# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

import os
from enum import Enum
import copy
import numpy as np
import string
from shapely.geometry import LineString, Point, Polygon
import json
import copy
import random
from random import sample
from collections import defaultdict

from ppocr.utils.logging import get_logger
from ppocr.data.imaug.vqa.augment import order_by_tbyx


class ClsLabelEncode(object):
    def __init__(self, label_list, **kwargs):
        self.label_list = label_list

    def __call__(self, data):
        label = data["label"]
        if label not in self.label_list:
            return None
        label = self.label_list.index(label)
        data["label"] = label
        return data


class DetLabelEncode(object):
    def __init__(self, **kwargs):
        pass

    def __call__(self, data):
        label = data["label"]
        label = json.loads(label)
        nBox = len(label)
        boxes, txts, txt_tags = [], [], []
        for bno in range(0, nBox):
            box = label[bno]["points"]
            txt = label[bno]["transcription"]
            boxes.append(box)
            txts.append(txt)
            if txt in ["*", "###"]:
                txt_tags.append(True)
            else:
                txt_tags.append(False)
        if len(boxes) == 0:
            return None
        boxes = self.expand_points_num(boxes)
        boxes = np.array(boxes, dtype=np.float32)
        txt_tags = np.array(txt_tags, dtype=np.bool_)

        data["polys"] = boxes
        data["texts"] = txts
        data["ignore_tags"] = txt_tags
        return data

    def order_points_clockwise(self, pts):
        rect = np.zeros((4, 2), dtype="float32")
        s = pts.sum(axis=1)
        rect[0] = pts[np.argmin(s)]
        rect[2] = pts[np.argmax(s)]
        tmp = np.delete(pts, (np.argmin(s), np.argmax(s)), axis=0)
        diff = np.diff(np.array(tmp), axis=1)
        rect[1] = tmp[np.argmin(diff)]
        rect[3] = tmp[np.argmax(diff)]
        return rect

    def expand_points_num(self, boxes):
        max_points_num = 0
        for box in boxes:
            if len(box) > max_points_num:
                max_points_num = len(box)
        ex_boxes = []
        for box in boxes:
            ex_box = box + [box[-1]] * (max_points_num - len(box))
            ex_boxes.append(ex_box)
        return ex_boxes


class BaseRecLabelEncode(object):
    """Convert between text-label and text-index"""

    def __init__(
        self,
        max_text_length,
        character_dict_path=None,
        use_space_char=False,
        lower=False,
    ):
        self.max_text_len = max_text_length
        self.beg_str = "sos"
        self.end_str = "eos"
        self.lower = lower

        if character_dict_path is None:
            logger = get_logger()
            logger.warning(
                "The character_dict_path is None, model can only recognize number and lower letters"
            )
            self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
            dict_character = list(self.character_str)
            self.lower = True
        else:
            self.character_str = []
            with open(character_dict_path, "rb") as fin:
                lines = fin.readlines()
                for line in lines:
                    line = line.decode("utf-8").strip("\n").strip("\r\n")
                    self.character_str.append(line)
            if use_space_char:
                self.character_str.append(" ")
            dict_character = list(self.character_str)
        dict_character = self.add_special_char(dict_character)
        self.dict = {}
        for i, char in enumerate(dict_character):
            self.dict[char] = i
        self.character = dict_character

    def add_special_char(self, dict_character):
        return dict_character

    def encode(self, text):
        """convert text-label into text-index.
        input:
            text: text labels of each image. [batch_size]

        output:
            text: concatenated text index for CTCLoss.
                    [sum(text_lengths)] = [text_index_0 + text_index_1 + ... + text_index_(n - 1)]
            length: length of each text. [batch_size]
        """
        if len(text) == 0 or len(text) > self.max_text_len:
            return None
        if self.lower:
            text = text.lower()
        text_list = []
        for char in text:
            if char not in self.dict:
                # logger = get_logger()
                # logger.warning('{} is not in dict'.format(char))
                continue
            text_list.append(self.dict[char])
        if len(text_list) == 0:
            return None
        return text_list


class CTCLabelEncode(BaseRecLabelEncode):
    """Convert between text-label and text-index"""

    def __init__(
        self, max_text_length, character_dict_path=None, use_space_char=False, **kwargs
    ):
        super(CTCLabelEncode, self).__init__(
            max_text_length, character_dict_path, use_space_char
        )

    def __call__(self, data):
        text = data["label"]
        text = self.encode(text)
        if text is None:
            return None
        data["length"] = np.array(len(text))
        text = text + [0] * (self.max_text_len - len(text))
        data["label"] = np.array(text)

        label = [0] * len(self.character)
        for x in text:
            label[x] += 1
        data["label_ace"] = np.array(label)
        return data

    def add_special_char(self, dict_character):
        dict_character = ["blank"] + dict_character
        return dict_character


class E2ELabelEncodeTest(BaseRecLabelEncode):
    def __init__(
        self, max_text_length, character_dict_path=None, use_space_char=False, **kwargs
    ):
        super(E2ELabelEncodeTest, self).__init__(
            max_text_length, character_dict_path, use_space_char
        )

    def __call__(self, data):
        import json

        padnum = len(self.dict)
        label = data["label"]
        label = json.loads(label)
        nBox = len(label)
        boxes, txts, txt_tags = [], [], []
        for bno in range(0, nBox):
            box = label[bno]["points"]
            txt = label[bno]["transcription"]
            boxes.append(box)
            txts.append(txt)
            if txt in ["*", "###"]:
                txt_tags.append(True)
            else:
                txt_tags.append(False)
        boxes = np.array(boxes, dtype=np.float32)
        txt_tags = np.array(txt_tags, dtype=np.bool_)
        data["polys"] = boxes
        data["ignore_tags"] = txt_tags
        temp_texts = []
        for text in txts:
            text = text.lower()
            text = self.encode(text)
            if text is None:
                return None
            text = text + [padnum] * (self.max_text_len - len(text))  # use 36 to pad
            temp_texts.append(text)
        data["texts"] = np.array(temp_texts)
        return data


class E2ELabelEncodeTrain(object):
    def __init__(self, **kwargs):
        pass

    def __call__(self, data):
        import json

        label = data["label"]
        label = json.loads(label)
        nBox = len(label)
        boxes, txts, txt_tags = [], [], []
        for bno in range(0, nBox):
            box = label[bno]["points"]
            txt = label[bno]["transcription"]
            boxes.append(box)
            txts.append(txt)
            if txt in ["*", "###"]:
                txt_tags.append(True)
            else:
                txt_tags.append(False)
        boxes = np.array(boxes, dtype=np.float32)
        txt_tags = np.array(txt_tags, dtype=np.bool_)

        data["polys"] = boxes
        data["texts"] = txts
        data["ignore_tags"] = txt_tags
        return data


class KieLabelEncode(object):
    def __init__(
        self, character_dict_path, class_path, norm=10, directed=False, **kwargs
    ):
        super(KieLabelEncode, self).__init__()
        self.dict = dict({"": 0})
        self.label2classid_map = dict()
        with open(character_dict_path, "r", encoding="utf-8") as fr:
            idx = 1
            for line in fr:
                char = line.strip()
                self.dict[char] = idx
                idx += 1
        with open(class_path, "r") as fin:
            lines = fin.readlines()
            for idx, line in enumerate(lines):
                line = line.strip("\n")
                self.label2classid_map[line] = idx
        self.norm = norm
        self.directed = directed

    def compute_relation(self, boxes):
        """Compute relation between every two boxes."""
        x1s, y1s = boxes[:, 0:1], boxes[:, 1:2]
        x2s, y2s = boxes[:, 4:5], boxes[:, 5:6]
        ws, hs = x2s - x1s + 1, np.maximum(y2s - y1s + 1, 1)
        dxs = (x1s[:, 0][None] - x1s) / self.norm
        dys = (y1s[:, 0][None] - y1s) / self.norm
        xhhs, xwhs = hs[:, 0][None] / hs, ws[:, 0][None] / hs
        whs = ws / hs + np.zeros_like(xhhs)
        relations = np.stack([dxs, dys, whs, xhhs, xwhs], -1)
        bboxes = np.concatenate([x1s, y1s, x2s, y2s], -1).astype(np.float32)
        return relations, bboxes

    def pad_text_indices(self, text_inds):
        """Pad text index to same length."""
        max_len = 300
        recoder_len = max([len(text_ind) for text_ind in text_inds])
        padded_text_inds = -np.ones((len(text_inds), max_len), np.int32)
        for idx, text_ind in enumerate(text_inds):
            padded_text_inds[idx, : len(text_ind)] = np.array(text_ind)
        return padded_text_inds, recoder_len

