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#  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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#  distributed under the License is distributed on an "AS IS" BASIS,
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import logging
from io import BytesIO
import re

from deepdoc.parser.utils import get_text
from rag.app import naive
from rag.nlp import rag_tokenizer, tokenize
from deepdoc.parser import PdfParser, ExcelParser, HtmlParser
from deepdoc.parser.figure_parser import vision_figure_parser_docx_wrapper_naive
from rag.app.naive import by_plaintext, PARSERS
from api.db.joint_services.tenant_model_service import get_composite_model_name_by_id
from common.constants import MAXIMUM_PAGE_NUMBER, MAXIMUM_TASK_PAGE_NUMBER
from common.parser_config_utils import normalize_layout_recognizer


class Pdf(PdfParser):
    def __call__(self, filename, binary=None, from_page=0, to_page=MAXIMUM_PAGE_NUMBER, 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, drop=False)
        callback(0.63, "Layout analysis ({:.2f}s)".format(timer() - start))
        logging.debug("layouts cost: {}s".format(timer() - start))

        start = timer()
        self._table_transformer_job(zoomin)
        callback(0.65, "Table analysis ({:.2f}s)".format(timer() - start))

        start = timer()
        self._text_merge()
        callback(0.67, "Text merged ({:.2f}s)".format(timer() - start))
        tbls = self._extract_table_figure(True, zoomin, True, True)
        self._concat_downward()

        sections = [(b["text"], self.get_position(b, zoomin)) for i, b in enumerate(self.boxes)]
        return [(txt, "") for txt, _ in sorted(sections, key=lambda x: (x[-1][0][0], x[-1][0][3], x[-1][0][1]))], tbls


def chunk(filename, binary=None, from_page=0, to_page=MAXIMUM_PAGE_NUMBER, lang="Chinese", callback=None, **kwargs):
    """
    Supported file formats are docx, pdf, excel, txt.
    One file forms a chunk which maintains original text order.
    """
    parser_config = kwargs.get("parser_config", {"chunk_token_num": 512, "delimiter": "\n!?。；！？", "layout_recognize": "DeepDOC"})
    eng = lang.lower() == "english"  # is_english(cks)

    if re.search(r"\.docx$", filename, re.IGNORECASE):
        callback(0.1, "Start to parse.")
        sections = naive.Docx()(filename, binary)
        cks = []
        image_idxs = []

        for text, image, table in sections:
            if table is not None:
                text = (text or "") + str(table)
                ck_type = "table"
            else:
                ck_type = "image" if image is not None else "text"

            if ck_type == "image":
                image_idxs.append(len(cks))

            cks.append({"text": text, "image": image, "ck_type": ck_type})

        vision_figure_parser_docx_wrapper_naive(cks, image_idxs, callback, **kwargs)
        sections = [ck["text"] for ck in cks if ck.get("text")]
        callback(0.8, "Finish parsing.")

    elif re.search(r"\.pdf$", filename, re.IGNORECASE):
        layout_recognize_raw = parser_config.get("layout_recognize", "DeepDOC")
        tenant_id = kwargs.get("tenant_id")
        if tenant_id and isinstance(layout_recognize_raw, str):
            try:
                layout_recognize_raw = get_composite_model_name_by_id(layout_recognize_raw)
            except LookupError:
                pass
        layout_recognizer, parser_model_name = normalize_layout_recognizer(layout_recognize_raw)

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

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

        sections, tbls, pdf_parser = 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,
            mistral_ocr_llm_name=parser_model_name,
            paddleocr_llm_name=parser_model_name,
            **kwargs,
        )

        if not sections and not tbls:
            return []

        if name in ["tcadp", "docling", "mineru", "paddleocr"]:
            parser_config["chunk_token_num"] = 0

        callback(0.8, "Finish parsing.")

        for (img, rows), poss in tbls:
            if not rows:
                continue
            sections.append((rows if isinstance(rows, str) else rows[0], [(p[0] + 1 - from_page, p[1], p[2], p[3], p[4]) for p in poss]))
        sections = [s for s, _ in sections if s]

    elif re.search(r"\.xlsx?$", filename, re.IGNORECASE):
        callback(0.1, "Start to parse.")
        excel_parser = ExcelParser()
        sections = excel_parser.html(binary, MAXIMUM_TASK_PAGE_NUMBER)

    elif re.search(r"\.(txt|md|markdown|mdx)$", filename, re.IGNORECASE):
        callback(0.1, "Start to parse.")
        txt = get_text(filename, binary)
        sections = txt.split("\n")
        sections = [s for s in sections if s]
        callback(0.8, "Finish parsing.")

    elif re.search(r"\.(htm|html)$", filename, re.IGNORECASE):
        callback(0.1, "Start to parse.")
        sections = HtmlParser()(filename, binary)
        sections = [s for s in sections if s]
        callback(0.8, "Finish parsing.")

    elif re.search(r"\.doc$", filename, re.IGNORECASE):
        callback(0.1, "Start to parse.")
        try:
            from tika import parser as tika_parser
        except Exception as e:
            callback(0.8, f"tika not available: {e}. Unsupported .doc parsing.")
            logging.warning(f"tika not available: {e}. Unsupported .doc parsing for {filename}.")
            return []

        binary = BytesIO(binary)
        doc_parsed = tika_parser.from_buffer(binary)
        if doc_parsed.get("content", None) is not None:
            sections = doc_parsed["content"].split("\n")
            sections = [s for s in sections if s]
        callback(0.8, "Finish parsing.")

    else:
        raise NotImplementedError("file type not supported yet(doc, docx, pdf, txt supported)")

    doc = {"docnm_kwd": filename, "title_tks": rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", filename))}
    doc["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(doc["title_tks"])
    tokenize(doc, "\n".join(sections), eng, language=lang)
    return [doc]


if __name__ == "__main__":
    import sys

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

    chunk(sys.argv[1], from_page=0, to_page=10, callback=dummy)
