# -*- coding: utf-8 -*-
from typing import Dict, List, Tuple, Union
import re
from transformers import (
    CamembertTokenizer,
    AutoTokenizer,
    pipeline,
)

_model_name = "wangchanberta-base-att-spm-uncased"
_tokenizer = CamembertTokenizer.from_pretrained(
        f'airesearch/{_model_name}',
        revision='main')
if _model_name == "wangchanberta-base-att-spm-uncased":
    _tokenizer.additional_special_tokens = ['<s>NOTUSED', '</s>NOTUSED', '<_>']


class ThaiNameTagger:
    def __init__(
        self,
        dataset_name: str = "thainer",
        grouped_entities: bool = True
    ):
        """
        This function tags named-entitiy from text in IOB format.

        Powered by wangchanberta from VISTEC-depa\
             AI Research Institute of Thailand

        :param str dataset_name:
            * *thainer* - ThaiNER dataset
            * *lst20* - LST20 Corpus
        :param bool grouped_entities: grouped entities
        """
        self.dataset_name = dataset_name
        self.grouped_entities = grouped_entities
        self.classify_tokens = pipeline(
            task='ner',
            tokenizer=_tokenizer,
            model=f'airesearch/{_model_name}',
            revision=f'finetuned@{self.dataset_name}-ner',
            ignore_labels=[],
            grouped_entities=self.grouped_entities)

    def _IOB(self, tag):
        if tag != "O":
            return "B-"+tag
        return "O"

    def _clear_tag(self, tag):
        return tag.replace('B-', '').replace('I-', '')

    def get_ner(
        self, text: str, tag: bool = False
    ) -> Union[List[Tuple[str, str]], str]:
        """
        This function tags named-entitiy from text in IOB format.
        Powered by wangchanberta from VISTEC-depa\
             AI Research Institute of Thailand

        :param str text: text in Thai to be tagged
        :param bool tag: output like html tag.
        :return: a list of tuple associated with tokenized word group, NER tag, \
                 and output like html tag (if the parameter `tag` is \
                 specified as `True`). \
                 Otherwise, return a list of tuple associated with tokenized \
                 word and NER tag
        :rtype: Union[list[tuple[str, str]]], str
        """
        text = re.sub(" ", "<_>", text)
        self.json_ner = self.classify_tokens(text)
        self.output = ""
        if self.grouped_entities and self.dataset_name == "thainer":
            self.sent_ner = [
                (
                    i['word'].replace("<_>", " ").replace('▁', ''),
                    self._IOB(i['entity_group'])
                ) for i in self.json_ner
            ]
        elif self.dataset_name == "thainer":
            self.sent_ner = [
                (
                    i['word'].replace("<_>", " ").replace('▁', ''), i['entity']
                ) for i in self.json_ner if i['word'] != '▁'
            ]
        elif self.grouped_entities and self.dataset_name == "lst20":
            self.sent_ner = [
                (
                    i['word'].replace("<_>", " ").replace('▁', ''),
                    i['entity_group'].replace('_', '-').replace('E-', 'I-')
                ) for i in self.json_ner
            ]
        else:
            self.sent_ner = [
                (
                    i['word'].replace("<_>", " ").replace('▁', ''),
                    i['entity'].replace('_', '-').replace('E-', 'I-')
                ) for i in self.json_ner
            ]
        if self.sent_ner[0][0] == '' and len(self.sent_ner) > 1:
            self.sent_ner = self.sent_ner[1:]
        for idx, (word, ner) in enumerate(self.sent_ner):
            if idx > 0 and ner.startswith("B-"):
                if (
                    self._clear_tag(ner) == self._clear_tag(
                        self.sent_ner[idx-1][1]
                    )
                ):
                    self.sent_ner[idx] = (word, ner.replace('B-', 'I-'))
        if tag:
            temp = ""
            sent = ""
            for idx, (word, ner) in enumerate(self.sent_ner):
                if ner.startswith("B-") and temp != "":
                    sent += "</" + temp + ">"
                    temp = ner[2:]
                    sent += "<" + temp + ">"
                elif ner.startswith("B-"):
                    temp = ner[2:]
                    sent += "<" + temp + ">"
                elif ner == "O" and temp != "":
                    sent += "</" + temp + ">"
                    temp = ""
                sent += word

                if idx == len(self.sent_ner) - 1 and temp != "":
                    sent += "</" + temp + ">"

            return sent
        else:
            return self.sent_ner


def segment(text: str) -> List[str]:
    """
    Subword tokenize. SentencePiece from wangchanberta model.

    :param str text: text to be tokenized
    :return: list of subwords
    :rtype: list[str]
    """
    if not text or not isinstance(text, str):
        return []

    return _tokenizer.tokenize(text)

# -*- coding: utf-8 -*-
from typing import Dict, List, Tuple, Union
import re
from transformers import (
    CamembertTokenizer,
    AutoTokenizer,
    pipeline,
)

_model_name = "wangchanberta-base-att-spm-uncased"
_tokenizer = CamembertTokenizer.from_pretrained(
        f'airesearch/{_model_name}',
        revision='main')
if _model_name == "wangchanberta-base-att-spm-uncased":
    _tokenizer.additional_special_tokens = ['<s>NOTUSED', '</s>NOTUSED', '<_>']


class ThaiNameTagger:
    def __init__(
        self,
        dataset_name: str = "thainer",
        grouped_entities: bool = True
    ):
        """
        This function tags named-entitiy from text in IOB format.

