diff --git a/Modules/charset_normalizer/__init__.py b/Modules/charset_normalizer/__init__.py index 8b13789..55991fc 100644 --- a/Modules/charset_normalizer/__init__.py +++ b/Modules/charset_normalizer/__init__.py @@ -1 +1,46 @@ +# -*- coding: utf-8 -*- +""" +Charset-Normalizer +~~~~~~~~~~~~~~ +The Real First Universal Charset Detector. +A library that helps you read text from an unknown charset encoding. +Motivated by chardet, This package is trying to resolve the issue by taking a new approach. +All IANA character set names for which the Python core library provides codecs are supported. +Basic usage: + >>> from charset_normalizer import from_bytes + >>> results = from_bytes('Bсеки човек има право на образование. Oбразованието!'.encode('utf_8')) + >>> best_guess = results.best() + >>> str(best_guess) + 'Bсеки човек има право на образование. Oбразованието!' + +Others methods and usages are available - see the full documentation +at . +:copyright: (c) 2021 by Ahmed TAHRI +:license: MIT, see LICENSE for more details. +""" +import logging + +from .api import from_bytes, from_fp, from_path, is_binary +from .legacy import detect +from .models import CharsetMatch, CharsetMatches +from .utils import set_logging_handler +from .version import VERSION, __version__ + +__all__ = ( + "from_fp", + "from_path", + "from_bytes", + "is_binary", + "detect", + "CharsetMatch", + "CharsetMatches", + "__version__", + "VERSION", + "set_logging_handler", +) + +# Attach a NullHandler to the top level logger by default +# https://docs.python.org/3.3/howto/logging.html#configuring-logging-for-a-library + +logging.getLogger("charset_normalizer").addHandler(logging.NullHandler()) diff --git a/Modules/charset_normalizer/__main__.py b/Modules/charset_normalizer/__main__.py index 8b13789..beae2ef 100644 --- a/Modules/charset_normalizer/__main__.py +++ b/Modules/charset_normalizer/__main__.py @@ -1 +1,4 @@ +from .cli import cli_detect +if __name__ == "__main__": + cli_detect() diff --git a/Modules/charset_normalizer/api.py b/Modules/charset_normalizer/api.py new file mode 100644 index 0000000..0ba08e3 --- /dev/null +++ b/Modules/charset_normalizer/api.py @@ -0,0 +1,626 @@ +import logging +from os import PathLike +from typing import BinaryIO, List, Optional, Set, Union + +from .cd import ( + coherence_ratio, + encoding_languages, + mb_encoding_languages, + merge_coherence_ratios, +) +from .constant import IANA_SUPPORTED, TOO_BIG_SEQUENCE, TOO_SMALL_SEQUENCE, TRACE +from .md import mess_ratio +from .models import CharsetMatch, CharsetMatches +from .utils import ( + any_specified_encoding, + cut_sequence_chunks, + iana_name, + identify_sig_or_bom, + is_cp_similar, + is_multi_byte_encoding, + should_strip_sig_or_bom, +) + +# Will most likely be controversial +# logging.addLevelName(TRACE, "TRACE") +logger = logging.getLogger("charset_normalizer") +explain_handler = logging.StreamHandler() +explain_handler.setFormatter( + logging.Formatter("%(asctime)s | %(levelname)s | %(message)s") +) + + +def from_bytes( + sequences: Union[bytes, bytearray], + steps: int = 5, + chunk_size: int = 512, + threshold: float = 0.2, + cp_isolation: Optional[List[str]] = None, + cp_exclusion: Optional[List[str]] = None, + preemptive_behaviour: bool = True, + explain: bool = False, + language_threshold: float = 0.1, + enable_fallback: bool = True, +) -> CharsetMatches: + """ + Given a raw bytes sequence, return the best possibles charset usable to render str objects. + If there is no results, it is a strong indicator that the source is binary/not text. + By default, the process will extract 5 blocks of 512o each to assess the mess and coherence of a given sequence. + And will give up a particular code page after 20% of measured mess. Those criteria are customizable at will. + + The preemptive behavior DOES NOT replace the traditional detection workflow, it prioritize a particular code page + but never take it for granted. Can improve the performance. + + You may want to focus your attention to some code page or/and not others, use cp_isolation and cp_exclusion for that + purpose. + + This function will strip the SIG in the payload/sequence every time except on UTF-16, UTF-32. + By default the library does not setup any handler other than the NullHandler, if you choose to set the 'explain' + toggle to True it will alter the logger configuration to add a StreamHandler that is suitable for debugging. + Custom logging format and handler can be set manually. + """ + + if not isinstance(sequences, (bytearray, bytes)): + raise TypeError( + "Expected object of type bytes or bytearray, got: {0}".format( + type(sequences) + ) + ) + + if explain: + previous_logger_level: int = logger.level + logger.addHandler(explain_handler) + logger.setLevel(TRACE) + + length: int = len(sequences) + + if length == 0: + logger.debug("Encoding detection on empty bytes, assuming utf_8 intention.") + if explain: + logger.removeHandler(explain_handler) + logger.setLevel(previous_logger_level or logging.WARNING) + return CharsetMatches([CharsetMatch(sequences, "utf_8", 0.0, False, [], "")]) + + if cp_isolation is not None: + logger.log( + TRACE, + "cp_isolation is set. use this flag for debugging purpose. " + "limited list of encoding allowed : %s.", + ", ".join(cp_isolation), + ) + cp_isolation = [iana_name(cp, False) for cp in cp_isolation] + else: + cp_isolation = [] + + if cp_exclusion is not None: + logger.log( + TRACE, + "cp_exclusion is set. use this flag for debugging purpose. " + "limited list of encoding excluded : %s.", + ", ".join(cp_exclusion), + ) + cp_exclusion = [iana_name(cp, False) for cp in cp_exclusion] + else: + cp_exclusion = [] + + if length <= (chunk_size * steps): + logger.log( + TRACE, + "override steps (%i) and chunk_size (%i) as content does not fit (%i byte(s) given) parameters.", + steps, + chunk_size, + length, + ) + steps = 1 + chunk_size = length + + if steps > 1 and length / steps < chunk_size: + chunk_size = int(length / steps) + + is_too_small_sequence: bool = len(sequences) < TOO_SMALL_SEQUENCE + is_too_large_sequence: bool = len(sequences) >= TOO_BIG_SEQUENCE + + if is_too_small_sequence: + logger.log( + TRACE, + "Trying to detect encoding from a tiny portion of ({}) byte(s).".format( + length + ), + ) + elif is_too_large_sequence: + logger.log( + TRACE, + "Using lazy str decoding because the payload is quite large, ({}) byte(s).".format( + length + ), + ) + + prioritized_encodings: List[str] = [] + + specified_encoding: Optional[str] = ( + any_specified_encoding(sequences) if preemptive_behaviour else None + ) + + if specified_encoding is not None: + prioritized_encodings.append(specified_encoding) + logger.log( + TRACE, + "Detected declarative mark in sequence. Priority +1 given for %s.", + specified_encoding, + ) + + tested: Set[str] = set() + tested_but_hard_failure: List[str] = [] + tested_but_soft_failure: List[str] = [] + + fallback_ascii: Optional[CharsetMatch] = None + fallback_u8: Optional[CharsetMatch] = None + fallback_specified: Optional[CharsetMatch] = None + + results: CharsetMatches = CharsetMatches() + + sig_encoding, sig_payload = identify_sig_or_bom(sequences) + + if sig_encoding is not None: + prioritized_encodings.append(sig_encoding) + logger.log( + TRACE, + "Detected a SIG or BOM mark on first %i byte(s). Priority +1 given for %s.", + len(sig_payload), + sig_encoding, + ) + + prioritized_encodings.append("ascii") + + if "utf_8" not in prioritized_encodings: + prioritized_encodings.append("utf_8") + + for encoding_iana in prioritized_encodings + IANA_SUPPORTED: + if cp_isolation and encoding_iana not in cp_isolation: + continue + + if cp_exclusion and encoding_iana in cp_exclusion: + continue + + if encoding_iana in tested: + continue + + tested.add(encoding_iana) + + decoded_payload: Optional[str] = None + bom_or_sig_available: bool = sig_encoding == encoding_iana + strip_sig_or_bom: bool = bom_or_sig_available and should_strip_sig_or_bom( + encoding_iana + ) + + if encoding_iana in {"utf_16", "utf_32"} and not bom_or_sig_available: + logger.log( + TRACE, + "Encoding %s won't be tested as-is because it require a BOM. Will try some sub-encoder LE/BE.", + encoding_iana, + ) + continue + if encoding_iana in {"utf_7"} and not bom_or_sig_available: + logger.log( + TRACE, + "Encoding %s won't be tested as-is because detection is unreliable without BOM/SIG.", + encoding_iana, + ) + continue + + try: + is_multi_byte_decoder: bool = is_multi_byte_encoding(encoding_iana) + except (ModuleNotFoundError, ImportError): + logger.log( + TRACE, + "Encoding %s does not provide an IncrementalDecoder", + encoding_iana, + ) + continue + + try: + if is_too_large_sequence and is_multi_byte_decoder is False: + str( + sequences[: int(50e4)] + if strip_sig_or_bom is False + else sequences[len(sig_payload) : int(50e4)], + encoding=encoding_iana, + ) + else: + decoded_payload = str( + sequences + if strip_sig_or_bom is False + else sequences[len(sig_payload) :], + encoding=encoding_iana, + ) + except (UnicodeDecodeError, LookupError) as e: + if not isinstance(e, LookupError): + logger.log( + TRACE, + "Code page %s does not fit given bytes sequence at ALL. %s", + encoding_iana, + str(e), + ) + tested_but_hard_failure.append(encoding_iana) + continue + + similar_soft_failure_test: bool = False + + for encoding_soft_failed in tested_but_soft_failure: + if is_cp_similar(encoding_iana, encoding_soft_failed): + similar_soft_failure_test = True + break + + if similar_soft_failure_test: + logger.log( + TRACE, + "%s is deemed too similar to code page %s and was consider unsuited already. Continuing!", + encoding_iana, + encoding_soft_failed, + ) + continue + + r_ = range( + 0 if not bom_or_sig_available else len(sig_payload), + length, + int(length / steps), + ) + + multi_byte_bonus: bool = ( + is_multi_byte_decoder + and decoded_payload is not None + and len(decoded_payload) < length + ) + + if multi_byte_bonus: + logger.log( + TRACE, + "Code page %s is a multi byte encoding table and it appear that at least one character " + "was encoded using n-bytes.", + encoding_iana, + ) + + max_chunk_gave_up: int = int(len(r_) / 4) + + max_chunk_gave_up = max(max_chunk_gave_up, 2) + early_stop_count: int = 0 + lazy_str_hard_failure = False + + md_chunks: List[str] = [] + md_ratios = [] + + try: + for chunk in cut_sequence_chunks( + sequences, + encoding_iana, + r_, + chunk_size, + bom_or_sig_available, + strip_sig_or_bom, + sig_payload, + is_multi_byte_decoder, + decoded_payload, + ): + md_chunks.append(chunk) + + md_ratios.append( + mess_ratio( + chunk, + threshold, + explain