    def list_to_numpy(self, ann_infos):
        """Convert bboxes, relations, texts and labels to ndarray."""
        boxes, text_inds = ann_infos["points"], ann_infos["text_inds"]
        boxes = np.array(boxes, np.int32)
        relations, bboxes = self.compute_relation(boxes)

        labels = ann_infos.get("labels", None)
        if labels is not None:
            labels = np.array(labels, np.int32)
            edges = ann_infos.get("edges", None)
            if edges is not None:
                labels = labels[:, None]
                edges = np.array(edges)
                edges = (edges[:, None] == edges[None, :]).astype(np.int32)
                if self.directed:
                    edges = (edges & labels == 1).astype(np.int32)
                np.fill_diagonal(edges, -1)
                labels = np.concatenate([labels, edges], -1)
        padded_text_inds, recoder_len = self.pad_text_indices(text_inds)
        max_num = 300
        temp_bboxes = np.zeros([max_num, 4])
        h, _ = bboxes.shape
        temp_bboxes[:h, :] = bboxes

        temp_relations = np.zeros([max_num, max_num, 5])
        temp_relations[:h, :h, :] = relations

        temp_padded_text_inds = np.zeros([max_num, max_num])
        temp_padded_text_inds[:h, :] = padded_text_inds

        temp_labels = np.zeros([max_num, max_num])
        temp_labels[:h, : h + 1] = labels

        tag = np.array([h, recoder_len])
        return dict(
            image=ann_infos["image"],
            points=temp_bboxes,
            relations=temp_relations,
            texts=temp_padded_text_inds,
            labels=temp_labels,
            tag=tag,
        )

    def convert_canonical(self, points_x, points_y):
        assert len(points_x) == 4
        assert len(points_y) == 4

        points = [Point(points_x[i], points_y[i]) for i in range(4)]

        polygon = Polygon([(p.x, p.y) for p in points])
        min_x, min_y, _, _ = polygon.bounds
        points_to_lefttop = [
            LineString([points[i], Point(min_x, min_y)]) for i in range(4)
        ]
        distances = np.array([line.length for line in points_to_lefttop])
        sort_dist_idx = np.argsort(distances)
        lefttop_idx = sort_dist_idx[0]

        if lefttop_idx == 0:
            point_orders = [0, 1, 2, 3]
        elif lefttop_idx == 1:
            point_orders = [1, 2, 3, 0]
        elif lefttop_idx == 2:
            point_orders = [2, 3, 0, 1]
        else:
            point_orders = [3, 0, 1, 2]

        sorted_points_x = [points_x[i] for i in point_orders]
        sorted_points_y = [points_y[j] for j in point_orders]

        return sorted_points_x, sorted_points_y

    def sort_vertex(self, points_x, points_y):
        assert len(points_x) == 4
        assert len(points_y) == 4

        x = np.array(points_x)
        y = np.array(points_y)
        center_x = np.sum(x) * 0.25
        center_y = np.sum(y) * 0.25

        x_arr = np.array(x - center_x)
        y_arr = np.array(y - center_y)

        angle = np.arctan2(y_arr, x_arr) * 180.0 / np.pi
        sort_idx = np.argsort(angle)

        sorted_points_x, sorted_points_y = [], []
        for i in range(4):
            sorted_points_x.append(points_x[sort_idx[i]])
            sorted_points_y.append(points_y[sort_idx[i]])

        return self.convert_canonical(sorted_points_x, sorted_points_y)

    def __call__(self, data):
        import json

        label = data["label"]
        annotations = json.loads(label)
        boxes, texts, text_inds, labels, edges = [], [], [], [], []
        for ann in annotations:
            box = ann["points"]
            x_list = [box[i][0] for i in range(4)]
            y_list = [box[i][1] for i in range(4)]
            sorted_x_list, sorted_y_list = self.sort_vertex(x_list, y_list)
            sorted_box = []
            for x, y in zip(sorted_x_list, sorted_y_list):
                sorted_box.append(x)
                sorted_box.append(y)
            boxes.append(sorted_box)
            text = ann["transcription"]
            texts.append(ann["transcription"])
            text_ind = [self.dict[c] for c in text if c in self.dict]
            text_inds.append(text_ind)
            if "label" in ann.keys():
                labels.append(self.label2classid_map[ann["label"]])
            elif "key_cls" in ann.keys():
                labels.append(ann["key_cls"])
            else:
                raise ValueError(
                    "Cannot found 'key_cls' in ann.keys(), please check your training annotation."
                )
            edges.append(ann.get("edge", 0))
        ann_infos = dict(
            image=data["image"],
            points=boxes,
            texts=texts,
            text_inds=text_inds,
            edges=edges,
            labels=labels,
        )

        return self.list_to_numpy(ann_infos)


class AttnLabelEncode(BaseRecLabelEncode):
    """Convert between text-label and text-index"""

    def __init__(
        self, max_text_length, character_dict_path=None, use_space_char=False, **kwargs
    ):
        super(AttnLabelEncode, self).__init__(
            max_text_length, character_dict_path, use_space_char
        )

    def add_special_char(self, dict_character):
        self.beg_str = "sos"
        self.end_str = "eos"
        dict_character = [self.beg_str] + dict_character + [self.end_str]
        return dict_character

    def __call__(self, data):
        text = data["label"]
        text = self.encode(text)
        if text is None:
            return None
        if len(text) >= self.max_text_len:
            return None
        data["length"] = np.array(len(text))
        text = (
            [0]
            + text
            + [len(self.character) - 1]
            + [0] * (self.max_text_len - len(text) - 2)
        )
        data["label"] = np.array(text)
        return data

    def get_ignored_tokens(self):
        beg_idx = self.get_beg_end_flag_idx("beg")
        end_idx = self.get_beg_end_flag_idx("end")
        return [beg_idx, end_idx]

    def get_beg_end_flag_idx(self, beg_or_end):
        if beg_or_end == "beg":
            idx = np.array(self.dict[self.beg_str])
        elif beg_or_end == "end":
            idx = np.array(self.dict[self.end_str])
        else:
            assert False, "Unsupported type %s in get_beg_end_flag_idx" % beg_or_end
        return idx


class RFLLabelEncode(BaseRecLabelEncode):
    """Convert between text-label and text-index"""

    def __init__(
        self, max_text_length, character_dict_path=None, use_space_char=False, **kwargs
    ):
        super(RFLLabelEncode, self).__init__(
            max_text_length, character_dict_path, use_space_char
        )

    def add_special_char(self, dict_character):
        self.beg_str = "sos"
        self.end_str = "eos"
        dict_character = [self.beg_str] + dict_character + [self.end_str]
        return dict_character

    def encode_cnt(self, text):
        cnt_label = [0.0] * len(self.character)
        for char_ in text:
            cnt_label[char_] += 1
        return np.array(cnt_label)

    def __call__(self, data):
        text = data["label"]
        text = self.encode(text)
        if text is None:
            return None
        if len(text) >= self.max_text_len:
            return None
        cnt_label = self.encode_cnt(text)
        data["length"] = np.array(len(text))
        text = (
            [0]
            + text
            + [len(self.character) - 1]
            + [0] * (self.max_text_len - len(text) - 2)
        )
        if len(text) != self.max_text_len:
            return None
        data["label"] = np.array(text)
        data["cnt_label"] = cnt_label
        return data

    def get_ignored_tokens(self):
        beg_idx = self.get_beg_end_flag_idx("beg")
        end_idx = self.get_beg_end_flag_idx("end")
        return [beg_idx, end_idx]

    def get_beg_end_flag_idx(self, beg_or_end):
        if beg_or_end == "beg":
            idx = np.array(self.dict[self.beg_str])
        elif beg_or_end == "end":
            idx = np.array(self.dict[self.end_str])
        else:
            assert False, "Unsupported type %s in get_beg_end_flag_idx" % beg_or_end
        return idx


class SEEDLabelEncode(BaseRecLabelEncode):
    """Convert between text-label and text-index"""

    def __init__(
        self, max_text_length, character_dict_path=None, use_space_char=False, **kwargs
    ):
        super(SEEDLabelEncode, self).__init__(
            max_text_length, character_dict_path, use_space_char
        )

