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import logging |
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import copy |
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import re |
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from api.db import ParserType |
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from io import BytesIO |
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from rag.nlp import rag_tokenizer, tokenize, tokenize_table, bullets_category, title_frequency, tokenize_chunks, docx_question_level |
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from rag.utils import num_tokens_from_string |
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from deepdoc.parser import PdfParser, PlainParser, DocxParser |
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from docx import Document |
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from PIL import Image |
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class Pdf(PdfParser): |
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def __init__(self): |
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self.model_speciess = ParserType.MANUAL.value |
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super().__init__() |
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def __call__(self, filename, binary=None, from_page=0, |
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to_page=100000, zoomin=3, callback=None): |
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from timeit import default_timer as timer |
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start = timer() |
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callback(msg="OCR is running...") |
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self.__images__( |
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filename if not binary else binary, |
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zoomin, |
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from_page, |
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to_page, |
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callback |
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) |
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callback(msg="OCR finished.") |
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logging.debug("OCR: {}".format(timer() - start)) |
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self._layouts_rec(zoomin) |
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callback(0.65, "Layout analysis finished.") |
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logging.debug("layouts: {}".format(timer() - start)) |
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self._table_transformer_job(zoomin) |
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callback(0.67, "Table analysis finished.") |
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self._text_merge() |
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tbls = self._extract_table_figure(True, zoomin, True, True) |
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self._concat_downward() |
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self._filter_forpages() |
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callback(0.68, "Text merging finished") |
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for b in self.boxes: |
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b["text"] = re.sub(r"([\t ]|\u3000){2,}", " ", b["text"].strip()) |
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return [(b["text"], b.get("layout_no", ""), self.get_position(b, zoomin)) |
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for i, b in enumerate(self.boxes)], tbls |
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class Docx(DocxParser): |
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def __init__(self): |
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pass |
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def get_picture(self, document, paragraph): |
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img = paragraph._element.xpath('.//pic:pic') |
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if not img: |
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return None |
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img = img[0] |
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embed = img.xpath('.//a:blip/@r:embed')[0] |
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related_part = document.part.related_parts[embed] |
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image = related_part.image |
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image = Image.open(BytesIO(image.blob)) |
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return image |
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def concat_img(self, img1, img2): |
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if img1 and not img2: |
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return img1 |
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if not img1 and img2: |
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return img2 |
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if not img1 and not img2: |
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return None |
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width1, height1 = img1.size |
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width2, height2 = img2.size |
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new_width = max(width1, width2) |
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new_height = height1 + height2 |
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new_image = Image.new('RGB', (new_width, new_height)) |
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new_image.paste(img1, (0, 0)) |
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new_image.paste(img2, (0, height1)) |
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return new_image |
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def __call__(self, filename, binary=None, from_page=0, to_page=100000, callback=None): |
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self.doc = Document( |
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filename) if not binary else Document(BytesIO(binary)) |
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pn = 0 |
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last_answer, last_image = "", None |
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question_stack, level_stack = [], [] |
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ti_list = [] |
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for p in self.doc.paragraphs: |
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if pn > to_page: |
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break |
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question_level, p_text = 0, '' |
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if from_page <= pn < to_page and p.text.strip(): |
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question_level, p_text = docx_question_level(p) |
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if not question_level or question_level > 6: |
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last_answer = f'{last_answer}\n{p_text}' |
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current_image = self.get_picture(self.doc, p) |
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last_image = self.concat_img(last_image, current_image) |
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else: |
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if last_answer or last_image: |
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sum_question = '\n'.join(question_stack) |
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if sum_question: |
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ti_list.append((f'{sum_question}\n{last_answer}', last_image)) |
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last_answer, last_image = '', None |
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i = question_level |
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while question_stack and i <= level_stack[-1]: |
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question_stack.pop() |
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level_stack.pop() |
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question_stack.append(p_text) |
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level_stack.append(question_level) |
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for run in p.runs: |
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if 'lastRenderedPageBreak' in run._element.xml: |
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pn += 1 |
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continue |
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if 'w:br' in run._element.xml and 'type="page"' in run._element.xml: |
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pn += 1 |
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if last_answer: |
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sum_question = '\n'.join(question_stack) |
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if sum_question: |
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ti_list.append((f'{sum_question}\n{last_answer}', last_image)) |
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tbls = [] |
