import torch
from torch import einsum, nn
import torch.nn.functional as F
from einops import rearrange, repeat

# helper functions

def exists(val):
    return val is not None

def default(val, d):
    return val if exists(val) else d

# normalization
# they use layernorm without bias, something that pytorch does not offer


class LayerNorm(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(dim))
        self.register_buffer("bias", torch.zeros(dim))

    def forward(self, x):
        return F.layer_norm(x, x.shape[-1:], self.weight, self.bias)

# residual


class Residual(nn.Module):
    def __init__(self, fn):
        super().__init__()
        self.fn = fn

    def forward(self, x, *args, **kwargs):
        return self.fn(x, *args, **kwargs) + x


# rotary positional embedding


class RotaryEmbedding(nn.Module):
    def __init__(self, dim):
        super().__init__()
        inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
        self.register_buffer("inv_freq", inv_freq)

    def forward(self, max_seq_len, *, device):
        seq = torch.arange(max_seq_len, device=device, dtype=self.inv_freq.dtype)
        freqs = einsum("i , j -> i j", seq, self.inv_freq)
        return torch.cat((freqs, freqs), dim=-1)


def rotate_half(x):
    x = rearrange(x, "... (j d) -> ... j d", j=2)
    x1, x2 = x.unbind(dim=-2)
    return torch.cat((-x2, x1), dim=-1)


def apply_rotary_pos_emb(pos, t):
    return (t * pos.cos()) + (rotate_half(t) * pos.sin())


# classic Noam Shazeer paper, except here they use SwiGLU instead of the more popular GEGLU for gating the feedforward


class SwiGLU(nn.Module):
    def forward(self, x):
        x, gate = x.chunk(2, dim=-1)
        return F.silu(gate) * x


# parallel attention and feedforward with residual
# discovered by Wang et al + EleutherAI from GPT-J fame

class ParallelTransformerBlock(nn.Module):
    def __init__(self, dim, dim_head=64, heads=8, ff_mult=4):
        super().__init__()
        self.norm = LayerNorm(dim)

        attn_inner_dim = dim_head * heads
        ff_inner_dim = dim * ff_mult
        self.fused_dims = (attn_inner_dim, dim_head, dim_head, (ff_inner_dim * 2))

        self.heads = heads
        self.scale = dim_head**-0.5
        self.rotary_emb = RotaryEmbedding(dim_head)

        self.fused_attn_ff_proj = nn.Linear(dim, sum(self.fused_dims), bias=False)
        self.attn_out = nn.Linear(attn_inner_dim, dim, bias=False)

        self.ff_out = nn.Sequential(
            SwiGLU(),
            nn.Linear(ff_inner_dim, dim, bias=False)
        )

        self.register_buffer("pos_emb", None, persistent=False)


    def get_rotary_embedding(self, n, device):
        if self.pos_emb is not None and self.pos_emb.shape[-2] >= n:
            return self.pos_emb[:n]

        pos_emb = self.rotary_emb(n, device=device)
        self.register_buffer("pos_emb", pos_emb, persistent=False)
        return pos_emb

    def forward(self, x, attn_mask=None):
        """
        einstein notation
        b - batch
        h - heads
        n, i, j - sequence length (base sequence length, source, target)
        d - feature dimension
        """

        n, device, h = x.shape[1], x.device, self.heads

        # pre layernorm

        x = self.norm(x)

        # attention queries, keys, values, and feedforward inner

        q, k, v, ff = self.fused_attn_ff_proj(x).split(self.fused_dims, dim=-1)

        # split heads
        # they use multi-query single-key-value attention, yet another Noam Shazeer paper
        # they found no performance loss past a certain scale, and more efficient decoding obviously

        q = rearrange(q, "b n (h d) -> b h n d", h=h)

        # rotary embeddings

        positions = self.get_rotary_embedding(n, device)
        q, k = map(lambda t: apply_rotary_pos_emb(positions, t), (q, k))

