"""Implementes the attention module for the transformer."""
from typing import Optional, Tuple

from einops import rearrange
from einops.layers.torch import Rearrange
import torch
from torch import einsum
from torch import nn
from torch import Tensor
import torch.nn.functional as F

from text_recognizer.networks.transformer.positional_encodings.rotary_embedding import (
    apply_rotary_pos_emb,
)


class Attention(nn.Module):
    def __init__(
        self,
        dim: int,
        num_heads: int,
        dim_head: int = 64,
        dropout_rate: float = 0.0,
        causal: bool = False,
    ) -> None:
        self.scale = dim ** -0.5
        self.num_heads = num_heads
        self.causal = causal
        inner_dim = dim * dim_head

        # Attnetion
        self.qkv_fn = nn.Sequential(
            nn.Linear(dim, 3 * inner_dim, bias=False),
            Rearrange("b n (qkv h d) -> qkv b h n d", qkv=3, h=self.num_heads),
        )
        self.dropout = nn.Dropout(dropout_rate)
        self.attn_fn = F.softmax

        # Feedforward
        self.fc = nn.Linear(inner_dim, dim)

    @staticmethod
    def _apply_rotary_emb(
        q: Tensor, k: Tensor, rotary_pos_emb: Tensor
    ) -> Tuple[Tensor, Tensor]:
        l = rotary_pos_emb.shape[-1]
        (ql, qr), (kl, kr) = map(lambda t: (t[..., :l], t[..., l:]), (q, k))
        ql, kl = apply_rotary_pos_emb(ql, kl, rotary_pos_emb)
        q = torch.cat((ql, qr), dim=-1)
        k = torch.cat((kl, kr), dim=-1)
        return q, k

    @staticmethod
    def _compute_input_mask(
        b: int,
        n: int,
        k: Tensor,
        mask: Optional[Tensor],
        context: Optional[Tensor],
        context_mask: Optional[Tensor],
        device: str,
    ) -> Optional[Tensor]:
        if any(x is not None for x in (mask, context_mask)):
            q_mask = (
                mask if mask is not None else torch.ones((b, n), device=device).bool()
            )
            k_mask = q_mask if context is not None else context_mask
            k_mask = (
                torch.ones((b, k.shape[-2]), device=device).bool()
                if k_mask is None
                else k_mask
            )
            q_mask = rearrange(q_mask, "b i -> b () i ()")
            k_mask = rearrange(k_mask, "b i -> b () () j")
            return q_mask * k_mask
        return

    @staticmethod
    def _apply_causal_mask(
        energy: Tensor, mask: Tensor, mask_value: Tensor, device: str
    ) -> Tensor:
        i, j = energy.shape[-2:]
        r = torch.arange(i, device=device)
        mask = rearrange(r, "i -> () () i ()") < rearrange(r, "j -> () () () j")
        mask = F.pad(mask, (j - i, 0), value=False)
        energy.masked_fill_(mask, mask_value)
        del mask
        return energy

    def forward(
        self,
        x: Tensor,
        context: Optional[Tensor],
        mask: Optional[Tensor],
        context_mask: Optional[Tensor],
        rotary_pos_emb: Optional[Tensor] = None,
    ) -> Tuple[Tensor, Tensor]:
        b, n, _, device = x.shape, x.device
        q, k, v = self.qkv_fn(x)
        q, k = (
            self._apply_rotary_emb(q, k, rotary_pos_emb)
            if rotary_pos_emb is not None
            else q,
            k,
        )

        input_mask = self._compute_input_mask(
            b, n, k, mask, context, context_mask, device
        )

        # Compute the attention
        energy = einsum("b h i d, b h j d -> b h i j", q, k) * self.scale
        mask_value = -torch.finfo(energy.dtype).max

        # Apply input mask
        if input_mask is not None:
            energy = energy.masked_fill_(~input_mask, mask_value)
            del input_mask

        if self.causal:
            energy = self._apply_causal_mask(energy, mask, mask_value, device)

        attn = self.attn_fn(energy, dim=-1)
        attn = self.dropout(attn)
        out = einsum("b h i j, b h j d -> b h i d", attn, v)
        out = rearrange(out, "b h n d -> b n (h d)")
        out = self.fc(out)
        return out, attn