is True and 1 <= len(cp_isolation) <= 2, + ) + ) + + if md_ratios[-1] >= threshold: + early_stop_count += 1 + + if (early_stop_count >= max_chunk_gave_up) or ( + bom_or_sig_available and strip_sig_or_bom is False + ): + break + except ( + UnicodeDecodeError + ) as e: # Lazy str loading may have missed something there + logger.log( + TRACE, + "LazyStr Loading: After MD chunk decode, code page %s does not fit given bytes sequence at ALL. %s", + encoding_iana, + str(e), + ) + early_stop_count = max_chunk_gave_up + lazy_str_hard_failure = True + + # We might want to check the sequence again with the whole content + # Only if initial MD tests passes + if ( + not lazy_str_hard_failure + and is_too_large_sequence + and not is_multi_byte_decoder + ): + try: + sequences[int(50e3) :].decode(encoding_iana, errors="strict") + except UnicodeDecodeError as e: + logger.log( + TRACE, + "LazyStr Loading: After final lookup, code page %s does not fit given bytes sequence at ALL. %s", + encoding_iana, + str(e), + ) + tested_but_hard_failure.append(encoding_iana) + continue + + mean_mess_ratio: float = sum(md_ratios) / len(md_ratios) if md_ratios else 0.0 + if mean_mess_ratio >= threshold or early_stop_count >= max_chunk_gave_up: + tested_but_soft_failure.append(encoding_iana) + logger.log( + TRACE, + "%s was excluded because of initial chaos probing. Gave up %i time(s). " + "Computed mean chaos is %f %%.", + encoding_iana, + early_stop_count, + round(mean_mess_ratio * 100, ndigits=3), + ) + # Preparing those fallbacks in case we got nothing. + if ( + enable_fallback + and encoding_iana in ["ascii", "utf_8", specified_encoding] + and not lazy_str_hard_failure + ): + fallback_entry = CharsetMatch( + sequences, encoding_iana, threshold, False, [], decoded_payload + ) + if encoding_iana == specified_encoding: + fallback_specified = fallback_entry + elif encoding_iana == "ascii": + fallback_ascii = fallback_entry + else: + fallback_u8 = fallback_entry + continue + + logger.log( + TRACE, + "%s passed initial chaos probing. Mean measured chaos is %f %%", + encoding_iana, + round(mean_mess_ratio * 100, ndigits=3), + ) + + if not is_multi_byte_decoder: + target_languages: List[str] = encoding_languages(encoding_iana) + else: + target_languages = mb_encoding_languages(encoding_iana) + + if target_languages: + logger.log( + TRACE, + "{} should target any language(s) of {}".format( + encoding_iana, str(target_languages) + ), + ) + + cd_ratios = [] + + # We shall skip the CD when its about ASCII + # Most of the time its not relevant to run "language-detection" on it. + if encoding_iana != "ascii": + for chunk in md_chunks: + chunk_languages = coherence_ratio( + chunk, + language_threshold, + ",".join(target_languages) if target_languages else None, + ) + + cd_ratios.append(chunk_languages) + + cd_ratios_merged = merge_coherence_ratios(cd_ratios) + + if cd_ratios_merged: + logger.log( + TRACE, + "We detected language {} using {}".format( + cd_ratios_merged, encoding_iana + ), + ) + + results.append( + CharsetMatch( + sequences, + encoding_iana, + mean_mess_ratio, + bom_or_sig_available, + cd_ratios_merged, + decoded_payload, + ) + ) + + if ( + encoding_iana in [specified_encoding, "ascii", "utf_8"] + and mean_mess_ratio < 0.1 + ): + logger.debug( + "Encoding detection: %s is most likely the one.", encoding_iana + ) + if explain: + logger.removeHandler(explain_handler) + logger.setLevel(previous_logger_level) + return CharsetMatches([results[encoding_iana]]) + + if encoding_iana == sig_encoding: + logger.debug( + "Encoding detection: %s is most likely the one as we detected a BOM or SIG within " + "the beginning of the sequence.", + encoding_iana, + ) + if explain: + logger.removeHandler(explain_handler) + logger.setLevel(previous_logger_level) + return CharsetMatches([results[encoding_iana]]) + + if len(results) == 0: + if fallback_u8 or fallback_ascii or fallback_specified: + logger.log( + TRACE, + "Nothing got out of the detection process. Using ASCII/UTF-8/Specified fallback.", + ) + + if fallback_specified: + logger.debug( + "Encoding detection: %s will be used as a fallback match", + fallback_specified.encoding, + ) + results.append(fallback_specified) + elif ( + (fallback_u8 and fallback_ascii is None) + or ( + fallback_u8 + and fallback_ascii + and fallback_u8.fingerprint != fallback_ascii.fingerprint + ) + or (fallback_u8 is not None) + ): + logger.debug("Encoding detection: utf_8 will be used as a fallback match") + results.append(fallback_u8) + elif fallback_ascii: + logger.debug("Encoding detection: ascii will be used as a fallback match") + results.append(fallback_ascii) + + if results: + logger.debug( + "Encoding detection: Found %s as plausible (best-candidate) for content. With %i alternatives.", + results.best().encoding, # type: ignore + len(results) - 1, + ) + else: + logger.debug("Encoding detection: Unable to determine any suitable charset.") + + if explain: + logger.removeHandler(explain_handler) + logger.setLevel(previous_logger_level) + + return results + + +def from_fp( + fp: BinaryIO, + steps: int = 5, + chunk_size: int = 512, + threshold: float = 0.20, + cp_isolation: Optional[List[str]] = None, + cp_exclusion: Optional[List[str]] = None, + preemptive_behaviour: bool = True, + explain: bool = False, + language_threshold: float = 0.1, + enable_fallback: bool = True, +) -> CharsetMatches: + """ + Same thing than the function from_bytes but using a file pointer that is already ready. + Will not close the file pointer. + """ + return from_bytes( + fp.read(), + steps, + chunk_size, + threshold, + cp_isolation, + cp_exclusion, + preemptive_behaviour, + explain, + language_threshold, + enable_fallback, + ) + + +def from_path( + path: Union[str, bytes, PathLike], # type: ignore[type-arg] + steps: int = 5, + chunk_size: int = 512, + threshold: float = 0.20, + cp_isolation: Optional[List[str]] = None, + cp_exclusion: Optional[List[str]] = None, + preemptive_behaviour: bool = True, + explain: bool = False, + language_threshold: float = 0.1, + enable_fallback: bool = True, +) -> CharsetMatches: + """ + Same thing than the function from_bytes but with one extra step. Opening and reading given file path in binary mode. + Can raise IOError. + """ + with open(path, "rb") as fp: + return from_fp( + fp, + steps, + chunk_size, + threshold, + cp_isolation, + cp_exclusion, + preemptive_behaviour, + explain, + language_threshold, + enable_fallback, + ) + + +def is_binary( + fp_or_path_or_payload: Union[PathLike, str, BinaryIO, bytes], # type: ignore[type-arg] + steps: int = 5, + chunk_size: int = 512, + threshold: float = 0.20, + cp_isolation: Optional[List[str]] = None, + cp_exclusion: Optional[List[str]] = None, + preemptive_behaviour: bool = True, + explain: bool = False, + language_threshold: float = 0.1, + enable_fallback: bool = False, +) -> bool: + """ + Detect if the given input (file, bytes, or path) points to a binary file. aka. not a string. + Based on the same main heuristic algorithms and default kwargs at the sole exception that fallbacks match + are disabled to be stricter around ASCII-compatible but unlikely to be a string. + """ + if isinstance(fp_or_path_or_payload, (str, PathLike)): + guesses = from_path( + fp_or_path_or_payload, + steps=steps, + chunk_size=chunk_size, + threshold=threshold, + cp_isolation=cp_isolation, + cp_exclusion=cp_exclusion, + preemptive_behaviour=preemptive_behaviour, + explain=explain, + language_threshold=language_threshold, + enable_fallback=enable_fallback, + ) + elif isinstance( + fp_or_path_or_payload, + ( + bytes, + bytearray, + ), + ): + guesses = from_bytes( + fp_or_path_or_payload, + steps=steps, + chunk_size=chunk_size, + threshold=threshold, + cp_isolation=cp_isolation, + cp_exclusion=cp_exclusion, + preemptive_behaviour=preemptive_behaviour, + explain=explain, + language_threshold=language_threshold, + enable_fallback=enable_fallback, + ) + else: + guesses = from_fp( + fp_or_path_or_payload, + steps=steps, + chunk_size=chunk_size, + threshold=threshold, + cp_isolation=cp_isolation, + cp_exclusion=cp_exclusion, + preemptive_behaviour=preemptive_behaviour, + explain=explain, + language_threshold=language_threshold, + enable_fallback=enable_fallback, + ) + + return not guesses diff --git a/Modules/charset_normalizer/cd.py b/Modules/charset_normalizer/cd.py new file mode 100644 index 0000000..4ea6760 --- /dev/null +++ b/Modules/charset_normalizer/cd.py @@ -0,0 +1,395 @@ +import importlib +from codecs import IncrementalDecoder +from collections import Counter +from functools import lru_cache +from typing import Counter as TypeCounter, Dict, List, Optional, Tuple + +from .constant import ( + FREQUENCIES, + KO_NAMES, + LANGUAGE_SUPPORTED_COUNT, + TOO_SMALL_SEQUENCE, + ZH_NAMES, +) +from .md import is_suspiciously_successive_range +from .models import CoherenceMatches +from .utils import ( + is_accentuated, + is_latin, + is_multi_byte_encoding, + is_unicode_range_secondary, + unicode_range, +) + + +def encoding_unicode_range(iana_name: str) -> List[str]: + """ + Return associated unicode ranges in a single byte code page. + """ + if is_multi_byte_encoding(iana_name): + raise IOError("Function not supported on multi-byte code page") + + decoder = importlib.import_module( + "encodings.