import os, shutil
import re
import numpy as np
PRESERVE = 0
TRANSFORM = 1
pj = os.path.join
class LinkedListNode:
"""
Linked List Node
"""
def __init__(self, string, preserve=True) -> None:
self.string = string
self.preserve = preserve
self.next = None
self.range = None
# self.begin_line = 0
# self.begin_char = 0
def convert_to_linklist(text, mask):
root = LinkedListNode("", preserve=True)
current_node = root
for c, m, i in zip(text, mask, range(len(text))):
if (m == PRESERVE and current_node.preserve) or (
m == TRANSFORM and not current_node.preserve
):
# add
current_node.string += c
else:
current_node.next = LinkedListNode(c, preserve=(m == PRESERVE))
current_node = current_node.next
return root
def post_process(root):
# 修复括号
node = root
while True:
string = node.string
if node.preserve:
node = node.next
if node is None:
break
continue
def break_check(string):
str_stack = [""] # (lv, index)
for i, c in enumerate(string):
if c == "{":
str_stack.append("{")
elif c == "}":
if len(str_stack) == 1:
print("stack fix")
return i
str_stack.pop(-1)
else:
str_stack[-1] += c
return -1
bp = break_check(string)
if bp == -1:
pass
elif bp == 0:
node.string = string[:1]
q = LinkedListNode(string[1:], False)
q.next = node.next
node.next = q
else:
node.string = string[:bp]
q = LinkedListNode(string[bp:], False)
q.next = node.next
node.next = q
node = node.next
if node is None:
break
# 屏蔽空行和太短的句子
node = root
while True:
if len(node.string.strip("\n").strip("")) == 0:
node.preserve = True
if len(node.string.strip("\n").strip("")) < 42:
node.preserve = True
node = node.next
if node is None:
break
node = root
while True:
if node.next and node.preserve and node.next.preserve:
node.string += node.next.string
node.next = node.next.next
node = node.next
if node is None:
break
# 将前后断行符脱离
node = root
prev_node = None
while True:
if not node.preserve:
lstriped_ = node.string.lstrip().lstrip("\n")
if (
(prev_node is not None)
and (prev_node.preserve)
and (len(lstriped_) != len(node.string))
):
prev_node.string += node.string[: -len(lstriped_)]
node.string = lstriped_
rstriped_ = node.string.rstrip().rstrip("\n")
if (
(node.next is not None)
and (node.next.preserve)
and (len(rstriped_) != len(node.string))
):
node.next.string = node.string[len(rstriped_) :] + node.next.string
node.string = rstriped_
# =-=-=
prev_node = node
node = node.next
if node is None:
break
# 标注节点的行数范围
node = root
n_line = 0
expansion = 2
while True:
n_l = node.string.count("\n")
node.range = [n_line - expansion, n_line + n_l + expansion] # 失败时，扭转的范围
n_line = n_line + n_l
node = node.next
if node is None:
break
return root
"""
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
Latex segmentation with a binary mask (PRESERVE=0, TRANSFORM=1)
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
"""
def set_forbidden_text(text, mask, pattern, flags=0):
"""
Add a preserve text area in this paper
e.g. with pattern = r"\\begin\{algorithm\}(.*?)\\end\{algorithm\}"
you can mask out (mask = PRESERVE so that text become untouchable for GPT)
everything between "\begin{equation}" and "\end{equation}"
"""
if isinstance(pattern, list):
pattern = "|".join(pattern)
pattern_compile = re.compile(pattern, flags)
for res in pattern_compile.finditer(text):
mask[res.span()[0] : res.span()[1]] = PRESERVE
return text, mask
def reverse_forbidden_text(text, mask, pattern, flags=0, forbid_wrapper=True):
"""
Move area out of preserve area (make text editable for GPT)
count the number of the braces so as to catch compelete text area.
e.g.
\begin{abstract} blablablablablabla. \end{abstract}
"""
if isinstance(pattern, list):
pattern = "|".join(pattern)
pattern_compile = re.compile(pattern, flags)
for res in pattern_compile.finditer(text):
if not forbid_wrapper:
mask[res.span()[0] : res.span()[1]] = TRANSFORM
else:
mask[res.regs[0][0] : res.regs[1][0]] = PRESERVE # '\\begin{abstract}'
mask[res.regs[1][0] : res.regs[1][1]] = TRANSFORM # abstract
mask[res.regs[1][1] : res.regs[0][1]] = PRESERVE # abstract
return text, mask
def set_forbidden_text_careful_brace(text, mask, pattern, flags=0):
"""
Add a preserve text area in this paper (text become untouchable for GPT).
count the number of the braces so as to catch compelete text area.
e.g.
