import copy
from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers, pre_tokenizers
from transformers import DebertaV2TokenizerFast


class DebertaV2JumanppTokenizerFast(DebertaV2TokenizerFast):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.juman_normalizer = normalizers.Sequence(
            [
                # cf. https://github.com/ku-nlp/rhoknp/blob/v1.3.0/src/rhoknp/units/sentence.py#L36
                normalizers.Replace("\r", ""),
                normalizers.Replace("\n", ""),
                # cf. https://github.com/ku-nlp/jumanpp/blob/v2.0.0-rc3/src/jumandic/shared/juman_format.cc#L44-L61
                normalizers.Replace("\t", "\\t"),
                normalizers.Replace(" ", "　"),
                normalizers.Replace('"', "”"),
                normalizers.Replace("<", "＜"),
                normalizers.Replace(">", "＞"),
            ]
        )
        self.juman_pre_tokenizer = pre_tokenizers.PreTokenizer.custom(JumanppPreTokenizer())

        self.default_normalizer = copy.deepcopy(self.backend_tokenizer.normalizer)
        self.default_pre_tokenizer = copy.deepcopy(self.backend_tokenizer.pre_tokenizer)

        self.backend_tokenizer.normalizer = normalizers.Sequence(
            [self.juman_normalizer, self.backend_tokenizer.normalizer]
        )
        self.backend_tokenizer.pre_tokenizer = pre_tokenizers.Sequence(
            [self.juman_pre_tokenizer, self.backend_tokenizer.pre_tokenizer]
        )

    def save_pretrained(self, *args, **kwargs):
        self.backend_tokenizer.normalizer = self.default_normalizer
        self.backend_tokenizer.pre_tokenizer = self.default_pre_tokenizer
        super().save_pretrained(*args, **kwargs)

        self.backend_tokenizer.normalizer = normalizers.Sequence(
            [self.juman_normalizer, self.backend_tokenizer.normalizer]
        )
        self.backend_tokenizer.pre_tokenizer = pre_tokenizers.Sequence(
            [self.juman_pre_tokenizer, self.backend_tokenizer.pre_tokenizer]
        )


class JumanppPreTokenizer:
    def __init__(self):
        try:
            import rhoknp
        except ImportError:
            raise ImportError(
                "You need to install rhoknp to use JumanppPreTokenizer. "
                "See https://github.com/ku-nlp/rhoknp for installation."
            )
        self.jumanpp = rhoknp.Jumanpp()

    def pre_tokenize(self, pretok: PreTokenizedString):
        pretok.split(self.jumanpp_split)

    def jumanpp_split(self, i: int, normalized_string: NormalizedString) -> List[NormalizedString]:
        offsets = [morpheme.span for morpheme in self.jumanpp.apply_to_sentence(str(normalized_string)).morphemes]
        if not offsets:
            doc = rhoknp.Document.from_raw_text(str(normalized_string))
            offsets = [morpheme.span for morpheme in self.jumanpp.apply_to_document(doc).morphemes]
        return [normalized_string[offset[0]:offset[1]] for offset in offsets]