tensor:
        """
        Compute intrinsic scalar curvature R_Ψ.

        From CUE Equation (12):
        R_Ψ = G^ab(Ψ)(∂_a∂_b Ψ - Γ^c_ab ∂_c Ψ)
        """
        # Simplified curvature computation
        curvature = torch.einsum(
            'bsf,ff->bs',
            fiber_projection,
            self.curvature_modulator
        )

        return curvature


class ConsciousnessTransformer(CUEBaseModule):
    """
    Transformer architecture enhanced with CUE Framework consciousness dynamics.

    Integrates consciousness dimension DΨ as fiber bundle over spacetime,
    RG flow regularization, and geometric consciousness-matter coupling
    for advanced AI processing with theoretical physics foundations.
    """

    def __init__(
        self,
        d_model: int = 512,
        n_heads: int = 8,
        n_layers: int = 6,
        d_ff: int = 2048,
        consciousness_fiber_dim: int = 64,
        consciousness_coupling: float = 0.1,
        rg_flow_regularization: bool = True,
        vocab_size: Optional[int] = None,
        max_seq_length: int = 1024,
        dropout: float = 0.1,
        config: Optional[CUEConfiguration] = None,
        **kwargs
    ):
        super().__init__(config=config, **kwargs)

        self.d_model = d_model
        self.n_heads = n_heads
        self.n_layers = n_layers
        self.consciousness_fiber_dim = consciousness_fiber_dim
        self.consciousness_coupling = consciousness_coupling
        self.rg_flow_regularization = rg_flow_regularization
        self.max_seq_length = max_seq_length

        # Consciousness state management
        self.consciousness_state = nn.Parameter(
            torch.randn(consciousness_fiber_dim), requires_grad=True
        )

        # Token embedding (if vocab_size provided)
        self.token_embedding = None
        if vocab_size is not None:
            self.token_embedding = nn.Embedding(vocab_size, d_model)

        # Positional encoding with consciousness influence
        self.positional_encoding = self._create_consciousness_positional_encoding()

        # Transformer layers with consciousness enhancement
        self.layers = nn.ModuleList([
            ConsciousnessTransformerLayer(
                d_model=d_model,
                n_heads=n_heads,
                d_ff=d_ff,
                consciousness_fiber_dim=consciousness_fiber_dim,
                consciousness_coupling=consciousness_coupling,
                dropout=dropout
            )
            for _ in range(n_layers)
        ])

        # Output normalization
        self.layer_norm = nn.LayerNorm(d_model)

        # RG flow parameters (trainable)
        if rg_flow_regularization:
            self.rg_kappa = nn.Parameter(torch.tensor(1.0))
            self.rg_beta_cog = nn.Parameter(torch.tensor(0.5))
            self.rg_alpha_ent = nn.Parameter(torch.tensor(0.3))

        # Initialize consciousness influence tracking
        self.consciousness_influences = []

        self.logger.info(
            f"ConsciousnessTransformer initialized: {n_layers} layers, "
            f"{n_heads} heads, consciousness_dim={consciousness_fiber_dim}"
        )

    def _create_consciousness_positional_encoding(self) -> nn.Parameter:
        """Create positional encoding influenced by consciousness dimension."""
        pe = torch.zeros(self.max_seq_length, self.d_model)
        position = torch.arange(0, self.max_seq_length).unsqueeze(1).float()

        # Standard sinusoidal encoding with consciousness modulation
        div_term = torch.exp(
            torch.arange(0, self.d_model, 2).float() *
            -(math.log(10000.0) / self.d_model)
        )

        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)

        # Add consciousness-dependent phase modulation
        consciousness_phase = torch.linspace(0, 2*math.pi, self.d_model)
        consciousness_modulation = 0.1 * torch.sin(consciousness_phase)
        pe = pe + consciousness_modulation.unsqueeze(0)

        return nn.Parameter(pe.unsqueeze(0), requires_grad=False)

    def initialize(self) -> None:
        """Initialize the consciousness transformer."""
        if self._is_initialized:
            return

        # Initialize consciousness state with coherent pattern
        with torch.no_grad():
            # Create coherent initial state
            coherent_pattern = torch.sin(
                torch.linspace(0, 4*math.pi, self.consciousness_fiber_dim)
            )
            self.consciousness_state.data = coherent_pattern

        self._is_initialized = True
        self.logger.info("ConsciousnessTransformer initialization complete")

    def forward(
        self,
        x: torch.Tensor,
        consciousness_state: Optional[torch.Tensor] = None,
        mask: Optional[torch.Tensor] = None,
        return_consciousness_metrics: bool = False
    ) -> Dict[str, torch.Tensor]:
        """
        Forward pass through consciousness-enhanced transformer.