{}".format(iana_name) + ).IncrementalDecoder + + p: IncrementalDecoder = decoder(errors="ignore") + seen_ranges: Dict[str, int] = {} + character_count: int = 0 + + for i in range(0x40, 0xFF): + chunk: str = p.decode(bytes([i])) + + if chunk: + character_range: Optional[str] = unicode_range(chunk) + + if character_range is None: + continue + + if is_unicode_range_secondary(character_range) is False: + if character_range not in seen_ranges: + seen_ranges[character_range] = 0 + seen_ranges[character_range] += 1 + character_count += 1 + + return sorted( + [ + character_range + for character_range in seen_ranges + if seen_ranges[character_range] / character_count >= 0.15 + ] + ) + + +def unicode_range_languages(primary_range: str) -> List[str]: + """ + Return inferred languages used with a unicode range. + """ + languages: List[str] = [] + + for language, characters in FREQUENCIES.items(): + for character in characters: + if unicode_range(character) == primary_range: + languages.append(language) + break + + return languages + + +@lru_cache() +def encoding_languages(iana_name: str) -> List[str]: + """ + Single-byte encoding language association. Some code page are heavily linked to particular language(s). + This function does the correspondence. + """ + unicode_ranges: List[str] = encoding_unicode_range(iana_name) + primary_range: Optional[str] = None + + for specified_range in unicode_ranges: + if "Latin" not in specified_range: + primary_range = specified_range + break + + if primary_range is None: + return ["Latin Based"] + + return unicode_range_languages(primary_range) + + +@lru_cache() +def mb_encoding_languages(iana_name: str) -> List[str]: + """ + Multi-byte encoding language association. Some code page are heavily linked to particular language(s). + This function does the correspondence. + """ + if ( + iana_name.startswith("shift_") + or iana_name.startswith("iso2022_jp") + or iana_name.startswith("euc_j") + or iana_name == "cp932" + ): + return ["Japanese"] + if iana_name.startswith("gb") or iana_name in ZH_NAMES: + return ["Chinese"] + if iana_name.startswith("iso2022_kr") or iana_name in KO_NAMES: + return ["Korean"] + + return [] + + +@lru_cache(maxsize=LANGUAGE_SUPPORTED_COUNT) +def get_target_features(language: str) -> Tuple[bool, bool]: + """ + Determine main aspects from a supported language if it contains accents and if is pure Latin. + """ + target_have_accents: bool = False + target_pure_latin: bool = True + + for character in FREQUENCIES[language]: + if not target_have_accents and is_accentuated(character): + target_have_accents = True + if target_pure_latin and is_latin(character) is False: + target_pure_latin = False + + return target_have_accents, target_pure_latin + + +def alphabet_languages( + characters: List[str], ignore_non_latin: bool = False +) -> List[str]: + """ + Return associated languages associated to given characters. + """ + languages: List[Tuple[str, float]] = [] + + source_have_accents = any(is_accentuated(character) for character in characters) + + for language, language_characters in FREQUENCIES.items(): + target_have_accents, target_pure_latin = get_target_features(language) + + if ignore_non_latin and target_pure_latin is False: + continue + + if target_have_accents is False and source_have_accents: + continue + + character_count: int = len(language_characters) + + character_match_count: int = len( + [c for c in language_characters if c in characters] + ) + + ratio: float = character_match_count / character_count + + if ratio >= 0.2: + languages.append((language, ratio)) + + languages = sorted(languages, key=lambda x: x[1], reverse=True) + + return [compatible_language[0] for compatible_language in languages] + + +def characters_popularity_compare( + language: str, ordered_characters: List[str] +) -> float: + """ + Determine if a ordered characters list (by occurrence from most appearance to rarest) match a particular language. + The result is a ratio between 0. (absolutely no correspondence) and 1. (near perfect fit). + Beware that is function is not strict on the match in order to ease the detection. (Meaning close match is 1.) + """ + if language not in FREQUENCIES: + raise ValueError("{} not available".format(language)) + + character_approved_count: int = 0 + FREQUENCIES_language_set = set(FREQUENCIES[language]) + + ordered_characters_count: int = len(ordered_characters) + target_language_characters_count: int = len(FREQUENCIES[language]) + + large_alphabet: bool = target_language_characters_count > 26 + + for character, character_rank in zip( + ordered_characters, range(0, ordered_characters_count) + ): + if character not in FREQUENCIES_language_set: + continue + + character_rank_in_language: int = FREQUENCIES[language].index(character) + expected_projection_ratio: float = ( + target_language_characters_count / ordered_characters_count + ) + character_rank_projection: int = int(character_rank * expected_projection_ratio) + + if ( + large_alphabet is False + and abs(character_rank_projection - character_rank_in_language) > 4 + ): + continue + + if ( + large_alphabet is True + and abs(character_rank_projection - character_rank_in_language) + < target_language_characters_count / 3 + ): + character_approved_count += 1 + continue + + characters_before_source: List[str] = FREQUENCIES[language][ + 0:character_rank_in_language + ] + characters_after_source: List[str] = FREQUENCIES[language][ + character_rank_in_language: + ] + characters_before: List[str] = ordered_characters[0:character_rank] + characters_after: List[str] = ordered_characters[character_rank:] + + before_match_count: int = len( + set(characters_before) & set(characters_before_source) + ) + + after_match_count: int = len( + set(characters_after) & set(characters_after_source) + ) + + if len(characters_before_source) == 0 and before_match_count <= 4: + character_approved_count += 1 + continue + + if len(characters_after_source) == 0 and after_match_count <= 4: + character_approved_count += 1 + continue + + if ( + before_match_count / len(characters_before_source) >= 0.4 + or after_match_count / len(characters_after_source) >= 0.4 + ): + character_approved_count += 1 + continue + + return character_approved_count / len(ordered_characters) + + +def alpha_unicode_split(decoded_sequence: str) -> List[str]: + """ + Given a decoded text sequence, return a list of str. Unicode range / alphabet separation. + Ex. a text containing English/Latin with a bit a Hebrew will return two items in the resulting list; + One containing the latin letters and the other hebrew. + """ + layers: Dict[str, str] = {} + + for character in decoded_sequence: + if character.isalpha() is False: + continue + + character_range: Optional[str] = unicode_range(character) + + if character_range is None: + continue + + layer_target_range: Optional[str] = None + + for discovered_range in layers: + if ( + is_suspiciously_successive_range(discovered_range, character_range) + is False + ): + layer_target_range = discovered_range + break + + if layer_target_range is None: + layer_target_range = character_range + + if layer_target_range not in layers: + layers[layer_target_range] = character.lower() + continue + + layers[layer_target_range] += character.lower() + + return list(layers.values()) + + +def merge_coherence_ratios(results: List[CoherenceMatches]) -> CoherenceMatches: + """ + This function merge results previously given by the function coherence_ratio. + The return type is the same as coherence_ratio. + """ + per_language_ratios: Dict[str, List[float]] = {} + for result in results: + for sub_result in result: + language, ratio = sub_result + if language not in per_language_ratios: + per_language_ratios[language] = [ratio] + continue + per_language_ratios[language].append(ratio) + + merge = [ + ( + language, + round( + sum(per_language_ratios[language]) / len(per_language_ratios[language]), + 4, + ), + ) + for language in per_language_ratios + ] + + return sorted(merge, key=lambda x: x[1], reverse=True) + + +def filter_alt_coherence_matches(results: CoherenceMatches) -> CoherenceMatches: + """ + We shall NOT return "English—" in CoherenceMatches because it is an alternative + of "English". This function only keeps the best match and remove the em-dash in it. + """ + index_results: Dict[str, List[float]] = dict() + + for result in results: + language, ratio = result + no_em_name: str = language.replace("—", "") + + if no_em_name not in index_results: + index_results[no_em_name] = [] + + index_results[no_em_name].append(ratio) + + if any(len(index_results[e]) > 1 for e in index_results): + filtered_results: CoherenceMatches = [] + + for language in index_results: + filtered_results.append((language, max(index_results[language]))) + + return filtered_results + + return results + + +@lru_cache(maxsize=2048) +def coherence_ratio( + decoded_sequence: str, threshold: float = 0.1, lg_inclusion: Optional[str] = None +) -> CoherenceMatches: + """ + Detect ANY language that can be identified in given sequence. The sequence will be analysed by layers. + A layer = Character extraction by alphabets/ranges. + """ + + results: List[Tuple[str, float]] = [] + ignore_non_latin: bool = False + + sufficient_match_count: int = 0 + + lg_inclusion_list = lg_inclusion.split(",") if lg_inclusion is not None else [] + if "Latin Based" in lg_inclusion_list: + ignore_non_latin = True + lg_inclusion_list.remove("Latin Based") + + for layer in alpha_unicode_split(decoded_sequence): + sequence_frequencies: TypeCounter[str] = Counter(layer) + most_common = sequence_frequencies.most_common() + + character_count: int = sum(o for c, o in most_common) + + if character_count <= TOO_SMALL_SEQUENCE: + continue + + popular_character_ordered: List[str] = [c for c, o in most_common] + + for language in lg_inclusion_list or alphabet_languages( + popular_character_ordered, ignore_non_latin + ): + ratio: float = characters_popularity_compare( + language, popular_character_ordered + ) + + if ratio < threshold: + continue + elif ratio >= 0.8: + sufficient_match_count += 1 + + results.append((language, round(ratio, 4))) + + if sufficient_match_count >= 3: + break + + return sorted( + filter_alt_coherence_matches(results), key=lambda x: x[1], reverse=True + ) diff --git a/Modules/charset_normalizer/constant.py b/Modules/charset_normalizer/constant.py new file mode 100644 index 0000000..8634904 --- /dev/null +++ b/Modules/charset_normalizer/constant.py @@ -0,0 +1,1995 @@ +# -*- coding: utf-8 -*- +from codecs import BOM_UTF8, BOM_UTF16_BE, BOM_UTF16_LE, BOM_UTF32_BE, BOM_UTF32_LE +from encodings.aliases import aliases +from re import IGNORECASE, compile as re_compile +from typing import Dict, List, Set, Union + +# Contain for each eligible encoding a list of/item bytes SIG/BOM +ENCODING_MARKS: Dict[str, Union[bytes, List[bytes]]] = { + "utf_8": BOM_UTF8, + "utf_7": [ + b"\x2b\x2f\x76\x38", + b"\x2b\x2f\x76\x39", + b"\x2b\x2f\x76\x2b", + b"\x2b\x2f\x76\x2f", + b"\x2b\x2f\x76\x38\x2d", + ], + "gb18030": b"\x84\x31\x95\x33", + "utf_32": [BOM_UTF32_BE, BOM_UTF32_LE], + "utf_16": [BOM_UTF16_BE, BOM_UTF16_LE], +} + +TOO_SMALL_SEQUENCE: int = 32 +TOO_BIG_SEQUENCE: int = int(10e6) + +UTF8_MAXIMAL_ALLOCATION: int = 1_112_064 + +# Up-to-date Unicode ucd/15.0.0 +UNICODE_RANGES_COMBINED: Dict[str, range] = { + "Control character": range(32), + "Basic Latin": range(32, 128), + "Latin-1 Supplement": range(128, 256), + "Latin Extended-A": range(256, 384), + "Latin Extended-B": range(384, 592), + "IPA Extensions": range(592, 688), + "Spacing Modifier Letters": range(688, 768), + "Combining Diacritical