    def add_special_char(self, dict_character):
        self.padding = "padding"
        self.end_str = "eos"
        self.unknown = "unknown"
        dict_character = dict_character + [self.end_str, self.padding, self.unknown]
        return dict_character

    def __call__(self, data):
        text = data["label"]
        text = self.encode(text)
        if text is None:
            return None
        if len(text) >= self.max_text_len:
            return None
        data["length"] = np.array(len(text)) + 1  # conclude eos
        text = (
            text
            + [len(self.character) - 3]
            + [len(self.character) - 2] * (self.max_text_len - len(text) - 1)
        )
        data["label"] = np.array(text)
        return data


class SRNLabelEncode(BaseRecLabelEncode):
    """Convert between text-label and text-index"""

    def __init__(
        self,
        max_text_length=25,
        character_dict_path=None,
        use_space_char=False,
        **kwargs,
    ):
        super(SRNLabelEncode, self).__init__(
            max_text_length, character_dict_path, use_space_char
        )

    def add_special_char(self, dict_character):
        dict_character = dict_character + [self.beg_str, self.end_str]
        return dict_character

    def __call__(self, data):
        text = data["label"]
        text = self.encode(text)
        char_num = len(self.character)
        if text is None:
            return None
        if len(text) > self.max_text_len:
            return None
        data["length"] = np.array(len(text))
        text = text + [char_num - 1] * (self.max_text_len - len(text))
        data["label"] = np.array(text)
        return data

    def get_ignored_tokens(self):
        beg_idx = self.get_beg_end_flag_idx("beg")
        end_idx = self.get_beg_end_flag_idx("end")
        return [beg_idx, end_idx]

    def get_beg_end_flag_idx(self, beg_or_end):
        if beg_or_end == "beg":
            idx = np.array(self.dict[self.beg_str])
        elif beg_or_end == "end":
            idx = np.array(self.dict[self.end_str])
        else:
            assert False, "Unsupported type %s in get_beg_end_flag_idx" % beg_or_end
        return idx


class TableLabelEncode(AttnLabelEncode):
    """Convert between text-label and text-index"""

    def __init__(
        self,
        max_text_length,
        character_dict_path,
        replace_empty_cell_token=False,
        merge_no_span_structure=False,
        learn_empty_box=False,
        loc_reg_num=4,
        **kwargs,
    ):
        self.max_text_len = max_text_length
        self.lower = False
        self.learn_empty_box = learn_empty_box
        self.merge_no_span_structure = merge_no_span_structure
        self.replace_empty_cell_token = replace_empty_cell_token

        dict_character = []
        with open(character_dict_path, "rb") as fin:
            lines = fin.readlines()
            for line in lines:
                line = line.decode("utf-8").strip("\n").strip("\r\n")
                dict_character.append(line)

        if self.merge_no_span_structure:
            if "<td></td>" not in dict_character:
                dict_character.append("<td></td>")
            if "<td>" in dict_character:
                dict_character.remove("<td>")

        dict_character = self.add_special_char(dict_character)
        self.dict = {}
        for i, char in enumerate(dict_character):
            self.dict[char] = i
        self.idx2char = {v: k for k, v in self.dict.items()}

        self.character = dict_character
        self.loc_reg_num = loc_reg_num
        self.pad_idx = self.dict[self.beg_str]
        self.start_idx = self.dict[self.beg_str]
        self.end_idx = self.dict[self.end_str]

        self.td_token = ["<td>", "<td", "<eb></eb>", "<td></td>"]
        self.empty_bbox_token_dict = {
            "[]": "<eb></eb>",
            "[' ']": "<eb1></eb1>",
            "['<b>', ' ', '</b>']": "<eb2></eb2>",
            "['\\u2028', '\\u2028']": "<eb3></eb3>",
            "['<sup>', ' ', '</sup>']": "<eb4></eb4>",
            "['<b>', '</b>']": "<eb5></eb5>",
            "['<i>', ' ', '</i>']": "<eb6></eb6>",
            "['<b>', '<i>', '</i>', '</b>']": "<eb7></eb7>",
            "['<b>', '<i>', ' ', '</i>', '</b>']": "<eb8></eb8>",
            "['<i>', '</i>']": "<eb9></eb9>",
            "['<b>', ' ', '\\u2028', ' ', '\\u2028', ' ', '</b>']": "<eb10></eb10>",
        }

    @property
    def _max_text_len(self):
        return self.max_text_len + 2

    def __call__(self, data):
        cells = data["cells"]
        structure = data["structure"]
        if self.merge_no_span_structure:
            structure = self._merge_no_span_structure(structure)
        if self.replace_empty_cell_token:
            structure = self._replace_empty_cell_token(structure, cells)
        # remove empty token and add " " to span token
        new_structure = []
        for token in structure:
            if token != "":
                if "span" in token and token[0] != " ":
                    token = " " + token
                new_structure.append(token)
        # encode structure
        structure = self.encode(new_structure)
        if structure is None:
            return None
        data["length"] = len(structure)
        structure = [self.start_idx] + structure + [self.end_idx]  # add sos abd eos
        structure = structure + [self.pad_idx] * (
            self._max_text_len - len(structure)
        )  # pad
        structure = np.array(structure)
        data["structure"] = structure

        if len(structure) > self._max_text_len:
            return None

        # encode box
        bboxes = np.zeros((self._max_text_len, self.loc_reg_num), dtype=np.float32)
        bbox_masks = np.zeros((self._max_text_len, 1), dtype=np.float32)

        bbox_idx = 0

        for i, token in enumerate(structure):
            if self.idx2char[token] in self.td_token:
                if "bbox" in cells[bbox_idx] and len(cells[bbox_idx]["tokens"]) > 0:
                    bbox = cells[bbox_idx]["bbox"].copy()
                    bbox = np.array(bbox, dtype=np.float32).reshape(-1)
                    bboxes[i] = bbox
                    bbox_masks[i] = 1.0
                if self.learn_empty_box:
                    bbox_masks[i] = 1.0
                bbox_idx += 1
        data["bboxes"] = bboxes
        data["bbox_masks"] = bbox_masks
        return data

    def _merge_no_span_structure(self, structure):
        """
        This code is refer from:
        https://github.com/JiaquanYe/TableMASTER-mmocr/blob/master/table_recognition/data_preprocess.py
        """
        new_structure = []
        i = 0
        while i < len(structure):
            token = structure[i]
            if token == "<td>":
                token = "<td></td>"
                i += 1
            new_structure.append(token)
            i += 1
        return new_structure

    def _replace_empty_cell_token(self, token_list, cells):
        """
        This fun code is refer from:
        https://github.com/JiaquanYe/TableMASTER-mmocr/blob/master/table_recognition/data_preprocess.py
        """

        bbox_idx = 0
        add_empty_bbox_token_list = []
        for token in token_list:
            if token in ["<td></td>", "<td", "<td>"]:
                if "bbox" not in cells[bbox_idx].keys():
                    content = str(cells[bbox_idx]["tokens"])
                    token = self.empty_bbox_token_dict[content]
                add_empty_bbox_token_list.append(token)
                bbox_idx += 1
            else:
                add_empty_bbox_token_list.append(token)
        return add_empty_bbox_token_list


class TableMasterLabelEncode(TableLabelEncode):
    """Convert between text-label and text-index"""

    def __init__(
        self,
        max_text_length,
        character_dict_path,
        replace_empty_cell_token=False,
        merge_no_span_structure=False,
        learn_empty_box=False,
        loc_reg_num=4,
        **kwargs,
    ):
        super(TableMasterLabelEncode, self).__init__(
            max_text_length,
            character_dict_path,
            replace_empty_cell_token,
            merge_no_span_structure,
            learn_empty_box,
            loc_reg_num,
            **kwargs,
        )
        self.pad_idx = self.dict[self.pad_str]
        self.unknown_idx = self.dict[self.unknown_str]

