Development-Project-Synergy-Finder
/
env
/Lib
/site-packages
/pip
/_vendor
/chardet
/sbcharsetprober.py
| ######################## BEGIN LICENSE BLOCK ######################## | |
| # The Original Code is Mozilla Universal charset detector code. | |
| # | |
| # The Initial Developer of the Original Code is | |
| # Netscape Communications Corporation. | |
| # Portions created by the Initial Developer are Copyright (C) 2001 | |
| # the Initial Developer. All Rights Reserved. | |
| # | |
| # Contributor(s): | |
| # Mark Pilgrim - port to Python | |
| # Shy Shalom - original C code | |
| # | |
| # This library is free software; you can redistribute it and/or | |
| # modify it under the terms of the GNU Lesser General Public | |
| # License as published by the Free Software Foundation; either | |
| # version 2.1 of the License, or (at your option) any later version. | |
| # | |
| # This library is distributed in the hope that it will be useful, | |
| # but WITHOUT ANY WARRANTY; without even the implied warranty of | |
| # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU | |
| # Lesser General Public License for more details. | |
| # | |
| # You should have received a copy of the GNU Lesser General Public | |
| # License along with this library; if not, write to the Free Software | |
| # Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA | |
| # 02110-1301 USA | |
| ######################### END LICENSE BLOCK ######################### | |
| from typing import Dict, List, NamedTuple, Optional, Union | |
| from .charsetprober import CharSetProber | |
| from .enums import CharacterCategory, ProbingState, SequenceLikelihood | |
| class SingleByteCharSetModel(NamedTuple): | |
| charset_name: str | |
| language: str | |
| char_to_order_map: Dict[int, int] | |
| language_model: Dict[int, Dict[int, int]] | |
| typical_positive_ratio: float | |
| keep_ascii_letters: bool | |
| alphabet: str | |
| class SingleByteCharSetProber(CharSetProber): | |
| SAMPLE_SIZE = 64 | |
| SB_ENOUGH_REL_THRESHOLD = 1024 # 0.25 * SAMPLE_SIZE^2 | |
| POSITIVE_SHORTCUT_THRESHOLD = 0.95 | |
| NEGATIVE_SHORTCUT_THRESHOLD = 0.05 | |
| def __init__( | |
| self, | |
| model: SingleByteCharSetModel, | |
| is_reversed: bool = False, | |
| name_prober: Optional[CharSetProber] = None, | |
| ) -> None: | |
| super().__init__() | |
| self._model = model | |
| # TRUE if we need to reverse every pair in the model lookup | |
| self._reversed = is_reversed | |
| # Optional auxiliary prober for name decision | |
| self._name_prober = name_prober | |
| self._last_order = 255 | |
| self._seq_counters: List[int] = [] | |
| self._total_seqs = 0 | |
| self._total_char = 0 | |
| self._control_char = 0 | |
| self._freq_char = 0 | |
| self.reset() | |
| def reset(self) -> None: | |
| super().reset() | |
| # char order of last character | |
| self._last_order = 255 | |
| self._seq_counters = [0] * SequenceLikelihood.get_num_categories() | |
| self._total_seqs = 0 | |
| self._total_char = 0 | |
| self._control_char = 0 | |
| # characters that fall in our sampling range | |
| self._freq_char = 0 | |
| def charset_name(self) -> Optional[str]: | |
| if self._name_prober: | |
| return self._name_prober.charset_name | |
| return self._model.charset_name | |
| def language(self) -> Optional[str]: | |
| if self._name_prober: | |
| return self._name_prober.language | |
| return self._model.language | |
| def feed(self, byte_str: Union[bytes, bytearray]) -> ProbingState: | |
| # TODO: Make filter_international_words keep things in self.alphabet | |
| if not self._model.keep_ascii_letters: | |
| byte_str = self.filter_international_words(byte_str) | |
| else: | |
| byte_str = self.remove_xml_tags(byte_str) | |
| if not byte_str: | |
| return self.state | |
| char_to_order_map = self._model.char_to_order_map | |
| language_model = self._model.language_model | |
| for char in byte_str: | |
| order = char_to_order_map.get(char, CharacterCategory.UNDEFINED) | |
| # XXX: This was SYMBOL_CAT_ORDER before, with a value of 250, but | |
| # CharacterCategory.SYMBOL is actually 253, so we use CONTROL | |
| # to make it closer to the original intent. The only difference | |
| # is whether or not we count digits and control characters for | |
| # _total_char purposes. | |
| if order < CharacterCategory.CONTROL: | |
| self._total_char += 1 | |
| if order < self.SAMPLE_SIZE: | |
| self._freq_char += 1 | |
| if self._last_order < self.SAMPLE_SIZE: | |
| self._total_seqs += 1 | |
| if not self._reversed: | |
| lm_cat = language_model[self._last_order][order] | |
| else: | |
| lm_cat = language_model[order][self._last_order] | |
| self._seq_counters[lm_cat] += 1 | |
| self._last_order = order | |
| charset_name = self._model.charset_name | |
| if self.state == ProbingState.DETECTING: | |
| if self._total_seqs > self.SB_ENOUGH_REL_THRESHOLD: | |
| confidence = self.get_confidence() | |
| if confidence > self.POSITIVE_SHORTCUT_THRESHOLD: | |
| self.logger.debug( | |
| "%s confidence = %s, we have a winner", charset_name, confidence | |
| ) | |
| self._state = ProbingState.FOUND_IT | |
| elif confidence < self.NEGATIVE_SHORTCUT_THRESHOLD: | |
| self.logger.debug( | |
| "%s confidence = %s, below negative shortcut threshold %s", | |
| charset_name, | |
| confidence, | |
| self.NEGATIVE_SHORTCUT_THRESHOLD, | |
| ) | |
| self._state = ProbingState.NOT_ME | |
| return self.state | |
| def get_confidence(self) -> float: | |
| r = 0.01 | |
| if self._total_seqs > 0: | |
| r = ( | |
| ( | |
| self._seq_counters[SequenceLikelihood.POSITIVE] | |
| + 0.25 * self._seq_counters[SequenceLikelihood.LIKELY] | |
| ) | |
| / self._total_seqs | |
| / self._model.typical_positive_ratio | |
| ) | |
| # The more control characters (proportionnaly to the size | |
| # of the text), the less confident we become in the current | |
| # charset. | |
| r = r * (self._total_char - self._control_char) / self._total_char | |
| r = r * self._freq_char / self._total_char | |
| if r >= 1.0: | |
| r = 0.99 | |
| return r | |