Hacked By AnonymousFox
# Licensed under the GPL: https://www.gnu.org/licenses/old-licenses/gpl-2.0.html
# For details: https://github.com/PyCQA/pylint/blob/main/LICENSE
# Copyright (c) https://github.com/PyCQA/pylint/blob/main/CONTRIBUTORS.txt
"""A similarities / code duplication command line tool and pylint checker.
The algorithm is based on comparing the hash value of n successive lines of a file.
First the files are read and any line that doesn't fulfill requirement are removed
(comments, docstrings...)
Those stripped lines are stored in the LineSet class which gives access to them.
Then each index of the stripped lines collection is associated with the hash of n
successive entries of the stripped lines starting at the current index (n is the
minimum common lines option).
The common hashes between both linesets are then looked for. If there are matches, then
the match indices in both linesets are stored and associated with the corresponding
couples (start line number/end line number) in both files.
This association is then post-processed to handle the case of successive matches. For
example if the minimum common lines setting is set to four, then the hashes are
computed with four lines. If one of match indices couple (12, 34) is the
successor of another one (11, 33) then it means that there are in fact five lines which
are common.
Once post-processed the values of association table are the result looked for, i.e.
start and end lines numbers of common lines in both files.
"""
from __future__ import annotations
import argparse
import copy
import functools
import itertools
import operator
import re
import sys
import warnings
from collections import defaultdict
from collections.abc import Callable, Generator, Iterable, Sequence
from getopt import getopt
from io import BufferedIOBase, BufferedReader, BytesIO
from itertools import chain, groupby
from typing import (
TYPE_CHECKING,
Any,
Dict,
List,
NamedTuple,
NewType,
NoReturn,
TextIO,
Tuple,
Union,
)
import astroid
from astroid import nodes
from pylint.checkers import BaseChecker, BaseRawFileChecker, table_lines_from_stats
from pylint.reporters.ureports.nodes import Section, Table
from pylint.typing import MessageDefinitionTuple, Options
from pylint.utils import LinterStats, decoding_stream
if TYPE_CHECKING:
from pylint.lint import PyLinter
DEFAULT_MIN_SIMILARITY_LINE = 4
REGEX_FOR_LINES_WITH_CONTENT = re.compile(r".*\w+")
# Index defines a location in a LineSet stripped lines collection
Index = NewType("Index", int)
# LineNumber defines a location in a LinesSet real lines collection (the whole file lines)
LineNumber = NewType("LineNumber", int)
# LineSpecifs holds characteristics of a line in a file
class LineSpecifs(NamedTuple):
line_number: LineNumber
text: str
# Links LinesChunk object to the starting indices (in lineset's stripped lines)
# of the different chunk of lines that are used to compute the hash
HashToIndex_T = Dict["LinesChunk", List[Index]]
# Links index in the lineset's stripped lines to the real lines in the file
IndexToLines_T = Dict[Index, "SuccessiveLinesLimits"]
# The types the streams read by pylint can take. Originating from astroid.nodes.Module.stream() and open()
STREAM_TYPES = Union[TextIO, BufferedReader, BytesIO]
class CplSuccessiveLinesLimits:
"""Holds a SuccessiveLinesLimits object for each checked file and counts the number
of common lines between both stripped lines collections extracted from both files.
"""
__slots__ = ("first_file", "second_file", "effective_cmn_lines_nb")
def __init__(
self,
first_file: SuccessiveLinesLimits,
second_file: SuccessiveLinesLimits,
effective_cmn_lines_nb: int,
) -> None:
self.first_file = first_file
self.second_file = second_file
self.effective_cmn_lines_nb = effective_cmn_lines_nb
# Links the indices to the starting line in both lineset's stripped lines to
# the start and end lines in both files
CplIndexToCplLines_T = Dict["LineSetStartCouple", CplSuccessiveLinesLimits]
class LinesChunk:
"""The LinesChunk object computes and stores the hash of some consecutive stripped
lines of a lineset.
"""
__slots__ = ("_fileid", "_index", "_hash")
def __init__(self, fileid: str, num_line: int, *lines: Iterable[str]) -> None:
self._fileid: str = fileid
"""The name of the file from which the LinesChunk object is generated."""
self._index: Index = Index(num_line)
"""The index in the stripped lines that is the starting of consecutive
lines.
