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https://github.com/jxxghp/MoviePilot.git
synced 2026-09-05 23:47:41 +08:00
refactor backend module architecture
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jxxghp
@@ -0,0 +1,221 @@
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import ast
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import logging
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import operator
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from functools import lru_cache
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from typing import Callable, List, Optional, Tuple
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import cn2an
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import regex as re
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from app.foundation.singleton import Singleton
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_custom_words_provider: Callable[[], object] = lambda: ()
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logger = logging.getLogger(__name__)
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def configure_custom_words_provider(provider: Callable[[], object]) -> None:
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"""注入用户识别词来源，领域层只负责解析和应用规则。"""
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global _custom_words_provider
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_custom_words_provider = provider
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def get_custom_words() -> object:
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"""返回当前自定义识别词原始配置。"""
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return _custom_words_provider()
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_COMBINED_WORD_RE = re.compile(r'^\s*(.*?)\s*=>\s*(.*?)\s*&&\s*(.*?)\s*<>\s*(.*?)\s*>>\s*(.*?)\s*$')
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_LEADING_ZERO_RE = re.compile(r"^0+")
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_EP_TOKEN_RE = re.compile(r"(?<![A-Za-z0-9_])EP(?![A-Za-z0-9_])")
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_IMPLICIT_EP_EXPRESSION_RE = re.compile(r"(?:\d|\))\s*EP|EP\s*(?:\d|\()")
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_EPISODE_OFFSET_OPS = {
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ast.Add: operator.add,
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ast.Sub: operator.sub,
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ast.Mult: operator.mul,
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ast.Div: operator.truediv,
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ast.FloorDiv: operator.floordiv,
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ast.Mod: operator.mod,
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}
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_EPISODE_OFFSET_UNARY_OPS = {
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ast.UAdd: operator.pos,
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ast.USub: operator.neg,
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}
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@lru_cache(maxsize=1024)
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def _compile_custom_word_regex(pattern: str):
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"""
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编译自定义识别词正则，缓存重复识别链路中反复使用的同一规则。
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"""
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return re.compile(pattern)
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def _calculate_episode_offset(offset: str, episode: int) -> int:
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"""
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按白名单算术语法计算集数偏移，避免执行任意表达式。
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"""
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if _IMPLICIT_EP_EXPRESSION_RE.search(offset):
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raise ValueError("EP 表达式不支持省略运算符")
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expression, replace_count = _EP_TOKEN_RE.subn(str(episode), offset)
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if "EP" in offset and replace_count == 0:
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raise ValueError("EP 占位符格式不正确")
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tree = ast.parse(expression, mode="eval")
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return int(_evaluate_episode_offset_node(tree.body))
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def _evaluate_episode_offset_node(node: ast.AST):
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"""
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递归计算集数偏移 AST 节点，仅允许数字和基础算术运算。
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"""
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if isinstance(node, ast.Constant) and isinstance(node.value, int):
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return node.value
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if isinstance(node, ast.BinOp) and type(node.op) in _EPISODE_OFFSET_OPS:
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left = _evaluate_episode_offset_node(node.left)
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right = _evaluate_episode_offset_node(node.right)
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return _EPISODE_OFFSET_OPS[type(node.op)](left, right)
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if isinstance(node, ast.UnaryOp) and type(node.op) in _EPISODE_OFFSET_UNARY_OPS:
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operand = _evaluate_episode_offset_node(node.operand)
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return _EPISODE_OFFSET_UNARY_OPS[type(node.op)](operand)
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raise ValueError("集数偏移表达式仅支持数字、EP、括号和基础算术运算符")
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def _format_episode_offset(episode_num_str: str, episode_num_offset_int: int) -> str:
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"""
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按原集数字符串格式返回偏移后的集数字符串。
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"""
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if not episode_num_str.isdigit():
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return cn2an.an2cn(episode_num_offset_int, "low")
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width = len(episode_num_str) if _LEADING_ZERO_RE.search(episode_num_str) else 0
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if episode_num_offset_int < 0:
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return f"-{str(abs(episode_num_offset_int)).zfill(width)}"
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return str(episode_num_offset_int).zfill(width)
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class WordsMatcher(metaclass=Singleton):
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"""
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自定义识别词匹配器。
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"""
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def prepare(self, title: str, custom_words: List[str] = None) -> Tuple[str, List[str]]:
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"""
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预处理标题，支持三种格式
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1：屏蔽词
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2：被替换词 => 替换词
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3：前定位词 <> 后定位词 >> 偏移量（EP）
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"""
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appley_words = []
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# 读取自定义识别词
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words: List[str] = custom_words or get_custom_words() or []
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for word in words:
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if not word or word.startswith("#"):
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continue
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try:
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word_info = self.__parse_word(word)
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if not word_info:
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continue
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word_type, params = word_info
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if word_type == "replace_and_offset":
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thc, bthc, pyq, pyh, offsets = params