        Powered by wangchanberta from VISTEC-depa\
             AI Research Institute of Thailand

        :param str dataset_name:
            * *thainer* - ThaiNER dataset
            * *lst20* - LST20 Corpus
        :param bool grouped_entities: grouped entities
        """
        self.dataset_name = dataset_name
        self.grouped_entities = grouped_entities
        self.classify_tokens = pipeline(
            task='ner',
            tokenizer=_tokenizer,
            model=f'airesearch/{_model_name}',
            revision=f'finetuned@{self.dataset_name}-ner',
            ignore_labels=[],
            grouped_entities=self.grouped_entities)

    def _IOB(self, tag):
        if tag != "O":
            return "B-"+tag
        return "O"

    def _clear_tag(self, tag):
        return tag.replace('B-', '').replace('I-', '')

    def get_ner(
        self, text: str, tag: bool = False
    ) -> Union[List[Tuple[str, str]], str]:
        """
        This function tags named-entitiy from text in IOB format.
        Powered by wangchanberta from VISTEC-depa\
             AI Research Institute of Thailand

        :param str text: text in Thai to be tagged
        :param bool tag: output like html tag.
        :return: a list of tuple associated with tokenized word group, NER tag, \
                 and output like html tag (if the parameter `tag` is \
                 specified as `True`). \
                 Otherwise, return a list of tuple associated with tokenized \
                 word and NER tag
        :rtype: Union[list[tuple[str, str]]], str
        """
        text = re.sub(" ", "<_>", text)
        self.json_ner = self.classify_tokens(text)
        self.output = ""
        if self.grouped_entities and self.dataset_name == "thainer":
            self.sent_ner = [
                (
                    i['word'].replace("<_>", " ").replace('▁', ''),
                    self._IOB(i['entity_group'])
                ) for i in self.json_ner
            ]
        elif self.dataset_name == "thainer":
            self.sent_ner = [
                (
                    i['word'].replace("<_>", " ").replace('▁', ''), i['entity']
                ) for i in self.json_ner if i['word'] != '▁'
            ]
        elif self.grouped_entities and self.dataset_name == "lst20":
            self.sent_ner = [
                (
                    i['word'].replace("<_>", " ").replace('▁', ''),
                    i['entity_group'].replace('_', '-').replace('E-', 'I-')
                ) for i in self.json_ner
            ]
        else:
            self.sent_ner = [
                (
                    i['word'].replace("<_>", " ").replace('▁', ''),
                    i['entity'].replace('_', '-').replace('E-', 'I-')
                ) for i in self.json_ner
            ]
        if self.sent_ner[0][0] == '' and len(self.sent_ner) > 1:
            self.sent_ner = self.sent_ner[1:]
        for idx, (word, ner) in enumerate(self.sent_ner):
            if idx > 0 and ner.startswith("B-"):
                if (
                    self._clear_tag(ner) == self._clear_tag(
                        self.sent_ner[idx-1][1]
                    )
                ):
                    self.sent_ner[idx] = (word, ner.replace('B-', 'I-'))
        if tag:
            temp = ""
            sent = ""
            for idx, (word, ner) in enumerate(self.sent_ner):
                if ner.startswith("B-") and temp != "":
                    sent += "</" + temp + ">"
                    temp = ner[2:]
                    sent += "<" + temp + ">"
                elif ner.startswith("B-"):
                    temp = ner[2:]
                    sent += "<" + temp + ">"
                elif ner == "O" and temp != "":
                    sent += "</" + temp + ">"
                    temp = ""
                sent += word

                if idx == len(self.sent_ner) - 1 and temp != "":
                    sent += "</" + temp + ">"

            return sent
        else:
            return self.sent_ner


def segment(text: str) -> List[str]:
    """
    Subword tokenize. SentencePiece from wangchanberta model.

    :param str text: text to be tokenized
    :return: list of subwords
    :rtype: list[str]
    """
    if not text or not isinstance(text, str):
        return []

    return _tokenizer.tokenize(text)

# -*- coding: utf-8 -*- This function tags named-entitiy from text in IOB format.          Powered by wangchanberta from VISTEC-depa\              AI Research Institute of Thailand          :param str dataset_name:             * *thainer* - ThaiNER dataset             * *lst20* - LST20 Corpus         :param bool grouped_entities: grouped entities This function tags named-entitiy from text in IOB format.         Powered by wangchanberta from VISTEC-depa\              AI Research Institute of Thailand          :param str text: text in Thai to be tagged         :param bool tag: output like html tag.         :return: a list of tuple associated with tokenized word group, NER tag, \                  and output like html tag (if the parameter `tag` is \                  specified as `True`). \                  Otherwise, return a list of tuple associated with tokenized \                  word and NER tag         :rtype: Union[list[tuple[str, str]]], str Subword tokenize. SentencePiece from wangchanberta model.      :param str text: text to be tokenized     :return: list of subwords     :rtype: list[str]