Marks": range(768, 880), + "Greek and Coptic": range(880, 1024), + "Cyrillic": range(1024, 1280), + "Cyrillic Supplement": range(1280, 1328), + "Armenian": range(1328, 1424), + "Hebrew": range(1424, 1536), + "Arabic": range(1536, 1792), + "Syriac": range(1792, 1872), + "Arabic Supplement": range(1872, 1920), + "Thaana": range(1920, 1984), + "NKo": range(1984, 2048), + "Samaritan": range(2048, 2112), + "Mandaic": range(2112, 2144), + "Syriac Supplement": range(2144, 2160), + "Arabic Extended-B": range(2160, 2208), + "Arabic Extended-A": range(2208, 2304), + "Devanagari": range(2304, 2432), + "Bengali": range(2432, 2560), + "Gurmukhi": range(2560, 2688), + "Gujarati": range(2688, 2816), + "Oriya": range(2816, 2944), + "Tamil": range(2944, 3072), + "Telugu": range(3072, 3200), + "Kannada": range(3200, 3328), + "Malayalam": range(3328, 3456), + "Sinhala": range(3456, 3584), + "Thai": range(3584, 3712), + "Lao": range(3712, 3840), + "Tibetan": range(3840, 4096), + "Myanmar": range(4096, 4256), + "Georgian": range(4256, 4352), + "Hangul Jamo": range(4352, 4608), + "Ethiopic": range(4608, 4992), + "Ethiopic Supplement": range(4992, 5024), + "Cherokee": range(5024, 5120), + "Unified Canadian Aboriginal Syllabics": range(5120, 5760), + "Ogham": range(5760, 5792), + "Runic": range(5792, 5888), + "Tagalog": range(5888, 5920), + "Hanunoo": range(5920, 5952), + "Buhid": range(5952, 5984), + "Tagbanwa": range(5984, 6016), + "Khmer": range(6016, 6144), + "Mongolian": range(6144, 6320), + "Unified Canadian Aboriginal Syllabics Extended": range(6320, 6400), + "Limbu": range(6400, 6480), + "Tai Le": range(6480, 6528), + "New Tai Lue": range(6528, 6624), + "Khmer Symbols": range(6624, 6656), + "Buginese": range(6656, 6688), + "Tai Tham": range(6688, 6832), + "Combining Diacritical Marks Extended": range(6832, 6912), + "Balinese": range(6912, 7040), + "Sundanese": range(7040, 7104), + "Batak": range(7104, 7168), + "Lepcha": range(7168, 7248), + "Ol Chiki": range(7248, 7296), + "Cyrillic Extended-C": range(7296, 7312), + "Georgian Extended": range(7312, 7360), + "Sundanese Supplement": range(7360, 7376), + "Vedic Extensions": range(7376, 7424), + "Phonetic Extensions": range(7424, 7552), + "Phonetic Extensions Supplement": range(7552, 7616), + "Combining Diacritical Marks Supplement": range(7616, 7680), + "Latin Extended Additional": range(7680, 7936), + "Greek Extended": range(7936, 8192), + "General Punctuation": range(8192, 8304), + "Superscripts and Subscripts": range(8304, 8352), + "Currency Symbols": range(8352, 8400), + "Combining Diacritical Marks for Symbols": range(8400, 8448), + "Letterlike Symbols": range(8448, 8528), + "Number Forms": range(8528, 8592), + "Arrows": range(8592, 8704), + "Mathematical Operators": range(8704, 8960), + "Miscellaneous Technical": range(8960, 9216), + "Control Pictures": range(9216, 9280), + "Optical Character Recognition": range(9280, 9312), + "Enclosed Alphanumerics": range(9312, 9472), + "Box Drawing": range(9472, 9600), + "Block Elements": range(9600, 9632), + "Geometric Shapes": range(9632, 9728), + "Miscellaneous Symbols": range(9728, 9984), + "Dingbats": range(9984, 10176), + "Miscellaneous Mathematical Symbols-A": range(10176, 10224), + "Supplemental Arrows-A": range(10224, 10240), + "Braille Patterns": range(10240, 10496), + "Supplemental Arrows-B": range(10496, 10624), + "Miscellaneous Mathematical Symbols-B": range(10624, 10752), + "Supplemental Mathematical Operators": range(10752, 11008), + "Miscellaneous Symbols and Arrows": range(11008, 11264), + "Glagolitic": range(11264, 11360), + "Latin Extended-C": range(11360, 11392), + "Coptic": range(11392, 11520), + "Georgian Supplement": range(11520, 11568), + "Tifinagh": range(11568, 11648), + "Ethiopic Extended": range(11648, 11744), + "Cyrillic Extended-A": range(11744, 11776), + "Supplemental Punctuation": range(11776, 11904), + "CJK Radicals Supplement": range(11904, 12032), + "Kangxi Radicals": range(12032, 12256), + "Ideographic Description Characters": range(12272, 12288), + "CJK Symbols and Punctuation": range(12288, 12352), + "Hiragana": range(12352, 12448), + "Katakana": range(12448, 12544), + "Bopomofo": range(12544, 12592), + "Hangul Compatibility Jamo": range(12592, 12688), + "Kanbun": range(12688, 12704), + "Bopomofo Extended": range(12704, 12736), + "CJK Strokes": range(12736, 12784), + "Katakana Phonetic Extensions": range(12784, 12800), + "Enclosed CJK Letters and Months": range(12800, 13056), + "CJK Compatibility": range(13056, 13312), + "CJK Unified Ideographs Extension A": range(13312, 19904), + "Yijing Hexagram Symbols": range(19904, 19968), + "CJK Unified Ideographs": range(19968, 40960), + "Yi Syllables": range(40960, 42128), + "Yi Radicals": range(42128, 42192), + "Lisu": range(42192, 42240), + "Vai": range(42240, 42560), + "Cyrillic Extended-B": range(42560, 42656), + "Bamum": range(42656, 42752), + "Modifier Tone Letters": range(42752, 42784), + "Latin Extended-D": range(42784, 43008), + "Syloti Nagri": range(43008, 43056), + "Common Indic Number Forms": range(43056, 43072), + "Phags-pa": range(43072, 43136), + "Saurashtra": range(43136, 43232), + "Devanagari Extended": range(43232, 43264), + "Kayah Li": range(43264, 43312), + "Rejang": range(43312, 43360), + "Hangul Jamo Extended-A": range(43360, 43392), + "Javanese": range(43392, 43488), + "Myanmar Extended-B": range(43488, 43520), + "Cham": range(43520, 43616), + "Myanmar Extended-A": range(43616, 43648), + "Tai Viet": range(43648, 43744), + "Meetei Mayek Extensions": range(43744, 43776), + "Ethiopic Extended-A": range(43776, 43824), + "Latin Extended-E": range(43824, 43888), + "Cherokee Supplement": range(43888, 43968), + "Meetei Mayek": range(43968, 44032), + "Hangul Syllables": range(44032, 55216), + "Hangul Jamo Extended-B": range(55216, 55296), + "High Surrogates": range(55296, 56192), + "High Private Use Surrogates": range(56192, 56320), + "Low Surrogates": range(56320, 57344), + "Private Use Area": range(57344, 63744), + "CJK Compatibility Ideographs": range(63744, 64256), + "Alphabetic Presentation Forms": range(64256, 64336), + "Arabic Presentation Forms-A": range(64336, 65024), + "Variation Selectors": range(65024, 65040), + "Vertical Forms": range(65040, 65056), + "Combining Half Marks": range(65056, 65072), + "CJK Compatibility Forms": range(65072, 65104), + "Small Form Variants": range(65104, 65136), + "Arabic Presentation Forms-B": range(65136, 65280), + "Halfwidth and Fullwidth Forms": range(65280, 65520), + "Specials": range(65520, 65536), + "Linear B Syllabary": range(65536, 65664), + "Linear B Ideograms": range(65664, 65792), + "Aegean Numbers": range(65792, 65856), + "Ancient Greek Numbers": range(65856, 65936), + "Ancient Symbols": range(65936, 66000), + "Phaistos Disc": range(66000, 66048), + "Lycian": range(66176, 66208), + "Carian": range(66208, 66272), + "Coptic Epact Numbers": range(66272, 66304), + "Old Italic": range(66304, 66352), + "Gothic": range(66352, 66384), + "Old Permic": range(66384, 66432), + "Ugaritic": range(66432, 66464), + "Old Persian": range(66464, 66528), + "Deseret": range(66560, 66640), + "Shavian": range(66640, 66688), + "Osmanya": range(66688, 66736), + "Osage": range(66736, 66816), + "Elbasan": range(66816, 66864), + "Caucasian Albanian": range(66864, 66928), + "Vithkuqi": range(66928, 67008), + "Linear A": range(67072, 67456), + "Latin Extended-F": range(67456, 67520), + "Cypriot Syllabary": range(67584, 67648), + "Imperial Aramaic": range(67648, 67680), + "Palmyrene": range(67680, 67712), + "Nabataean": range(67712, 67760), + "Hatran": range(67808, 67840), + "Phoenician": range(67840, 67872), + "Lydian": range(67872, 67904), + "Meroitic Hieroglyphs": range(67968, 68000), + "Meroitic Cursive": range(68000, 68096), + "Kharoshthi": range(68096, 68192), + "Old South Arabian": range(68192, 68224), + "Old North Arabian": range(68224, 68256), + "Manichaean": range(68288, 68352), + "Avestan": range(68352, 68416), + "Inscriptional Parthian": range(68416, 68448), + "Inscriptional Pahlavi": range(68448, 68480), + "Psalter Pahlavi": range(68480, 68528), + "Old Turkic": range(68608, 68688), + "Old Hungarian": range(68736, 68864), + "Hanifi Rohingya": range(68864, 68928), + "Rumi Numeral Symbols": range(69216, 69248), + "Yezidi": range(69248, 69312), + "Arabic Extended-C": range(69312, 69376), + "Old Sogdian": range(69376, 69424), + "Sogdian": range(69424, 69488), + "Old Uyghur": range(69488, 69552), + "Chorasmian": range(69552, 69600), + "Elymaic": range(69600, 69632), + "Brahmi": range(69632, 69760), + "Kaithi": range(69760, 69840), + "Sora Sompeng": range(69840, 69888), + "Chakma": range(69888, 69968), + "Mahajani": range(69968, 70016), + "Sharada": range(70016, 70112), + "Sinhala Archaic Numbers": range(70112, 70144), + "Khojki": range(70144, 70224), + "Multani": range(70272, 70320), + "Khudawadi": range(70320, 70400), + "Grantha": range(70400, 70528), + "Newa": range(70656, 70784), + "Tirhuta": range(70784, 70880), + "Siddham": range(71040, 71168), + "Modi": range(71168, 71264), + "Mongolian Supplement": range(71264, 71296), + "Takri": range(71296, 71376), + "Ahom": range(71424, 71504), + "Dogra": range(71680, 71760), + "Warang Citi": range(71840, 71936), + "Dives Akuru": range(71936, 72032), + "Nandinagari": range(72096, 72192), + "Zanabazar Square": range(72192, 72272), + "Soyombo": range(72272, 72368), + "Unified Canadian Aboriginal Syllabics Extended-A": range(72368, 72384), + "Pau Cin Hau": range(72384, 72448), + "Devanagari Extended-A": range(72448, 72544), + "Bhaiksuki": range(72704, 72816), + "Marchen": range(72816, 72896), + "Masaram Gondi": range(72960, 73056), + "Gunjala Gondi": range(73056, 73136), + "Makasar": range(73440, 73472), + "Kawi": range(73472, 73568), + "Lisu Supplement": range(73648, 73664), + "Tamil Supplement": range(73664, 73728), + "Cuneiform": range(73728, 74752), + "Cuneiform Numbers and Punctuation": range(74752, 74880), + "Early Dynastic Cuneiform": range(74880, 75088), + "Cypro-Minoan": range(77712, 77824), + "Egyptian Hieroglyphs": range(77824, 78896), + "Egyptian Hieroglyph Format Controls": range(78896, 78944), + "Anatolian Hieroglyphs": range(82944, 83584), + "Bamum Supplement": range(92160, 92736), + "Mro": range(92736, 92784), + "Tangsa": range(92784, 92880), + "Bassa Vah": range(92880, 92928), + "Pahawh Hmong": range(92928, 93072), + "Medefaidrin": range(93760, 93856), + "Miao": range(93952, 94112), + "Ideographic Symbols and Punctuation": range(94176, 94208), + "Tangut": range(94208, 