#
#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
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#  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
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#      http://www.apache.org/licenses/LICENSE-2.0
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#  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.
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import logging
import copy
import re

from deepdoc.parser.figure_parser import vision_figure_parser_pdf_wrapper
from common.constants import ParserType
from rag.nlp import rag_tokenizer, tokenize, tokenize_table, add_positions, bullets_category, title_frequency, \
    tokenize_chunks, attach_media_context
from deepdoc.parser import PdfParser
import numpy as np
from rag.app.naive import by_plaintext, PARSERS
from common.parser_config_utils import normalize_layout_recognizer


class Pdf(PdfParser):
    def __init__(self):
        self.model_speciess = ParserType.PAPER.value
        super().__init__()

    def __call__(self, filename, binary=None, from_page=0,
                 to_page=100000, zoomin=3, callback=None):
        from timeit import default_timer as timer
        start = timer()
        callback(msg="OCR started")
        self.__images__(
            filename if not binary else binary,
            zoomin,
            from_page,
            to_page,
            callback
        )
        callback(msg="OCR finished ({:.2f}s)".format(timer() - start))

        start = timer()
        self._layouts_rec(zoomin)
        callback(0.63, "Layout analysis ({:.2f}s)".format(timer() - start))
        logging.debug(f"layouts cost: {timer() - start}s")

        start = timer()
        self._table_transformer_job(zoomin)
        callback(0.68, "Table analysis ({:.2f}s)".format(timer() - start))

        start = timer()
        self._text_merge()
        tbls = self._extract_table_figure(True, zoomin, True, True)
        column_width = np.median([b["x1"] - b["x0"] for b in self.boxes])
        self._concat_downward()
        self._filter_forpages()
        callback(0.75, "Text merged ({:.2f}s)".format(timer() - start))

        # clean mess
        if column_width < self.page_images[0].size[0] / zoomin / 2:
            logging.debug("two_column................... {} {}".format(column_width,
                                                                       self.page_images[0].size[0] / zoomin / 2))
            self.boxes = self.sort_X_by_page(self.boxes, column_width / 2)
        for b in self.boxes:
            b["text"] = re.sub(r"([\t 　]|\u3000){2,}", " ", b["text"].strip())

        def _begin(txt):
            return re.match(
                "[0-9. 一、i]*(introduction|abstract|摘要|引言|keywords|key words|关键词|background|背景|目录|前言|contents)",
                txt.lower().strip())

        if from_page > 0:
            return {
                "title": "",
                "authors": "",
                "abstract": "",
                "sections": [(b["text"] + self._line_tag(b, zoomin), b.get("layoutno", "")) for b in self.boxes if
                             re.match(r"(text|title)", b.get("layoutno", "text"))],
                "tables": tbls
            }
        # get title and authors
        title = ""
        authors = []
        i = 0
        while i < min(32, len(self.boxes) - 1):
            b = self.boxes[i]
            i += 1
            if b.get("layoutno", "").find("title") >= 0:
                title = b["text"]
                if _begin(title):
                    title = ""
                    break
                for j in range(3):
                    if _begin(self.boxes[i + j]["text"]):
                        break
                    authors.append(self.boxes[i + j]["text"])
                    break
                break
        # get abstract
        abstr = ""
        i = 0
        while i + 1 < min(32, len(self.boxes)):
            b = self.boxes[i]
            i += 1
            txt = b["text"].lower().strip()
            if re.match("(abstract|摘要)", txt):
                if len(txt.split()) > 32 or len(txt) > 64:
                    abstr = txt + self._line_tag(b, zoomin)
                    break
                txt = self.boxes[i]["text"].lower().strip()
                if len(txt.split()) > 32 or len(txt) > 64:
                    abstr = txt + self._line_tag(self.boxes[i], zoomin)
                i += 1
                break
        if not abstr:
            i = 0

        callback(
            0.8, "Page {}~{}: Text merging finished".format(
                from_page, min(
                    to_page, self.total_page)))
        for b in self.boxes:
            logging.debug("{} {}".format(b["text"], b.get("layoutno")))
        logging.debug("{}".format(tbls))

        return {
            "title": title,
            "authors": " ".join(authors),
            "abstract": abstr,
            "sections": [(b["text"] + self._line_tag(b, zoomin), b.get("layoutno", "")) for b in self.boxes[i:] if
                         re.match(r"(text|title)", b.get("layoutno", "text"))],
            "tables": tbls
        }


def chunk(filename, binary=None, from_page=0, to_page=100000,
          lang="Chinese", callback=None, **kwargs):
    """
        Only pdf is supported.
        The abstract of the paper will be sliced as an entire chunk, and will not be sliced partly.
    """
    parser_config = kwargs.get(
        "parser_config", {
            "chunk_token_num": 512, "delimiter": "\n!?。；！？", "layout_recognize": "DeepDOC"})
    if re.search(r"\.pdf$", filename, re.IGNORECASE):
        layout_recognizer, parser_model_name = normalize_layout_recognizer(
            parser_config.get("layout_recognize", "DeepDOC")
        )

        if isinstance(layout_recognizer, bool):
            layout_recognizer = "DeepDOC" if layout_recognizer else "Plain Text"

        name = layout_recognizer.strip().lower()
        pdf_parser = PARSERS.get(name, by_plaintext)
        callback(0.1, "Start to parse.")