"""
self._hash: int = sum(hash(lin) for lin in lines)
"""The hash of some consecutive lines."""
def __eq__(self, o: Any) -> bool:
if not isinstance(o, LinesChunk):
return NotImplemented
return self._hash == o._hash
def __hash__(self) -> int:
return self._hash
def __repr__(self) -> str:
return (
f"<LinesChunk object for file {self._fileid} ({self._index}, {self._hash})>"
)
def __str__(self) -> str:
return (
f"LinesChunk object for file {self._fileid}, starting at line {self._index} \n"
f"Hash is {self._hash}"
)
class SuccessiveLinesLimits:
"""A class to handle the numbering of begin and end of successive lines.
:note: Only the end line number can be updated.
"""
__slots__ = ("_start", "_end")
def __init__(self, start: LineNumber, end: LineNumber) -> None:
self._start: LineNumber = start
self._end: LineNumber = end
@property
def start(self) -> LineNumber:
return self._start
@property
def end(self) -> LineNumber:
return self._end
@end.setter
def end(self, value: LineNumber) -> None:
self._end = value
def __repr__(self) -> str:
return f"<SuccessiveLinesLimits <{self._start};{self._end}>>"
class LineSetStartCouple(NamedTuple):
"""Indices in both linesets that mark the beginning of successive lines."""
fst_lineset_index: Index
snd_lineset_index: Index
def __repr__(self) -> str:
return (
f"<LineSetStartCouple <{self.fst_lineset_index};{self.snd_lineset_index}>>"
)
def __eq__(self, other: Any) -> bool:
if not isinstance(other, LineSetStartCouple):
return NotImplemented
return (
self.fst_lineset_index == other.fst_lineset_index
and self.snd_lineset_index == other.snd_lineset_index
)
def __hash__(self) -> int:
return hash(self.fst_lineset_index) + hash(self.snd_lineset_index)
def increment(self, value: Index) -> LineSetStartCouple:
return LineSetStartCouple(
Index(self.fst_lineset_index + value),
Index(self.snd_lineset_index + value),
)
LinesChunkLimits_T = Tuple["LineSet", LineNumber, LineNumber]
def hash_lineset(
lineset: LineSet, min_common_lines: int = DEFAULT_MIN_SIMILARITY_LINE
) -> tuple[HashToIndex_T, IndexToLines_T]:
"""Return two dicts.
The first associates the hash of successive stripped lines of a lineset
to the indices of the starting lines.
The second dict, associates the index of the starting line in the lineset's stripped lines to the
couple [start, end] lines number in the corresponding file.
:param lineset: lineset object (i.e the lines in a file)
:param min_common_lines: number of successive lines that are used to compute the hash
:return: a dict linking hashes to corresponding start index and a dict that links this
index to the start and end lines in the file
"""
hash2index = defaultdict(list)
index2lines = {}
# Comments, docstring and other specific patterns maybe excluded -> call to stripped_lines
# to get only what is desired
lines = tuple(x.text for x in lineset.stripped_lines)
# Need different iterators on same lines but each one is shifted 1 from the precedent
shifted_lines = [iter(lines[i:]) for i in range(min_common_lines)]
for i, *succ_lines in enumerate(zip(*shifted_lines)):
start_linenumber = LineNumber(lineset.stripped_lines[i].line_number)
try:
end_linenumber = lineset.stripped_lines[i + min_common_lines].line_number
except IndexError:
end_linenumber = LineNumber(lineset.stripped_lines[-1].line_number + 1)
index = Index(i)
index2lines[index] = SuccessiveLinesLimits(
start=start_linenumber, end=end_linenumber
)
l_c = LinesChunk(lineset.name, index, *succ_lines)
hash2index[l_c].append(index)
return hash2index, index2lines
def remove_successive(all_couples: CplIndexToCplLines_T) -> None:
"""Removes all successive entries in the dictionary in argument.
:param all_couples: collection that has to be cleaned up from successive entries.
The keys are couples of indices that mark the beginning of common entries
in both linesets. The values have two parts. The first one is the couple
of starting and ending line numbers of common successive lines in the first file.
The second part is the same for the second file.
For example consider the following dict:
>>> all_couples
{(11, 34): ([5, 9], [27, 31]),
(23, 79): ([15, 19], [45, 49]),
(12, 35): ([6, 10], [28, 32])}
There are two successive keys (11, 34) and (12, 35).
It means there are two consecutive similar chunks of lines in both files.