    @property
    def _max_text_len(self):
        return self.max_text_len

    def add_special_char(self, dict_character):
        self.beg_str = "<SOS>"
        self.end_str = "<EOS>"
        self.unknown_str = "<UKN>"
        self.pad_str = "<PAD>"
        dict_character = dict_character
        dict_character = dict_character + [
            self.unknown_str,
            self.beg_str,
            self.end_str,
            self.pad_str,
        ]
        return dict_character


class TableBoxEncode(object):
    def __init__(self, in_box_format="xyxy", out_box_format="xyxy", **kwargs):
        assert out_box_format in ["xywh", "xyxy", "xyxyxyxy"]
        self.in_box_format = in_box_format
        self.out_box_format = out_box_format

    def __call__(self, data):
        img_height, img_width = data["image"].shape[:2]
        bboxes = data["bboxes"]
        if self.in_box_format != self.out_box_format:
            if self.out_box_format == "xywh":
                if self.in_box_format == "xyxyxyxy":
                    bboxes = self.xyxyxyxy2xywh(bboxes)
                elif self.in_box_format == "xyxy":
                    bboxes = self.xyxy2xywh(bboxes)

        bboxes[:, 0::2] /= img_width
        bboxes[:, 1::2] /= img_height
        data["bboxes"] = bboxes
        return data

    def xyxyxyxy2xywh(self, boxes):
        new_bboxes = np.zeros([len(boxes), 4])
        new_bboxes[:, 0] = boxes[:, 0::2].min()  # x1
        new_bboxes[:, 1] = boxes[:, 1::2].min()  # y1
        new_bboxes[:, 2] = boxes[:, 0::2].max() - new_bboxes[:, 0]  # w
        new_bboxes[:, 3] = boxes[:, 1::2].max() - new_bboxes[:, 1]  # h
        return new_bboxes

    def xyxy2xywh(self, bboxes):
        new_bboxes = np.empty_like(bboxes)
        new_bboxes[:, 0] = (bboxes[:, 0] + bboxes[:, 2]) / 2  # x center
        new_bboxes[:, 1] = (bboxes[:, 1] + bboxes[:, 3]) / 2  # y center
        new_bboxes[:, 2] = bboxes[:, 2] - bboxes[:, 0]  # width
        new_bboxes[:, 3] = bboxes[:, 3] - bboxes[:, 1]  # height
        return new_bboxes


class SARLabelEncode(BaseRecLabelEncode):
    """Convert between text-label and text-index"""

    def __init__(
        self, max_text_length, character_dict_path=None, use_space_char=False, **kwargs
    ):
        super(SARLabelEncode, self).__init__(
            max_text_length, character_dict_path, use_space_char
        )

    def add_special_char(self, dict_character):
        beg_end_str = "<BOS/EOS>"
        unknown_str = "<UKN>"
        padding_str = "<PAD>"
        dict_character = dict_character + [unknown_str]
        self.unknown_idx = len(dict_character) - 1
        dict_character = dict_character + [beg_end_str]
        self.start_idx = len(dict_character) - 1
        self.end_idx = len(dict_character) - 1
        dict_character = dict_character + [padding_str]
        self.padding_idx = len(dict_character) - 1

        return dict_character

    def __call__(self, data):
        text = data["label"]
        text = self.encode(text)
        if text is None:
            return None
        if len(text) >= self.max_text_len - 1:
            return None
        data["length"] = np.array(len(text))
        target = [self.start_idx] + text + [self.end_idx]
        padded_text = [self.padding_idx for _ in range(self.max_text_len)]

        padded_text[: len(target)] = target
        data["label"] = np.array(padded_text)
        return data

    def get_ignored_tokens(self):
        return [self.padding_idx]


class SATRNLabelEncode(BaseRecLabelEncode):
    """Convert between text-label and text-index"""

    def __init__(
        self,
        max_text_length,
        character_dict_path=None,
        use_space_char=False,
        lower=False,
        **kwargs,
    ):
        super(SATRNLabelEncode, self).__init__(
            max_text_length, character_dict_path, use_space_char
        )
        self.lower = lower

    def add_special_char(self, dict_character):
        beg_end_str = "<BOS/EOS>"
        unknown_str = "<UKN>"
        padding_str = "<PAD>"
        dict_character = dict_character + [unknown_str]
        self.unknown_idx = len(dict_character) - 1
        dict_character = dict_character + [beg_end_str]
        self.start_idx = len(dict_character) - 1
        self.end_idx = len(dict_character) - 1
        dict_character = dict_character + [padding_str]
        self.padding_idx = len(dict_character) - 1

        return dict_character

    def encode(self, text):
        if self.lower:
            text = text.lower()
        text_list = []
        for char in text:
            text_list.append(self.dict.get(char, self.unknown_idx))
        if len(text_list) == 0:
            return None
        return text_list

    def __call__(self, data):
        text = data["label"]
        text = self.encode(text)
        if text is None:
            return None
        data["length"] = np.array(len(text))
        target = [self.start_idx] + text + [self.end_idx]
        padded_text = [self.padding_idx for _ in range(self.max_text_len)]
        if len(target) > self.max_text_len:
            padded_text = target[: self.max_text_len]
        else:
            padded_text[: len(target)] = target
        data["label"] = np.array(padded_text)
        return data

    def get_ignored_tokens(self):
        return [self.padding_idx]


class PRENLabelEncode(BaseRecLabelEncode):
    def __init__(
        self, max_text_length, character_dict_path, use_space_char=False, **kwargs
    ):
        super(PRENLabelEncode, self).__init__(
            max_text_length, character_dict_path, use_space_char
        )

    def add_special_char(self, dict_character):
        padding_str = "<PAD>"  # 0
        end_str = "<EOS>"  # 1
        unknown_str = "<UNK>"  # 2

        dict_character = [padding_str, end_str, unknown_str] + dict_character
        self.padding_idx = 0
        self.end_idx = 1
        self.unknown_idx = 2

        return dict_character

    def encode(self, text):
        if len(text) == 0 or len(text) >= self.max_text_len:
            return None
        if self.lower:
            text = text.lower()
        text_list = []
        for char in text:
            if char not in self.dict:
                text_list.append(self.unknown_idx)
            else:
                text_list.append(self.dict[char])
        text_list.append(self.end_idx)
        if len(text_list) < self.max_text_len:
            text_list += [self.padding_idx] * (self.max_text_len - len(text_list))
        return text_list

    def __call__(self, data):
        text = data["label"]
        encoded_text = self.encode(text)
        if encoded_text is None:
            return None
        data["label"] = np.array(encoded_text)
        return data


class VQATokenLabelEncode(object):
    """
    Label encode for NLP VQA methods
    """

    def __init__(
        self,
        class_path,
        contains_re=False,
        add_special_ids=False,
        algorithm="LayoutXLM",
        use_textline_bbox_info=True,
        order_method=None,
        infer_mode=False,
        ocr_engine=None,
        **kwargs,
    ):
        super(VQATokenLabelEncode, self).__init__()
        from paddlenlp.transformers import (
            LayoutXLMTokenizer,
            LayoutLMTokenizer,
            LayoutLMv2Tokenizer,
        )
        from ppocr.utils.utility import load_vqa_bio_label_maps

        tokenizer_dict = {
            "LayoutXLM": {
                "class": LayoutXLMTokenizer,
                "pretrained_model": "layoutxlm-base-uncased",
            },
            "LayoutLM": {
                "class": LayoutLMTokenizer,
                "pretrained_model": "layoutlm-base-uncased",
            },
            "LayoutLMv2": {
                "class": LayoutLMv2Tokenizer,
                "pretrained_model": "layoutlmv2-base-uncased",
            },
        }
        self.contains_re = contains_re
        tokenizer_config = tokenizer_dict[algorithm]
        self.tokenizer = tokenizer_config["class"].from_pretrained(
            tokenizer_config["pretrained_model"]
        )
        self.label2id_map, id2label_map = load_vqa_bio_label_maps(class_path)
        self.add_special_ids = add_special_ids
        self.infer_mode = infer_mode
        self.ocr_engine = ocr_engine
        self.use_textline_bbox_info = use_textline_bbox_info
        self.order_method = order_method
        assert self.order_method in [None, "tb-yx"]

    def split_bbox(self, bbox, text, tokenizer):
        words = text.split()
        token_bboxes = []
        curr_word_idx = 0
        x1, y1, x2, y2 = bbox
        unit_w = (x2 - x1) / len(text)
        for idx, word in enumerate(words):
            curr_w = len(word) * unit_w
            word_bbox = [x1, y1, x1 + curr_w, y2]
            token_bboxes.extend([word_bbox] * len(tokenizer.tokenize(word)))
            x1 += (len(word) + 1) * unit_w
        return token_bboxes

    def filter_empty_contents(self, ocr_info):
        """
        find out the empty texts and remove the links
        """
        new_ocr_info = []
        empty_index = []
        for idx, info in enumerate(ocr_info):
            if len(info["transcription"]) > 0:
                new_ocr_info.append(copy.deepcopy(info))
            else:
                empty_index.append(info["id"])

        for idx, info in enumerate(new_ocr_info):
            new_link = []
            for link in info["linking"]:
                if link[0] in empty_index or link[1] in empty_index:
                    continue
                new_link.append(link)
            new_ocr_info[idx]["linking"] = new_link
        return new_ocr_info

    def __call__(self, data):
        # load bbox and label info
        ocr_info = self._load_ocr_info(data)

        for idx in range(len(ocr_info)):
            if "bbox" not in ocr_info[idx]:
                ocr_info[idx]["bbox"] = self.trans_poly_to_bbox(ocr_info[idx]["points"])

        if self.order_method == "tb-yx":
            ocr_info = order_by_tbyx(ocr_info)

