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charset_normalizer
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md.py
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from functools import lru_cache from logging import getLogger from typing import List, Optional from .constant import ( COMMON_SAFE_ASCII_CHARACTERS, TRACE, UNICODE_SECONDARY_RANGE_KEYWORD, ) from .utils import ( is_accentuated, is_arabic, is_arabic_isolated_form, is_case_variable, is_cjk, is_emoticon, is_hangul, is_hiragana, is_katakana, is_latin, is_punctuation, is_separator, is_symbol, is_thai, is_unprintable, remove_accent, unicode_range, ) class MessDetectorPlugin: """ Base abstract class used for mess detection plugins. All detectors MUST extend and implement given methods. """ def eligible(self, character: str) -> bool: """ Determine if given character should be fed in. """ raise NotImplementedError # pragma: nocover def feed(self, character: str) -> None: """ The main routine to be executed upon character. Insert the logic in witch the text would be considered chaotic. """ raise NotImplementedError # pragma: nocover def reset(self) -> None: # pragma: no cover """ Permit to reset the plugin to the initial state. """ raise NotImplementedError @property def ratio(self) -> float: """ Compute the chaos ratio based on what your feed() has seen. Must NOT be lower than 0.; No restriction gt 0. """ raise NotImplementedError # pragma: nocover class TooManySymbolOrPunctuationPlugin(MessDetectorPlugin): def __init__(self) -> None: self._punctuation_count: int = 0 self._symbol_count: int = 0 self._character_count: int = 0 self._last_printable_char: Optional[str] = None self._frenzy_symbol_in_word: bool = False def eligible(self, character: str) -> bool: return character.isprintable() def feed(self, character: str) -> None: self._character_count += 1 if ( character != self._last_printable_char and character not in COMMON_SAFE_ASCII_CHARACTERS ): if is_punctuation(character): self._punctuation_count += 1 elif ( character.isdigit() is False and is_symbol(character) and is_emoticon(character) is False ): self._symbol_count += 2 self._last_printable_char = character def reset(self) -> None: # pragma: no cover self._punctuation_count = 0 self._character_count = 0 self._symbol_count = 0 @property def ratio(self) -> float: if self._character_count == 0: return 0.0 ratio_of_punctuation: float = ( self._punctuation_count + self._symbol_count ) / self._character_count return ratio_of_punctuation if ratio_of_punctuation >= 0.3 else 0.0 class TooManyAccentuatedPlugin(MessDetectorPlugin): def __init__(self) -> None: self._character_count: int = 0 self._accentuated_count: int = 0 def eligible(self, character: str) -> bool: return character.isalpha() def feed(self, character: str) -> None: self._character_count += 1 if is_accentuated(character): self._accentuated_count += 1 def reset(self) -> None: # pragma: no cover self._character_count = 0 self._accentuated_count = 0 @property def ratio(self) -> float: if self._character_count < 8: return 0.0 ratio_of_accentuation: float = self._accentuated_count / self._character_count return ratio_of_accentuation if ratio_of_accentuation >= 0.35 else 0.0 class UnprintablePlugin(MessDetectorPlugin): def __init__(self) -> None: self._unprintable_count: int = 0 self._character_count: int = 0 def eligible(self, character: str) -> bool: return True def feed(self, character: str) -> None: if is_unprintable(character): self._unprintable_count += 1 self._character_count += 1 def reset(self) -> None: # pragma: no cover self._unprintable_count = 0 @property def ratio(self) -> float: if self._character_count == 0: return 0.0 return (self._unprintable_count * 8) / self._character_count class SuspiciousDuplicateAccentPlugin(MessDetectorPlugin): def __init__(self) -> None: self._successive_count: int = 0 self._character_count: int = 0 