        # scale

        q = q * self.scale

        # similarity

        sim = einsum("b h i d, b j d -> b h i j", q, k)


        # extra attention mask - for masking out attention from text CLS token to padding

        if exists(attn_mask):
            attn_mask = rearrange(attn_mask, 'b i j -> b 1 i j')
            sim = sim.masked_fill(~attn_mask, -torch.finfo(sim.dtype).max)

        # attention

        sim = sim - sim.amax(dim=-1, keepdim=True).detach()
        attn = sim.softmax(dim=-1)

        # aggregate values

        out = einsum("b h i j, b j d -> b h i d", attn, v)

        # merge heads

        out = rearrange(out, "b h n d -> b n (h d)")
        return self.attn_out(out) + self.ff_out(ff)

# cross attention - using multi-query + one-headed key / values as in PaLM w/ optional parallel feedforward

class CrossAttention(nn.Module):
    def __init__(
        self,
        dim,
        *,
        context_dim=None,
        dim_head=64,
        heads=12,
        parallel_ff=False,
        ff_mult=4,
        norm_context=False
    ):
        super().__init__()
        self.heads = heads
        self.scale = dim_head ** -0.5
        inner_dim = heads * dim_head
        context_dim = default(context_dim, dim)

        self.norm = LayerNorm(dim)
        self.context_norm = LayerNorm(context_dim) if norm_context else nn.Identity()

        self.to_q = nn.Linear(dim, inner_dim, bias=False)
        self.to_kv = nn.Linear(context_dim, dim_head * 2, bias=False)
        self.to_out = nn.Linear(inner_dim, dim, bias=False)

        # whether to have parallel feedforward

        ff_inner_dim = ff_mult * dim

        self.ff = nn.Sequential(
            nn.Linear(dim, ff_inner_dim * 2, bias=False),
            SwiGLU(),
            nn.Linear(ff_inner_dim, dim, bias=False)
        ) if parallel_ff else None

    def forward(self, x, context, mask):
        """
        einstein notation
        b - batch
        h - heads
        n, i, j - sequence length (base sequence length, source, target)
        d - feature dimension
        """

        # pre-layernorm, for queries and context

        x = self.norm(x)
        context = self.context_norm(context)

        # get queries

        q = self.to_q(x)
        q = rearrange(q, 'b n (h d) -> b h n d', h = self.heads)

        # scale

        q = q * self.scale

        # get key / values

        k, v = self.to_kv(context).chunk(2, dim=-1)

        # query / key similarity

        sim = einsum('b h i d, b j d -> b h i j', q, k)

        # attention
        mask = mask.unsqueeze(1).repeat(1,self.heads,1,1)
        sim = sim + mask  # context mask
        sim = sim - sim.amax(dim=-1, keepdim=True)
        attn = sim.softmax(dim=-1)

        # aggregate

        out = einsum('b h i j, b j d -> b h i d', attn, v)

        # merge and combine heads

        out = rearrange(out, 'b h n d -> b n (h d)')
        out = self.to_out(out)

        # add parallel feedforward (for multimodal layers)

        if exists(self.ff):
            out = out + self.ff(x)

        return out


class Cross_model(nn.Module):
    def __init__(
        self,
        dim=512,
        layer_num=4,
        dim_head=64,
        heads=8,
        ff_mult=4
    ):
        super().__init__()

        self.layers = nn.ModuleList([])


        for ind in range(layer_num):
            self.layers.append(nn.ModuleList([
                Residual(CrossAttention(dim=dim, dim_head=dim_head, heads=heads, parallel_ff=True, ff_mult=ff_mult)),
                Residual(ParallelTransformerBlock(dim=dim, dim_head=dim_head, heads=heads, ff_mult=ff_mult))
            ]))

    def forward(
        self,
        query_tokens,
        context_tokens,
        mask
    ):

        for cross_attn, self_attn_ff in self.layers:
            query_tokens = cross_attn(query_tokens, context_tokens,mask)
            query_tokens = self_attn_ff(query_tokens)

        return query_tokens