Thus remove last entry and update the last line numbers in the first entry
>>> remove_successive(all_couples)
>>> all_couples
{(11, 34): ([5, 10], [27, 32]),
(23, 79): ([15, 19], [45, 49])}
"""
couple: LineSetStartCouple
for couple in tuple(all_couples.keys()):
to_remove = []
test = couple.increment(Index(1))
while test in all_couples:
all_couples[couple].first_file.end = all_couples[test].first_file.end
all_couples[couple].second_file.end = all_couples[test].second_file.end
all_couples[couple].effective_cmn_lines_nb += 1
to_remove.append(test)
test = test.increment(Index(1))
for target in to_remove:
try:
all_couples.pop(target)
except KeyError:
pass
def filter_noncode_lines(
ls_1: LineSet,
stindex_1: Index,
ls_2: LineSet,
stindex_2: Index,
common_lines_nb: int,
) -> int:
"""Return the effective number of common lines between lineset1
and lineset2 filtered from non code lines.
That is to say the number of common successive stripped
lines except those that do not contain code (for example
a line with only an ending parenthesis)
:param ls_1: first lineset
:param stindex_1: first lineset starting index
:param ls_2: second lineset
:param stindex_2: second lineset starting index
:param common_lines_nb: number of common successive stripped lines before being filtered from non code lines
:return: the number of common successive stripped lines that contain code
"""
stripped_l1 = [
lspecif.text
for lspecif in ls_1.stripped_lines[stindex_1 : stindex_1 + common_lines_nb]
if REGEX_FOR_LINES_WITH_CONTENT.match(lspecif.text)
]
stripped_l2 = [
lspecif.text
for lspecif in ls_2.stripped_lines[stindex_2 : stindex_2 + common_lines_nb]
if REGEX_FOR_LINES_WITH_CONTENT.match(lspecif.text)
]
return sum(sline_1 == sline_2 for sline_1, sline_2 in zip(stripped_l1, stripped_l2))
class Commonality(NamedTuple):
cmn_lines_nb: int
fst_lset: LineSet
fst_file_start: LineNumber
fst_file_end: LineNumber
snd_lset: LineSet
snd_file_start: LineNumber
snd_file_end: LineNumber
class Similar:
"""Finds copy-pasted lines of code in a project."""
def __init__(
self,
min_lines: int = DEFAULT_MIN_SIMILARITY_LINE,
ignore_comments: bool = False,
ignore_docstrings: bool = False,
ignore_imports: bool = False,
ignore_signatures: bool = False,
) -> None:
# If we run in pylint mode we link the namespace objects
if isinstance(self, BaseChecker):
self.namespace = self.linter.config
else:
self.namespace = argparse.Namespace()
self.namespace.min_similarity_lines = min_lines
self.namespace.ignore_comments = ignore_comments
self.namespace.ignore_docstrings = ignore_docstrings
self.namespace.ignore_imports = ignore_imports
self.namespace.ignore_signatures = ignore_signatures
self.linesets: list[LineSet] = []
def append_stream(
self, streamid: str, stream: STREAM_TYPES, encoding: str | None = None
) -> None:
"""Append a file to search for similarities."""
if isinstance(stream, BufferedIOBase):
if encoding is None:
raise ValueError
readlines = decoding_stream(stream, encoding).readlines
else:
# hint parameter is incorrectly typed as non-optional
readlines = stream.readlines # type: ignore[assignment]
try:
lines = readlines()
except UnicodeDecodeError:
lines = []
self.linesets.append(
LineSet(
streamid,
lines,
self.namespace.ignore_comments,
self.namespace.ignore_docstrings,
self.namespace.ignore_imports,
self.namespace.ignore_signatures,
line_enabled_callback=self.linter._is_one_message_enabled
if hasattr(self, "linter")
else None,
)
)
def run(self) -> None:
"""Start looking for similarities and display results on stdout."""
if self.namespace.min_similarity_lines == 0:
return
self._display_sims(self._compute_sims())
def _compute_sims(self) -> list[tuple[int, set[LinesChunkLimits_T]]]:
"""Compute similarities in appended files."""
no_duplicates: dict[int, list[set[LinesChunkLimits_T]]] = defaultdict(list)
for commonality in self._iter_sims():
num = commonality.cmn_lines_nb
lineset1 = commonality.fst_lset
start_line_1 = commonality.fst_file_start
end_line_1 = commonality.fst_file_end
lineset2 = commonality.snd_lset
start_line_2 = commonality.snd_file_start
end_line_2 = commonality.snd_file_end
duplicate = no_duplicates[num]
couples: set[LinesChunkLimits_T]
for couples in duplicate:
if (lineset1, start_line_1, end_line_1) in couples or (
lineset2,
start_line_2,
end_line_2,
) in couples:
break
else:
duplicate.append(
{
(lineset1, start_line_1, end_line_1),
(lineset2, start_line_2, end_line_2),
}
)
sims: list[tuple[int, set[LinesChunkLimits_T]]] = []
ensembles: list[set[LinesChunkLimits_T]]
for num, ensembles in no_duplicates.items():
cpls: set[LinesChunkLimits_T]
for cpls in ensembles:
sims.append((num, cpls))
sims.sort()
sims.reverse()
return sims
def _display_sims(
self, similarities: list[tuple[int, set[LinesChunkLimits_T]]]
) -> None:
"""Display computed similarities on stdout."""