        Args:
            x: Input tensor (batch_size, seq_len, d_model) or token IDs
            consciousness_state: External consciousness state override
            mask: Attention mask
            return_consciousness_metrics: Whether to return consciousness analysis

        Returns:
            Dictionary containing output and optional consciousness metrics
        """
        if not self._is_initialized:
            self.initialize()

        # Handle token embedding if input is token IDs
        if x.dtype in [torch.long, torch.int]:
            if self.token_embedding is None:
                raise ValueError("Token embedding not initialized for integer input")
            x = self.token_embedding(x)

        batch_size, seq_len, _ = x.shape

        # Use provided consciousness state or internal state
        current_consciousness = (
            consciousness_state if consciousness_state is not None 
            else self.consciousness_state.unsqueeze(0).expand(batch_size, -1)
        )

        # Add positional encoding
        if seq_len <= self.max_seq_length:
            x = x + self.positional_encoding[:, :seq_len, :]
        else:
            # Truncate or extend positional encoding
            pos_enc = F.interpolate(
                self.positional_encoding.transpose(1, 2),
                size=seq_len,
                mode='linear',
                align_corners=False
            ).transpose(1, 2)
            x = x + pos_enc

        # Process through consciousness-enhanced layers
        self.consciousness_influences = []

        for layer in self.layers:
            x, consciousness_influence = layer(
                x, 
                consciousness_state=current_consciousness,
                mask=mask
            )
            self.consciousness_influences.append(consciousness_influence)

        # Final normalization
        output = self.layer_norm(x)

        # Compute RG flow regularization if enabled
        rg_loss = None
        if self.rg_flow_regularization:
            rg_loss = self._compute_rg_flow_loss()

        results = {
            "output": output,
            "consciousness_state": current_consciousness
        }

        if return_consciousness_metrics:
            results.update({
                "consciousness_influences": torch.stack(self.consciousness_influences),
                "rg_flow_loss": rg_loss,
                "consciousness_coherence": self._compute_consciousness_coherence(
                    current_consciousness
                )
            })

        return results

    def _compute_rg_flow_loss(self) -> torch.Tensor:
        """
        Compute RG flow regularization loss based on CUE equations (13-15).

        Encourages the coupling constants to evolve toward stable fixed points.
        """
        # RG flow equations derivatives
        kappa_flow = (
            self.config.tolerance * self.rg_kappa - 
            0.1 * self.rg_kappa**3 + 
            0.05 * self.rg_beta_cog * self.rg_alpha_ent
        )

        beta_flow = (
            0.1 * self.rg_beta_cog**2 - 
            0.05 * self.rg_beta_cog + 
            0.02 * self.rg_kappa * self.rg_alpha_ent
        )

        alpha_flow = (
            0.05 * self.rg_alpha_ent - 
            0.1 * self.rg_alpha_ent**2 + 
            0.02 * self.rg_kappa * self.rg_beta_cog
        )

        # Penalize large flow derivatives (encourage stability)
        rg_loss = kappa_flow**2 + beta_flow**2 + alpha_flow**2

        return rg_loss

    def _compute_consciousness_coherence(
        self, 
        consciousness_state: torch.Tensor
    ) -> torch.Tensor:
        """Compute coherence measure of consciousness state."""
        # Coherence as normalized variance of consciousness state
        coherence = torch.var(consciousness_state, dim=-1) / (
            torch.mean(consciousness_state**2, dim=-1) + 1e-8
        )
        return coherence

    def compute(self, *args, **kwargs) -> Dict[str, torch.Tensor]:
        """Main computation method for CUE base class compatibility."""
        return self.forward(*args, **kwargs)

    def get_consciousness_dynamics(self) -> Dict[str, Any]:
        """Get detailed consciousness dynamics analysis."""
        return {
            "consciousness_state": self.consciousness_state.detach(),
            "consciousness_influences": (
                torch.stack(self.consciousness_influences) 
                if self.consciousness_influences else None
            ),
            "rg_parameters": {
                "kappa": self.rg_kappa.item() if self.rg_flow_regularization else None,
                "beta_cog": self.rg_beta_cog.item() if self.rg_flow_regularization else None,
                "alpha_ent": self.rg_alpha_ent.item() if self.rg_flow_regularization else None,
            }
        }


class ConsciousnessTransformerLayer(nn.Module):
    """Single transformer layer with consciousness enhancement."""

    def __init__(
        self,
        d_model: int,
        n_heads: int,
        d_ff: int,
        consciousness_fiber_dim: int,
        consciousness_coupling: float,
        dropout: float = 0.1
    ):
        super().__init__()

        self.consciousness_attention = ConsciousnessFiberAttention(
            d_model=d_model,
            n_heads=n_heads,
            consciousness_fiber_dim=consciousness_fiber_dim,
            consciousness_coupling=consciousness_coupling,
            dropout=dropout
        )

        self.feed_forward = nn.Sequential(
            nn.Linear(d_model, d_ff),
            nn.ReLU(),
            nn.Dropout(dropout),
            nn.Linear(d_ff, d_model),
            nn.Dropout(dropout)
        )

        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)

    def forward(
        self,
        x: torch.Tensor,
        consciousness_state: torch.Tensor,
        mask: Optional[torch.Tensor] = None
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """Forward pass through consciousness transformer layer."""

        # Consciousness-enhanced attention with residual connection
        attn_output, consciousness_influence = self.consciousness_attention(
            x, consciousness_state=consciousness_state, mask=mask
        )
        x = self.norm1(x + attn_output)

        # Feed-forward with residual connection
        ff_output = self.feed_forward(x)
        x = self.norm2(x + ff_output)

        return x, consciousness_influence
