from abc import ABC, abstractmethod
from itertools import groupby
from typing import List, Optional, Tuple

import torch
from torch import Tensor
from torch.nn.utils.rnn import pad_sequence


class CharsetAdapter:
    """Transforms labels according to the target charset."""

    def __init__(self, target_charset) -> None:
        super().__init__()
        self.charset = target_charset
        self.lowercase_only = target_charset == target_charset.lower()
        self.uppercase_only = target_charset == target_charset.upper()

    def __call__(self, label):
        if self.lowercase_only:
            label = label.lower()
        elif self.uppercase_only:
            label = label.upper()
        return label


class BaseTokenizer(ABC):

    def __init__(
        self, charset: str, specials_first: tuple = (), specials_last: tuple = ()
    ) -> None:
        self._itos = specials_first + tuple(charset + "[UNK]") + specials_last
        self._stoi = {s: i for i, s in enumerate(self._itos)}

    def __len__(self):
        return len(self._itos)

    def _tok2ids(self, tokens: str) -> List[int]:
        return [self._stoi[s] for s in tokens]

    def _ids2tok(self, token_ids: List[int], join: bool = True) -> str:
        tokens = [self._itos[i] for i in token_ids]
        return "".join(tokens) if join else tokens

    @abstractmethod
    def encode(
        self, labels: List[str], device: Optional[torch.device] = None
    ) -> Tensor:
        """Encode a batch of labels to a representation suitable for the model.

        Args:
            labels: List of labels. Each can be of arbitrary length.
            device: Create tensor on this device.

        Returns:
            Batched tensor representation padded to the max label length. Shape: N, L
        """
        raise NotImplementedError

    @abstractmethod
    def _filter(self, probs: Tensor, ids: Tensor) -> Tuple[Tensor, List[int]]:
        """Internal method which performs the necessary filtering prior to decoding."""
        raise NotImplementedError

    def decode(
        self, token_dists: Tensor, raw: bool = False
    ) -> Tuple[List[str], List[Tensor]]:
        """Decode a batch of token distributions.

        Args:
            token_dists: softmax probabilities over the token distribution. Shape: N, L, C
            raw: return unprocessed labels (will return list of list of strings)

        Returns:
            list of string labels (arbitrary length) and
            their corresponding sequence probabilities as a list of Tensors
        """
        batch_tokens = []
        batch_probs = []
        for dist in token_dists:
            probs, ids = dist.max(-1)  # greedy selection
            if not raw:
                probs, ids = self._filter(probs, ids)
            tokens = self._ids2tok(ids, not raw)
            batch_tokens.append(tokens)
            batch_probs.append(probs)
        return batch_tokens, batch_probs


class Tokenizer(BaseTokenizer):
    BOS = "[B]"
    EOS = "[E]"
    PAD = "[P]"

    def __init__(self, charset: str) -> None:
        specials_first = (self.EOS,)
        specials_last = (self.BOS, self.PAD)
        super().__init__(charset, specials_first, specials_last)
        self.eos_id, self.bos_id, self.pad_id = [
            self._stoi[s] for s in specials_first + specials_last
        ]

    def encode(
        self, labels: List[str], device: Optional[torch.device] = None
    ) -> Tensor:
        batch = [
            torch.as_tensor(
                [self.bos_id] + self._tok2ids(y) + [self.eos_id],
                dtype=torch.long,
                device=device,
            )
            for y in labels
        ]
        return pad_sequence(batch, batch_first=True, padding_value=self.pad_id)

    def _filter(self, probs: Tensor, ids: Tensor) -> Tuple[Tensor, List[int]]:
        ids = ids.tolist()
        try:
            eos_idx = ids.index(self.eos_id)
        except ValueError:
            eos_idx = len(ids)  # Nothing to truncate.
        # Truncate after EOS
        ids = ids[:eos_idx]
        # but include prob. for EOS (if it exists)
        probs = probs[: eos_idx + 1]
        return probs, ids


class CTCTokenizer(BaseTokenizer):
    BLANK = "[B]"

    def __init__(self, charset: str) -> None:
        # BLANK uses index == 0 by default
        super().__init__(charset, specials_first=(self.BLANK,))
        self.blank_id = self._stoi[self.BLANK]

    def encode(
        self, labels: List[str], device: Optional[torch.device] = None
    ) -> Tensor:
        # We use a padded representation since we don't want to use CUDNN's CTC implementation
        batch = [
            torch.as_tensor(self._tok2ids(y), dtype=torch.long, device=device)
            for y in labels
        ]
        return pad_sequence(batch, batch_first=True, padding_value=self.blank_id)

    def _filter(self, probs: Tensor, ids: Tensor) -> Tuple[Tensor, List[int]]:
        # Best path decoding:
        ids = list(zip(*groupby(ids.tolist())))[0]  # Remove duplicate tokens
        ids = [x for x in ids if x != self.blank_id]  # Remove BLANKs
        # `probs` is just pass-through since all positions are considered part of the path
        return probs, ids