from typing import Dict, List, Optional, Tuple
import torch
import torch.nn as nn
from torch import Tensor
import numpy as np
from einops import rearrange, repeat

class ExpertBase(nn.Module):
    def __init__(self, input_dim: int, hidden_dim: int, output_dim: int):
        super().__init__()
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.output_dim = output_dim
        
    def forward(self, x: Tensor) -> Tensor:
        raise NotImplementedError

class MLPExpert(ExpertBase):
    def __init__(self, input_dim: int, hidden_dim: int, output_dim: int):
        super().__init__(input_dim, hidden_dim, output_dim)
        self.net = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, output_dim)
        )
        
    def forward(self, x: Tensor) -> Tensor:
        return self.net(x)

class ConvExpert(ExpertBase):
    def __init__(self, input_dim: int, hidden_dim: int, output_dim: int):
        super().__init__(input_dim, hidden_dim, output_dim)
        self.net = nn.Sequential(
            nn.Conv1d(input_dim, hidden_dim, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.Conv1d(hidden_dim, output_dim, kernel_size=3, padding=1)
        )
        
    def forward(self, x: Tensor) -> Tensor:
        # Convert [batch, seq_len, channels] to [batch, channels, seq_len]
        x = x.transpose(1, 2)
        x = self.net(x)
        # Convert back to [batch, seq_len, channels]
        return x.transpose(1, 2)

class TransformerExpert(ExpertBase):
    def __init__(self, input_dim: int, hidden_dim: int, output_dim: int):
        super().__init__(input_dim, hidden_dim, output_dim)
        self.attention = nn.MultiheadAttention(input_dim, num_heads=8)
        self.norm1 = nn.LayerNorm(input_dim)
        self.norm2 = nn.LayerNorm(input_dim)
        self.ffn = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, output_dim)
        )
        
    def forward(self, x: Tensor) -> Tensor:
        # Self attention
        attended, _ = self.attention(x, x, x)
        x = self.norm1(x + attended)
        
        # Feed forward
        x = self.norm2(x + self.ffn(x))
        return x

class HierarchicalContextExpert(ExpertBase):
    def __init__(
        self,
        input_dim: int,
        hidden_dim: int,
        output_dim: int,
        num_levels: int = 3,
        chunk_size: int = 512,
        overlap: int = 64,
        max_context_length: Optional[int] = None,
        use_checkpointing: bool = False
    ):
        super().__init__(input_dim, hidden_dim, output_dim)
        self.num_levels = num_levels
        self.chunk_size = chunk_size
        self.overlap = overlap
        self.max_context_length = max_context_length or (chunk_size * (2 ** (num_levels - 1)))
        self.use_checkpointing = use_checkpointing
        
        # Create experts for each level
        self.local_experts = nn.ModuleList([
            TransformerExpert(input_dim, hidden_dim, output_dim)
            for _ in range(num_levels)
        ])
        
        # Cross attention between levels
        self.cross_experts = nn.ModuleList([
            TransformerExpert(output_dim, hidden_dim, output_dim)
            for _ in range(num_levels - 1)
        ])
        
        # Memory cache for each level
        self.reset_caches()
        
    def reset_caches(self):
        self.memory_cache = [[] for _ in range(self.num_levels)]
        
    def _process_level(
        self,
        x: Tensor,
        level: int,
        chunk_size: int
    ) -> Tuple[Tensor, List[Tensor]]:
        B, L, D = x.shape
        
        # Split into chunks with overlap
        chunks = []
        memories = []
        
        for i in range(0, L, chunk_size - self.overlap):
            end = min(i + chunk_size, L)
            chunk = x[:, i:end]
            
            # Process chunk
            processed = self.local_experts[level](chunk)
            chunks.append(processed)
            
            # Extract memory
            if end < L:  # Not the last chunk
                memory = processed[:, -self.overlap:]
                memories.append(memory)
        
        # Combine chunks
        output = torch.cat(chunks, dim=1)
        if L > chunk_size:  # Trim overlap
            output = output[:, :L]
            
        return output, memories
        
    def forward(self, x: Tensor) -> Tensor:
        B, L, D = x.shape
        assert L <= self.max_context_length, f"Input length {L} exceeds maximum context length {self.max_context_length}"
        
        # Process each level
        current = x
        outputs = []
        
        for level in range(self.num_levels):
            chunk_size = self.chunk_size * (2 ** level)
            processed, memories = self._process_level(current, level, chunk_size)
            
            # Cache memories
            self.memory_cache[level].extend(memories)
            
            # Add to outputs
            outputs.append(processed)
            
            # Prepare input for next level
            if level < self.num_levels - 1:
                # Apply cross attention between levels
                current = self.cross_experts[level](processed)
        