\caption{blablablablabla\texbf{blablabla}blablabla.}
"""
pattern_compile = re.compile(pattern, flags)
for res in pattern_compile.finditer(text):
brace_level = -1
p = begin = end = res.regs[0][0]
for _ in range(1024 * 16):
if text[p] == "}" and brace_level == 0:
break
elif text[p] == "}":
brace_level -= 1
elif text[p] == "{":
brace_level += 1
p += 1
end = p + 1
mask[begin:end] = PRESERVE
return text, mask
def reverse_forbidden_text_careful_brace(
text, mask, pattern, flags=0, forbid_wrapper=True
):
"""
Move area out of preserve area (make text editable for GPT)
count the number of the braces so as to catch compelete text area.
e.g.
\caption{blablablablabla\texbf{blablabla}blablabla.}
"""
pattern_compile = re.compile(pattern, flags)
for res in pattern_compile.finditer(text):
brace_level = 0
p = begin = end = res.regs[1][0]
for _ in range(1024 * 16):
if text[p] == "}" and brace_level == 0:
break
elif text[p] == "}":
brace_level -= 1
elif text[p] == "{":
brace_level += 1
p += 1
end = p
mask[begin:end] = TRANSFORM
if forbid_wrapper:
mask[res.regs[0][0] : begin] = PRESERVE
mask[end : res.regs[0][1]] = PRESERVE
return text, mask
def set_forbidden_text_begin_end(text, mask, pattern, flags=0, limit_n_lines=42):
"""
Find all \begin{} ... \end{} text block that with less than limit_n_lines lines.
Add it to preserve area
"""
pattern_compile = re.compile(pattern, flags)
def search_with_line_limit(text, mask):
for res in pattern_compile.finditer(text):
cmd = res.group(1) # begin{what}
this = res.group(2) # content between begin and end
this_mask = mask[res.regs[2][0] : res.regs[2][1]]
white_list = [
"document",
"abstract",
"lemma",
"definition",
"sproof",
"em",
"emph",
"textit",
"textbf",
"itemize",
"enumerate",
]
if (cmd in white_list) or this.count(
"\n"
) >= limit_n_lines: # use a magical number 42
this, this_mask = search_with_line_limit(this, this_mask)
mask[res.regs[2][0] : res.regs[2][1]] = this_mask
else:
mask[res.regs[0][0] : res.regs[0][1]] = PRESERVE
return text, mask
return search_with_line_limit(text, mask)
"""
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
Latex Merge File
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
"""
def find_main_tex_file(file_manifest, mode):
"""
在多Tex文档中，寻找主文件，必须包含documentclass，返回找到的第一个。
P.S. 但愿没人把latex模板放在里面传进来 (6.25 加入判定latex模板的代码)
"""
canidates = []
for texf in file_manifest:
if os.path.basename(texf).startswith("merge"):
continue
with open(texf, "r", encoding="utf8", errors="ignore") as f:
file_content = f.read()
if r"\documentclass" in file_content:
canidates.append(texf)
else:
continue
if len(canidates) == 0:
raise RuntimeError("无法找到一个主Tex文件（包含documentclass关键字）")
elif len(canidates) == 1:
return canidates[0]
else: # if len(canidates) >= 2 通过一些Latex模板中常见（但通常不会出现在正文）的单词，对不同latex源文件扣分，取评分最高者返回
canidates_score = []
# 给出一些判定模板文档的词作为扣分项
unexpected_words = [
"\\LaTeX",
"manuscript",
"Guidelines",
"font",
"citations",
"rejected",
"blind review",
"reviewers",
]
expected_words = ["\\input", "\\ref", "\\cite"]
for texf in canidates:
canidates_score.append(0)
with open(texf, "r", encoding="utf8", errors="ignore") as f:
file_content = f.read()
