"""Lightning model for base Transformers."""
from collections.abc import Sequence
from typing import Optional, Tuple, Type

import torch
from omegaconf import DictConfig
from torch import nn, Tensor

from text_recognizer.data.tokenizer import Tokenizer
from text_recognizer.models.base import LitBase
from text_recognizer.models.metrics.cer import CharacterErrorRate
from text_recognizer.models.metrics.wer import WordErrorRate


class LitTransformer(LitBase):
    """A PyTorch Lightning model for transformer networks."""

    def __init__(
        self,
        network: Type[nn.Module],
        loss_fn: Type[nn.Module],
        optimizer_config: DictConfig,
        tokenizer: Tokenizer,
        lr_scheduler_config: Optional[DictConfig] = None,
        max_output_len: int = 682,
    ) -> None:
        super().__init__(
            network,
            loss_fn,
            optimizer_config,
            lr_scheduler_config,
            tokenizer,
        )
        self.max_output_len = max_output_len
        self.ignore_indices = set([self.start_index, self.end_index, self.pad_index])
        self.val_cer = CharacterErrorRate(self.ignore_indices)
        self.test_cer = CharacterErrorRate(self.ignore_indices)
        self.val_wer = WordErrorRate(self.ignore_indices)
        self.test_wer = WordErrorRate(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
        preds = self.predict(data)
        pred_text, target_text = self.get_text(preds, targets)
        self.val_acc(pred_text, target_text)
        self.log("val/acc", self.val_acc, on_step=False, on_epoch=True)
        self.val_cer(pred_text, target_text)
        self.log("val/cer", self.val_cer, on_step=False, on_epoch=True, prog_bar=True)
        self.val_wer(pred_text, target_text)
        self.log("val/wer", self.val_wer, 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.
        preds = self(data)
        pred_text, target_text = self.get_text(preds, targets)
        self.test_acc(pred_text, target_text)
        self.log("test/acc", self.test_acc, on_step=False, on_epoch=True)
        self.test_cer(pred_text, target_text)
        self.log("test/cer", self.test_cer, on_step=False, on_epoch=True, prog_bar=True)
        self.test_wer(pred_text, target_text)
        self.log("test/wer", self.test_wer, on_step=False, on_epoch=True, prog_bar=True)

    def get_text(
        self, preds: Tensor, targets: Tensor
    ) -> Tuple[Sequence[str], Sequence[str]]:
        pred_text = [self.tokenizer.decode(p) for p in preds]
        target_text = [self.tokenizer.decode(t) for t in targets]
        return pred_text, target_text

    @torch.no_grad()
    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.
        """
        start_index = self.tokenizer.start_index
        end_index = self.tokenizer.start_index
        pad_index = self.tokenizer.start_index
        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] = start_index

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

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

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

        return output