        # for re
        train_re = self.contains_re and not self.infer_mode
        if train_re:
            ocr_info = self.filter_empty_contents(ocr_info)

        height, width, _ = data["image"].shape

        words_list = []
        bbox_list = []
        input_ids_list = []
        token_type_ids_list = []
        segment_offset_id = []
        gt_label_list = []

        entities = []

        if train_re:
            relations = []
            id2label = {}
            entity_id_to_index_map = {}
            empty_entity = set()

        data["ocr_info"] = copy.deepcopy(ocr_info)

        for info in ocr_info:
            text = info["transcription"]
            if len(text) <= 0:
                continue
            if train_re:
                # for re
                if len(text) == 0:
                    empty_entity.add(info["id"])
                    continue
                id2label[info["id"]] = info["label"]
                relations.extend([tuple(sorted(l)) for l in info["linking"]])
            # smooth_box
            info["bbox"] = self.trans_poly_to_bbox(info["points"])

            encode_res = self.tokenizer.encode(
                text,
                pad_to_max_seq_len=False,
                return_attention_mask=True,
                return_token_type_ids=True,
            )

            if not self.add_special_ids:
                # TODO: use tok.all_special_ids to remove
                encode_res["input_ids"] = encode_res["input_ids"][1:-1]
                encode_res["token_type_ids"] = encode_res["token_type_ids"][1:-1]
                encode_res["attention_mask"] = encode_res["attention_mask"][1:-1]

            if self.use_textline_bbox_info:
                bbox = [info["bbox"]] * len(encode_res["input_ids"])
            else:
                bbox = self.split_bbox(
                    info["bbox"], info["transcription"], self.tokenizer
                )
            if len(bbox) <= 0:
                continue
            bbox = self._smooth_box(bbox, height, width)
            if self.add_special_ids:
                bbox.insert(0, [0, 0, 0, 0])
                bbox.append([0, 0, 0, 0])

            # parse label
            if not self.infer_mode:
                label = info["label"]
                gt_label = self._parse_label(label, encode_res)

            # construct entities for re
            if train_re:
                if gt_label[0] != self.label2id_map["O"]:
                    entity_id_to_index_map[info["id"]] = len(entities)
                    label = label.upper()
                    entities.append(
                        {
                            "start": len(input_ids_list),
                            "end": len(input_ids_list) + len(encode_res["input_ids"]),
                            "label": label.upper(),
                        }
                    )
            else:
                entities.append(
                    {
                        "start": len(input_ids_list),
                        "end": len(input_ids_list) + len(encode_res["input_ids"]),
                        "label": "O",
                    }
                )
            input_ids_list.extend(encode_res["input_ids"])
            token_type_ids_list.extend(encode_res["token_type_ids"])
            bbox_list.extend(bbox)
            words_list.append(text)
            segment_offset_id.append(len(input_ids_list))
            if not self.infer_mode:
                gt_label_list.extend(gt_label)

        data["input_ids"] = input_ids_list
        data["token_type_ids"] = token_type_ids_list
        data["bbox"] = bbox_list
        data["attention_mask"] = [1] * len(input_ids_list)
        data["labels"] = gt_label_list
        data["segment_offset_id"] = segment_offset_id
        data["tokenizer_params"] = dict(
            padding_side=self.tokenizer.padding_side,
            pad_token_type_id=self.tokenizer.pad_token_type_id,
            pad_token_id=self.tokenizer.pad_token_id,
        )
        data["entities"] = entities

        if train_re:
            data["relations"] = relations
            data["id2label"] = id2label
            data["empty_entity"] = empty_entity
            data["entity_id_to_index_map"] = entity_id_to_index_map
        return data

    def trans_poly_to_bbox(self, poly):
        x1 = int(np.min([p[0] for p in poly]))
        x2 = int(np.max([p[0] for p in poly]))
        y1 = int(np.min([p[1] for p in poly]))
        y2 = int(np.max([p[1] for p in poly]))
        return [x1, y1, x2, y2]

    def _load_ocr_info(self, data):
        if self.infer_mode:
            ocr_result = self.ocr_engine.ocr(data["image"], cls=False)[0]
            ocr_info = []
            for res in ocr_result:
                ocr_info.append(
                    {
                        "transcription": res[1][0],
                        "bbox": self.trans_poly_to_bbox(res[0]),
                        "points": res[0],
                    }
                )
            return ocr_info
        else:
            info = data["label"]
            # read text info
            info_dict = json.loads(info)
            return info_dict

    def _smooth_box(self, bboxes, height, width):
        bboxes = np.array(bboxes)
        bboxes[:, 0] = bboxes[:, 0] * 1000 / width
        bboxes[:, 2] = bboxes[:, 2] * 1000 / width
        bboxes[:, 1] = bboxes[:, 1] * 1000 / height
        bboxes[:, 3] = bboxes[:, 3] * 1000 / height
        bboxes = bboxes.astype("int64").tolist()
        return bboxes

    def _parse_label(self, label, encode_res):
        gt_label = []
        if label.lower() in ["other", "others", "ignore"]:
            gt_label.extend([0] * len(encode_res["input_ids"]))
        else:
            gt_label.append(self.label2id_map[("b-" + label).upper()])
            gt_label.extend(
                [self.label2id_map[("i-" + label).upper()]]
                * (len(encode_res["input_ids"]) - 1)
            )
        return gt_label


class MultiLabelEncode(BaseRecLabelEncode):
    def __init__(
        self,
        max_text_length,
        character_dict_path=None,
        use_space_char=False,
        gtc_encode=None,
        **kwargs,
    ):
        super(MultiLabelEncode, self).__init__(
            max_text_length, character_dict_path, use_space_char
        )

        self.ctc_encode = CTCLabelEncode(
            max_text_length, character_dict_path, use_space_char, **kwargs
        )
        self.gtc_encode_type = gtc_encode
        if gtc_encode is None:
            self.gtc_encode = SARLabelEncode(
                max_text_length, character_dict_path, use_space_char, **kwargs
            )
        else:
            self.gtc_encode = eval(gtc_encode)(
                max_text_length, character_dict_path, use_space_char, **kwargs
            )

    def __call__(self, data):
        data_ctc = copy.deepcopy(data)
        data_gtc = copy.deepcopy(data)
        data_out = dict()
        data_out["img_path"] = data.get("img_path", None)
        data_out["image"] = data["image"]
        ctc = self.ctc_encode.__call__(data_ctc)
        gtc = self.gtc_encode.__call__(data_gtc)
        if ctc is None or gtc is None:
            return None
        data_out["label_ctc"] = ctc["label"]
        if self.gtc_encode_type is not None:
            data_out["label_gtc"] = gtc["label"]
        else:
            data_out["label_sar"] = gtc["label"]
        data_out["length"] = ctc["length"]
        return data_out


class NRTRLabelEncode(BaseRecLabelEncode):
    """Convert between text-label and text-index"""

    def __init__(
        self, max_text_length, character_dict_path=None, use_space_char=False, **kwargs
    ):
        super(NRTRLabelEncode, self).__init__(
            max_text_length, character_dict_path, use_space_char
        )

    def __call__(self, data):
        text = data["label"]
        text = self.encode(text)
        if text is None:
            return None
        if len(text) >= self.max_text_len - 1:
            return None
        data["length"] = np.array(len(text))
        text.insert(0, 2)
        text.append(3)
        text = text + [0] * (self.max_text_len - len(text))
        data["label"] = np.array(text)
        return data

    def add_special_char(self, dict_character):
        dict_character = ["blank", "<unk>", "<s>", "</s>"] + dict_character
        return dict_character


class ParseQLabelEncode(BaseRecLabelEncode):
    """Convert between text-label and text-index"""

