#!/usr/bin/env python3
"""
AETHER-Micro Decoder Layer

Transformer Decoder Layer: Attention + MoE
"""

import torch
import torch.nn as nn

from .configuration_aether_micro import AETHERMicroConfig
from .normalization import AETHERMicroRMSNorm
from .attention import AETHERMicroAttention
from .moe import AETHERMicroMoE
from .latent_thought import AETHERMicroLatentThought
from .self_evaluation import AETHERMicroSelfEvalHead


class AETHERMicroDecoderLayer(nn.Module):
    """
    Transformer Decoder Layer

    Structure:
    1. Input LayerNorm
    2. Self-Attention (RoPE + GQA)
    3. Post-Attention LayerNorm
    4. MoE FFN (Heterogeneous experts)
    5. Residual connections
    """

    def __init__(self, config: AETHERMicroConfig):
        super().__init__()
        self.hidden_size = config.hidden_size

        # Self-Attention
        self.input_layernorm = AETHERMicroRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.self_attn = AETHERMicroAttention(config)

        # Latent Thought Loop (Block 1)
        if config.enable_latent_thought:
            self.latent_thought = AETHERMicroLatentThought(config)

        # Self Evaluation (Block 4)
        if config.enable_self_eval:
            self.self_evaluation = AETHERMicroSelfEvalHead(config)

        # MoE FFN
        self.post_attention_layernorm = AETHERMicroRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.mlp = AETHERMicroMoE(config)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor = None,
        position_ids: torch.LongTensor = None,
        disable_ltl: bool = False,
    ) -> torch.Tensor:
        """
        Args:
            hidden_states: (batch_size, seq_length, hidden_size)
            attention_mask: (batch_size, 1, seq_length, seq_length)
            position_ids: (batch_size, seq_length)

        Returns:
            hidden_states: (batch_size, seq_length, hidden_size)
        """
        residual = hidden_states

        # Self-Attention
        hidden_states = self.input_layernorm(hidden_states)
        hidden_states = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
        )
        hidden_states = residual + hidden_states

        # Latent Thought Loop (Block 1) - Checkpoint-safe
        if hasattr(self, 'latent_thought') and not disable_ltl:
            result = self.latent_thought(hidden_states)
            # Tuple unpacking (gradient checkpointing 호환)
            if isinstance(result, tuple):
                hidden_states = result[0]
                # metrics는 체크포인팅 모드에서는 무시됨
            else:
                hidden_states = result

        # MoE FFN
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + hidden_states

        # Self Evaluation (Block 4)
        # NOTE: self_evaluation returns (quality, overall) tuple for metrics
        # We don't modify hidden_states - just compute quality scores
        if hasattr(self, 'self_evaluation'):
            _ = self.self_evaluation(hidden_states)  # Compute but don't assign

        return hidden_states