        # Combine outputs from all levels
        final = sum(outputs) / len(outputs)
        
        return final

class LongContextMoE(nn.Module):
    def __init__(
        self,
        input_dim: int,
        hidden_dim: int,
        output_dim: int,
        num_experts_per_type: int = 4,
        max_context_length: Optional[int] = None
    ):
        super().__init__()
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.output_dim = output_dim
        self.max_context_length = max_context_length or 4194304  # Default 4M tokens
        
        # Create different types of experts
        self.experts = nn.ModuleDict({
            'mlp': nn.ModuleList([MLPExpert(input_dim, hidden_dim, output_dim) 
                                for _ in range(num_experts_per_type)]),
            'conv': nn.ModuleList([ConvExpert(input_dim, hidden_dim, output_dim)
                                for _ in range(num_experts_per_type)]),
            'transformer': nn.ModuleList([TransformerExpert(input_dim, hidden_dim, output_dim)
                                      for _ in range(num_experts_per_type)]),
            'hierarchical': nn.ModuleList([HierarchicalContextExpert(input_dim, hidden_dim, output_dim)
                                       for _ in range(num_experts_per_type)])
        })
        
        # Router network
        self.router = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, len(self.experts) * num_experts_per_type)
        )
        
    def forward(self, x: Tensor) -> Tensor:
        B, L, D = x.shape
        assert L <= self.max_context_length, f"Input length {L} exceeds maximum context length {self.max_context_length}"
        
        # Get routing probabilities
        route_logits = self.router(x.mean(dim=1))  # [B, num_experts_total]
        route_probs = torch.softmax(route_logits, dim=-1)
        
        # Split routing probabilities by expert type
        num_expert_types = len(self.experts)
        route_probs = route_probs.view(B, num_expert_types, -1)
        
        # Process input through each expert type
        outputs = []
        for i, (expert_type, experts) in enumerate(self.experts.items()):
            expert_probs = route_probs[:, i]  # [B, num_experts_per_type]
            
            # Process through each expert of this type
            expert_outputs = []
            for j, expert in enumerate(experts):
                out = expert(x)
                expert_outputs.append(out * expert_probs[:, j].view(B, 1, 1))
            
            # Combine experts of same type
            type_output = sum(expert_outputs)
            outputs.append(type_output)
        
        # Combine all expert types
        return sum(outputs)

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, List, Tuple
import numpy as np
from einops import rearrange, repeat

from .rust_experts import QuantumInspiredExpert as RustQuantumExpert
from .rust_experts import NeuralCompressionExpert as RustCompressionExpert

class MetalAcceleratedAttention(nn.Module):
    """Efficient attention implementation optimized for Apple Metal"""
    
    def __init__(
        self,
        dim: int,
        num_heads: int = 8,
        dropout: float = 0.1,
        max_seq_length: int = 1024
    ):
        super().__init__()
        self.num_heads = num_heads
        self.head_dim = dim // num_heads
        self.scaling = self.head_dim ** -0.5
        
        self.qkv = nn.Linear(dim, dim * 3)
        self.proj = nn.Linear(dim, dim)
        self.dropout = nn.Dropout(dropout)
        
        # Create causal mask once
        mask = torch.triu(torch.ones(max_seq_length, max_seq_length), diagonal=1).bool()
        self.register_buffer("mask", mask)
        
    def forward(self, x: torch.Tensor, use_cache: bool = True) -> torch.Tensor:
        B, L, D = x.shape
        
        # Move to Metal device if available
        device = torch.device("mps") if torch.backends.mps.is_available() else x.device
        x = x.to(device)
        
        # Project to q, k, v
        qkv = self.qkv(x)
        q, k, v = rearrange(qkv, 'b n (three h d) -> three b h n d', three=3, h=self.num_heads)
        
        # Compute attention scores with memory-efficient implementation
        scale = self.scaling
        
        # Split processing into chunks for memory efficiency
        chunk_size = min(L, 256)  # Process 256 tokens at a time
        output_chunks = []
        
        for chunk_start in range(0, L, chunk_size):
            chunk_end = min(chunk_start + chunk_size, L)
            
            # Get current chunk
            q_chunk = q[:, :, chunk_start:chunk_end]
            
            # Compute attention scores for this chunk
            attn_weights = torch.matmul(q_chunk, k.transpose(-2, -1)) * scale
            
            # Apply causal mask
            mask_chunk = self.mask[chunk_start:chunk_end, :L]
            attn_weights = attn_weights.masked_fill(mask_chunk, float('-inf'))
            
            # Compute softmax with better numerical stability
            max_score = torch.max(attn_weights, dim=-1, keepdim=True)[0]
            exp_weights = torch.exp(attn_weights - max_score)
            exp_weights = exp_weights.masked_fill(mask_chunk, 0.0)
            
            # Normalize
            attn_weights = exp_weights / (exp_weights.sum(dim=-1, keepdim=True) + 1e-6)
            
            # Apply attention to values
            chunk_output = torch.matmul(attn_weights, v)
            output_chunks.append(chunk_output)
        
