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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 tika import parser
import re
from io import BytesIO

from deepdoc.parser.utils import get_text
from rag.nlp import bullets_category, is_english,remove_contents_table, \
    hierarchical_merge, make_colon_as_title, naive_merge, random_choices, tokenize_table, \
    tokenize_chunks
from rag.nlp import rag_tokenizer
from deepdoc.parser import PdfParser, DocxParser, PlainParser, HtmlParser


class Pdf(PdfParser):
    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(0.67, "Layout analysis ({:.2f}s)".format(timer() - start))
        logging.debug("layouts: {}".format(timer() - start))

        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)
        self._naive_vertical_merge()
        self._filter_forpages()
        self._merge_with_same_bullet()
        callback(0.8, "Text extraction ({:.2f}s)".format(timer() - start))

        return [(b["text"] + self._line_tag(b, zoomin), b.get("layoutno", ""))
                for b in self.boxes], tbls


def chunk(filename, binary=None, from_page=0, to_page=100000,
          lang="Chinese", callback=None, **kwargs):
    """
        Supported file formats are docx, pdf, txt.
        Since a book is long and not all the parts are useful, if it's a PDF,
        please setup the page ranges for every book in order eliminate negative effects and save elapsed computing time.
    """
    doc = {
        "docnm_kwd": filename,
        "title_tks": rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", filename))
    }
    doc["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(doc["title_tks"])
    pdf_parser = None
    sections, tbls = [], []
    if re.search(r"\.docx$", filename, re.IGNORECASE):
        callback(0.1, "Start to parse.")
        doc_parser = DocxParser()
        # TODO: table of contents need to be removed
        sections, tbls = doc_parser(
            binary if binary else filename, from_page=from_page, to_page=to_page)
        remove_contents_table(sections, eng=is_english(
            random_choices([t for t, _ in sections], k=200)))
        tbls = [((None, lns), None) for lns in tbls]
        callback(0.8, "Finish parsing.")

    elif re.search(r"\.pdf$", filename, re.IGNORECASE):
        pdf_parser = Pdf()
        if kwargs.get("layout_recognize", "DeepDOC") == "Plain Text":
            pdf_parser = PlainParser()
        sections, tbls = pdf_parser(filename if not binary else binary,
                                    from_page=from_page, to_page=to_page, callback=callback)

    elif re.search(r"\.txt$", filename, re.IGNORECASE):
        callback(0.1, "Start to parse.")
        txt = get_text(filename, binary)
        sections = txt.split("\n")
        sections = [(line, "") for line in sections if line]
        remove_contents_table(sections, eng=is_english(
            random_choices([t for t, _ in sections], k=200)))
        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 = [(line, "") for line in sections if line]
        remove_contents_table(sections, eng=is_english(
            random_choices([t for t, _ in sections], k=200)))
        callback(0.8, "Finish parsing.")

    elif re.search(r"\.doc$", filename, re.IGNORECASE):
        callback(0.1, "Start to parse.")
        binary = BytesIO(binary)
        doc_parsed = parser.from_buffer(binary)
        sections = doc_parsed['content'].split('\n')
        sections = [(line, "") for line in sections if line]
        remove_contents_table(sections, eng=is_english(
            random_choices([t for t, _ in sections], k=200)))
        callback(0.8, "Finish parsing.")

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

    make_colon_as_title(sections)
    bull = bullets_category(
        [t for t in random_choices([t for t, _ in sections], k=100)])
    if bull >= 0:
        chunks = ["\n".join(ck)
                  for ck in hierarchical_merge(bull, sections, 5)]
    else:
        sections = [s.split("@") for s, _ in sections]
        sections = [(pr[0], "@" + pr[1]) if len(pr) == 2 else (pr[0], '') for pr in sections ]
        chunks = naive_merge(
            sections, kwargs.get(
                "chunk_token_num", 256), kwargs.get(
                "delimer", "\n。；！？"))

    # is it English
    # is_english(random_choices([t for t, _ in sections], k=218))
    eng = lang.lower() == "english"

    res = tokenize_table(tbls, doc, eng)
    res.extend(tokenize_chunks(chunks, doc, eng, pdf_parser))

    return res


if __name__ == "__main__":
    import sys

    def dummy(prog=None, msg=""):
        pass
    chunk(sys.argv[1], from_page=1, to_page=10, callback=dummy)
