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|
import re
from typing import Callable, Dict, Iterable, List, NamedTuple, Optional, Tuple, Union
from prompt_toolkit.document import Document
from prompt_toolkit.filters import FilterOrBool, to_filter
from prompt_toolkit.formatted_text import AnyFormattedText, StyleAndTextTuples
from .base import CompleteEvent, Completer, Completion
from .word_completer import WordCompleter
__all__ = [
"FuzzyCompleter",
"FuzzyWordCompleter",
]
class FuzzyCompleter(Completer):
"""
Fuzzy completion.
This wraps any other completer and turns it into a fuzzy completer.
If the list of words is: ["leopard" , "gorilla", "dinosaur", "cat", "bee"]
Then trying to complete "oar" would yield "leopard" and "dinosaur", but not
the others, because they match the regular expression 'o.*a.*r'.
Similar, in another application "djm" could expand to "django_migrations".
The results are sorted by relevance, which is defined as the start position
and the length of the match.
Notice that this is not really a tool to work around spelling mistakes,
like what would be possible with difflib. The purpose is rather to have a
quicker or more intuitive way to filter the given completions, especially
when many completions have a common prefix.
Fuzzy algorithm is based on this post:
https://blog.amjith.com/fuzzyfinder-in-10-lines-of-python
:param completer: A :class:`~.Completer` instance.
:param WORD: When True, use WORD characters.
:param pattern: Regex pattern which selects the characters before the
cursor that are considered for the fuzzy matching.
:param enable_fuzzy: (bool or `Filter`) Enabled the fuzzy behavior. For
easily turning fuzzyness on or off according to a certain condition.
"""
def __init__(
self,
completer: Completer,
WORD: bool = False,
pattern: Optional[str] = None,
enable_fuzzy: FilterOrBool = True,
):
assert pattern is None or pattern.startswith("^")
self.completer = completer
self.pattern = pattern
self.WORD = WORD
self.pattern = pattern
self.enable_fuzzy = to_filter(enable_fuzzy)
def get_completions(
self, document: Document, complete_event: CompleteEvent
) -> Iterable[Completion]:
if self.enable_fuzzy():
return self._get_fuzzy_completions(document, complete_event)
else:
return self.completer.get_completions(document, complete_event)
def _get_pattern(self) -> str:
if self.pattern:
return self.pattern
if self.WORD:
return r"[^\s]+"
return "^[a-zA-Z0-9_]*"
def _get_fuzzy_completions(
self, document: Document, complete_event: CompleteEvent
) -> Iterable[Completion]:
word_before_cursor = document.get_word_before_cursor(
pattern=re.compile(self._get_pattern())
)
# Get completions
document2 = Document(
text=document.text[: document.cursor_position - len(word_before_cursor)],
cursor_position=document.cursor_position - len(word_before_cursor),
)
completions = list(self.completer.get_completions(document2, complete_event))
fuzzy_matches: List[_FuzzyMatch] = []
pat = ".*?".join(map(re.escape, word_before_cursor))
pat = f"(?=({pat}))" # lookahead regex to manage overlapping matches
regex = re.compile(pat, re.IGNORECASE)
for compl in completions:
matches = list(regex.finditer(compl.text))
if matches:
# Prefer the match, closest to the left, then shortest.
best = min(matches, key=lambda m: (m.start(), len(m.group(1))))
fuzzy_matches.append(
_FuzzyMatch(len(best.group(1)), best.start(), compl)
)
def sort_key(fuzzy_match: "_FuzzyMatch") -> Tuple[int, int]:
"Sort by start position, then by the length of the match."
return fuzzy_match.start_pos, fuzzy_match.match_length
fuzzy_matches = sorted(fuzzy_matches, key=sort_key)
for match in fuzzy_matches:
# Include these completions, but set the correct `display`
# attribute and `start_position`.
yield Completion(
text=match.completion.text,
start_position=match.completion.start_position
- len(word_before_cursor),
display_meta=match.completion.display_meta,
display=self._get_display(match, word_before_cursor),
style=match.completion.style,
)
def _get_display(
self, fuzzy_match: "_FuzzyMatch", word_before_cursor: str
) -> AnyFormattedText:
"""
Generate formatted text for the display label.
"""
m = fuzzy_match
word = m.completion.text
if m.match_length == 0:
# No highlighting when we have zero length matches (no input text).
# In this case, use the original display text (which can include
# additional styling or characters).
return m.completion.display
result: StyleAndTextTuples = []
# Text before match.
result.append(("class:fuzzymatch.outside", word[: m.start_pos]))
# The match itself.
characters = list(word_before_cursor)
for c in word[m.start_pos : m.start_pos + m.match_length]:
classname = "class:fuzzymatch.inside"
if characters and c.lower() == characters[0].lower():
classname += ".character"
del characters[0]
result.append((classname, c))
# Text after match.
result.append(
("class:fuzzymatch.outside", word[m.start_pos + m.match_length :])
)
return result
class FuzzyWordCompleter(Completer):
"""
Fuzzy completion on a list of words.
(This is basically a `WordCompleter` wrapped in a `FuzzyCompleter`.)
:param words: List of words or callable that returns a list of words.
:param meta_dict: Optional dict mapping words to their meta-information.
:param WORD: When True, use WORD characters.
"""
def __init__(
self,
words: Union[List[str], Callable[[], List[str]]],
meta_dict: Optional[Dict[str, str]] = None,
WORD: bool = False,
) -> None:
self.words = words
self.meta_dict = meta_dict or {}
self.WORD = WORD
self.word_completer = WordCompleter(
words=self.words, WORD=self.WORD, meta_dict=self.meta_dict
)
self.fuzzy_completer = FuzzyCompleter(self.word_completer, WORD=self.WORD)
def get_completions(
self, document: Document, complete_event: CompleteEvent
) -> Iterable[Completion]:
return self.fuzzy_completer.get_completions(document, complete_event)
class _FuzzyMatch(NamedTuple):
match_length: int
start_pos: int
completion: Completion
|
