"""
Multi-Head Attention with explicit KV cache for SLM.
Qualcomm-safe: No FlashAttention, no fused ops, clean ONNX export.
"""

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

from .config import SLMConfig
from .rope import RotaryEmbedding
from .kv_cache import KVCache


class MultiHeadAttention(nn.Module):
    """Multi-Head Self-Attention with RoPE and explicit KV cache.

    Design choices for Qualcomm compatibility:
    - Standard attention (no FlashAttention)
    - No grouped/multi-query attention (simpler, v1.1 will add GQA)
    - Explicit KV cache management
    - Clean tensor operations for ONNX export
    """

    def __init__(self, config: SLMConfig, layer_idx: int):
        """Initialize attention layer.

        Args:
            config: Model configuration
            layer_idx: Index of this layer (for KV cache)
        """
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx

        self.hidden_size = config.hidden_size
        self.num_heads = config.num_heads
        self.head_dim = config.head_dim
        self.dropout = config.attention_dropout

        # Q, K, V projections
        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
        self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
        self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)

        # Output projection
        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)

        # Rotary embeddings
        self.rotary_emb = RotaryEmbedding(
            dim=self.head_dim,
            max_position_embeddings=config.max_position_embeddings,
            base=config.rope_theta,
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_ids: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        kv_cache: Optional[KVCache] = None,
        use_cache: bool = False,
    ) -> Tuple[torch.Tensor, Optional[KVCache]]:
        """Forward pass for attention.

        Args:
            hidden_states: Input tensor [batch, seq_len, hidden_size]
            position_ids: Position indices [batch, seq_len]
            attention_mask: Causal mask [batch, 1, seq_len, kv_seq_len]
            kv_cache: Optional KV cache for inference
            use_cache: Whether to use/update KV cache

        Returns:
            Tuple of (output, kv_cache)
        """
        batch_size, seq_len, _ = hidden_states.shape

        # Project to Q, K, V
        query = self.q_proj(hidden_states)
        key = self.k_proj(hidden_states)
        value = self.v_proj(hidden_states)

        # Reshape: [batch, seq, hidden] -> [batch, seq, heads, head_dim]
        query = query.view(batch_size, seq_len, self.num_heads, self.head_dim)
        key = key.view(batch_size, seq_len, self.num_heads, self.head_dim)
        value = value.view(batch_size, seq_len, self.num_heads, self.head_dim)

        # Transpose for attention: [batch, heads, seq, head_dim]
        query = query.transpose(1, 2)
        key = key.transpose(1, 2)
        value = value.transpose(1, 2)

        # Apply rotary embeddings to Q and K
        query, key = self.rotary_emb(query, key, position_ids)

        # Handle KV cache
        if use_cache and kv_cache is not None:
            # Get the position to write to cache
            cache_position = position_ids[0, 0].item()

            # Update cache and get full K, V
            key, value = kv_cache.update(
                layer_idx=self.layer_idx,
                key=key,
                value=value,
                position=cache_position,
            )

        # Compute attention scores
        # [batch, heads, seq, head_dim] @ [batch, heads, head_dim, kv_seq]
        # -> [batch, heads, seq, kv_seq]
        scale = 1.0 / (self.head_dim ** 0.5)
        attn_weights = torch.matmul(query, key.transpose(-2, -1)) * scale

        # Apply causal mask
        if attention_mask is not None:
            attn_weights = attn_weights + attention_mask

        # Softmax and dropout
        attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)

        if self.training and self.dropout > 0:
            attn_weights = F.dropout(attn_weights, p=self.dropout)

        # Apply attention to values
        # [batch, heads, seq, kv_seq] @ [batch, heads, kv_seq, head_dim]
        # -> [batch, heads, seq, head_dim]
        attn_output = torch.matmul(attn_weights, value)

        # Reshape back: [batch, heads, seq, head_dim] -> [batch, seq, hidden]
        attn_output = attn_output.transpose(1, 2).contiguous()
        attn_output = attn_output.view(batch_size, seq_len, self.hidden_size)

        # Output projection
        output = self.o_proj(attn_output)

        return output, kv_cache


def create_causal_mask(
    seq_len: int,
    kv_seq_len: int,
    dtype: torch.dtype,
    device: torch.device,
) -> torch.Tensor:
    """Create a causal attention mask.

    Args:
        seq_len: Query sequence length
        kv_seq_len: Key/value sequence length
        dtype: Data type for mask
        device: Device for mask

    Returns:
        Causal mask tensor [1, 1, seq_len, kv_seq_len]
    """
    # Create lower triangular mask
    mask = torch.full((seq_len, kv_seq_len), float("-inf"), dtype=dtype, device=device)

    # For decode (seq_len=1), we can attend to all previous tokens
    if seq_len == 1:
        mask = torch.zeros((seq_len, kv_seq_len), dtype=dtype, device=device)
    else:
        # For prefill, create standard causal mask
        # Position i can attend to positions 0..i
        for i in range(seq_len):
            # Offset for KV cache
            offset = kv_seq_len - seq_len
            mask[i, : offset + i + 1] = 0.0

    return mask.unsqueeze(0).unsqueeze(0)