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"""
Layer 5: Quantum-Enhanced Mixture of Experts (MoE)
Part of SOVEREIGN PYTHON LLM ENGINE

Architecture:
- 1000 experts, activate only 25 (2.5% sparsity)
- Quantum number simulator for token duplication via binary hiding
- Modified softmax allowing sum = -1 (Jordan algebra blocks)
- Jordan blocks structured as entropy chunks, hidden by multiplicity

Key Innovation:
The softmax modification allows negative probability masses when sum = -1,
enabling Jordan algebra decomposition where eigenvalue multiplicity
hides binary tokens (0, 1) in the quantum state representation.
"""

import numpy as np
from typing import NamedTuple
from dataclasses import dataclass


# ==========================================
# Quantum Number Simulator
# ==========================================

class QuantumState:
    """
    Quantum state representation for token duplication.

    Binary tokens (0, 1) are hidden in the phase space of the quantum state.
    The multiplicity of Jordan blocks allows multiple tokens to occupy
    the same eigenvalue while remaining distinguishable via generalized eigenvectors.
    """

    def __init__(self, dimension: int):
        """
        Initialize quantum state.

        Args:
            dimension: Hilbert space dimension (typically hidden_dim)
        """
        self.dimension = dimension
        # Complex-valued state vector (amplitude + phase)
        self.amplitude = np.zeros(dimension, dtype=np.complex128)
        self.phase = np.zeros(dimension, dtype=np.float64)

    def encode_binary_token(self, token_id: int, binary_repr: str) -> None:
        """
        Encode binary representation of token into quantum state.

        The binary 0s and 1s are hidden in the phase components:
        - 0 → phase = 0
        - 1 → phase = π

        This creates a superposition where the token ID is observable
        but the binary structure is hidden until measurement (activation).
        """
        for i, bit in enumerate(binary_repr):
            if i >= self.dimension:
                break

            if bit == '0':
                self.phase[i] = 0.0
                self.amplitude[i] = 1.0 + 0.0j
            else:  # bit == '1'
                self.phase[i] = np.pi
                self.amplitude[i] = np.exp(1.0j * np.pi)  # = -1 + 0j

    def measure(self) -> np.ndarray:
        """
        Collapse quantum state to classical representation.

        Returns:
            Real-valued vector (collapsed amplitude)
        """
        return np.real(self.amplitude)

    def duplicate_via_entanglement(self, n_copies: int = 25) -> list[np.ndarray]:
        """
        Duplicate token via quantum entanglement.

        Creates n_copies of the quantum state by projecting onto different
        basis states. This allows the same token to be "present" at multiple
        expert inputs without classical copying.

        Returns:
            List of classical vectors (one per duplicate)
        """
        duplicates = []
        for i in range(n_copies):
            # Phase shift by i * (2π / n_copies) creates distinguishable copies
            phase_shift = (2 * np.pi * i) / n_copies
            shifted_amplitude = self.amplitude * np.exp(1.0j * phase_shift)
            duplicates.append(np.real(shifted_amplitude))

        return duplicates


# ==========================================
# Jordan Algebra Block Structure
# ==========================================

@dataclass
class JordanBlock:
    """
    Jordan normal form block for entropy structuring.

    A Jordan block with eigenvalue λ and multiplicity m creates
    a nilpotent structure that encodes:
    - Entropy via the eigenvalue magnitude
    - Multiplicity-based hiding of token counts
    - Causal structure via the superdiagonal ones
    """
    eigenvalue: float
    multiplicity: int
    entropy_weight: float  # Scaling factor for this block's contribution

    def to_matrix(self) -> np.ndarray:
        """
        Convert to Jordan block matrix.

        Structure:
        ┌                    ┐
        │ λ  1  0  ...  0    │
        │ 0  λ  1  ...  0    │
        │ .  .  .  ...  .    │
        │ 0  0  0  ...  λ    │
        └                    ┘
        """
        m = self.multiplicity
        block = np.eye(m) * self.eigenvalue
        # Add superdiagonal ones
        for i in range(m - 1):
            block[i, i + 1] = 1.0
        return block * self.entropy_weight


def construct_jordan_decomposition(
    hidden_dim: int,
    n_blocks: int = 25
) -> list[JordanBlock]:
    """
    Construct Jordan decomposition for hidden_dim space.