        if name == "deepdoc":
            pdf_parser = Pdf()
            paper = pdf_parser(filename if not binary else binary,
                               from_page=from_page, to_page=to_page, callback=callback)
            sections = paper.get("sections", [])
        else:
            kwargs.pop("parse_method", None)
            kwargs.pop("mineru_llm_name", None)
            sections, tables, pdf_parser = pdf_parser(
                filename=filename,
                binary=binary,
                from_page=from_page,
                to_page=to_page,
                lang=lang,
                callback=callback,
                pdf_cls=Pdf,
                layout_recognizer=layout_recognizer,
                mineru_llm_name=parser_model_name,
                parse_method="paper",
                **kwargs
            )

            paper = {
                "title": filename,
                "authors": " ",
                "abstract": "",
                "sections": sections,
                "tables": tables
            }

        tbls = paper["tables"]
        tbls = vision_figure_parser_pdf_wrapper(
            tbls=tbls,
            sections=sections,
            callback=callback,
            **kwargs,
        )
        paper["tables"] = tbls
    else:
        raise NotImplementedError("file type not supported yet(pdf supported)")

    doc = {"docnm_kwd": filename, "authors_tks": rag_tokenizer.tokenize(paper["authors"]),
           "title_tks": rag_tokenizer.tokenize(paper["title"] if paper["title"] else filename)}
    doc["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(doc["title_tks"])
    doc["authors_sm_tks"] = rag_tokenizer.fine_grained_tokenize(doc["authors_tks"])
    # is it English
    eng = lang.lower() == "english"  # pdf_parser.is_english
    logging.debug("It's English.....{}".format(eng))

    res = tokenize_table(paper["tables"], doc, eng)

    if paper["abstract"]:
        d = copy.deepcopy(doc)
        txt = pdf_parser.remove_tag(paper["abstract"])
        d["important_kwd"] = ["abstract", "总结", "概括", "summary", "summarize"]
        d["important_tks"] = " ".join(d["important_kwd"])
        d["image"], poss = pdf_parser.crop(
            paper["abstract"], need_position=True)
        add_positions(d, poss)
        tokenize(d, txt, eng)
        res.append(d)

    sorted_sections = paper["sections"]
    # set pivot using the most frequent type of title,
    # then merge between 2 pivot
    bull = bullets_category([txt for txt, _ in sorted_sections])
    most_level, levels = title_frequency(bull, sorted_sections)
    assert len(sorted_sections) == len(levels)
    sec_ids = []
    sid = 0
    for i, lvl in enumerate(levels):
        if lvl <= most_level and i > 0 and lvl != levels[i - 1]:
            sid += 1
        sec_ids.append(sid)
        logging.debug("{} {} {} {}".format(lvl, sorted_sections[i][0], most_level, sid))

    chunks = []
    last_sid = -2
    for (txt, _), sec_id in zip(sorted_sections, sec_ids):
        if sec_id == last_sid:
            if chunks:
                chunks[-1] += "\n" + txt
                continue
        chunks.append(txt)
        last_sid = sec_id
    res.extend(tokenize_chunks(chunks, doc, eng, pdf_parser))
    table_ctx = max(0, int(parser_config.get("table_context_size", 0) or 0))
    image_ctx = max(0, int(parser_config.get("image_context_size", 0) or 0))
    if table_ctx or image_ctx:
        attach_media_context(res, table_ctx, image_ctx)
    return res


"""
    readed = [0] * len(paper["lines"])
    # find colon firstly
    i = 0
    while i + 1 < len(paper["lines"]):
        txt = pdf_parser.remove_tag(paper["lines"][i][0])
        j = i
        if txt.strip("\n").strip()[-1] not in ":：":
            i += 1
            continue
        i += 1
        while i < len(paper["lines"]) and not paper["lines"][i][0]:
            i += 1
        if i >= len(paper["lines"]): break
        proj = [paper["lines"][i][0].strip()]
        i += 1
        while i < len(paper["lines"]) and paper["lines"][i][0].strip()[0] == proj[-1][0]:
            proj.append(paper["lines"][i])
            i += 1
        for k in range(j, i): readed[k] = True
        txt = txt[::-1]
        if eng:
            r = re.search(r"(.*?) ([\\.;?!]|$)", txt)
            txt = r.group(1)[::-1] if r else txt[::-1]
        else:
            r = re.search(r"(.*?) ([。？；！]|$)", txt)
            txt = r.group(1)[::-1] if r else txt[::-1]
        for p in proj:
            d = copy.deepcopy(doc)
            txt += "\n" + pdf_parser.remove_tag(p)
            d["image"], poss = pdf_parser.crop(p, need_position=True)
            add_positions(d, poss)
            tokenize(d, txt, eng)
            res.append(d)

    i = 0
    chunk = []
    tk_cnt = 0
    def add_chunk():
        nonlocal chunk, res, doc, pdf_parser, tk_cnt
        d = copy.deepcopy(doc)
        ck = "\n".join(chunk)
        tokenize(d, pdf_parser.remove_tag(ck), pdf_parser.is_english)
        d["image"], poss = pdf_parser.crop(ck, need_position=True)
        add_positions(d, poss)
        res.append(d)
        chunk = []
        tk_cnt = 0

    while i < len(paper["lines"]):
        if tk_cnt > 128:
            add_chunk()
        if readed[i]:
            i += 1
            continue
        readed[i] = True
        txt, layouts = paper["lines"][i]
        txt_ = pdf_parser.remove_tag(txt)
        i += 1
        cnt = num_tokens_from_string(txt_)
        if any([
            layouts.find("title") >= 0 and chunk,
            cnt + tk_cnt > 128 and tk_cnt > 32,
        ]):
            add_chunk()
            chunk = [txt]
            tk_cnt = cnt
        else:
            chunk.append(txt)
            tk_cnt += cnt

    if chunk: add_chunk()
    for i, d in enumerate(res):
        print(d)
        # d["image"].save(f"./logs/{i}.jpg")
    return res
"""

if __name__ == "__main__":
    import sys


    def dummy(prog=None, msg=""):
        pass


    chunk(sys.argv[1], callback=dummy)