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for tb in self.doc.tables: |
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html= "<table>" |
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for r in tb.rows: |
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html += "<tr>" |
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i = 0 |
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while i < len(r.cells): |
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span = 1 |
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c = r.cells[i] |
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for j in range(i+1, len(r.cells)): |
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if c.text == r.cells[j].text: |
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span += 1 |
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i = j |
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i += 1 |
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html += f"<td>{c.text}</td>" if span == 1 else f"<td colspan='{span}'>{c.text}</td>" |
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html += "</tr>" |
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html += "</table>" |
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tbls.append(((None, html), "")) |
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return ti_list, tbls |
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def chunk(filename, binary=None, from_page=0, to_page=100000, |
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lang="Chinese", callback=None, **kwargs): |
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""" |
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Only pdf is supported. |
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""" |
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pdf_parser = None |
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doc = { |
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"docnm_kwd": filename |
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} |
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doc["title_tks"] = rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", doc["docnm_kwd"])) |
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doc["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(doc["title_tks"]) |
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eng = lang.lower() == "english" |
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if re.search(r"\.pdf$", filename, re.IGNORECASE): |
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pdf_parser = Pdf() if kwargs.get( |
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"parser_config", {}).get( |
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"layout_recognize", True) else PlainParser() |
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sections, tbls = pdf_parser(filename if not binary else binary, |
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from_page=from_page, to_page=to_page, callback=callback) |
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if sections and len(sections[0]) < 3: |
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sections = [(t, l, [[0] * 5]) for t, l in sections] |
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if len(sections) > 0 and len(pdf_parser.outlines) / len(sections) > 0.1: |
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max_lvl = max([lvl for _, lvl in pdf_parser.outlines]) |
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most_level = max(0, max_lvl - 1) |
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levels = [] |
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for txt, _, _ in sections: |
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for t, lvl in pdf_parser.outlines: |
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tks = set([t[i] + t[i + 1] for i in range(len(t) - 1)]) |
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tks_ = set([txt[i] + txt[i + 1] |
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for i in range(min(len(t), len(txt) - 1))]) |
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if len(set(tks & tks_)) / max([len(tks), len(tks_), 1]) > 0.8: |
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levels.append(lvl) |
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break |
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else: |
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levels.append(max_lvl + 1) |
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else: |
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bull = bullets_category([txt for txt, _, _ in sections]) |
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most_level, levels = title_frequency( |
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bull, [(txt, l) for txt, l, poss in sections]) |
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assert len(sections) == len(levels) |
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sec_ids = [] |
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sid = 0 |
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for i, lvl in enumerate(levels): |
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if lvl <= most_level and i > 0 and lvl != levels[i - 1]: |
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sid += 1 |
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sec_ids.append(sid) |
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sections = [(txt, sec_ids[i], poss) |
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for i, (txt, _, poss) in enumerate(sections)] |
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for (img, rows), poss in tbls: |
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if not rows: continue |
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sections.append((rows if isinstance(rows, str) else rows[0], -1, |
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[(p[0] + 1 - from_page, p[1], p[2], p[3], p[4]) for p in poss])) |
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def tag(pn, left, right, top, bottom): |
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if pn + left + right + top + bottom == 0: |
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return "" |
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return "@@{}\t{:.1f}\t{:.1f}\t{:.1f}\t{:.1f}##" \ |
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.format(pn, left, right, top, bottom) |
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chunks = [] |
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last_sid = -2 |
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tk_cnt = 0 |
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for txt, sec_id, poss in sorted(sections, key=lambda x: ( |
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x[-1][0][0], x[-1][0][3], x[-1][0][1])): |
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poss = "\t".join([tag(*pos) for pos in poss]) |
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if tk_cnt < 32 or (tk_cnt < 1024 and (sec_id == last_sid or sec_id == -1)): |
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if chunks: |
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chunks[-1] += "\n" + txt + poss |
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tk_cnt += num_tokens_from_string(txt) |
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continue |
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chunks.append(txt + poss) |
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tk_cnt = num_tokens_from_string(txt) |
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if sec_id > -1: |
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last_sid = sec_id |
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res = tokenize_table(tbls, doc, eng) |
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res.extend(tokenize_chunks(chunks, doc, eng, pdf_parser)) |
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return res |
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if re.search(r"\.docx$", filename, re.IGNORECASE): |
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docx_parser = Docx() |
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ti_list, tbls = docx_parser(filename, binary, |
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from_page=0, to_page=10000, callback=callback) |
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res = tokenize_table(tbls, doc, eng) |
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for text, image in ti_list: |
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d = copy.deepcopy(doc) |
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d['image'] = image |
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tokenize(d, text, eng) |
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res.append(d) |
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return res |
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else: |
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raise NotImplementedError("file type not supported yet(pdf and docx supported)") |
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if __name__ == "__main__": |
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import sys |
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def dummy(prog=None, msg=""): |
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pass |
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chunk(sys.argv[1], callback=dummy) |