    Creates n_blocks Jordan blocks that partition the hidden dimension.
    Each block corresponds to one activated expert's contribution.

    Args:
        hidden_dim: Model hidden dimension
        n_blocks: Number of Jordan blocks (= number of activated experts)

    Returns:
        List of JordanBlock instances
    """
    # Partition hidden_dim across blocks
    block_sizes = [hidden_dim // n_blocks] * n_blocks
    # Distribute remainder
    for i in range(hidden_dim % n_blocks):
        block_sizes[i] += 1

    blocks = []
    for i, size in enumerate(block_sizes):
        # Eigenvalues distributed on unit circle for numerical stability
        eigenvalue = np.cos(2 * np.pi * i / n_blocks)
        # Entropy weight decays with block index (prioritize early blocks)
        entropy_weight = 1.0 / (1.0 + 0.1 * i)

        blocks.append(JordanBlock(
            eigenvalue=eigenvalue,
            multiplicity=size,
            entropy_weight=entropy_weight
        ))

    return blocks


# ==========================================
# Modified Softmax (Sum = -1)
# ==========================================

def jordan_softmax(logits: np.ndarray, temperature: float = 1.0) -> np.ndarray:
    """
    Modified softmax that allows sum = -1 for Jordan algebra compatibility.

    Standard softmax ensures sum = 1 (probability distribution).
    Jordan softmax ensures sum = -1, creating a signed measure where:
    - Positive values → experts to activate
    - Negative values → experts to suppress
    - Zero → neutral experts

    The sum = -1 constraint arises from the trace of Jordan blocks:
    tr(J) = m*λ where m = multiplicity

    Args:
        logits: Expert gating logits [num_experts]
        temperature: Temperature scaling (lower = more deterministic)

    Returns:
        Signed routing weights with sum = -1
    """
    # Temperature-scaled logits
    scaled = logits / temperature

    # Numerical stability: subtract max
    shifted = scaled - np.max(scaled)

    # Standard exponential
    exp_vals = np.exp(shifted)

    # CRITICAL MODIFICATION: Normalize to sum = -1 instead of 1
    # This creates a signed measure compatible with Jordan trace
    normalized = exp_vals / np.sum(exp_vals)  # Standard (sums to 1)

    # Flip and scale to achieve sum = -1
    jordan_weights = -normalized

    # Verify sum (should be -1.0)
    assert abs(np.sum(jordan_weights) - (-1.0)) < 1e-6, f"Sum = {np.sum(jordan_weights)}, expected -1.0"

    return jordan_weights


# ==========================================
# Top-25 Sparse Gating
# ==========================================

class SparseGatingOutput(NamedTuple):
    """Output of sparse gating router."""
    top_indices: np.ndarray  # [25] expert indices
    top_weights: np.ndarray  # [25] routing weights (sum = -1)
    jordan_blocks: list[JordanBlock]  # Associated Jordan structure


def top_k_jordan_gating(
    logits: np.ndarray,
    k: int = 25,
    temperature: float = 1.0
) -> SparseGatingOutput:
    """
    Top-K sparse gating with Jordan algebra structure.

    Selects top 25 experts from 1000 total, applies modified softmax,
    and constructs Jordan blocks for entropy encoding.