report = self._get_similarity_report(similarities)
print(report)
def _get_similarity_report(
self, similarities: list[tuple[int, set[LinesChunkLimits_T]]]
) -> str:
"""Create a report from similarities."""
report: str = ""
duplicated_line_number: int = 0
for number, couples in similarities:
report += f"\n{number} similar lines in {len(couples)} files\n"
couples_l = sorted(couples)
line_set = start_line = end_line = None
for line_set, start_line, end_line in couples_l:
report += f"=={line_set.name}:[{start_line}:{end_line}]\n"
if line_set:
for line in line_set._real_lines[start_line:end_line]:
report += f" {line.rstrip()}\n" if line.rstrip() else "\n"
duplicated_line_number += number * (len(couples_l) - 1)
total_line_number: int = sum(len(lineset) for lineset in self.linesets)
report += (
f"TOTAL lines={total_line_number} "
f"duplicates={duplicated_line_number} "
f"percent={duplicated_line_number * 100.0 / total_line_number:.2f}\n"
)
return report
# pylint: disable = too-many-locals
def _find_common(
self, lineset1: LineSet, lineset2: LineSet
) -> Generator[Commonality, None, None]:
"""Find similarities in the two given linesets.
This the core of the algorithm. The idea is to compute the hashes of a
minimal number of successive lines of each lineset and then compare the
hashes. Every match of such comparison is stored in a dict that links the
couple of starting indices in both linesets to the couple of corresponding
starting and ending lines in both files.
Last regroups all successive couples in a bigger one. It allows to take into
account common chunk of lines that have more than the minimal number of
successive lines required.
"""
hash_to_index_1: HashToIndex_T
hash_to_index_2: HashToIndex_T
index_to_lines_1: IndexToLines_T
index_to_lines_2: IndexToLines_T
hash_to_index_1, index_to_lines_1 = hash_lineset(
lineset1, self.namespace.min_similarity_lines
)
hash_to_index_2, index_to_lines_2 = hash_lineset(
lineset2, self.namespace.min_similarity_lines
)
hash_1: frozenset[LinesChunk] = frozenset(hash_to_index_1.keys())
hash_2: frozenset[LinesChunk] = frozenset(hash_to_index_2.keys())
common_hashes: Iterable[LinesChunk] = sorted(
hash_1 & hash_2, key=lambda m: hash_to_index_1[m][0]
)
# all_couples is a dict that links the couple of indices in both linesets that mark the beginning of
# successive common lines, to the corresponding starting and ending number lines in both files
all_couples: CplIndexToCplLines_T = {}
for c_hash in sorted(common_hashes, key=operator.attrgetter("_index")):
for indices_in_linesets in itertools.product(
hash_to_index_1[c_hash], hash_to_index_2[c_hash]
):
index_1 = indices_in_linesets[0]
index_2 = indices_in_linesets[1]
all_couples[
LineSetStartCouple(index_1, index_2)
] = CplSuccessiveLinesLimits(
copy.copy(index_to_lines_1[index_1]),
copy.copy(index_to_lines_2[index_2]),
effective_cmn_lines_nb=self.namespace.min_similarity_lines,
)
remove_successive(all_couples)
for cml_stripped_l, cmn_l in all_couples.items():
start_index_1 = cml_stripped_l.fst_lineset_index
start_index_2 = cml_stripped_l.snd_lineset_index
nb_common_lines = cmn_l.effective_cmn_lines_nb
com = Commonality(
cmn_lines_nb=nb_common_lines,
fst_lset=lineset1,
fst_file_start=cmn_l.first_file.start,
fst_file_end=cmn_l.first_file.end,
snd_lset=lineset2,
snd_file_start=cmn_l.second_file.start,
snd_file_end=cmn_l.second_file.end,
)
eff_cmn_nb = filter_noncode_lines(
lineset1, start_index_1, lineset2, start_index_2, nb_common_lines
)
if eff_cmn_nb > self.namespace.min_similarity_lines:
yield com
def _iter_sims(self) -> Generator[Commonality, None, None]:
"""Iterate on similarities among all files, by making a Cartesian
product.
"""
for idx, lineset in enumerate(self.linesets[:-1]):
for lineset2 in self.linesets[idx + 1 :]:
yield from self._find_common(lineset, lineset2)
def get_map_data(self) -> list[LineSet]:
"""Returns the data we can use for a map/reduce process.