        # Combine chunks
        output = torch.cat(output_chunks, dim=2)
        output = rearrange(output, 'b h n d -> b n (h d)')
        
        # Project back
        output = self.proj(output)
        output = self.dropout(output)
        
        return output.to(x.device)

class QuantumFusionExpert(nn.Module):
    """Expert that combines quantum-inspired processing with neural networks"""
    
    def __init__(
        self,
        input_dim: int,
        hidden_dim: int,
        output_dim: int,
        num_qubits: int = 8
    ):
        super().__init__()
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.output_dim = output_dim
        
        # Initialize Rust quantum expert
        self.rust_expert = RustQuantumExpert(
            input_dim,
            hidden_dim,
            hidden_dim,
            num_qubits=num_qubits
        )
        
        # Neural layers
        self.pre_quantum = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.LayerNorm(hidden_dim),
            nn.GELU()
        )
        
        self.post_quantum = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim),
            nn.LayerNorm(hidden_dim),
            nn.GELU(),
            nn.Linear(hidden_dim, output_dim)
        )
        
        # Quantum mixing parameters
        self.quantum_mixer = nn.Parameter(torch.randn(3, hidden_dim))
        
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        # Pre-process input
        x = self.pre_quantum(x)
        
        # Process with Rust quantum expert
        quantum_state = self.rust_expert.forward(x.cpu().numpy())
        quantum_state = torch.from_numpy(quantum_state)
        
        # Apply quantum mixing
        if torch.backends.mps.is_available():
            quantum_state = quantum_state.to("mps")
            self.quantum_mixer.data = self.quantum_mixer.data.to("mps")
            
        mixed = torch.einsum('b...d,qd->b...qd', quantum_state, self.quantum_mixer)
        mixed = mixed.mean(dim=-2)
        
        # Post-process
        output = self.post_quantum(mixed)
        
        return output

class CompressedAttentionExpert(nn.Module):
    """Expert that combines neural compression with efficient attention"""
    
    def __init__(
        self,
        input_dim: int,
        hidden_dim: int,
        output_dim: int,
        num_heads: int = 8,
        compression_ratio: float = 0.25
    ):
        super().__init__()
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.output_dim = output_dim
        
        # Initialize Rust compression expert
        self.compression = RustCompressionExpert(
            input_dim,
            hidden_dim,
            hidden_dim,
            compression_ratio=compression_ratio
        )
        
        # Metal-accelerated attention
        self.attention = MetalAcceleratedAttention(
            dim=hidden_dim,
            num_heads=num_heads
        )
        
        # Output projection
        self.output_proj = nn.Linear(hidden_dim, output_dim)
        
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        # Compress input using Rust expert
        compressed = self.compression.forward(x.cpu().numpy())
        compressed = torch.from_numpy(compressed)
        
        # Process with attention
        if torch.backends.mps.is_available():
            compressed = compressed.to("mps")
            
        attention_output = self.attention(compressed)
        
        # Project to output dimension
        output = self.output_proj(attention_output)
        
        return output

class HierarchicalFusionExpert(nn.Module):
    """Expert that combines quantum and compressed attention hierarchically"""
    
    def __init__(
        self,
        input_dim: int,
        hidden_dim: int,
        output_dim: int,
        num_levels: int = 3
    ):
        super().__init__()
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.output_dim = output_dim
        self.num_levels = num_levels
        
        # Create experts for each level
        self.quantum_experts = nn.ModuleList([
            QuantumFusionExpert(
                input_dim if i == 0 else hidden_dim,
                hidden_dim,
                hidden_dim
            ) for i in range(num_levels)
        ])
        
        self.attention_experts = nn.ModuleList([
            CompressedAttentionExpert(
                hidden_dim,
                hidden_dim,
                hidden_dim if i < num_levels-1 else output_dim
            ) for i in range(num_levels)
        ])
        
        # Level mixing
        self.level_mixing = nn.Parameter(torch.randn(num_levels, 2))
        
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        batch_size, seq_len, _ = x.shape
        
        # Process through levels
        current = x
        level_outputs = []
        
        for i in range(self.num_levels):
            # Process through both expert types
            quantum_output = self.quantum_experts[i](current)
            attention_output = self.attention_experts[i](current)
            
            # Mix outputs for this level
            if torch.backends.mps.is_available():
                quantum_output = quantum_output.to("mps")
                attention_output = attention_output.to("mps")
                self.level_mixing.data = self.level_mixing.data.to("mps")
                
            level_weights = F.softmax(self.level_mixing[i], dim=-1)
            level_output = (
                level_weights[0] * quantum_output +
                level_weights[1] * attention_output
            )
            
            level_outputs.append(level_output)
            current = level_output
            
        # Combine all levels with residual connections
        output = sum(level_outputs)
        
        return output