self._last_latin_character: Optional[str] = None def eligible(self, character: str) -> bool: return character.isalpha() and is_latin(character) def feed(self, character: str) -> None: self._character_count += 1 if ( self._last_latin_character is not None and is_accentuated(character) and is_accentuated(self._last_latin_character) ): if character.isupper() and self._last_latin_character.isupper(): self._successive_count += 1 # Worse if its the same char duplicated with different accent. if remove_accent(character) == remove_accent(self._last_latin_character): self._successive_count += 1 self._last_latin_character = character def reset(self) -> None: # pragma: no cover self._successive_count = 0 self._character_count = 0 self._last_latin_character = None @property def ratio(self) -> float: if self._character_count == 0: return 0.0 return (self._successive_count * 2) / self._character_count class SuspiciousRange(MessDetectorPlugin): def __init__(self) -> None: self._suspicious_successive_range_count: int = 0 self._character_count: int = 0 self._last_printable_seen: Optional[str] = None def eligible(self, character: str) -> bool: return character.isprintable() def feed(self, character: str) -> None: self._character_count += 1 if ( character.isspace() or is_punctuation(character) or character in COMMON_SAFE_ASCII_CHARACTERS ): self._last_printable_seen = None return if self._last_printable_seen is None: self._last_printable_seen = character return unicode_range_a: Optional[str] = unicode_range(self._last_printable_seen) unicode_range_b: Optional[str] = unicode_range(character) if is_suspiciously_successive_range(unicode_range_a, unicode_range_b): self._suspicious_successive_range_count += 1 self._last_printable_seen = character def reset(self) -> None: # pragma: no cover self._character_count = 0 self._suspicious_successive_range_count = 0 self._last_printable_seen = None @property def ratio(self) -> float: if self._character_count <= 24: return 0.0 ratio_of_suspicious_range_usage: float = ( self._suspicious_successive_range_count * 2 ) / self._character_count return ratio_of_suspicious_range_usage class SuperWeirdWordPlugin(MessDetectorPlugin): def __init__(self) -> None: self._word_count: int = 0 self._bad_word_count: int = 0 self._foreign_long_count: int = 0 self._is_current_word_bad: bool = False self._foreign_long_watch: bool = False self._character_count: int = 0 self._bad_character_count: int = 0 self._buffer: str = "" self._buffer_accent_count: int = 0 def eligible(self, character: str) -> bool: return True def feed(self, character: str) -> None: if character.isalpha(): self._buffer += character if is_accentuated(character): self._buffer_accent_count += 1 if ( self._foreign_long_watch is False and (is_latin(character) is False or is_accentuated(character)) and is_cjk(character) is False and is_hangul(character) is False and is_katakana(character) is False and is_hiragana(character) is False and is_thai(character) is False ): self._foreign_long_watch = True return if not self._buffer: return if ( character.isspace() or is_punctuation(character) or is_separator(character) ) and self._buffer: self._word_count += 1 buffer_length: int = len(self._buffer) self._character_count += buffer_length if buffer_length >= 4: if self._buffer_accent_count / buffer_length > 0.34: self._is_current_word_bad = True # Word/Buffer ending with an upper case accentuated letter are so rare, # that we will consider them all as suspicious. Same weight as foreign_long suspicious. if ( is_accentuated(self._buffer[-1]) and self._buffer[-1].isupper() and all(_.isupper() for _ in self._buffer) is False ): self._foreign_long_count += 1 self._is_current_word_bad = True if buffer_length >= 24 and self._foreign_long_watch: camel_case_dst = [ i for c, i in zip(self._buffer, range(0, buffer_length)) if c.isupper() ] probable_camel_cased: bool = False if camel_case_dst