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# 替换词
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title, message, state = self.__replace_regex(title, thc, bthc)
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if state:
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# 替换词成功再进行集偏移
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title, message, state = self.__episode_offset(title, pyq, pyh, offsets)
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elif word_type == "replace":
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title, message, state = self.__replace_regex(title, params[0], params[1])
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elif word_type == "offset":
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title, message, state = self.__episode_offset(title, params[0], params[1], params[2])
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else: # block
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title, message, state = self.__replace_regex(title, params[0], "")
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if state:
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appley_words.append(word)
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except Exception as err:
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logger.warning(f"自定义识别词 {word} 预处理标题失败：{str(err)} - 标题：{title}")
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return title, appley_words
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@staticmethod
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def __parse_word(word: str) -> Optional[Tuple[str, Tuple[str, ...]]]:
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"""
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解析识别词格式。复杂识别词保留原来的字段含义，只把多次正则提取合并为一次。
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"""
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if word.count(" => ") and word.count(" && ") and word.count(" >> ") and word.count(" <> "):
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word_match = _COMBINED_WORD_RE.match(word)
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if not word_match:
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raise ValueError("复杂识别词格式不正确")
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return "replace_and_offset", tuple(item.strip() for item in word_match.groups())
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if word.count(" => "):
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strings = word.split(" => ")
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return "replace", (strings[0], strings[1])
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if word.count(" >> ") and word.count(" <> "):
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strings = word.split(" <> ")
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offsets = strings[1].split(" >> ")
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strings[1] = offsets[0]
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return "offset", (strings[0], strings[1], offsets[1])
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if not word.strip():
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return None
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return "block", (word,)
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@staticmethod
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def __replace_regex(title: str, replaced: str, replace: str) -> Tuple[str, str, bool]:
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"""
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正则替换
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"""
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try:
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replaced_re = _compile_custom_word_regex(r'%s' % replaced)
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title, count = replaced_re.subn(r'%s' % replace, title)
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return title, "", count > 0
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except Exception as err:
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logger.warning(f"自定义识别词正则替换失败：{str(err)} - 标题：{title}，被替换词：{replaced}，替换词：{replace}")
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return title, str(err), False
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@staticmethod
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def __episode_offset(title: str, front: str, back: str, offset: str) -> Tuple[str, str, bool]:
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"""
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集数偏移
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"""
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try:
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if back and not _compile_custom_word_regex(r'%s' % back).search(title):
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return title, "", False
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if front and not _compile_custom_word_regex(r'%s' % front).search(title):
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return title, "", False
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offset_word_info_re = _compile_custom_word_regex(
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r'(?<=%s.*?)[0-9一二三四五六七八九十]+(?=.*?%s)' % (front, back)
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)
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episode_nums_str = offset_word_info_re.findall(title)
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if not episode_nums_str:
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return title, "", False
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episode_nums_offset_str = []
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offset_order_flag = False
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for episode_num_str in episode_nums_str:
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episode_num_int = int(cn2an.cn2an(episode_num_str, "smart"))
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episode_num_offset_int = _calculate_episode_offset(offset, episode_num_int)
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# 向前偏移
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if episode_num_int > episode_num_offset_int:
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offset_order_flag = True
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# 向后偏移
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elif episode_num_int < episode_num_offset_int:
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offset_order_flag = False
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episode_num_offset_str = _format_episode_offset(
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episode_num_str, episode_num_offset_int
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)
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episode_nums_offset_str.append(episode_num_offset_str)
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episode_nums_dict = dict(zip(episode_nums_str, episode_nums_offset_str))
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# 集数向前偏移，集数按升序处理
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if offset_order_flag:
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episode_nums_list = sorted(episode_nums_dict.items(), key=lambda x: x[1])
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# 集数向后偏移，集数按降序处理
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else:
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episode_nums_list = sorted(episode_nums_dict.items(), key=lambda x: x[1], reverse=True)
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for episode_num in episode_nums_list:
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episode_offset_re = _compile_custom_word_regex(
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r'(?<=%s.*?)%s(?=.*?%s)' % (front, episode_num[0], back)
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)
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title = episode_offset_re.sub(r'%s' % episode_num[1], title)
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return title, "", True
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except Exception as err:
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logger.warning(f"自定义识别词集数偏移失败：{str(err)} - 标题：{title}，前定位词：{front}，后定位词：{back}，偏移量：{offset}")
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return title, str(err), False
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