import torch

# from transformers import BertTokenizerFast

from third_party.colbert.modeling.hf_colbert import class_factory
from third_party.colbert.infra import ColBERTConfig
from third_party.colbert.modeling.tokenization.utils import _split_into_batches, _sort_by_length, _insert_prefix_token
from third_party.colbert.parameters import DEVICE

class DocTokenizer():
    def __init__(self, config: ColBERTConfig):
        HF_ColBERT = class_factory(config.checkpoint)
        self.tok = HF_ColBERT.raw_tokenizer_from_pretrained(config.checkpoint)

        self.config = config
        self.doc_maxlen = config.doc_maxlen

        self.D_marker_token, self.D_marker_token_id = self.config.doc_token, self.tok.convert_tokens_to_ids(self.config.doc_token_id)
        self.cls_token, self.cls_token_id = self.tok.cls_token, self.tok.cls_token_id
        self.sep_token, self.sep_token_id = self.tok.sep_token, self.tok.sep_token_id

    def tokenize(self, batch_text, add_special_tokens=False):
        assert type(batch_text) in [list, tuple], (type(batch_text))

        tokens = [self.tok.tokenize(x, add_special_tokens=False).to(DEVICE) for x in batch_text]

        if not add_special_tokens:
            return tokens

        prefix, suffix = [self.cls_token, self.D_marker_token], [self.sep_token]
        tokens = [prefix + lst + suffix for lst in tokens]

        return tokens

    def encode(self, batch_text, add_special_tokens=False):
        assert type(batch_text) in [list, tuple], (type(batch_text))

        ids = self.tok(batch_text, add_special_tokens=False).to(DEVICE)['input_ids']

        if not add_special_tokens:
            return ids

        prefix, suffix = [self.cls_token_id, self.D_marker_token_id], [self.sep_token_id]
        ids = [prefix + lst + suffix for lst in ids]

        return ids
        
    def tensorize(self, batch_text, bsize=None):
        assert type(batch_text) in [list, tuple], (type(batch_text))

        obj = self.tok(batch_text, padding='longest', truncation='longest_first',
                       return_tensors='pt', max_length=(self.doc_maxlen - 1)).to(DEVICE)

        ids = _insert_prefix_token(obj['input_ids'], self.D_marker_token_id)
        mask = _insert_prefix_token(obj['attention_mask'], 1)

        if bsize:
            ids, mask, reverse_indices = _sort_by_length(ids, mask, bsize)
            batches = _split_into_batches(ids, mask, bsize)
            return batches, reverse_indices

        return ids, mask
