"""PyTorch Lightning model for base Transformers."""
from typing import Sequence, Tuple, Type

import attr
import torch
from torch import Tensor

from text_recognizer.data.mappings import AbstractMapping
from text_recognizer.models.metrics import CharacterErrorRate
from text_recognizer.models.base import BaseLitModel


@attr.s(auto_attribs=True)
class TransformerLitModel(BaseLitModel):
    """A PyTorch Lightning model for transformer networks."""
    mapping: Type[AbstractMapping] = attr.ib()
    start_token: str = attr.ib()
    end_token: str = attr.ib()
    pad_token: str = attr.ib()

    start_index: Tensor = attr.ib(init=False)
    end_index: Tensor = attr.ib(init=False)
    pad_index: Tensor = attr.ib(init=False)

    ignore_indices: Sequence[str] = attr.ib(init=False)
    val_cer: CharacterErrorRate = attr.ib(init=False)
    test_cer: CharacterErrorRate = attr.ib(init=False)

    def __attrs_post_init__(self) -> None:
        """Post init configuration."""
        self.start_index = self.mapping.get_index(self.start_token)
        self.end_index = self.mapping.get_index(self.end_token)
        self.pad_index = self.mapping.get_index(self.pad_token)
        self.ignore_indices = [self.start_index, self.end_index, self.pad_index]
        self.val_cer = CharacterErrorRate(self.ignore_indices)
        self.test_cer = CharacterErrorRate(self.ignore_indices)

    def forward(self, data: Tensor) -> Tensor:
        """Forward pass with the transformer network."""
        return self.predict(data)

    def training_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> Tensor:
        """Training step."""
        data, targets = batch
        logits = self.network(data, targets[:, :-1])
        loss = self.loss_fn(logits, targets[:, 1:])
        self.log("train/loss", loss)
        return loss

    def validation_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
        """Validation step."""
        data, targets = batch

        # Compute the loss.
        logits = self.network(data, targets[:-1])
        loss = self.loss_fn(logits, targets[1:])
        self.log("val/loss", loss, prog_bar=True)

        # Get the token prediction.
        pred = self(data)
        self.val_cer(pred, targets)
        self.log("val/cer", self.val_cer, on_step=False, on_epoch=True, prog_bar=True)

    def test_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
        """Test step."""
        data, targets = batch

        # Compute the text prediction.
        pred = self(data)
        self.test_cer(pred, targets)
        self.log("test/cer", self.test_cer, on_step=False, on_epoch=True, prog_bar=True)

    def predict(self, x: Tensor) -> Tensor:
        """Predicts text in image.
        
        Args:
            x (Tensor): Image(s) to extract text from.

        Shapes:
            - x: :math: `(B, H, W)`
            - output: :math: `(B, S)`

        Returns:
            Tensor: A tensor of token indices of the predictions from the model.
        """
        bsz = x.shape[0]

        # Encode image(s) to latent vectors.
        z = self.network.encode(x)

        # Create a placeholder matrix for storing outputs from the network
        output = torch.ones((bsz, self.max_output_len), dtype=torch.long).to(x.device)
        output[:, 0] = self.start_index

        for i in range(1, self.max_output_len):
            context = output[:, :i]  # (bsz, i)
            logits = self.network.decode(z, context)  # (i, bsz, c)
            tokens = torch.argmax(logits, dim=-1)  # (i, bsz)
            output[:, i : i + 1] = tokens[-1:]

            # Early stopping of prediction loop if token is end or padding token.
            if (
                output[:, i - 1] == self.end_index | output[: i - 1] == self.pad_index
            ).all():
                break

        # Set all tokens after end token to pad token.
        for i in range(1, self.max_output_len):
            idx = (
                output[:, i - 1] == self.end_index | output[:, i - 1] == self.pad_index
            )
            output[idx, i] = self.pad_index

        return output