"""Vector quantized encoder, transformer decoder."""
from pathlib import Path
from typing import Tuple, Optional

import torch
from torch import Tensor

from text_recognizer.networks.vqvae.vqvae import VQVAE
from text_recognizer.networks.conv_transformer import ConvTransformer
from text_recognizer.networks.transformer.layers import Decoder


class VqTransformer(ConvTransformer):
    """Convolutional encoder and transformer decoder network."""

    def __init__(
        self,
        input_dims: Tuple[int, int, int],
        encoder_dim: int,
        hidden_dim: int,
        dropout_rate: float,
        num_classes: int,
        pad_index: Tensor,
        encoder: VQVAE,
        decoder: Decoder,
        no_grad: bool,
        pretrained_encoder_path: Optional[str] = None,
    ) -> None:
        super().__init__(
            input_dims=input_dims,
            encoder_dim=encoder_dim,
            hidden_dim=hidden_dim,
            dropout_rate=dropout_rate,
            num_classes=num_classes,
            pad_index=pad_index,
            encoder=encoder,
            decoder=decoder,
        )
        # For typing
        self.encoder: VQVAE

        self.no_grad = no_grad

        if pretrained_encoder_path is not None:
            self.pretrained_encoder_path = (
                Path(__file__).resolve().parents[2] / pretrained_encoder_path
            )
            self._setup_encoder()
        else:
            self.pretrained_encoder_path = None

    def _load_pretrained_encoder(self) -> None:
        self.encoder.load_state_dict(
            torch.load(self.pretrained_encoder_path)["state_dict"]["network"]
        )

    def _setup_encoder(self) -> None:
        """Remove unecessary layers."""
        self._load_pretrained_encoder()
        del self.encoder.decoder
        # del self.encoder.post_codebook_conv

    def _encode(self, x: Tensor) -> Tuple[Tensor, Tensor]:
        z_e = self.encoder.encode(x)
        z_q, commitment_loss = self.encoder.quantize(z_e)
        return z_q, commitment_loss

    def encode(self, x: Tensor) -> Tuple[Tensor, Tensor]:
        """Encodes an image into a discrete (VQ) latent representation.

        Args:
            x (Tensor): Image tensor.

        Shape:
            - x: :math: `(B, C, H, W)`
            - z: :math: `(B, Sx, E)`

            where Sx is the length of the flattened feature maps projected from
            the encoder. E latent dimension for each pixel in the projected
            feature maps.

        Returns:
            Tensor: A Latent embedding of the image.
        """
        if self.no_grad:
            with torch.no_grad():
                z_q, commitment_loss = self._encode(x)
        else:
            z_q, commitment_loss = self._encode(x)

        z = self.latent_encoder(z_q)

        # Permute tensor from [B, E, Ho * Wo] to [B, Sx, E]
        z = z.permute(0, 2, 1)
        return z, commitment_loss

    def forward(self, x: Tensor, context: Tensor) -> Tensor:
        """Encodes images into word piece logtis.

        Args:
            x (Tensor): Input image(s).
            context (Tensor): Target word embeddings.

        Shapes:
            - x: :math: `(B, C, H, W)`
            - context: :math: `(B, Sy, T)`

            where B is the batch size, C is the number of input channels, H is
            the image height and W is the image width.

        Returns:
            Tensor: Sequence of logits.
        """
        z, commitment_loss = self.encode(x)
        logits = self.decode(z, context)
        return logits, commitment_loss