import re
import sys
import typing as tp
import unicodedata

import torch
from sacremoses import MosesPunctNormalizer
from sentence_splitter import SentenceSplitter
from transformers import AutoModelForSeq2SeqLM, NllbTokenizer

L1 = "spa_Latn"
L2 = "agr_Latn"
LANGUAGES = {
    "Spanish | spa": L1,
    "Awajun | agr": L2,
}

def get_non_printing_char_replacer(replace_by: str = " ") -> tp.Callable[[str], str]:
    non_printable_map = {
        ord(c): replace_by
        for c in (chr(i) for i in range(sys.maxunicode + 1))
        # same as \p{C} in perl
        # see https://www.unicode.org/reports/tr44/#General_Category_Values
        if unicodedata.category(c) in {"C", "Cc", "Cf", "Cs", "Co", "Cn"}
    }

    def replace_non_printing_char(line) -> str:
        return line.translate(non_printable_map)

    return replace_non_printing_char

class TextPreprocessor:
    """
    Mimic the text preprocessing made for the NLLB model.
    This code is adapted from the Stopes repo of the NLLB team:
    https://github.com/facebookresearch/stopes/blob/main/stopes/pipelines/monolingual/monolingual_line_processor.py#L214
    """

    def __init__(self, lang="en"):
        self.mpn = MosesPunctNormalizer(lang=lang)
        self.mpn.substitutions = [
            (re.compile(r), sub) for r, sub in self.mpn.substitutions
        ]
        self.replace_nonprint = get_non_printing_char_replacer(" ")

    def __call__(self, text: str) -> str:
        clean = self.mpn.normalize(text)
        clean = self.replace_nonprint(clean)
        # replace 𝓕𝔯𝔞𝔫𝔠𝔢𝔰𝔠𝔞 by Francesca
        clean = unicodedata.normalize("NFKC", clean)
        return clean

def fix_tokenizer(tokenizer, new_lang=L2):
    """Add a new language token to the tokenizer vocabulary
    (this should be done each time after its initialization)
    """
    old_len = len(tokenizer) - int(new_lang in tokenizer.added_tokens_encoder)
    tokenizer.lang_code_to_id[new_lang] = old_len - 1
    tokenizer.id_to_lang_code[old_len - 1] = new_lang
    # always move "mask" to the last position
    tokenizer.fairseq_tokens_to_ids["<mask>"] = (
        len(tokenizer.sp_model)
        + len(tokenizer.lang_code_to_id)
        + tokenizer.fairseq_offset
    )

    tokenizer.fairseq_tokens_to_ids.update(tokenizer.lang_code_to_id)
    tokenizer.fairseq_ids_to_tokens = {
        v: k for k, v in tokenizer.fairseq_tokens_to_ids.items()
    }
    if new_lang not in tokenizer._additional_special_tokens:
        tokenizer._additional_special_tokens.append(new_lang)
    # clear the added token encoder; otherwise a new token may end up there by mistake
    tokenizer.added_tokens_encoder = {}
    tokenizer.added_tokens_decoder = {}

def sentenize_with_fillers(text, splitter, fix_double_space=True, ignore_errors=False):
    """Apply a sentence splitter and return the sentences and all separators before and after them"""
    if fix_double_space:
        text = re.sub(" +", " ", text)
    sentences = splitter.split(text)
    fillers = []
    i = 0
    for sentence in sentences:
        start_idx = text.find(sentence, i)
        if ignore_errors and start_idx == -1:
            # print(f"sent not found after {i}: `{sentence}`")
            start_idx = i + 1
        assert start_idx != -1, f"sent not found after {i}: `{sentence}`"
        fillers.append(text[i:start_idx])
        i = start_idx + len(sentence)
    fillers.append(text[i:])
    return sentences, fillers