"""PyTorch Lightning model for base Transformers."""
from typing import Dict, List, Optional, Union, Tuple, Type

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

from text_recognizer.data.emnist import emnist_mapping
from text_recognizer.data.mappings import AbstractMapping
from text_recognizer.models.metrics import CharacterErrorRate
from text_recognizer.models.base import LitBaseModel


@attr.s
class TransformerLitModel(LitBaseModel):
    """A PyTorch Lightning model for transformer networks."""

    network: Type[nn.Module] = attr.ib()
    criterion_config: DictConfig = attr.ib(converter=DictConfig)
    optimizer_config: DictConfig = attr.ib(converter=DictConfig)
    lr_scheduler_config: DictConfig = attr.ib(converter=DictConfig)
    monitor: str = attr.ib()
    mapping: Type[AbstractMapping] = attr.ib()

    def __attrs_post_init__(self) -> None:
        super().__init__(
            network=self.network,
            optimizer_config=self.optimizer_config,
            lr_scheduler_config=self.lr_scheduler_config,
            criterion_config=self.criterion_config,
            monitor=self.monitor,
        )
        self.mapping, ignore_tokens = self.configure_mapping(mapping)
        self.val_cer = CharacterErrorRate(ignore_tokens)
        self.test_cer = CharacterErrorRate(ignore_tokens)

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

    @staticmethod
    def configure_mapping(mapping: Optional[List[str]]) -> Tuple[List[str], List[int]]:
        """Configure mapping."""
        # TODO: Fix me!!!
        mapping, inverse_mapping, _ = emnist_mapping(["\n"])
        start_index = inverse_mapping["<s>"]
        end_index = inverse_mapping["<e>"]
        pad_index = inverse_mapping["<p>"]
        ignore_tokens = [start_index, end_index, pad_index]
        # TODO: add case for sentence pieces
        return mapping, ignore_tokens

    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

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

        pred = self.network.predict(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
        pred = self.network.predict(data)
        self.test_cer(pred, targets)
        self.log("test/cer", self.test_cer, on_step=False, on_epoch=True, prog_bar=True)