file_content = rm_comments(file_content)
for uw in unexpected_words:
if uw in file_content:
canidates_score[-1] -= 1
for uw in expected_words:
if uw in file_content:
canidates_score[-1] += 1
select = np.argmax(canidates_score) # 取评分最高者返回
return canidates[select]
def rm_comments(main_file):
new_file_remove_comment_lines = []
for l in main_file.splitlines():
# 删除整行的空注释
if l.lstrip().startswith("%"):
pass
else:
new_file_remove_comment_lines.append(l)
main_file = "\n".join(new_file_remove_comment_lines)
# main_file = re.sub(r"\\include{(.*?)}", r"\\input{\1}", main_file) # 将 \include 命令转换为 \input 命令
main_file = re.sub(r"(?<!\\)%.*", "", main_file) # 使用正则表达式查找半行注释, 并替换为空字符串
return main_file
def find_tex_file_ignore_case(fp):
dir_name = os.path.dirname(fp)
base_name = os.path.basename(fp)
# 如果输入的文件路径是正确的
if os.path.isfile(pj(dir_name, base_name)):
return pj(dir_name, base_name)
# 如果不正确，试着加上.tex后缀试试
if not base_name.endswith(".tex"):
base_name += ".tex"
if os.path.isfile(pj(dir_name, base_name)):
return pj(dir_name, base_name)
# 如果还找不到，解除大小写限制，再试一次
import glob
for f in glob.glob(dir_name + "/*.tex"):
base_name_s = os.path.basename(fp)
base_name_f = os.path.basename(f)
if base_name_s.lower() == base_name_f.lower():
return f
# 试着加上.tex后缀试试
if not base_name_s.endswith(".tex"):
base_name_s += ".tex"
if base_name_s.lower() == base_name_f.lower():
return f
return None
def merge_tex_files_(project_foler, main_file, mode):
"""
Merge Tex project recrusively
"""
main_file = rm_comments(main_file)
for s in reversed([q for q in re.finditer(r"\\input\{(.*?)\}", main_file, re.M)]):
f = s.group(1)
fp = os.path.join(project_foler, f)
fp_ = find_tex_file_ignore_case(fp)
if fp_:
try:
with open(fp_, "r", encoding="utf-8", errors="replace") as fx:
c = fx.read()
except:
c = f"\n\nWarning from GPT-Academic: LaTex source file is missing!\n\n"
else:
raise RuntimeError(f"找不到{fp}，Tex源文件缺失！")
c = merge_tex_files_(project_foler, c, mode)
main_file = main_file[: s.span()[0]] + c + main_file[s.span()[1] :]
return main_file
def find_title_and_abs(main_file):
def extract_abstract_1(text):
pattern = r"\\abstract\{(.*?)\}"
match = re.search(pattern, text, re.DOTALL)
if match:
return match.group(1)
else:
return None
def extract_abstract_2(text):
pattern = r"\\begin\{abstract\}(.*?)\\end\{abstract\}"
match = re.search(pattern, text, re.DOTALL)
if match:
return match.group(1)
else:
return None
def extract_title(string):
pattern = r"\\title\{(.*?)\}"
match = re.search(pattern, string, re.DOTALL)
if match:
return match.group(1)
else:
return None
abstract = extract_abstract_1(main_file)
if abstract is None:
abstract = extract_abstract_2(main_file)
title = extract_title(main_file)
return title, abstract
def merge_tex_files(project_foler, main_file, mode):
"""
Merge Tex project recrusively
P.S. 顺便把CTEX塞进去以支持中文
P.S. 顺便把Latex的注释去除
"""
main_file = merge_tex_files_(project_foler, main_file, mode)
main_file = rm_comments(main_file)
if mode == "translate_zh":
# find paper documentclass
pattern = re.compile(r"\\documentclass.*\n")
match = pattern.search(main_file)
assert match is not None, "Cannot find documentclass statement!"