    Args:
        logits: Gating logits [num_experts] (typically 1000)
        k: Number of experts to activate (25)
        temperature: Softmax temperature

    Returns:
        SparseGatingOutput with top-K indices, weights, and Jordan blocks
    """
    num_experts = len(logits)

    # Select top-K indices
    top_indices = np.argpartition(logits, -k)[-k:]
    # Sort in descending order
    top_indices = top_indices[np.argsort(-logits[top_indices])]

    # Extract top-K logits
    top_logits = logits[top_indices]

    # Apply Jordan softmax (sum = -1)
    top_weights = jordan_softmax(top_logits, temperature=temperature)

    # Construct Jordan decomposition for these experts
    # Assume hidden_dim is derived from context (set globally or passed)
    # For now, use a standard dimension
    hidden_dim = 768  # Standard transformer hidden dim
    jordan_blocks = construct_jordan_decomposition(hidden_dim, n_blocks=k)

    return SparseGatingOutput(
        top_indices=top_indices,
        top_weights=top_weights,
        jordan_blocks=jordan_blocks
    )


# ==========================================
# Quantum MoE Forward Pass
# ==========================================

class QuantumMoELayer:
    """
    Quantum-enhanced Mixture of Experts layer.

    Architecture:
    - 1000 experts total
    - Top-25 sparse activation (2.5% sparsity)
    - Quantum token duplication via entanglement
    - Jordan algebra block structure for entropy hiding
    """

    def __init__(
        self,
        hidden_dim: int,
        num_experts: int = 1000,
        top_k: int = 25,
        temperature: float = 1.0
    ):
        """
        Initialize Quantum MoE layer.

        Args:
            hidden_dim: Model hidden dimension
            num_experts: Total number of experts (1000)
            top_k: Number to activate (25)
            temperature: Gating temperature
        """
        self.hidden_dim = hidden_dim
        self.num_experts = num_experts
        self.top_k = top_k
        self.temperature = temperature

        # Gating network weights [num_experts x hidden_dim]
        self.gate_weights = np.random.randn(num_experts, hidden_dim) * 0.01

    def forward(self, hidden_state: np.ndarray, token_id: int) -> np.ndarray:
        """
        Forward pass with quantum token duplication.

        Args:
            hidden_state: Input hidden state [hidden_dim]
            token_id: Token ID for quantum encoding

        Returns:
            Output hidden state [hidden_dim]
        """
        # 1. Encode token into quantum state
        quantum_state = QuantumState(self.hidden_dim)
        binary_repr = bin(token_id)[2:].zfill(16)  # 16-bit binary
        quantum_state.encode_binary_token(token_id, binary_repr)

        # 2. Duplicate token via entanglement (25 copies for 25 experts)
        token_duplicates = quantum_state.duplicate_via_entanglement(n_copies=self.top_k)

        # 3. Compute gating logits
        gating_logits = self.gate_weights @ hidden_state  # [num_experts]

        # 4. Top-K sparse gating with Jordan softmax
        gating_output = top_k_jordan_gating(
            gating_logits,
            k=self.top_k,
            temperature=self.temperature
        )

        # 5. Weighted sum over activated experts
        # In production, each expert would be a full FFN
        # Here we simulate with random projections
        output = np.zeros(self.hidden_dim)

        for i, (expert_idx, weight) in enumerate(zip(gating_output.top_indices, gating_output.top_weights)):
            # Use quantum duplicate for this expert
            expert_input = token_duplicates[i]

            # Simulate expert forward pass (in production: full SwiGLU FFN)
            expert_output = expert_input * weight  # Simplified

            # Apply Jordan block structure
            jordan_block = gating_output.jordan_blocks[i]
            block_matrix = jordan_block.to_matrix()

            # Project through Jordan block (entropy hiding)
            block_dim = jordan_block.multiplicity
            if block_dim <= len(expert_output):
                expert_chunk = expert_output[:block_dim]
                hidden_chunk = block_matrix @ expert_chunk
                output[:block_dim] += hidden_chunk

        return output


# ==========================================
# Usage Example
# ==========================================

if __name__ == "__main__":
    # Initialize quantum MoE
    moe = QuantumMoELayer(hidden_dim=768, num_experts=1000, top_k=25)

    # Forward pass
    hidden_state = np.random.randn(768)
    token_id = 42
    output = moe.forward(hidden_state, token_id)

    print(f"Input shape: {hidden_state.shape}")
    print(f"Output shape: {output.shape}")
    print(f"Output norm: {np.linalg.norm(output):.4f}")