    BOS = "[B]"
    EOS = "[E]"
    PAD = "[P]"

    def __init__(
        self, max_text_length, character_dict_path=None, use_space_char=False, **kwargs
    ):
        super(ParseQLabelEncode, self).__init__(
            max_text_length, character_dict_path, use_space_char
        )

    def __call__(self, data):
        text = data["label"]
        text = self.encode(text)
        if text is None:
            return None
        if len(text) >= self.max_text_len - 2:
            return None
        data["length"] = np.array(len(text))
        text = [self.dict[self.BOS]] + text + [self.dict[self.EOS]]
        text = text + [self.dict[self.PAD]] * (self.max_text_len - len(text))
        data["label"] = np.array(text)
        return data

    def add_special_char(self, dict_character):
        dict_character = [self.EOS] + dict_character + [self.BOS, self.PAD]
        return dict_character


class ViTSTRLabelEncode(BaseRecLabelEncode):
    """Convert between text-label and text-index"""

    def __init__(
        self,
        max_text_length,
        character_dict_path=None,
        use_space_char=False,
        ignore_index=0,
        **kwargs,
    ):
        super(ViTSTRLabelEncode, self).__init__(
            max_text_length, character_dict_path, use_space_char
        )
        self.ignore_index = ignore_index

    def __call__(self, data):
        text = data["label"]
        text = self.encode(text)
        if text is None:
            return None
        if len(text) >= self.max_text_len:
            return None
        data["length"] = np.array(len(text))
        text.insert(0, self.ignore_index)
        text.append(1)
        text = text + [self.ignore_index] * (self.max_text_len + 2 - len(text))
        data["label"] = np.array(text)
        return data

    def add_special_char(self, dict_character):
        dict_character = ["<s>", "</s>"] + dict_character
        return dict_character


class ABINetLabelEncode(BaseRecLabelEncode):
    """Convert between text-label and text-index"""

    def __init__(
        self,
        max_text_length,
        character_dict_path=None,
        use_space_char=False,
        ignore_index=100,
        **kwargs,
    ):
        super(ABINetLabelEncode, self).__init__(
            max_text_length, character_dict_path, use_space_char
        )
        self.ignore_index = ignore_index

    def __call__(self, data):
        text = data["label"]
        text = self.encode(text)
        if text is None:
            return None
        if len(text) >= self.max_text_len:
            return None
        data["length"] = np.array(len(text))
        text.append(0)
        text = text + [self.ignore_index] * (self.max_text_len + 1 - len(text))
        data["label"] = np.array(text)
        return data

    def add_special_char(self, dict_character):
        dict_character = ["</s>"] + dict_character
        return dict_character


class SRLabelEncode(BaseRecLabelEncode):
    def __init__(
        self, max_text_length, character_dict_path=None, use_space_char=False, **kwargs
    ):
        super(SRLabelEncode, self).__init__(
            max_text_length, character_dict_path, use_space_char
        )
        self.dic = {}
        with open(character_dict_path, "r") as fin:
            for line in fin.readlines():
                line = line.strip()
                character, sequence = line.split()
                self.dic[character] = sequence
        english_stroke_alphabet = "0123456789"
        self.english_stroke_dict = {}
        for index in range(len(english_stroke_alphabet)):
            self.english_stroke_dict[english_stroke_alphabet[index]] = index

    def encode(self, label):
        stroke_sequence = ""
        for character in label:
            if character not in self.dic:
                continue
            else:
                stroke_sequence += self.dic[character]
        stroke_sequence += "0"
        label = stroke_sequence

        length = len(label)

        input_tensor = np.zeros(self.max_text_len).astype("int64")
        for j in range(length - 1):
            input_tensor[j + 1] = self.english_stroke_dict[label[j]]

        return length, input_tensor

    def __call__(self, data):
        text = data["label"]
        length, input_tensor = self.encode(text)

        data["length"] = length
        data["input_tensor"] = input_tensor
        if text is None:
            return None
        return data


class SPINLabelEncode(AttnLabelEncode):
    """Convert between text-label and text-index"""

    def __init__(
        self,
        max_text_length,
        character_dict_path=None,
        use_space_char=False,
        lower=True,
        **kwargs,
    ):
        super(SPINLabelEncode, self).__init__(
            max_text_length, character_dict_path, use_space_char
        )
        self.lower = lower

    def add_special_char(self, dict_character):
        self.beg_str = "sos"
        self.end_str = "eos"
        dict_character = [self.beg_str] + [self.end_str] + dict_character
        return dict_character

    def __call__(self, data):
        text = data["label"]
        text = self.encode(text)
        if text is None:
            return None
        if len(text) > self.max_text_len:
            return None
        data["length"] = np.array(len(text))
        target = [0] + text + [1]
        padded_text = [0 for _ in range(self.max_text_len + 2)]

        padded_text[: len(target)] = target
        data["label"] = np.array(padded_text)
        return data


class VLLabelEncode(BaseRecLabelEncode):
    """Convert between text-label and text-index"""

    def __init__(
        self, max_text_length, character_dict_path=None, use_space_char=False, **kwargs
    ):
        super(VLLabelEncode, self).__init__(
            max_text_length, character_dict_path, use_space_char
        )
        self.dict = {}
        for i, char in enumerate(self.character):
            self.dict[char] = i

    def __call__(self, data):
        text = data["label"]  # original string
        # generate occluded text
        len_str = len(text)
        if len_str <= 0:
            return None
        change_num = 1
        order = list(range(len_str))
        change_id = sample(order, change_num)[0]
        label_sub = text[change_id]
        if change_id == (len_str - 1):
            label_res = text[:change_id]
        elif change_id == 0:
            label_res = text[1:]
        else:
            label_res = text[:change_id] + text[change_id + 1 :]

        data["label_res"] = label_res  # remaining string
        data["label_sub"] = label_sub  # occluded character
        data["label_id"] = change_id  # character index
        # encode label
        text = self.encode(text)
        if text is None:
            return None
        text = [i + 1 for i in text]
        data["length"] = np.array(len(text))
        text = text + [0] * (self.max_text_len - len(text))
        data["label"] = np.array(text)
        label_res = self.encode(label_res)
        label_sub = self.encode(label_sub)
        if label_res is None:
            label_res = []
        else:
            label_res = [i + 1 for i in label_res]
        if label_sub is None:
            label_sub = []
        else:
            label_sub = [i + 1 for i in label_sub]
        data["length_res"] = np.array(len(label_res))
        data["length_sub"] = np.array(len(label_sub))
        label_res = label_res + [0] * (self.max_text_len - len(label_res))
        label_sub = label_sub + [0] * (self.max_text_len - len(label_sub))
        data["label_res"] = np.array(label_res)
        data["label_sub"] = np.array(label_sub)
        return data


class CTLabelEncode(object):
    def __init__(self, **kwargs):
        pass

    def __call__(self, data):
        label = data["label"]

        label = json.loads(label)
        nBox = len(label)
        boxes, txts = [], []
        for bno in range(0, nBox):
            box = label[bno]["points"]
            box = np.array(box)

            boxes.append(box)
            txt = label[bno]["transcription"]
            txts.append(txt)

        if len(boxes) == 0:
            return None

        data["polys"] = boxes
        data["texts"] = txts
        return data


class CANLabelEncode(BaseRecLabelEncode):
    def __init__(
        self,
        character_dict_path,
        max_text_length=100,
        use_space_char=False,
        lower=True,
        **kwargs,
    ):
        super(CANLabelEncode, self).__init__(
            max_text_length, character_dict_path, use_space_char, lower
        )

    def encode(self, text_seq):
        text_seq_encoded = []
        for text in text_seq:
            if text not in self.character:
                continue
            text_seq_encoded.append(self.dict.get(text))
        if len(text_seq_encoded) == 0:
            return None
        return text_seq_encoded

    def __call__(self, data):
        label = data["label"]
        if isinstance(label, str):
            label = label.strip().split()
        label.append(self.end_str)
        data["label"] = self.encode(label)
        return data


class CPPDLabelEncode(BaseRecLabelEncode):
    """Convert between text-label and text-index"""

    def __init__(
        self,
        max_text_length,
        character_dict_path=None,
        use_space_char=False,
        ch=False,
        ignore_index=100,
        **kwargs,
    ):
        super(CPPDLabelEncode, self).__init__(
            max_text_length, character_dict_path, use_space_char
        )
        self.ch = ch
        self.ignore_index = ignore_index

    def __call__(self, data):
        text = data["label"]
        if self.ch:
            text, text_node_index, text_node_num = self.encodech(text)
            if text is None:
                return None
            if len(text) > self.max_text_len:
                return None
            data["length"] = np.array(len(text))