and (len(camel_case_dst) / buffer_length <= 0.3): probable_camel_cased = True if not probable_camel_cased: self._foreign_long_count += 1 self._is_current_word_bad = True if self._is_current_word_bad: self._bad_word_count += 1 self._bad_character_count += len(self._buffer) self._is_current_word_bad = False self._foreign_long_watch = False self._buffer = "" self._buffer_accent_count = 0 elif ( character not in {"<", ">", "-", "=", "~", "|", "_"} and character.isdigit() is False and is_symbol(character) ): self._is_current_word_bad = True self._buffer += character def reset(self) -> None: # pragma: no cover self._buffer = "" self._is_current_word_bad = False self._foreign_long_watch = False self._bad_word_count = 0 self._word_count = 0 self._character_count = 0 self._bad_character_count = 0 self._foreign_long_count = 0 @property def ratio(self) -> float: if self._word_count <= 10 and self._foreign_long_count == 0: return 0.0 return self._bad_character_count / self._character_count class CjkInvalidStopPlugin(MessDetectorPlugin): """ GB(Chinese) based encoding often render the stop incorrectly when the content does not fit and can be easily detected. Searching for the overuse of '丅' and '丄'. """ def __init__(self) -> None: self._wrong_stop_count: int = 0 self._cjk_character_count: int = 0 def eligible(self, character: str) -> bool: return True def feed(self, character: str) -> None: if character in {"丅", "丄"}: self._wrong_stop_count += 1 return if is_cjk(character): self._cjk_character_count += 1 def reset(self) -> None: # pragma: no cover self._wrong_stop_count = 0 self._cjk_character_count = 0 @property def ratio(self) -> float: if self._cjk_character_count < 16: return 0.0 return self._wrong_stop_count / self._cjk_character_count class ArchaicUpperLowerPlugin(MessDetectorPlugin): def __init__(self) -> None: self._buf: bool = False self._character_count_since_last_sep: int = 0 self._successive_upper_lower_count: int = 0 self._successive_upper_lower_count_final: int = 0 self._character_count: int = 0 self._last_alpha_seen: Optional[str] = None self._current_ascii_only: bool = True def eligible(self, character: str) -> bool: return True def feed(self, character: str) -> None: is_concerned = character.isalpha() and is_case_variable(character) chunk_sep = is_concerned is False if chunk_sep and self._character_count_since_last_sep > 0: if ( self._character_count_since_last_sep <= 64 and character.isdigit() is False and self._current_ascii_only is False ): self._successive_upper_lower_count_final += ( self._successive_upper_lower_count ) self._successive_upper_lower_count = 0 self._character_count_since_last_sep = 0 self._last_alpha_seen = None self._buf = False self._character_count += 1 self._current_ascii_only = True return if self._current_ascii_only is True and character.isascii() is False: self._current_ascii_only = False if self._last_alpha_seen is not None: if (character.isupper() and self._last_alpha_seen.islower()) or ( character.islower() and self._last_alpha_seen.isupper() ): if self._buf is True: self._successive_upper_lower_count += 2 self._buf = False else: self._buf = True else: self._buf = False self._character_count += 1 self._character_count_since_last_sep += 1 self._last_alpha_seen = character def reset(self) -> None: # pragma: no cover self._character_count = 0 self._character_count_since_last_sep = 0 self._successive_upper_lower_count = 0 self._successive_upper_lower_count_final = 0 self._last_alpha_seen = None self._buf = False self._current_ascii_only = True @property def ratio(self) -> float: if self._character_count == 0: return 0.0 return self._successive_upper_lower_count_final / self._character_count class ArabicIsolatedFormPlugin(MessDetectorPlugin): def __init__(self) -> None: self._character_count: int = 0 self._isolated_form_count: int = 0 def reset(self) -> None: # pragma: no cover self._character_count = 0 self._isolated_form_count = 0 def eligible(self, character: str) -> bool: return is_arabic(character) def feed(self, character: str) -> None: self._character_count += 1 if is_arabic_isolated_form(character): self._isolated_form_count += 1 @property def ratio(self) -> float: if self._character_count < 8: return 0.0 isolated_form_usage: float = self._isolated_form_count / self._character_count return isolated_form_usage @lru_cache(maxsize=1024) def is_suspiciously_successive_range( unicode_range_a: Optional[str], unicode_range_b: Optional[str] ) -> bool: """ Determine if two Unicode range seen next to each other can be considered as suspicious. """ if unicode_range_a is None or unicode_range_b is None: return True if unicode_range_a == unicode_range_b: return False if "Latin" in unicode_range_a and "Latin" in unicode_range_b: return False if "Emoticons" in unicode_range_a or "Emoticons" in unicode_range_b: return False # Latin characters can be accompanied with a combining diacritical mark # eg. Vietnamese. if ("Latin" in unicode_range_a or "Latin" in unicode_range_b) and ( "Combining" in unicode_range_a or "Combining" in unicode_range_b ): return False keywords_range_a, keywords_range_b = unicode_range_a.split( " " ), unicode_range_b.split(" ") for el in keywords_range_a: if el in UNICODE_SECONDARY_RANGE_KEYWORD: continue if el in keywords_range_b: return False # Japanese Exception range_a_jp_chars, range_b_jp_chars = ( unicode_range_a in ( "Hiragana", "Katakana", ), unicode_range_b in ("Hiragana", "Katakana"), ) if (range_a_jp_chars or range_b_jp_chars) and ( "CJK" in unicode_range_a or "CJK" in unicode_range_b ): return False if range_a_jp_chars and range_b_jp_chars: return False if "Hangul" in unicode_range_a or "Hangul" in unicode_range_b: if "CJK" in unicode_range_a or "CJK" in unicode_range_b: return False if unicode_range_a == "Basic Latin" or unicode_range_b == "Basic Latin": return False # Chinese/Japanese use dedicated range for punctuation and/or separators. if ("CJK" in unicode_range_a or "CJK" in unicode_range_b) or ( unicode_range_a in ["Katakana", "Hiragana"] and unicode_range_b in ["Katakana", "Hiragana"] ): if "Punctuation" in unicode_range_a or "Punctuation" in unicode_range_b: return False if "Forms" in unicode_range_a or "Forms" in unicode_range_b: return False if unicode_range_a == "Basic Latin" or unicode_range_b == "Basic Latin": return False return True @lru_cache(maxsize=2048) def mess_ratio( decoded_sequence: str, maximum_threshold: float = 0.2, debug: bool = False ) -> float: """ Compute a mess ratio given a decoded bytes sequence. The maximum threshold does stop the computation earlier. """ detectors: List[MessDetectorPlugin] = [ md_class() for md_class in MessDetectorPlugin.__subclasses__() ] length: int = len(decoded_sequence) + 1 mean_mess_ratio: float = 0.0 if length < 512: intermediary_mean_mess_ratio_calc: int = 32 elif length <= 1024: intermediary_mean_mess_ratio_calc = 64 else: intermediary_mean_mess_ratio_calc = 128 for character, index in zip(decoded_sequence + "\n", range(length)): for detector in detectors: if detector.eligible(character): detector.feed(character) if ( index > 0 and index % intermediary_mean_mess_ratio_calc == 0 ) or index == length - 1: mean_mess_ratio = sum(dt.ratio for dt in detectors) if mean_mess_ratio >= maximum_threshold: break if debug: logger = getLogger("charset_normalizer") logger.log( TRACE, "Mess-detector extended-analysis start. " f"intermediary_mean_mess_ratio_calc={intermediary_mean_mess_ratio_calc} mean_mess_ratio={mean_mess_ratio} " f"maximum_threshold={maximum_threshold}", ) if len(decoded_sequence) > 16: logger.log(TRACE, f"Starting with: {decoded_sequence[:16]}") logger.log(TRACE, f"Ending with: {decoded_sequence[-16::]}") for dt in detectors: # pragma: nocover logger.log(TRACE, f"{dt.__class__}: {dt.ratio}") return round(mean_mess_ratio, 3)