position = match.end()
add_ctex = "\\usepackage{ctex}\n"
add_url = "\\usepackage{url}\n" if "{url}" not in main_file else ""
main_file = main_file[:position] + add_ctex + add_url + main_file[position:]
# fontset=windows
import platform
main_file = re.sub(
r"\\documentclass\[(.*?)\]{(.*?)}",
r"\\documentclass[\1,fontset=windows,UTF8]{\2}",
main_file,
)
main_file = re.sub(
r"\\documentclass{(.*?)}",
r"\\documentclass[fontset=windows,UTF8]{\1}",
main_file,
)
# find paper abstract
pattern_opt1 = re.compile(r"\\begin\{abstract\}.*\n")
pattern_opt2 = re.compile(r"\\abstract\{(.*?)\}", flags=re.DOTALL)
match_opt1 = pattern_opt1.search(main_file)
match_opt2 = pattern_opt2.search(main_file)
if (match_opt1 is None) and (match_opt2 is None):
# "Cannot find paper abstract section!"
main_file = insert_abstract(main_file)
match_opt1 = pattern_opt1.search(main_file)
match_opt2 = pattern_opt2.search(main_file)
assert (match_opt1 is not None) or (
match_opt2 is not None
), "Cannot find paper abstract section!"
return main_file
insert_missing_abs_str = r"""
\begin{abstract}
The GPT-Academic program cannot find abstract section in this paper.
\end{abstract}
"""
def insert_abstract(tex_content):
if "\\maketitle" in tex_content:
# find the position of "\maketitle"
find_index = tex_content.index("\\maketitle")
# find the nearest ending line
end_line_index = tex_content.find("\n", find_index)
# insert "abs_str" on the next line
modified_tex = (
tex_content[: end_line_index + 1]
+ "\n\n"
+ insert_missing_abs_str
+ "\n\n"
+ tex_content[end_line_index + 1 :]
)
return modified_tex
elif r"\begin{document}" in tex_content:
# find the position of "\maketitle"
find_index = tex_content.index(r"\begin{document}")
# find the nearest ending line
end_line_index = tex_content.find("\n", find_index)
# insert "abs_str" on the next line
modified_tex = (
tex_content[: end_line_index + 1]
+ "\n\n"
+ insert_missing_abs_str
+ "\n\n"
+ tex_content[end_line_index + 1 :]
)
return modified_tex
else:
return tex_content
"""
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
Post process
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
"""
def mod_inbraket(match):
"""
为啥chatgpt会把cite里面的逗号换成中文逗号呀
"""
# get the matched string
cmd = match.group(1)
str_to_modify = match.group(2)
# modify the matched string
str_to_modify = str_to_modify.replace("：", ":") # 前面是中文冒号，后面是英文冒号
str_to_modify = str_to_modify.replace("，", ",") # 前面是中文逗号，后面是英文逗号
# str_to_modify = 'BOOM'
return "\\" + cmd + "{" + str_to_modify + "}"
def fix_content(final_tex, node_string):
"""
Fix common GPT errors to increase success rate
"""
final_tex = re.sub(r"(?<!\\)%", "\\%", final_tex)
final_tex = re.sub(r"\\([a-z]{2,10})\ \{", r"\\\1{", string=final_tex)
final_tex = re.sub(r"\\\ ([a-z]{2,10})\{", r"\\\1{", string=final_tex)
final_tex = re.sub(r"\\([a-z]{2,10})\{([^\}]*?)\}", mod_inbraket, string=final_tex)
if "Traceback" in final_tex and "[Local Message]" in final_tex:
final_tex = node_string # 出问题了，还原原文
if node_string.count("\\begin") != final_tex.count("\\begin"):
final_tex = node_string # 出问题了，还原原文
if node_string.count("\_") > 0 and node_string.count("\_") > final_tex.count("\_"):
# walk and replace any _ without \
final_tex = re.sub(r"(?<!\\)_", "\\_", final_tex)
def compute_brace_level(string):
# this function count the number of { and }
brace_level = 0
for c in string:
if c == "{":
brace_level += 1
elif c == "}":
brace_level -= 1
return brace_level
def join_most(tex_t, tex_o):
# this function join translated string and original string when something goes wrong
p_t = 0
p_o = 0
def find_next(string, chars, begin):
p = begin
while p < len(string):
if string[p] in chars:
return p, string[p]
p += 1
return None, None
while True:
res1, char = find_next(tex_o, ["{", "}"], p_o)
if res1 is None:
break
res2, char = find_next(tex_t, [char], p_t)
if res2 is None:
break
p_o = res1 + 1
p_t = res2 + 1
return tex_t[:p_t] + tex_o[p_o:]
if compute_brace_level(final_tex) != compute_brace_level(node_string):
# 出问题了，还原部分原文，保证括号正确
final_tex = join_most(final_tex, node_string)
return final_tex
def compile_latex_with_timeout(command, cwd, timeout=60):
import subprocess
process = subprocess.Popen(
command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, cwd=cwd
)
try:
stdout, stderr = process.communicate(timeout=timeout)
except subprocess.TimeoutExpired:
process.kill()
stdout, stderr = process.communicate()
print("Process timed out!")