            text_pos_node = [1] * (len(text) + 1) + [0] * (
                self.max_text_len - len(text)
            )

            text.append(0)  # eos
            text = text + [self.ignore_index] * (self.max_text_len + 1 - len(text))

            data["label"] = np.array(text)
            data["label_node"] = np.array(text_node_num + text_pos_node)
            data["label_index"] = np.array(text_node_index)
            return data
        else:
            text, text_char_node, ch_order = self.encode(text)
            if text is None:
                return None
            if len(text) >= self.max_text_len:
                return None
            data["length"] = np.array(len(text))

            text_pos_node = [1] * (len(text) + 1) + [0] * (
                self.max_text_len - len(text)
            )

            text.append(0)  # eos

            text = text + [self.ignore_index] * (self.max_text_len + 1 - len(text))
            data["label"] = np.array(text)
            data["label_node"] = np.array(text_char_node + text_pos_node)
            data["label_order"] = np.array(ch_order)

            return data

    def add_special_char(self, dict_character):
        dict_character = ["</s>"] + dict_character
        self.num_character = len(dict_character)
        return dict_character

    def encode(self, text):
        """ """
        if len(text) == 0 or len(text) > self.max_text_len:
            return None, None, None
        if self.lower:
            text = text.lower()
        text_node = [0 for _ in range(self.num_character)]
        text_node[0] = 1
        text_list = []
        ch_order = []
        order = 1
        for char in text:
            if char not in self.dict:
                continue
            text_list.append(self.dict[char])
            text_node[self.dict[char]] += 1
            ch_order.append([self.dict[char], text_node[self.dict[char]], order])
            order += 1

        no_ch_order = []
        for char in self.character:
            if char not in text:
                no_ch_order.append([self.dict[char], 1, 0])
        random.shuffle(no_ch_order)
        ch_order = ch_order + no_ch_order
        ch_order = ch_order[: self.max_text_len + 1]

        if len(text_list) == 0:
            return None, None, None
        return text_list, text_node, ch_order.sort()

    def encodech(self, text):
        """ """
        if len(text) == 0 or len(text) > self.max_text_len:
            return None, None, None
        if self.lower:
            text = text.lower()
        text_node_dict = {}
        text_node_dict.update({0: 1})
        character_index = [_ for _ in range(self.num_character)]
        text_list = []
        for char in text:
            if char not in self.dict:
                continue
            i_c = self.dict[char]
            text_list.append(i_c)
            if i_c in text_node_dict.keys():
                text_node_dict[i_c] += 1
            else:
                text_node_dict.update({i_c: 1})
        for ic in list(text_node_dict.keys()):
            character_index.remove(ic)
        none_char_index = sample(character_index, 37 - len(list(text_node_dict.keys())))
        for ic in none_char_index:
            text_node_dict[ic] = 0

        text_node_index = sorted(text_node_dict)
        text_node_num = [text_node_dict[k] for k in text_node_index]
        if len(text_list) == 0:
            return None, None, None
        return text_list, text_node_index, text_node_num


class LatexOCRLabelEncode(object):
    def __init__(
        self,
        rec_char_dict_path,
        **kwargs,
    ):
        # Set the TOKENIZERS_PARALLELISM environment variable to 'false' to suppress
        # the warning: "The current process just got forked, Disabling parallelism to avoid deadlocks..
        #  To disable this warning, please explicitly set TOKENIZERS_PARALLELISM=(true | false)" from tokenizers
        os.environ["TOKENIZERS_PARALLELISM"] = "false"
        from tokenizers import Tokenizer as TokenizerFast

        self.tokenizer = TokenizerFast.from_file(rec_char_dict_path)
        self.model_input_names = ["input_ids", "token_type_ids", "attention_mask"]
        self.pad_token_id = 0
        self.bos_token_id = 1
        self.eos_token_id = 2

    def _convert_encoding(
        self,
        encoding,
        return_token_type_ids=None,
        return_attention_mask=None,
        return_overflowing_tokens=False,
        return_special_tokens_mask=False,
        return_offsets_mapping=False,
        return_length=False,
        verbose=True,
    ):

        if return_token_type_ids is None:
            return_token_type_ids = "token_type_ids" in self.model_input_names
        if return_attention_mask is None:
            return_attention_mask = "attention_mask" in self.model_input_names

        if return_overflowing_tokens and encoding.overflowing is not None:
            encodings = [encoding] + encoding.overflowing
        else:
            encodings = [encoding]

        encoding_dict = defaultdict(list)
        for e in encodings:
            encoding_dict["input_ids"].append(e.ids)

            if return_token_type_ids:
                encoding_dict["token_type_ids"].append(e.type_ids)
            if return_attention_mask:
                encoding_dict["attention_mask"].append(e.attention_mask)
            if return_special_tokens_mask:
                encoding_dict["special_tokens_mask"].append(e.special_tokens_mask)
            if return_offsets_mapping:
                encoding_dict["offset_mapping"].append(e.offsets)
            if return_length:
                encoding_dict["length"].append(len(e.ids))

        return encoding_dict, encodings

    def encode(
        self,
        text,
        text_pair=None,
        return_token_type_ids=False,
        add_special_tokens=True,
        is_split_into_words=False,
    ):
        batched_input = text
        encodings = self.tokenizer.encode_batch(
            batched_input,
            add_special_tokens=add_special_tokens,
            is_pretokenized=is_split_into_words,
        )
        tokens_and_encodings = [
            self._convert_encoding(
                encoding=encoding,
                return_token_type_ids=False,
                return_attention_mask=None,
                return_overflowing_tokens=False,
                return_special_tokens_mask=False,
                return_offsets_mapping=False,
                return_length=False,
                verbose=True,
            )
            for encoding in encodings
        ]
        sanitized_tokens = {}
        for key in tokens_and_encodings[0][0].keys():
            stack = [e for item, _ in tokens_and_encodings for e in item[key]]
            sanitized_tokens[key] = stack
        return sanitized_tokens

    def __call__(self, eqs):
        topk = self.encode(eqs)
        for k, p in zip(topk, [[self.bos_token_id, self.eos_token_id], [1, 1]]):
            process_seq = [[p[0]] + x + [p[1]] for x in topk[k]]
            max_length = 0
            for seq in process_seq:
                max_length = max(max_length, len(seq))
            labels = np.zeros((len(process_seq), max_length), dtype="int64")
            for idx, seq in enumerate(process_seq):
                l = len(seq)
                labels[idx][:l] = seq
            topk[k] = labels
        return (
            np.array(topk["input_ids"]).astype(np.int64),
            np.array(topk["attention_mask"]).astype(np.int64),
            max_length,
        )


class ExplicitEnum(str, Enum):
    """
    Enum with more explicit error message for missing values.
    """

    @classmethod
    def _missing_(cls, value):
        raise ValueError(
            f"{value} is not a valid {cls.__name__}, please select one of {list(cls._value2member_map_.keys())}"
        )


class TruncationStrategy(ExplicitEnum):
    """
    Possible values for the `truncation` argument in [`PreTrainedTokenizerBase.__call__`]. Useful for tab-completion in
    an IDE.
    """

    ONLY_FIRST = "only_first"
    ONLY_SECOND = "only_second"
    LONGEST_FIRST = "longest_first"
    DO_NOT_TRUNCATE = "do_not_truncate"


class PaddingStrategy(ExplicitEnum):
    """
    Possible values for the `padding` argument in [`PreTrainedTokenizerBase.__call__`]. Useful for tab-completion in an
    IDE.
    """

    LONGEST = "longest"
    MAX_LENGTH = "max_length"
    DO_NOT_PAD = "do_not_pad"


class UniMERNetLabelEncode(object):