100352), + "Tangut Components": range(100352, 101120), + "Khitan Small Script": range(101120, 101632), + "Tangut Supplement": range(101632, 101760), + "Kana Extended-B": range(110576, 110592), + "Kana Supplement": range(110592, 110848), + "Kana Extended-A": range(110848, 110896), + "Small Kana Extension": range(110896, 110960), + "Nushu": range(110960, 111360), + "Duployan": range(113664, 113824), + "Shorthand Format Controls": range(113824, 113840), + "Znamenny Musical Notation": range(118528, 118736), + "Byzantine Musical Symbols": range(118784, 119040), + "Musical Symbols": range(119040, 119296), + "Ancient Greek Musical Notation": range(119296, 119376), + "Kaktovik Numerals": range(119488, 119520), + "Mayan Numerals": range(119520, 119552), + "Tai Xuan Jing Symbols": range(119552, 119648), + "Counting Rod Numerals": range(119648, 119680), + "Mathematical Alphanumeric Symbols": range(119808, 120832), + "Sutton SignWriting": range(120832, 121520), + "Latin Extended-G": range(122624, 122880), + "Glagolitic Supplement": range(122880, 122928), + "Cyrillic Extended-D": range(122928, 123024), + "Nyiakeng Puachue Hmong": range(123136, 123216), + "Toto": range(123536, 123584), + "Wancho": range(123584, 123648), + "Nag Mundari": range(124112, 124160), + "Ethiopic Extended-B": range(124896, 124928), + "Mende Kikakui": range(124928, 125152), + "Adlam": range(125184, 125280), + "Indic Siyaq Numbers": range(126064, 126144), + "Ottoman Siyaq Numbers": range(126208, 126288), + "Arabic Mathematical Alphabetic Symbols": range(126464, 126720), + "Mahjong Tiles": range(126976, 127024), + "Domino Tiles": range(127024, 127136), + "Playing Cards": range(127136, 127232), + "Enclosed Alphanumeric Supplement": range(127232, 127488), + "Enclosed Ideographic Supplement": range(127488, 127744), + "Miscellaneous Symbols and Pictographs": range(127744, 128512), + "Emoticons range(Emoji)": range(128512, 128592), + "Ornamental Dingbats": range(128592, 128640), + "Transport and Map Symbols": range(128640, 128768), + "Alchemical Symbols": range(128768, 128896), + "Geometric Shapes Extended": range(128896, 129024), + "Supplemental Arrows-C": range(129024, 129280), + "Supplemental Symbols and Pictographs": range(129280, 129536), + "Chess Symbols": range(129536, 129648), + "Symbols and Pictographs Extended-A": range(129648, 129792), + "Symbols for Legacy Computing": range(129792, 130048), + "CJK Unified Ideographs Extension B": range(131072, 173792), + "CJK Unified Ideographs Extension C": range(173824, 177984), + "CJK Unified Ideographs Extension D": range(177984, 178208), + "CJK Unified Ideographs Extension E": range(178208, 183984), + "CJK Unified Ideographs Extension F": range(183984, 191472), + "CJK Compatibility Ideographs Supplement": range(194560, 195104), + "CJK Unified Ideographs Extension G": range(196608, 201552), + "CJK Unified Ideographs Extension H": range(201552, 205744), + "Tags": range(917504, 917632), + "Variation Selectors Supplement": range(917760, 918000), + "Supplementary Private Use Area-A": range(983040, 1048576), + "Supplementary Private Use Area-B": range(1048576, 1114112), +} + + +UNICODE_SECONDARY_RANGE_KEYWORD: List[str] = [ + "Supplement", + "Extended", + "Extensions", + "Modifier", + "Marks", + "Punctuation", + "Symbols", + "Forms", + "Operators", + "Miscellaneous", + "Drawing", + "Block", + "Shapes", + "Supplemental", + "Tags", +] + +RE_POSSIBLE_ENCODING_INDICATION = re_compile( + r"(?:(?:encoding)|(?:charset)|(?:coding))(?:[\:= ]{1,10})(?:[\"\']?)([a-zA-Z0-9\-_]+)(?:[\"\']?)", + IGNORECASE, +) + +IANA_NO_ALIASES = [ + "cp720", + "cp737", + "cp856", + "cp874", + "cp875", + "cp1006", + "koi8_r", + "koi8_t", + "koi8_u", +] + +IANA_SUPPORTED: List[str] = sorted( + filter( + lambda x: x.endswith("_codec") is False + and x not in {"rot_13", "tactis", "mbcs"}, + list(set(aliases.values())) + IANA_NO_ALIASES, + ) +) + +IANA_SUPPORTED_COUNT: int = len(IANA_SUPPORTED) + +# pre-computed code page that are similar using the function cp_similarity. +IANA_SUPPORTED_SIMILAR: Dict[str, List[str]] = { + "cp037": ["cp1026", "cp1140", "cp273", "cp500"], + "cp1026": ["cp037", "cp1140", "cp273", "cp500"], + "cp1125": ["cp866"], + "cp1140": ["cp037", "cp1026", "cp273", "cp500"], + "cp1250": ["iso8859_2"], + "cp1251": ["kz1048", "ptcp154"], + "cp1252": ["iso8859_15", "iso8859_9", "latin_1"], + "cp1253": ["iso8859_7"], + "cp1254": ["iso8859_15", "iso8859_9", "latin_1"], + "cp1257": ["iso8859_13"], + "cp273": ["cp037", "cp1026", "cp1140", "cp500"], + "cp437": ["cp850", "cp858", "cp860", "cp861", "cp862", "cp863", "cp865"], + "cp500": ["cp037", "cp1026", "cp1140", "cp273"], + "cp850": ["cp437", "cp857", "cp858", "cp865"], + "cp857": ["cp850", "cp858", "cp865"], + "cp858": ["cp437", "cp850", "cp857", "cp865"], + "cp860": ["cp437", "cp861", "cp862", "cp863", "cp865"], + "cp861": ["cp437", "cp860", "cp862", "cp863", "cp865"], + "cp862": ["cp437", "cp860", "cp861", "cp863", "cp865"], + "cp863": ["cp437", "cp860", "cp861", "cp862", "cp865"], + "cp865": ["cp437", "cp850", "cp857", "cp858", "cp860", "cp861", "cp862", "cp863"], + "cp866": ["cp1125"], + "iso8859_10": ["iso8859_14", "iso8859_15", "iso8859_4", "iso8859_9", "latin_1"], + "iso8859_11": ["tis_620"], + "iso8859_13": ["cp1257"], + "iso8859_14": [ + "iso8859_10", + "iso8859_15", + "iso8859_16", + "iso8859_3", + "iso8859_9", + "latin_1", + ], + "iso8859_15": [ + "cp1252", + "cp1254", + "iso8859_10", + "iso8859_14", + "iso8859_16", + "iso8859_3", + "iso8859_9", + "latin_1", + ], + "iso8859_16": [ + "iso8859_14", + "iso8859_15", + "iso8859_2", + "iso8859_3", + "iso8859_9", + "latin_1", + ], + "iso8859_2": ["cp1250", "iso8859_16", "iso8859_4"], + "iso8859_3": ["iso8859_14", "iso8859_15", "iso8859_16", "iso8859_9", "latin_1"], + "iso8859_4": ["iso8859_10", "iso8859_2", "iso8859_9", "latin_1"], + "iso8859_7": ["cp1253"], + "iso8859_9": [ + "cp1252", + "cp1254", + "cp1258", + "iso8859_10", + "iso8859_14", + "iso8859_15", + "iso8859_16", + "iso8859_3", + "iso8859_4", + "latin_1", + ], + "kz1048": ["cp1251", "ptcp154"], + "latin_1": [ + "cp1252", + "cp1254", + "cp1258", + "iso8859_10", + "iso8859_14", + "iso8859_15", + "iso8859_16", + "iso8859_3", + "iso8859_4", + "iso8859_9", + ], + "mac_iceland": ["mac_roman", "mac_turkish"], + "mac_roman": ["mac_iceland", "mac_turkish"], + "mac_turkish": ["mac_iceland", "mac_roman"], + "ptcp154": ["cp1251", "kz1048"], + "tis_620": ["iso8859_11"], +} + + +CHARDET_CORRESPONDENCE: Dict[str, str] = { + "iso2022_kr": "ISO-2022-KR", + "iso2022_jp": "ISO-2022-JP", + "euc_kr": "EUC-KR", + "tis_620": "TIS-620", + "utf_32": "UTF-32", + "euc_jp": "EUC-JP", + "koi8_r": "KOI8-R", + "iso8859_1": "ISO-8859-1", + "iso8859_2": "ISO-8859-2", + "iso8859_5": "ISO-8859-5", + "iso8859_6": "ISO-8859-6", + "iso8859_7": "ISO-8859-7", + "iso8859_8": "ISO-8859-8", + "utf_16": "UTF-16", + "cp855": "IBM855", + "mac_cyrillic": "MacCyrillic", + "gb2312": "GB2312", + "gb18030": "GB18030", + "cp932": "CP932", + "cp866": "IBM866", + "utf_8": "utf-8", + "utf_8_sig": "UTF-8-SIG", + "shift_jis": "SHIFT_JIS", + "big5": "Big5", + "cp1250": "windows-1250", + "cp1251": "windows-1251", + "cp1252": "Windows-1252", + "cp1253": "windows-1253", + "cp1255": "windows-1255", + "cp1256": "windows-1256", + "cp1254": "Windows-1254", + "cp949": "CP949", +} + + +COMMON_SAFE_ASCII_CHARACTERS: Set[str] = { + "<", + ">", + "=", + ":", + "/", + "&", + ";", + "{", + "}", + "[", + "]", + ",", + "|", + '"', + "-", +} + + +KO_NAMES: Set[str] = {"johab", "cp949", "euc_kr"} +ZH_NAMES: Set[str] = {"big5", "cp950", "big5hkscs", "hz"} + +# Logging LEVEL below DEBUG +TRACE: int = 5 + + +# Language label that contain the em dash "—" +# character are to be considered alternative seq to origin +FREQUENCIES: Dict[str, List[str]] = { + "English": [ + "e", + "a", + "t", + "i", + "o", + "n", + "s", + "r", + "h", + "l", + "d", + "c", + "u", + "m", + "f", + "p", + "g", + "w", + "y", + "b", + "v", + "k", + "x", + "j", + "z", + "q", + ], + "English—": [ + "e", + "a", + "t", + "i", + "o", + "n", + "s", + "r", + "h", + "l", + "d", + "c", + "m", + "u", + "f", + "p", + "g", + "w", + "b", + "y", + "v", + "k", + "j", + "x", + "z", + "q", + ], + "German": [ + "e", + "n", + "i", + "r", + "s", + "t", + "a", + "d", + "h", + "u", + "l", + "g", + "o", + "c", + "m", + "b", + "f", + "k", + "w", + "z", + "p", + "v", + "ü", + "ä", + "ö", + "j", + ], + "French": [ + "e", + "a", + "s", + "n", + "i", + "t", + "r", + "l", + "u", + "o", + "d", + "c", + "p", + "m", + "é", + "v", + "g", + "f", + "b", + "h", + "q", + "à", + "x", + "è", + "y", + "j", + ], + "Dutch": [ + "e", + "n", + "a", + "i", + "r", + "t", + "o", + "d", + "s", + "l", + "g", + "h", + "v", + "m", + "u", + "k", + "c", + "p", + "b", + "w", + "j", + "z", + "f", + "y", + "x", + "ë", + ], + "Italian": [ + "e", + "i", + "a", + "o", + "n", + "l", + "t", + "r", + "s", + "c", + "d", + "u", + "p", + "m", + "g", + "v", + "f", + "b", + "z", + "h", + "q", + "è", + "à", + "k", + "y", + "ò", + ], + "Polish": [ + "a", + "i", + "o", + "e", + "n", + "r", + "z", + "w", + "s", + "c", + "t", + "k", + "y", + "d", + "p", + "m", + "u", + "l", + "j", + "ł", + "g", + "b", + "h", + "ą", + "ę", + "ó", + ], + "Spanish": [ + "e", + "a", + "o", + "n", + "s", + "r", + "i", + "l", + "d", + "t", + "c", + "u", + "m", + "p", + "b", + "g", + "v", + "f", + "y", + "ó", + "h", + "q", + "í", + "j", + "z", + "á", + ], + "Russian": [ + "о", + "а", + "е", + "и", + "н", + "с", + "т", + "р", + "в", + "л", + "к", + "м", + "д", + "п", + "у", + "г", + "я", + "ы", + "з", + "б", + "й", + "ь", + "ч", + "х", + "ж", + "ц", + ], + # Jap-Kanji + "Japanese": [ + "人", + "一", + "大", + "亅", + "丁", + "丨", + "竹", + "笑", + "口", + "日", + "今", + "二", + "彳", + "行", + "十", + "土", + "丶", + "寸", + "寺", + "時", + "乙", + "丿", + "乂", + "气", + "気", + "冂", + "巾", + "亠", + "市", + "目", + "儿", + "見", + "八", + "小", + "凵", + "県", + "月", + "彐", + "門", + "間", + "木", + "東", + "山", + "出", + "本", + "中", + "刀", + "分", + "耳", + "又", + "取", + "最", + "言", + "田", + "心", + "思", + "刂", + "前", + "京", + "尹", + "事", + "生", + "厶", + "云", + "会", + "未", + "来", + "白", + "冫", + "楽", + "灬", + "馬", + "尸", + "尺", + "駅", + "明", + "耂", + "者", + "了", + "阝", + "都", + "高", + "卜", + "占", + "厂", + "广", + "店", + "子", + "申", + "奄", + "亻", + "俺", + "上", + "方", + "冖", + "学", + "衣", + "艮", + "食", + "自", + ], + # Jap-Katakana + "Japanese—": [ + "ー", + "ン", + "ス", + "・", + "ル", + "ト", + "リ", + "イ", + "ア", + "ラ", + "ッ", + "ク", + "ド", + "シ", + "レ", + "ジ", + "タ", + "フ", + "ロ", + "カ", + "テ", + "マ", + "ィ", + "グ", + "バ", + "ム", + "プ", + "オ", + "コ", + "デ", + "ニ", + "ウ", + "メ", + "サ", + "ビ", + "ナ", + "ブ", + "ャ", + "エ", + "ュ", + "チ", + "キ", + "ズ", + "ダ", + "パ", + "ミ", + "ェ", + "ョ", + "ハ", + "セ", + "ベ", + "ガ", + "モ", + "ツ", + "ネ", + "ボ", + "ソ", + "ノ", + "ァ", + "ヴ", + "ワ", + "ポ", + "ペ", + "ピ", + "ケ", + "ゴ", + "ギ", + "ザ", + "ホ", + "ゲ", + "ォ", + "ヤ", + "ヒ", + "ユ", + "ヨ", + "ヘ", + "ゼ", + "ヌ", + "ゥ", + "ゾ", + "ヶ", + "ヂ", + "ヲ", + "ヅ", + "ヵ", + "ヱ", + "ヰ", + "ヮ", + "ヽ", + "゠", + "ヾ", + "ヷ", + "ヿ", + "ヸ", + "ヹ", + "ヺ", + ], + # Jap-Hiragana + "Japanese——": [ + "の", + "に", + "る", + "た", + "と", + "は", + "し", + "い", + "を", + "で", + "て", + "が", + "な", + "れ", + "か", + "ら", + "さ", + "っ", + "り", + "す", + "あ", + "も", + "こ", + "ま", + "う", + "く", + "よ", + "き", + "ん", + "め", + "お", + "け", + "そ", + "つ", + "だ", + "や", + "え", + "ど", + "わ", + "ち", + "み", + "せ", + "じ", + "ば", + "へ", + "び", + "ず", + "ろ", + "ほ", + "げ", + "む", + "べ", + "ひ", + "ょ", + "ゆ", + "ぶ", + "ご", + "ゃ", + "ね", + "ふ", + "ぐ", + "ぎ", + "ぼ", + "ゅ", + "づ", + "ざ", + "ぞ", + "ぬ", + "ぜ", + "ぱ", + "ぽ", + "ぷ", + "ぴ", + "ぃ", + "ぁ", + "ぇ", + "ぺ", + "ゞ", + "ぢ", + "ぉ", + "ぅ", + "ゐ", + "ゝ", + "ゑ", + "゛", + "゜", + "ゎ", + "ゔ", + "゚", + "ゟ", + "゙", + "ゕ", + "ゖ", + ], + "Portuguese": [ + "a", + "e", + "o", + "s", + "i", + "r", + "d", + "n", + "t", + "m", + "u", + "c", + "l", + "p", + "g", + "v", + "b", + "f", + "h", + "ã", + "q", + "é", + "ç", + "á", + "z", + "í", + ], + "Swedish": [ + "e", + "a", + "n", + "r", + "t", + "s", + "i", + "l", + "d", + "o", + "m", + "k", + "g", + "v", + "h", + "f", + "u", + "p", + "ä", + "c", + "b", + "ö", + "å", + "y", + "j", + "x", + ], + "Chinese": [ + "的", + "一", + "是", + "不", + "了", + "在", + "人", + "有", + "我", + "他", + "这", + "个", + "们", + "中", + "来", + "上", + "大", + "为", + "和", + "国", + "地", + "到", + "以", + "说", + "时", + "要", + "就", + "出", + "会", + "可", + "也", + "你", + "对", + "生", + "能", + "而", + "子", + "那", + "得", + "于", + "着", + "下", + "自", + "之", + "年", + "过", + "发", + "后", + "作", + "里", + "用", + "道", + "行", + "所", + "然", + "家", + "种", + "事", + "成", + "方", + "多", + "经", + "么", + "去", + "法", + "学", + "如", + "都", + "同", + "现", + "当", + "没", + "动", + "面", + "起", + "看", + "定", + "天", + "分", + "还", + "进", + "好", + "小", + "部", + "其", + "些", + "主", + "样", + "理", + "心", + "她", + "本", + "前", + "开", + "但", + "因", + "只", + "从", + "想", + "实", + ], + "Ukrainian": [ + "о", + "а", + "н", + "і", + "и", + "р", + "в", + "т", + "е", + "с", + "к", + "л", + "у", + "д", + "м", + "п", + "з", + "я", + "ь", + "б", + "г", + "й", + "ч", + "х", + "ц", + "ї", + ], + "Norwegian": [ + "e", + "r", + "n", + "t", + "a", + "s", + "i", + "o", + "l", + "d", + "g", + "k", + "m", + "v", + "f", + "p", + "u", + "b", + "h", + "å", + "y", + "j", + "ø", + "c", + "æ", + "w", + ], + "Finnish": [ + "a", + "i", + "n", + "t", + "e", + "s", + "l", + "o", + "u", + "k", + "ä", + "m", + "r", + "v", + "j", + "h", + "p", + "y", + "d", + "ö", + "g", + "c", + "b", + "f", + "w", + "z", + ], + "Vietnamese": [ + "n", + "h", + "t", + "i", + "c", + "g", + "a", + "o", + "u", + "m", + "l", + "r", + "à", + "đ", + "s", + "e", + "v", + "p", + "b", + "y", + "ư", + "d", + "á", + "k", + "ộ", + "ế", + ], + "Czech": [ + "o", + "e", + "a", + "n", + "t", + "s", + "i", + "l", + "v", + "r", + "k", + "d", + "u", + "m", + "p", + "í", + "c", + "h", + "z", + "á", + "y", + "j", + "b", + "ě", + "é", + "ř", + ], + "Hungarian": [ + "e", + "a", + "t", + "l", + "s", + "n", + "k", + "r", + "i", + "o", + "z", + "á", + "é", + "g", + "m", + "b", + "y", + "v", + "d", + "h", + "u", + "p", + "j", + "ö", + "f", + "c", + ], + "Korean": [ + "이", + "다", + "에", + "의", + "는", + "로", + "하", + "을", + "가", + "고", + "지", + "서", + "한", + "은", + "기", + "으", + "년", + "대", + "사", + "시", + "를", + "리", + "도", + "인", + "스", + "일", + ], + "Indonesian": [ + "a", + "n", + "e", + "i", + "r", + "t", + "u", + "s", + "d", + "k", + "m", + "l", + "g", + "p", + "b", + "o", + "h", + "y", + "j", + "c", + "w", + "f", + "v", + "z", + "x", + "q", + ], + "Turkish": [ + "a", + "e", + "i", + "n", + "r", + "l", + "ı", + "k", + "d", + "t", + "s", + "m", + "y", + "u", + "o", + "b", + "ü", + "ş", + "v", + "g", + "z", + "h", + "c", + "p", + "ç", + "ğ", + ], + "Romanian": [ + "e", + "i", + "a", + "r", + "n", + "t", + "u", + "l", + "o", + "c", + "s", + "d", + "p", + "m", + "ă", + "f", + "v", + "î", + "g", + "b", + "ș", + "ț", + "z", + "h", + "â", + "j", + ], + "Farsi": [ + "ا", + "ی", + "ر", + "د", + "ن", + "ه", + "و", + "م", + "ت", + "ب", + "س", + "ل", + "ک", + "ش", + "ز", + "ف", + "گ", + "ع", + "خ", + "ق", + "ج", + "آ", + "پ", + "ح", + "ط", + "ص", + ], + "Arabic": [ + "ا", + "ل", + "ي", + "م", + "و", + "ن", + "ر", + "ت", + "ب", + "ة", + "ع", + "د", + "س", + "ف", + "ه", + "ك", + "ق", + "أ", + "ح", + "ج", + "ش", + "ط", + "ص", + "ى", + "خ", + "إ", + ], + "Danish": [ + "e", + "r", + "n", + "t", + "a", + "i", + "s", + "d", + "l", + "o", + "g", + "m", + "k", + "f", + "v", + "u", + "b", + "h", + "p", + "å", + "y", + "ø", + "æ", + "c", + "j", + "w", + ], + "Serbian": [ + "а", + "и", + "о", + "е", + "н", + "р", + "с", + "у", + "т", + "к", + "ј", + "в", + "д", + "м", + "п", + "л", + "г", + "з", + "б", + "a", + "i", + "e", + "o", + "n", + "ц", + "ш", + ], + "Lithuanian": [ + "i", + "a", + "s", + "o", + "r", + "e", + "t", + "n", + "u", + "k", + "m", + "l", + "p", + "v", + "d", + "j", + "g", + "ė", + "b", + "y", + "ų", + "š", + "ž", + "c", + "ą", + "į", + ], + "Slovene": [ + "e", + "a", + "i", + "o", + "n", + "r", + "s", + "l", + "t", + "j", + "v", + "k", + "d", + "p", + "m", + "u", + "z", + "b", + "g", + "h", + "č", + "c", + "š", + "ž", + "f", + "y", + ], + "Slovak": [ + "o", + "a", + "e", + "n", + "i", + "r", + "v", + "t", + "s", + "l", + "k", + "d", + "m", + "p", + "u", + "c", + "h", + "j", + "b", + "z", + "á", + "y", + "ý", + "í", + "č", + "é", + ], + "Hebrew": [ + "י", + "ו", + "ה", + "ל", + "ר", + "ב", + "ת", + "מ", + "א", + "ש", + "נ", + "ע", + "ם", + "ד", + "ק", + "ח", + "פ", + "ס", + "כ", + "ג", + "ט", + "צ", + "ן", + "ז", + "ך", + ], + "Bulgarian": [ + "а", + "и", + "о", + "е", + "н", + "т", + "р", + "с", + "в", + "л", + "к", + "д", + "п", + "м", + "з", + "г", + "я", + "ъ", + "у", + "б", + "ч", + "ц", + "й", + "ж", + "щ", + "х", + ], + "Croatian": [ + "a", + "i", + "o", + "e", + "n", + "r", + "j", + "s", + "t", + "u", + "k", + "l", + "v", + "d", + "m", + "p", + "g", + "z", + "b", + "c", + "č", + "h", + "š", + "ž", + "ć", + "f", + ], + "Hindi": [ + "क", + "र", + "स", + "न", + "त", + "म", + "ह", + "प", + "य", + "ल", + "व", + "ज", + "द", + "ग", + "ब", + "श", + "ट", + "अ", + "ए", + "थ", + "भ", + "ड", + "च", + "ध", + "ष", + "इ", + ], + "Estonian": [ + "a", + "i", + "e", + "s", + "t", + "l", + "u", + "n", + "o", + "k", + "r", + "d", + "m", + "v", + "g", + "p", + "j", + "h", + "ä", + "b", + "õ", + "ü", + "f", + "c", + "ö", + "y", + ], + "Thai": [ + "า", + "น", + "ร", + "อ", + "ก", + "เ", + "ง", + "ม", + "ย", + "ล", + "ว", + "ด", + "ท", + "ส", + "ต", + "ะ", + "ป", + "บ", + "ค", + "ห", + "แ", + "จ", + "พ", + "ช", + "ข", + "ใ", + ], + "Greek": [ + "α", + "τ", + "ο", + "ι", + "ε", + "ν", + "ρ", + "σ", + "κ", + "η", + "π", + "ς", + "υ", + "μ", + "λ", + "ί", + "ό", + "ά", + "γ", + "έ", + "δ", + "ή", + "ω", + "χ", + "θ", + "ύ", + ], + "Tamil": [ + "க", + "த", + "ப", + "ட", + "ர", + "ம", + "ல", + "ன", + "வ", + "ற", + "ய", + "ள", + "ச", + "ந", + "இ", + "ண", + "அ", + "ஆ", + "ழ", + "ங", + "எ", + "உ", + "ஒ", + "ஸ", + ], + "Kazakh": [ + "а", + "ы", + "е", + "н", + "т", + "р", + "л", + "і", + "д", + "с", + "м", + "қ", + "к", + "о", + "б", + "и", + "у", + "ғ", + "ж", + "ң", + "з", + "ш", + "й", + "п", + "г", + "ө", + ], +} + +LANGUAGE_SUPPORTED_COUNT: int = len(FREQUENCIES) diff --git a/Modules/charset_normalizer/legacy.py b/Modules/charset_normalizer/legacy.py new file mode 100644 index 0000000..43aad21 --- /dev/null +++ b/Modules/charset_normalizer/legacy.py @@ -0,0 +1,54 @@ +from typing import Any, Dict, Optional, Union +from warnings import warn + +from .api import from_bytes +from .constant import CHARDET_CORRESPONDENCE + + +def detect( + byte_str: bytes, should_rename_legacy: bool = False, **kwargs: Any +) -> Dict[str, Optional[Union[str, float]]]: + """ + chardet legacy method + Detect the encoding of the given byte string. It should be mostly backward-compatible. + Encoding name will match Chardet own writing whenever possible. (Not on encoding name unsupported by it) + This function is deprecated and should be used to migrate your project easily, consult the documentation for + further information. Not planned for removal. + + :param byte_str: The byte sequence to examine. + :param should_rename_legacy: Should we rename legacy encodings + to their more modern equivalents? + """ + if len(kwargs): + warn( + f"charset-normalizer disregard arguments '{','.join(list(kwargs.keys()))}' in legacy function detect()" + ) + + if not isinstance(byte_str, (bytearray, bytes)): + raise TypeError( # pragma: nocover + "Expected object of type bytes or bytearray, got: " + "{0}".format(type(byte_str)) + ) + + if isinstance(byte_str, bytearray): + byte_str = bytes(byte_str) + + r = from_bytes(byte_str).best() + + encoding = r.encoding if r is not None else None + language = r.language if r is not None and r.language != "Unknown" else "" + confidence = 1.0 - r.chaos if r is not None else None + + # Note: CharsetNormalizer does not return 'UTF-8-SIG' as the sig get stripped in the detection/normalization process + # but chardet does return 'utf-8-sig' and it is a valid codec name. + if r is not None and encoding == "utf_8" and r.bom: + encoding += "_sig" + + if should_rename_legacy is False and encoding in CHARDET_CORRESPONDENCE: + encoding = CHARDET_CORRESPONDENCE[encoding] + + return { + "encoding": encoding, + "language": language, + "confidence": confidence, + } diff --git a/Modules/charset_normalizer/md.py b/Modules/charset_normalizer/md.py new file mode 100644 index 0000000..77897aa --- /dev/null +++ b/Modules/charset_normalizer/md.py @@ -0,0 +1,615 @@ +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) diff --git a/Modules/charset_normalizer/models.py b/Modules/charset_normalizer/models.py new file mode 100644 index 0000000..a760b9c --- /dev/null +++ b/Modules/charset_normalizer/models.py @@ -0,0 +1,340 @@ +from encodings.aliases import aliases +from hashlib import sha256 +from json import dumps +from typing import Any, Dict, Iterator, List, Optional, Tuple, Union + +from .constant import TOO_BIG_SEQUENCE +from .utils import iana_name, is_multi_byte_encoding, unicode_range + + +class CharsetMatch: + def __init__( + self, + payload: bytes, + guessed_encoding: str, + mean_mess_ratio: float, + has_sig_or_bom: bool, + languages: "CoherenceMatches", + decoded_payload: Optional[str] = None, + ): + self._payload: bytes = payload + + self._encoding: str = guessed_encoding + self._mean_mess_ratio: float = mean_mess_ratio + self._languages: CoherenceMatches = languages + self._has_sig_or_bom: bool = has_sig_or_bom + self._unicode_ranges: Optional[List[str]] = None + + self._leaves: List[CharsetMatch] = [] + self._mean_coherence_ratio: float = 0.0 + + self._output_payload: Optional[bytes] = None + self._output_encoding: Optional[str] = None + + self._string: Optional[str] = decoded_payload + + def __eq__(self, other: object) -> bool: + if not isinstance(other, CharsetMatch): + raise TypeError( + "__eq__ cannot be invoked on {} and {}.".format( + str(other.