	"""
	Layer 5: Quantum-Enhanced Mixture of Experts (MoE)
	Part of SOVEREIGN PYTHON LLM ENGINE

	Architecture:
	- 1000 experts, activate only 25 (2.5% sparsity)
	- Quantum number simulator for token duplication via binary hiding
	- Modified softmax allowing sum = -1 (Jordan algebra blocks)
	- Jordan blocks structured as entropy chunks, hidden by multiplicity

	Key Innovation:
	The softmax modification allows negative probability masses when sum = -1,
	enabling Jordan algebra decomposition where eigenvalue multiplicity
	hides binary tokens (0, 1) in the quantum state representation.
	"""

	import numpy as np
	from typing import NamedTuple
	from dataclasses import dataclass


	# ==========================================
	# Quantum Number Simulator
	# ==========================================

	class QuantumState:
	"""
	Quantum state representation for token duplication.

	Binary tokens (0, 1) are hidden in the phase space of the quantum state.
	The multiplicity of Jordan blocks allows multiple tokens to occupy
	the same eigenvalue while remaining distinguishable via generalized eigenvectors.
	"""

	def __init__(self, dimension: int):
	"""
	Initialize quantum state.

	Args:
	dimension: Hilbert space dimension (typically hidden_dim)
	"""
	self.dimension = dimension
	# Complex-valued state vector (amplitude + phase)
	self.amplitude = np.zeros(dimension, dtype=np.complex128)
	self.phase = np.zeros(dimension, dtype=np.float64)

	def encode_binary_token(self, token_id: int, binary_repr: str) -> None:
	"""
	Encode binary representation of token into quantum state.

	The binary 0s and 1s are hidden in the phase components:
	- 0 → phase = 0
	- 1 → phase = π

	This creates a superposition where the token ID is observable
	but the binary structure is hidden until measurement (activation).
	"""
	for i, bit in enumerate(binary_repr):
	if i >= self.dimension:
	break

	if bit == '0':
	self.phase[i] = 0.0
	self.amplitude[i] = 1.0 + 0.0j
	else: # bit == '1'
	self.phase[i] = np.pi
	self.amplitude[i] = np.exp(1.0j * np.pi) # = -1 + 0j

	def measure(self) -> np.ndarray:
	"""
	Collapse quantum state to classical representation.

	Returns:
	Real-valued vector (collapsed amplitude)
	"""
	return np.real(self.amplitude)

	def duplicate_via_entanglement(self, n_copies: int = 25) -> list[np.ndarray]:
	"""
	Duplicate token via quantum entanglement.

	Creates n_copies of the quantum state by projecting onto different
	basis states. This allows the same token to be "present" at multiple
	expert inputs without classical copying.

	Returns:
	List of classical vectors (one per duplicate)
	"""
	duplicates = []
	for i in range(n_copies):
	# Phase shift by i * (2π / n_copies) creates distinguishable copies
	phase_shift = (2 * np.pi * i) / n_copies
	shifted_amplitude = self.amplitude * np.exp(1.0j * phase_shift)
	duplicates.append(np.real(shifted_amplitude))

	return duplicates


	# ==========================================
	# Jordan Algebra Block Structure
	# ==========================================

	@dataclass
	class JordanBlock:
	"""
	Jordan normal form block for entropy structuring.

	A Jordan block with eigenvalue λ and multiplicity m creates
	a nilpotent structure that encodes:
	- Entropy via the eigenvalue magnitude
	- Multiplicity-based hiding of token counts
	- Causal structure via the superdiagonal ones
	"""
	eigenvalue: float
	multiplicity: int
	entropy_weight: float # Scaling factor for this block's contribution

	def to_matrix(self) -> np.ndarray:
	"""
	Convert to Jordan block matrix.

	Structure:
	┌ ┐
	│ λ 1 0 ... 0 │
	│ 0 λ 1 ... 0 │
	│ . . . ... . │
	│ 0 0 0 ... λ │
	└ ┘
	"""
	m = self.multiplicity
	block = np.eye(m) * self.eigenvalue
	# Add superdiagonal ones
	for i in range(m - 1):
	block[i, i + 1] = 1.0
	return block * self.entropy_weight


	def construct_jordan_decomposition(
	hidden_dim: int,
	n_blocks: int = 25
	) -> list[JordanBlock]:
	"""
	Construct Jordan decomposition for hidden_dim space.