    SPECIAL_TOKENS_ATTRIBUTES = [
        "bos_token",
        "eos_token",
        "unk_token",
        "sep_token",
        "pad_token",
        "cls_token",
        "mask_token",
        "additional_special_tokens",
    ]

    def __init__(
        self,
        rec_char_dict_path,
        max_seq_len,
        **kwargs,
    ):
        # Set the TOKENIZERS_PARALLELISM environment variable to 'false' to suppress
        # the warning: "The current process just got forked, Disabling parallelism to avoid deadlocks..
        #  To disable this warning, please explicitly set TOKENIZERS_PARALLELISM=(true | false)" from tokenizers
        os.environ["TOKENIZERS_PARALLELISM"] = "false"
        from tokenizers import Tokenizer as TokenizerFast
        from tokenizers import AddedToken

        self._unk_token = "<unk>"
        self._bos_token = "<s>"
        self._eos_token = "</s>"
        self._pad_token = "<pad>"
        self._sep_token = None
        self._cls_token = None
        self._mask_token = None
        self._additional_special_tokens = []
        self.model_input_names = ["input_ids", "token_type_ids", "attention_mask"]
        self.max_seq_len = max_seq_len
        self.pad_token_id = 1
        self.bos_token_id = 0
        self.eos_token_id = 2
        self.padding_side = "right"
        self.pad_token = "<pad>"
        self.pad_token_type_id = 0
        self.pad_to_multiple_of = None
        fast_tokenizer_file = os.path.join(rec_char_dict_path, "tokenizer.json")
        tokenizer_config_file = os.path.join(
            rec_char_dict_path, "tokenizer_config.json"
        )
        self.tokenizer = TokenizerFast.from_file(fast_tokenizer_file)
        added_tokens_decoder = {}
        added_tokens_map = {}

        if tokenizer_config_file is not None:
            with open(
                tokenizer_config_file, encoding="utf-8"
            ) as tokenizer_config_handle:
                init_kwargs = json.load(tokenizer_config_handle)
                if "added_tokens_decoder" in init_kwargs:
                    for idx, token in init_kwargs["added_tokens_decoder"].items():
                        if isinstance(token, dict):
                            token = AddedToken(**token)
                        if isinstance(token, AddedToken):
                            added_tokens_decoder[int(idx)] = token
                            added_tokens_map[str(token)] = token
                        else:
                            raise ValueError(
                                f"Found a {token.__class__} in the saved `added_tokens_decoder`, should be a dictionary or an AddedToken instance"
                            )
                init_kwargs["added_tokens_decoder"] = added_tokens_decoder
                added_tokens_decoder = init_kwargs.pop("added_tokens_decoder", {})
                tokens_to_add = [
                    token
                    for index, token in sorted(
                        added_tokens_decoder.items(), key=lambda x: x[0]
                    )
                    if token not in added_tokens_decoder
                ]
                added_tokens_encoder = self.added_tokens_encoder(added_tokens_decoder)
                encoder = list(added_tokens_encoder.keys()) + [
                    str(token) for token in tokens_to_add
                ]
                tokens_to_add += [
                    token
                    for token in self.all_special_tokens_extended
                    if token not in encoder and token not in tokens_to_add
                ]
                if len(tokens_to_add) > 0:
                    is_last_special = None
                    tokens = []
                    special_tokens = self.all_special_tokens
                    for token in tokens_to_add:
                        is_special = (
                            (token.special or str(token) in special_tokens)
                            if isinstance(token, AddedToken)
                            else str(token) in special_tokens
                        )
                        if is_last_special is None or is_last_special == is_special:
                            tokens.append(token)
                        else:
                            self._add_tokens(tokens, special_tokens=is_last_special)
                            tokens = [token]
                        is_last_special = is_special
                    if tokens:
                        self._add_tokens(tokens, special_tokens=is_last_special)

    def _add_tokens(self, new_tokens, special_tokens=False) -> int:
        if special_tokens:
            return self.tokenizer.add_special_tokens(new_tokens)

        return self.tokenizer.add_tokens(new_tokens)

    def added_tokens_encoder(self, added_tokens_decoder):
        return {
            k.content: v
            for v, k in sorted(added_tokens_decoder.items(), key=lambda item: item[0])
        }

    @property
    def all_special_tokens(self):
        all_toks = [str(s) for s in self.all_special_tokens_extended]
        return all_toks

    @property
    def all_special_tokens_extended(self):
        all_tokens = []
        seen = set()
        for value in self.special_tokens_map_extended.values():
            if isinstance(value, (list, tuple)):
                tokens_to_add = [token for token in value if str(token) not in seen]
            else:
                tokens_to_add = [value] if str(value) not in seen else []
            seen.update(map(str, tokens_to_add))
            all_tokens.extend(tokens_to_add)
        return all_tokens

    @property
    def special_tokens_map_extended(self):
        set_attr = {}
        for attr in self.SPECIAL_TOKENS_ATTRIBUTES:
            attr_value = getattr(self, "_" + attr)
            if attr_value:
                set_attr[attr] = attr_value
        return set_attr

    def set_truncation_and_padding(
        self,
        padding_strategy,
        truncation_strategy,
        max_length,
        stride,
        pad_to_multiple_of,
    ):
        _truncation = self.tokenizer.truncation
        _padding = self.tokenizer.padding
        # Set truncation and padding on the backend tokenizer
        if truncation_strategy == TruncationStrategy.DO_NOT_TRUNCATE:
            if _truncation is not None:
                self._tokenizer.no_truncation()
        else:
            target = {
                "max_length": max_length,
                "stride": stride,
                "strategy": truncation_strategy.value,
                "direction": "right",
            }

            if _truncation is None:
                current = None
            else:
                current = {k: _truncation.get(k, None) for k in target}

            if current != target:
                self.tokenizer.enable_truncation(**target)
        if padding_strategy == PaddingStrategy.DO_NOT_PAD:
            if _padding is not None:
                self.tokenizer.no_padding()
        else:
            length = (
                max_length if padding_strategy == PaddingStrategy.MAX_LENGTH else None
            )
            target = {
                "length": length,
                "direction": self.padding_side,
                "pad_id": self.pad_token_id,
                "pad_token": self.pad_token,
                "pad_type_id": self.pad_token_type_id,
                "pad_to_multiple_of": pad_to_multiple_of,
            }
            if _padding != target:
                self.tokenizer.enable_padding(**target)

    def _convert_encoding(
        self,
        encoding,
        return_token_type_ids=None,
        return_attention_mask=None,
        return_overflowing_tokens=False,
        return_special_tokens_mask=False,
        return_offsets_mapping=False,
        return_length=False,
        verbose=True,
    ):

        if return_token_type_ids is None:
            return_token_type_ids = "token_type_ids" in self.model_input_names
        if return_attention_mask is None:
            return_attention_mask = "attention_mask" in self.model_input_names

        if return_overflowing_tokens and encoding.overflowing is not None:
            encodings = [encoding] + encoding.overflowing
        else:
            encodings = [encoding]

        encoding_dict = defaultdict(list)
        for e in encodings:
            encoding_dict["input_ids"].append(e.ids)
            if return_token_type_ids:
                encoding_dict["token_type_ids"].append(e.type_ids)
            if return_attention_mask:
                encoding_dict["attention_mask"].append(e.attention_mask)
            if return_special_tokens_mask:
                encoding_dict["special_tokens_mask"].append(e.special_tokens_mask)
            if return_offsets_mapping:
                encoding_dict["offset_mapping"].append(e.offsets)
            if return_length:
                encoding_dict["length"].append(len(e.ids))

        return encoding_dict, encodings

    def encode(
        self,
        text,
        text_pair=None,
        return_token_type_ids=False,
        add_special_tokens=True,
        is_split_into_words=False,
    ):
        batched_input = text
        self.set_truncation_and_padding(
            padding_strategy=PaddingStrategy.LONGEST,
            truncation_strategy=TruncationStrategy.LONGEST_FIRST,
            max_length=self.max_seq_len,
            stride=0,
            pad_to_multiple_of=None,
        )
        encodings = self.tokenizer.encode_batch(
            batched_input,
            add_special_tokens=add_special_tokens,
            is_pretokenized=is_split_into_words,
        )

        tokens_and_encodings = [
            self._convert_encoding(
                encoding=encoding,
                return_token_type_ids=False,
                return_attention_mask=None,
                return_overflowing_tokens=False,
                return_special_tokens_mask=False,
                return_offsets_mapping=False,
                return_length=False,
                verbose=True,
            )
            for encoding in encodings
        ]
        sanitized_tokens = {}
        for key in tokens_and_encodings[0][0].keys():
            stack = [e for item, _ in tokens_and_encodings for e in item[key]]
            sanitized_tokens[key] = stack
        return sanitized_tokens

    def __call__(self, data):
        eqs = data["label"]
        topk = self.encode([eqs])
        for k, p in zip(topk, [[self.bos_token_id, self.eos_token_id], [1, 1]]):
            process_seq = [x for x in topk[k]]
            max_length = 0
            for seq in process_seq:
                max_length = max(max_length, len(seq))
        data["label"] = np.array(topk["input_ids"]).astype(np.int64)[0]
        data["attention_mask"] = np.array(topk["attention_mask"]).astype(np.int64)[0]
        return data