__class__), str(self.__class__) + ) + ) + return self.encoding == other.encoding and self.fingerprint == other.fingerprint + + def __lt__(self, other: object) -> bool: + """ + Implemented to make sorted available upon CharsetMatches items. + """ + if not isinstance(other, CharsetMatch): + raise ValueError + + chaos_difference: float = abs(self.chaos - other.chaos) + coherence_difference: float = abs(self.coherence - other.coherence) + + # Below 1% difference --> Use Coherence + if chaos_difference < 0.01 and coherence_difference > 0.02: + return self.coherence > other.coherence + elif chaos_difference < 0.01 and coherence_difference <= 0.02: + # When having a difficult decision, use the result that decoded as many multi-byte as possible. + # preserve RAM usage! + if len(self._payload) >= TOO_BIG_SEQUENCE: + return self.chaos < other.chaos + return self.multi_byte_usage > other.multi_byte_usage + + return self.chaos < other.chaos + + @property + def multi_byte_usage(self) -> float: + return 1.0 - (len(str(self)) / len(self.raw)) + + def __str__(self) -> str: + # Lazy Str Loading + if self._string is None: + self._string = str(self._payload, self._encoding, "strict") + return self._string + + def __repr__(self) -> str: + return "".format(self.encoding, self.fingerprint) + + def add_submatch(self, other: "CharsetMatch") -> None: + if not isinstance(other, CharsetMatch) or other == self: + raise ValueError( + "Unable to add instance <{}> as a submatch of a CharsetMatch".format( + other.__class__ + ) + ) + + other._string = None # Unload RAM usage; dirty trick. + self._leaves.append(other) + + @property + def encoding(self) -> str: + return self._encoding + + @property + def encoding_aliases(self) -> List[str]: + """ + Encoding name are known by many name, using this could help when searching for IBM855 when it's listed as CP855. + """ + also_known_as: List[str] = [] + for u, p in aliases.items(): + if self.encoding == u: + also_known_as.append(p) + elif self.encoding == p: + also_known_as.append(u) + return also_known_as + + @property + def bom(self) -> bool: + return self._has_sig_or_bom + + @property + def byte_order_mark(self) -> bool: + return self._has_sig_or_bom + + @property + def languages(self) -> List[str]: + """ + Return the complete list of possible languages found in decoded sequence. + Usually not really useful. Returned list may be empty even if 'language' property return something != 'Unknown'. + """ + return [e[0] for e in self._languages] + + @property + def language(self) -> str: + """ + Most probable language found in decoded sequence. If none were detected or inferred, the property will return + "Unknown". + """ + if not self._languages: + # Trying to infer the language based on the given encoding + # Its either English or we should not pronounce ourselves in certain cases. + if "ascii" in self.could_be_from_charset: + return "English" + + # doing it there to avoid circular import + from charset_normalizer.cd import encoding_languages, mb_encoding_languages + + languages = ( + mb_encoding_languages(self.encoding) + if is_multi_byte_encoding(self.encoding) + else encoding_languages(self.encoding) + ) + + if len(languages) == 0 or "Latin Based" in languages: + return "Unknown" + + return languages[0] + + return self._languages[0][0] + + @property + def chaos(self) -> float: + return self._mean_mess_ratio + + @property + def coherence(self) -> float: + if not self._languages: + return 0.0 + return self._languages[0][1] + + @property + def percent_chaos(self) -> float: + return round(self.chaos * 100, ndigits=3) + + @property + def percent_coherence(self) -> float: + return round(self.coherence * 100, ndigits=3) + + @property + def raw(self) -> bytes: + """ + Original untouched bytes. + """ + return self._payload + + @property + def submatch(self) -> List["CharsetMatch"]: + return self._leaves + + @property + def has_submatch(self) -> bool: + return len(self._leaves) > 0 + + @property + def alphabets(self) -> List[str]: + if self._unicode_ranges is not None: + return self._unicode_ranges + # list detected ranges + detected_ranges: List[Optional[str]] = [ + unicode_range(char) for char in str(self) + ] + # filter and sort + self._unicode_ranges = sorted(list({r for r in detected_ranges if r})) + return self._unicode_ranges + + @property + def could_be_from_charset(self) -> List[str]: + """ + The complete list of encoding that output the exact SAME str result and therefore could be the originating + encoding. + This list does include the encoding available in property 'encoding'. + """ + return [self._encoding] + [m.encoding for m in self._leaves] + + def output(self, encoding: str = "utf_8") -> bytes: + """ + Method to get re-encoded bytes payload using given target encoding. Default to UTF-8. + Any errors will be simply ignored by the encoder NOT replaced. + """ + if self._output_encoding is None or self._output_encoding != encoding: + self._output_encoding = encoding + self._output_payload = str(self).encode(encoding, "replace") + + return self._output_payload # type: ignore + + @property + def fingerprint(self) -> str: + """ + Retrieve the unique SHA256 computed using the transformed (re-encoded) payload. Not the original one. + """ + return sha256(self.output()).hexdigest() + + +class CharsetMatches: + """ + Container with every CharsetMatch items ordered by default from most probable to the less one. + Act like a list(iterable) but does not implements all related methods. + """ + + def __init__(self, results: Optional[List[CharsetMatch]] = None): + self._results: List[CharsetMatch] = sorted(results) if results else [] + + def __iter__(self) -> Iterator[CharsetMatch]: + yield from self._results + + def __getitem__(self, item: Union[int, str]) -> CharsetMatch: + """ + Retrieve a single item either by its position or encoding name (alias may be used here). + Raise KeyError upon invalid index or encoding not present in results. + """ + if isinstance(item, int): + return self._results[item] + if isinstance(item, str): + item = iana_name(item, False) + for result in self._results: + if item in result.could_be_from_charset: + return result + raise KeyError + + def __len__(self) -> int: + return len(self._results) + + def __bool__(self) -> bool: + return len(self._results) > 0 + + def append(self, item: CharsetMatch) -> None: + """ + Insert a single match. Will be inserted accordingly to preserve sort. + Can be inserted as a submatch. + """ + if not isinstance(item, CharsetMatch): + raise ValueError( + "Cannot append instance '{}' to CharsetMatches".format( + str(item.__class__) + ) + ) + # We should disable the submatch factoring when the input file is too heavy (conserve RAM usage) + if len(item.raw) <= TOO_BIG_SEQUENCE: + for match in self._results: + if match.fingerprint == item.fingerprint and match.chaos == item.chaos: + match.add_submatch(item) + return + self._results.append(item) + self._results = sorted(self._results) + + def best(self) -> Optional["CharsetMatch"]: + """ + Simply return the first match. Strict equivalent to matches[0]. + """ + if not self._results: + return None + return self._results[0] + + def first(self) -> Optional["CharsetMatch"]: + """ + Redundant method, call the method best(). Kept for BC reasons. + """ + return self.best() + + +CoherenceMatch = Tuple[str, float] +CoherenceMatches = List[CoherenceMatch] + + +class CliDetectionResult: + def __init__( + self, + path: str, + encoding: Optional[str], + encoding_aliases: List[str], + alternative_encodings: List[str], + language: str, + alphabets: List[str], + has_sig_or_bom: bool, + chaos: float, + coherence: float, + unicode_path: Optional[str], + is_preferred: bool, + ): + self.path: str = path + self.unicode_path: Optional[str] = unicode_path + self.encoding: Optional[str] = encoding + self.encoding_aliases: List[str] = encoding_aliases + self.alternative_encodings: List[str] = alternative_encodings + self.language: str = language + self.alphabets: List[str] = alphabets + self.has_sig_or_bom: bool = has_sig_or_bom + self.chaos: float = chaos + self.coherence: float = coherence + self.is_preferred: bool = is_preferred + + @property + def __dict__(self) -> Dict[str, Any]: # type: ignore + return { + "path": self.path, + "encoding": self.encoding, + "encoding_aliases": self.encoding_aliases, + "alternative_encodings": self.alternative_encodings, + "language": self.language, + "alphabets": self.alphabets, + "has_sig_or_bom": self.has_sig_or_bom, + "chaos": self.chaos, + "coherence": self.coherence, + "unicode_path": self.unicode_path, + "is_preferred": self.is_preferred, + } + + def to_json(self) -> str: + return dumps(self.