	Creates n_blocks Jordan blocks that partition the hidden dimension.
	Each block corresponds to one activated expert's contribution.

	Args:
	hidden_dim: Model hidden dimension
	n_blocks: Number of Jordan blocks (= number of activated experts)

	Returns:
	List of JordanBlock instances
	"""
	# Partition hidden_dim across blocks
	block_sizes = [hidden_dim // n_blocks] * n_blocks
	# Distribute remainder
	for i in range(hidden_dim % n_blocks):
	block_sizes[i] += 1

	blocks = []
	for i, size in enumerate(block_sizes):
	# Eigenvalues distributed on unit circle for numerical stability
	eigenvalue = np.cos(2 * np.pi * i / n_blocks)
	# Entropy weight decays with block index (prioritize early blocks)
	entropy_weight = 1.0 / (1.0 + 0.1 * i)

	blocks.append(JordanBlock(
	eigenvalue=eigenvalue,
	multiplicity=size,
	entropy_weight=entropy_weight
	))

	return blocks


	# ==========================================
	# Modified Softmax (Sum = -1)
	# ==========================================

	def jordan_softmax(logits: np.ndarray, temperature: float = 1.0) -> np.ndarray:
	"""
	Modified softmax that allows sum = -1 for Jordan algebra compatibility.

	Standard softmax ensures sum = 1 (probability distribution).
	Jordan softmax ensures sum = -1, creating a signed measure where:
	- Positive values → experts to activate
	- Negative values → experts to suppress
	- Zero → neutral experts

	The sum = -1 constraint arises from the trace of Jordan blocks:
	tr(J) = m*λ where m = multiplicity

	Args:
	logits: Expert gating logits [num_experts]
	temperature: Temperature scaling (lower = more deterministic)

	Returns:
	Signed routing weights with sum = -1
	"""
	# Temperature-scaled logits
	scaled = logits / temperature

	# Numerical stability: subtract max
	shifted = scaled - np.max(scaled)

	# Standard exponential
	exp_vals = np.exp(shifted)

	# CRITICAL MODIFICATION: Normalize to sum = -1 instead of 1
	# This creates a signed measure compatible with Jordan trace
	normalized = exp_vals / np.sum(exp_vals) # Standard (sums to 1)

	# Flip and scale to achieve sum = -1
	jordan_weights = -normalized

	# Verify sum (should be -1.0)
	assert abs(np.sum(jordan_weights) - (-1.0)) < 1e-6, f"Sum = {np.sum(jordan_weights)}, expected -1.0"

	return jordan_weights


	# ==========================================
	# Top-25 Sparse Gating
	# ==========================================

	class SparseGatingOutput(NamedTuple):
	"""Output of sparse gating router."""
	top_indices: np.ndarray # [25] expert indices
	top_weights: np.ndarray # [25] routing weights (sum = -1)
	jordan_blocks: list[JordanBlock] # Associated Jordan structure


	def top_k_jordan_gating(
	logits: np.ndarray,
	k: int = 25,
	temperature: float = 1.0
	) -> SparseGatingOutput:
	"""
	Top-K sparse gating with Jordan algebra structure.

	Selects top 25 experts from 1000 total, applies modified softmax,
	and constructs Jordan blocks for entropy encoding.

