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//opt/alt/python37/lib/python3.7/site-packages/charset_normalizer/md.py
from functools import lru_cache from typing import List, Optional from .constant import COMMON_SAFE_ASCII_CHARACTERS, UNICODE_SECONDARY_RANGE_KEYWORD from .utils import ( is_accentuated, is_ascii, is_case_variable, is_cjk, is_emoticon, is_hangul, is_hiragana, is_katakana, is_latin, is_punctuation, is_separator, is_symbol, is_thai, 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 = 0 # type: int self._symbol_count = 0 # type: int self._character_count = 0 # type: int self._last_printable_char = None # type: Optional[str] self._frenzy_symbol_in_word = False # type: bool 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 = ( self._punctuation_count + self._symbol_count ) / self._character_count # type: float return ratio_of_punctuation if ratio_of_punctuation >= 0.3 else 0.0 class TooManyAccentuatedPlugin(MessDetectorPlugin): def __init__(self) -> None: self._character_count = 0 # type: int self._accentuated_count = 0 # type: int 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 == 0: return 0.0 ratio_of_accentuation = ( self._accentuated_count / self._character_count ) # type: float return ratio_of_accentuation if ratio_of_accentuation >= 0.35 else 0.0 class UnprintablePlugin(MessDetectorPlugin): def __init__(self) -> None: self._unprintable_count = 0 # type: int self._character_count = 0 # type: int def eligible(self, character: str) -> bool: return True def feed(self, character: str) -> None: if ( character.isspace() is False # includes \n \t \r \v and character.isprintable() is False and character != "\x1A" # Why? Its the ASCII substitute 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 = 0 # type: int self._character_count = 0 # type: int self._last_latin_character = None # type: Optional[str] 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 = 0 # type: int self._character_count = 0 # type: int self._last_printable_seen = None # type: Optional[str] 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 = unicode_range( self._last_printable_seen ) # type: Optional[str] unicode_range_b = unicode_range(character) # type: Optional[str] 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 == 0: return 0.0 ratio_of_suspicious_range_usage = ( self._suspicious_successive_range_count * 2 ) / self._character_count # type: float if ratio_of_suspicious_range_usage < 0.1: return 0.0 return ratio_of_suspicious_range_usage class SuperWeirdWordPlugin(MessDetectorPlugin): def __init__(self) -> None: self._word_count = 0 # type: int self._bad_word_count = 0 # type: int self._foreign_long_count = 0 # type: int self._is_current_word_bad = False # type: bool self._foreign_long_watch = False # type: bool self._character_count = 0 # type: int self._bad_character_count = 0 # type: int self._buffer = "" # type: str self._buffer_accent_count = 0 # type: int def eligible(self, character: str) -> bool: return True def feed(self, character: str) -> None: if character.isalpha(): self._buffer = "".join([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 = len(self._buffer) # type: int 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 a 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(): self._foreign_long_count += 1 self._is_current_word_bad = True if buffer_length >= 24 and self._foreign_long_watch: 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 = 0 # type: int self._cjk_character_count = 0 # type: int 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 = False # type: bool self._character_count_since_last_sep = 0 # type: int self._successive_upper_lower_count = 0 # type: int self._successive_upper_lower_count_final = 0 # type: int self._character_count = 0 # type: int self._last_alpha_seen = None # type: Optional[str] self._current_ascii_only = True # type: bool 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 is_ascii(character) 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 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 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 = [ md_class() for md_class in MessDetectorPlugin.__subclasses__() ] # type: List[MessDetectorPlugin] length = len(decoded_sequence) + 1 # type: int mean_mess_ratio = 0.0 # type: float if length < 512: intermediary_mean_mess_ratio_calc = 32 # type: int 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: for dt in detectors: # pragma: nocover print(dt.__class__, dt.ratio) return round(mean_mess_ratio, 3)