__dict__, ensure_ascii=True, indent=4) diff --git a/Modules/charset_normalizer/py.typed b/Modules/charset_normalizer/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/Modules/charset_normalizer/utils.py b/Modules/charset_normalizer/utils.py new file mode 100644 index 0000000..e5cbbf4 --- /dev/null +++ b/Modules/charset_normalizer/utils.py @@ -0,0 +1,421 @@ +import importlib +import logging +import unicodedata +from codecs import IncrementalDecoder +from encodings.aliases import aliases +from functools import lru_cache +from re import findall +from typing import Generator, List, Optional, Set, Tuple, Union + +from _multibytecodec import MultibyteIncrementalDecoder + +from .constant import ( + ENCODING_MARKS, + IANA_SUPPORTED_SIMILAR, + RE_POSSIBLE_ENCODING_INDICATION, + UNICODE_RANGES_COMBINED, + UNICODE_SECONDARY_RANGE_KEYWORD, + UTF8_MAXIMAL_ALLOCATION, +) + + +@lru_cache(maxsize=UTF8_MAXIMAL_ALLOCATION) +def is_accentuated(character: str) -> bool: + try: + description: str = unicodedata.name(character) + except ValueError: + return False + return ( + "WITH GRAVE" in description + or "WITH ACUTE" in description + or "WITH CEDILLA" in description + or "WITH DIAERESIS" in description + or "WITH CIRCUMFLEX" in description + or "WITH TILDE" in description + or "WITH MACRON" in description + or "WITH RING ABOVE" in description + ) + + +@lru_cache(maxsize=UTF8_MAXIMAL_ALLOCATION) +def remove_accent(character: str) -> str: + decomposed: str = unicodedata.decomposition(character) + if not decomposed: + return character + + codes: List[str] = decomposed.split(" ") + + return chr(int(codes[0], 16)) + + +@lru_cache(maxsize=UTF8_MAXIMAL_ALLOCATION) +def unicode_range(character: str) -> Optional[str]: + """ + Retrieve the Unicode range official name from a single character. + """ + character_ord: int = ord(character) + + for range_name, ord_range in UNICODE_RANGES_COMBINED.items(): + if character_ord in ord_range: + return range_name + + return None + + +@lru_cache(maxsize=UTF8_MAXIMAL_ALLOCATION) +def is_latin(character: str) -> bool: + try: + description: str = unicodedata.name(character) + except ValueError: + return False + return "LATIN" in description + + +@lru_cache(maxsize=UTF8_MAXIMAL_ALLOCATION) +def is_punctuation(character: str) -> bool: + character_category: str = unicodedata.category(character) + + if "P" in character_category: + return True + + character_range: Optional[str] = unicode_range(character) + + if character_range is None: + return False + + return "Punctuation" in character_range + + +@lru_cache(maxsize=UTF8_MAXIMAL_ALLOCATION) +def is_symbol(character: str) -> bool: + character_category: str = unicodedata.category(character) + + if "S" in character_category or "N" in character_category: + return True + + character_range: Optional[str] = unicode_range(character) + + if character_range is None: + return False + + return "Forms" in character_range and character_category != "Lo" + + +@lru_cache(maxsize=UTF8_MAXIMAL_ALLOCATION) +def is_emoticon(character: str) -> bool: + character_range: Optional[str] = unicode_range(character) + + if character_range is None: + return False + + return "Emoticons" in character_range or "Pictographs" in character_range + + +@lru_cache(maxsize=UTF8_MAXIMAL_ALLOCATION) +def is_separator(character: str) -> bool: + if character.isspace() or character in {"｜", "+", "<", ">"}: + return True + + character_category: str = unicodedata.category(character) + + return "Z" in character_category or character_category in {"Po", "Pd", "Pc"} + + +@lru_cache(maxsize=UTF8_MAXIMAL_ALLOCATION) +def is_case_variable(character: str) -> bool: + return character.islower() != character.isupper() + + +@lru_cache(maxsize=UTF8_MAXIMAL_ALLOCATION) +def is_cjk(character: str) -> bool: + try: + character_name = unicodedata.name(character) + except ValueError: + return False + + return "CJK" in character_name + + +@lru_cache(maxsize=UTF8_MAXIMAL_ALLOCATION) +def is_hiragana(character: str) -> bool: + try: + character_name = unicodedata.name(character) + except ValueError: + return False + + return "HIRAGANA" in character_name + + +@lru_cache(maxsize=UTF8_MAXIMAL_ALLOCATION) +def is_katakana(character: str) -> bool: + try: + character_name = unicodedata.name(character) + except ValueError: + return False + + return "KATAKANA" in character_name + + +@lru_cache(maxsize=UTF8_MAXIMAL_ALLOCATION) +def is_hangul(character: str) -> bool: + try: + character_name = unicodedata.name(character) + except ValueError: + return False + + return "HANGUL" in character_name + + +@lru_cache(maxsize=UTF8_MAXIMAL_ALLOCATION) +def is_thai(character: str) -> bool: + try: + character_name = unicodedata.name(character) + except ValueError: + return False + + return "THAI" in character_name + + +@lru_cache(maxsize=UTF8_MAXIMAL_ALLOCATION) +def is_arabic(character: str) -> bool: + try: + character_name = unicodedata.name(character) + except ValueError: + return False + + return "ARABIC" in character_name + + +@lru_cache(maxsize=UTF8_MAXIMAL_ALLOCATION) +def is_arabic_isolated_form(character: str) -> bool: + try: + character_name = unicodedata.name(character) + except ValueError: + return False + + return "ARABIC" in character_name and "ISOLATED FORM" in character_name + + +@lru_cache(maxsize=len(UNICODE_RANGES_COMBINED)) +def is_unicode_range_secondary(range_name: str) -> bool: + return any(keyword in range_name for keyword in UNICODE_SECONDARY_RANGE_KEYWORD) + + +@lru_cache(maxsize=UTF8_MAXIMAL_ALLOCATION) +def is_unprintable(character: str) -> bool: + return ( + character.isspace() is False # includes \n \t \r \v + and character.isprintable() is False + and character != "\x1A" # Why? Its the ASCII substitute character. + and character != "\ufeff" # bug discovered in Python, + # Zero Width No-Break Space located in Arabic Presentation Forms-B, Unicode 1.1 not acknowledged as space. + ) + + +def any_specified_encoding(sequence: bytes, search_zone: int = 8192) -> Optional[str]: + """ + Extract using ASCII-only decoder any specified encoding in the first n-bytes. + """ + if not isinstance(sequence, bytes): + raise TypeError + + seq_len: int = len(sequence) + + results: List[str] = findall( + RE_POSSIBLE_ENCODING_INDICATION, + sequence[: min(seq_len, search_zone)].decode("ascii", errors="ignore"), + ) + + if len(results) == 0: + return None + + for specified_encoding in results: + specified_encoding = specified_encoding.lower().replace("-", "_") + + encoding_alias: str + encoding_iana: str + + for encoding_alias, encoding_iana in aliases.items(): + if encoding_alias == specified_encoding: + return encoding_iana + if encoding_iana == specified_encoding: + return encoding_iana + + return None + + +@lru_cache(maxsize=128) +def is_multi_byte_encoding(name: str) -> bool: + """ + Verify is a specific encoding is a multi byte one based on it IANA name + """ + return name in { + "utf_8", + "utf_8_sig", + "utf_16", + "utf_16_be", + "utf_16_le", + "utf_32", + "utf_32_le", + "utf_32_be", + "utf_7", + } or issubclass( + importlib.import_module("encodings.{}".format(name)).IncrementalDecoder, + MultibyteIncrementalDecoder, + ) + + +def identify_sig_or_bom(sequence: bytes) -> Tuple[Optional[str], bytes]: + """ + Identify and extract SIG/BOM in given sequence. + """ + + for iana_encoding in ENCODING_MARKS: + marks: Union[bytes, List[bytes]] = ENCODING_MARKS[iana_encoding] + + if isinstance(marks, bytes): + marks = [marks] + + for mark in marks: + if sequence.startswith(mark): + return iana_encoding, mark + + return None, b"" + + +def should_strip_sig_or_bom(iana_encoding: str) -> bool: + return iana_encoding not in {"utf_16", "utf_32"} + + +def iana_name(cp_name: str, strict: bool = True) -> str: + cp_name = cp_name.lower().replace("-", "_") + + encoding_alias: str + encoding_iana: str + + for encoding_alias, encoding_iana in aliases.items(): + if cp_name in [encoding_alias, encoding_iana]: + return encoding_iana + + if strict: + raise ValueError("Unable to retrieve IANA for '{}'".format(cp_name)) + + return cp_name + + +def range_scan(decoded_sequence: str) -> List[str]: + ranges: Set[str] = set() + + for character in decoded_sequence: + character_range: Optional[str] = unicode_range(character) + + if character_range is None: + continue + + ranges.add(character_range) + + return list(ranges) + + +def cp_similarity(iana_name_a: str, iana_name_b: str) -> float: + if is_multi_byte_encoding(iana_name_a) or is_multi_byte_encoding(iana_name_b): + return 0.0 + + decoder_a = importlib.import_module( + "encodings.{}".format(iana_name_a) + ).IncrementalDecoder + decoder_b = importlib.import_module( + "encodings.{}".format(iana_name_b) + ).IncrementalDecoder + + id_a: IncrementalDecoder = decoder_a(errors="ignore") + id_b: IncrementalDecoder = decoder_b(errors="ignore") + + character_match_count: int = 0 + + for i in range(255): + to_be_decoded: bytes = bytes([i]) + if id_a.decode(to_be_decoded) == id_b.decode(to_be_decoded): + character_match_count += 1 + + return character_match_count / 254 + + +def is_cp_similar(iana_name_a: str, iana_name_b: str) -> bool: + """ + Determine if two code page are at least 80% similar. IANA_SUPPORTED_SIMILAR dict was generated using + the function cp_similarity. + """ + return ( + iana_name_a in IANA_SUPPORTED_SIMILAR + and iana_name_b in IANA_SUPPORTED_SIMILAR[iana_name_a] + ) + + +def set_logging_handler( + name: str = "charset_normalizer", + level: int = logging.INFO, + format_string: str = "%(asctime)s | %(levelname)s | %(message)s", +) -> None: + logger = logging.getLogger(name) + logger.setLevel(level) + + handler = logging.StreamHandler() + handler.setFormatter(logging.Formatter(format_string)) + logger.addHandler(handler) + + +def cut_sequence_chunks( + sequences: bytes, + encoding_iana: str, + offsets: range, + chunk_size: int, + bom_or_sig_available: bool, + strip_sig_or_bom: bool, + sig_payload: bytes, + is_multi_byte_decoder: bool, + decoded_payload: Optional[str] = None, +) -> Generator[str, None, None]: + if decoded_payload and is_multi_byte_decoder is False: + for i in offsets: + chunk = decoded_payload[i : i + chunk_size] + if not chunk: + break + yield chunk + else: + for i in offsets: + chunk_end = i + chunk_size + if chunk_end > len(sequences) + 8: + continue + + cut_sequence = sequences[i : i + chunk_size] + + if bom_or_sig_available and strip_sig_or_bom is False: + cut_sequence = sig_payload + cut_sequence + + chunk = cut_sequence.decode( + encoding_iana, + errors="ignore" if is_multi_byte_decoder else "strict", + ) + + # multi-byte bad cutting detector and adjustment + # not the cleanest way to perform that fix but clever enough for now. + if is_multi_byte_decoder and i > 0: + chunk_partial_size_chk: int = min(chunk_size, 16) + + if ( + decoded_payload + and chunk[:chunk_partial_size_chk] not in decoded_payload + ): + for j in range(i, i - 4, -1): + cut_sequence = sequences[j:chunk_end] + + if bom_or_sig_available and strip_sig_or_bom is False: + cut_sequence = sig_payload + cut_sequence + + chunk = cut_sequence.decode(encoding_iana, errors="ignore") + + if chunk[:chunk_partial_size_chk] in decoded_payload: + break + + yield chunk diff --git a/Modules/charset_normalizer/version.py b/Modules/charset_normalizer/version.py new file mode 100644 index 0000000..5a4da4f --- /dev/null +++ b/Modules/charset_normalizer/version.py @@ -0,0 +1,6 @@ +""" +Expose version +""" + +__version__ = "3.3.2" +VERSION = __version__.split(".")