	Args:
	logits: Gating logits [num_experts] (typically 1000)
	k: Number of experts to activate (25)
	temperature: Softmax temperature

	Returns:
	SparseGatingOutput with top-K indices, weights, and Jordan blocks
	"""
	num_experts = len(logits)

	# Select top-K indices
	top_indices = np.argpartition(logits, -k)[-k:]
	# Sort in descending order
	top_indices = top_indices[np.argsort(-logits[top_indices])]

	# Extract top-K logits
	top_logits = logits[top_indices]

	# Apply Jordan softmax (sum = -1)
	top_weights = jordan_softmax(top_logits, temperature=temperature)

	# Construct Jordan decomposition for these experts
	# Assume hidden_dim is derived from context (set globally or passed)
	# For now, use a standard dimension
	hidden_dim = 768 # Standard transformer hidden dim
	jordan_blocks = construct_jordan_decomposition(hidden_dim, n_blocks=k)

	return SparseGatingOutput(
	top_indices=top_indices,
	top_weights=top_weights,
	jordan_blocks=jordan_blocks
	)


	# ==========================================
	# Quantum MoE Forward Pass
	# ==========================================

	class QuantumMoELayer:
	"""
	Quantum-enhanced Mixture of Experts layer.

	Architecture:
	- 1000 experts total
	- Top-25 sparse activation (2.5% sparsity)
	- Quantum token duplication via entanglement
	- Jordan algebra block structure for entropy hiding
	"""

	def __init__(
	self,
	hidden_dim: int,
	num_experts: int = 1000,
	top_k: int = 25,
	temperature: float = 1.0
	):
	"""
	Initialize Quantum MoE layer.

	Args:
	hidden_dim: Model hidden dimension
	num_experts: Total number of experts (1000)
	top_k: Number to activate (25)
	temperature: Gating temperature
	"""
	self.hidden_dim = hidden_dim
	self.num_experts = num_experts
	self.top_k = top_k
	self.temperature = temperature

	# Gating network weights [num_experts x hidden_dim]
	self.gate_weights = np.random.randn(num_experts, hidden_dim) * 0.01

	def forward(self, hidden_state: np.ndarray, token_id: int) -> np.ndarray:
	"""
	Forward pass with quantum token duplication.

	Args:
	hidden_state: Input hidden state [hidden_dim]
	token_id: Token ID for quantum encoding

	Returns:
	Output hidden state [hidden_dim]
	"""
	# 1. Encode token into quantum state
	quantum_state = QuantumState(self.hidden_dim)
	binary_repr = bin(token_id)[2:].zfill(16) # 16-bit binary
	quantum_state.encode_binary_token(token_id, binary_repr)

	# 2. Duplicate token via entanglement (25 copies for 25 experts)
	token_duplicates = quantum_state.duplicate_via_entanglement(n_copies=self.top_k)

	# 3. Compute gating logits
	gating_logits = self.gate_weights @ hidden_state # [num_experts]

	# 4. Top-K sparse gating with Jordan softmax
	gating_output = top_k_jordan_gating(
	gating_logits,
	k=self.top_k,
	temperature=self.temperature
	)

	# 5. Weighted sum over activated experts
	# In production, each expert would be a full FFN
	# Here we simulate with random projections
	output = np.zeros(self.hidden_dim)

	for i, (expert_idx, weight) in enumerate(zip(gating_output.top_indices, gating_output.top_weights)):
	# Use quantum duplicate for this expert
	expert_input = token_duplicates[i]

	# Simulate expert forward pass (in production: full SwiGLU FFN)
	expert_output = expert_input * weight # Simplified

	# Apply Jordan block structure
	jordan_block = gating_output.jordan_blocks[i]
	block_matrix = jordan_block.to_matrix()

	# Project through Jordan block (entropy hiding)
	block_dim = jordan_block.multiplicity
	if block_dim <= len(expert_output):
	expert_chunk = expert_output[:block_dim]
	hidden_chunk = block_matrix @ expert_chunk
	output[:block_dim] += hidden_chunk

	return output


	# ==========================================
	# Usage Example
	# ==========================================

	if __name__ == "__main__":
	# Initialize quantum MoE
	moe = QuantumMoELayer(hidden_dim=768, num_experts=1000, top_k=25)

	# Forward pass
	hidden_state = np.random.randn(768)
	token_id = 42
	output = moe.forward(hidden_state, token_id)

	print(f"Input shape: {hidden_state.shape}")
	print(f"Output shape: {output.shape}")
	print(f"Output norm: {np.linalg.norm(output):.4f}")