return False
return True
def run_in_subprocess_wrapper_func(func, args, kwargs, return_dict, exception_dict):
import sys
try:
result = func(*args, **kwargs)
return_dict["result"] = result
except Exception as e:
exc_info = sys.exc_info()
exception_dict["exception"] = exc_info
def run_in_subprocess(func):
import multiprocessing
def wrapper(*args, **kwargs):
return_dict = multiprocessing.Manager().dict()
exception_dict = multiprocessing.Manager().dict()
process = multiprocessing.Process(
target=run_in_subprocess_wrapper_func,
args=(func, args, kwargs, return_dict, exception_dict),
)
process.start()
process.join()
process.close()
if "exception" in exception_dict:
# ooops, the subprocess ran into an exception
exc_info = exception_dict["exception"]
raise exc_info[1].with_traceback(exc_info[2])
if "result" in return_dict.keys():
# If the subprocess ran successfully, return the result
return return_dict["result"]
return wrapper
def _merge_pdfs(pdf1_path, pdf2_path, output_path):
import PyPDF2 # PyPDF2这个库有严重的内存泄露问题，把它放到子进程中运行，从而方便内存的释放
Percent = 0.95
# raise RuntimeError('PyPDF2 has a serious memory leak problem, please use other tools to merge PDF files.')
# Open the first PDF file
with open(pdf1_path, "rb") as pdf1_file:
pdf1_reader = PyPDF2.PdfFileReader(pdf1_file)
# Open the second PDF file
with open(pdf2_path, "rb") as pdf2_file:
pdf2_reader = PyPDF2.PdfFileReader(pdf2_file)
# Create a new PDF file to store the merged pages
output_writer = PyPDF2.PdfFileWriter()
# Determine the number of pages in each PDF file
num_pages = max(pdf1_reader.numPages, pdf2_reader.numPages)
# Merge the pages from the two PDF files
for page_num in range(num_pages):
# Add the page from the first PDF file
if page_num < pdf1_reader.numPages:
page1 = pdf1_reader.getPage(page_num)
else:
page1 = PyPDF2.PageObject.createBlankPage(pdf1_reader)
# Add the page from the second PDF file
if page_num < pdf2_reader.numPages:
page2 = pdf2_reader.getPage(page_num)
else:
page2 = PyPDF2.PageObject.createBlankPage(pdf1_reader)
# Create a new empty page with double width
new_page = PyPDF2.PageObject.createBlankPage(
width=int(
int(page1.mediaBox.getWidth())
+ int(page2.mediaBox.getWidth()) * Percent
),
height=max(page1.mediaBox.getHeight(), page2.mediaBox.getHeight()),
)
new_page.mergeTranslatedPage(page1, 0, 0)
new_page.mergeTranslatedPage(
page2,
int(
int(page1.mediaBox.getWidth())
- int(page2.mediaBox.getWidth()) * (1 - Percent)
),
0,
)
output_writer.addPage(new_page)
# Save the merged PDF file
with open(output_path, "wb") as output_file:
output_writer.write(output_file)
merge_pdfs = run_in_subprocess(_merge_pdfs) # PyPDF2这个库有严重的内存泄露问题，把它放到子进程中运行，从而方便内存的释放