import logging
import os
import warnings

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

from einops import rearrange

XFORMERS_AVAILABLE = False


class Attention(nn.Module):
    def __init__(
        self,
        dim: int,
        num_heads: int = 8,
        qkv_bias: bool = True,
        proj_bias: bool = True,
        attn_drop: float = 0.0,
        proj_drop: float = 0.0,
        norm_layer: nn.Module = nn.LayerNorm,
        qk_norm: bool = False,
        fused_attn: bool = True,  # use F.scaled_dot_product_attention or not
        rope=None,
    ) -> None:
        super().__init__()
        assert dim % num_heads == 0, "dim should be divisible by num_heads"
        self.num_heads = num_heads
        self.head_dim = dim // num_heads
        self.scale = self.head_dim**-0.5
        self.fused_attn = fused_attn

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
        self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim, bias=proj_bias)
        self.proj_drop = nn.Dropout(proj_drop)
        self.rope = rope

    def forward(self, 
        x: torch.Tensor, 
        pos=None, 
        attn_mask=None, 
        past_key_values=None, 
        use_cache=False
    ) -> Union[torch.Tensor, Tuple[torch.Tensor, Tuple]]:
        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
        q, k, v = qkv.unbind(0)

        pos_k = pos
        if use_cache:
            k = k.unsqueeze(2)
            v = v.unsqueeze(2)
            if past_key_values is not None:
                past_k, past_v = past_key_values
                k = torch.cat([past_k, k], dim=2)
                v = torch.cat([past_v, v], dim=2)
                
            new_kv = (k, v)
            a, b, c, d, e = k.shape
            k = k.reshape(a, b, c*d, e)
            v = v.reshape(a, b, c*d, e)
            if pos_k is not None:
                #print(pos_k.shape)
                pos_k = pos_k.repeat(1, c, 1)
                #print(pos_k.shape)

        q, k = self.q_norm(q), self.k_norm(k)

        if self.rope is not None:
            q = self.rope(q, pos)
            k = self.rope(k, pos_k)

        if self.fused_attn:
            x = F.scaled_dot_product_attention(
                q,
                k,
                v,
                attn_mask=attn_mask,
                dropout_p=self.attn_drop.p if self.training else 0.0,
            )

        else:
            q = q * self.scale
            attn = q @ k.transpose(-2, -1)

            # Mask
            if attn_mask is not None:
                assert attn_mask.shape[-2:] == (N, N), f"Expected mask shape [..., {N}, {N}], got {attn_mask.shape}"
                attn = attn + attn_mask

            attn = attn.softmax(dim=-1)
            attn = self.attn_drop(attn)
            x = attn @ v

        x = x.transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        if use_cache:
            return x, new_kv
        return x


class MemEffAttention(Attention):
    def forward(self, x: Tensor, attn_bias=None, pos=None) -> Tensor:
        assert pos is None
        if not XFORMERS_AVAILABLE:
            if attn_bias is not None:
                raise AssertionError("xFormers is required for using nested tensors")
            return super().forward(x)

        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)

        q, k, v = unbind(qkv, 2)

        x = memory_efficient_attention(q, k, v, attn_bias=attn_bias)
        x = x.reshape([B, N, C])

        x = self.proj(x)
        x = self.proj_drop(x)

        return x