In this case we are returning this instance's Linesets, that is all file
information that will later be used for vectorisation.
"""
return self.linesets
def combine_mapreduce_data(self, linesets_collection: list[list[LineSet]]) -> None:
"""Reduces and recombines data into a format that we can report on.
The partner function of get_map_data()
"""
self.linesets = [line for lineset in linesets_collection for line in lineset]
def stripped_lines(
lines: Iterable[str],
ignore_comments: bool,
ignore_docstrings: bool,
ignore_imports: bool,
ignore_signatures: bool,
line_enabled_callback: Callable[[str, int], bool] | None = None,
) -> list[LineSpecifs]:
"""Return tuples of line/line number/line type with leading/trailing white-space and
any ignored code features removed.
:param lines: a collection of lines
:param ignore_comments: if true, any comment in the lines collection is removed from the result
:param ignore_docstrings: if true, any line that is a docstring is removed from the result
:param ignore_imports: if true, any line that is an import is removed from the result
:param ignore_signatures: if true, any line that is part of a function signature is removed from the result
:param line_enabled_callback: If called with "R0801" and a line number, a return value of False will disregard
the line
:return: the collection of line/line number/line type tuples
"""
if ignore_imports or ignore_signatures:
tree = astroid.parse("".join(lines))
if ignore_imports:
node_is_import_by_lineno = (
(node.lineno, isinstance(node, (nodes.Import, nodes.ImportFrom)))
for node in tree.body
)
line_begins_import = {
lineno: all(is_import for _, is_import in node_is_import_group)
for lineno, node_is_import_group in groupby(
node_is_import_by_lineno, key=lambda x: x[0] # type: ignore[no-any-return]
)
}
current_line_is_import = False
if ignore_signatures:
def _get_functions(
functions: list[nodes.NodeNG], tree: nodes.NodeNG
) -> list[nodes.NodeNG]:
"""Recursively get all functions including nested in the classes from the
tree.
"""
for node in tree.body:
if isinstance(node, (nodes.FunctionDef, nodes.AsyncFunctionDef)):
functions.append(node)
if isinstance(
node,
(nodes.ClassDef, nodes.FunctionDef, nodes.AsyncFunctionDef),
):
_get_functions(functions, node)
return functions
functions = _get_functions([], tree)
signature_lines = set(
chain(
*(
range(
func.lineno,
func.body[0].lineno if func.body else func.tolineno + 1,
)
for func in functions
)
)
)
strippedlines = []
docstring = None
for lineno, line in enumerate(lines, start=1):
if line_enabled_callback is not None and not line_enabled_callback(
"R0801", lineno
):
continue
line = line.strip()
if ignore_docstrings:
if not docstring:
if line.startswith('"""') or line.startswith("'''"):
docstring = line[:3]
line = line[3:]
elif line.startswith('r"""') or line.startswith("r'''"):
docstring = line[1:4]
line = line[4:]
if docstring:
if line.endswith(docstring):
docstring = None
line = ""
if ignore_imports:
current_line_is_import = line_begins_import.get(
lineno, current_line_is_import
)
if current_line_is_import:
line = ""
if ignore_comments:
line = line.split("#", 1)[0].strip()
if ignore_signatures and lineno in signature_lines:
line = ""
if line:
strippedlines.append(
LineSpecifs(text=line, line_number=LineNumber(lineno - 1))
)
return strippedlines
@functools.total_ordering
class LineSet:
"""Holds and indexes all the lines of a single source file.
Allows for correspondence between real lines of the source file and stripped ones, which
are the real ones from which undesired patterns have been removed.
"""
def __init__(
self,
name: str,
lines: list[str],
ignore_comments: bool = False,
ignore_docstrings: bool = False,
ignore_imports: bool = False,
ignore_signatures: bool = False,
line_enabled_callback: Callable[[str, int], bool] | None = None,
) -> None:
self.name = name
self._real_lines = lines
self._stripped_lines = stripped_lines(
lines,
ignore_comments,
ignore_docstrings,
ignore_imports,
ignore_signatures,
line_enabled_callback=line_enabled_callback,
)
def __str__(self) -> str:
return f"<Lineset for {self.name}>"
def __len__(self) -> int:
return len(self._real_lines)
def __getitem__(self, index: int) -> LineSpecifs:
return self._stripped_lines[index]
def __lt__(self, other: LineSet) -> bool:
return self.name < other.name
def __hash__(self) -> int:
return id(self)
def __eq__(self, other: Any) -> bool:
if not isinstance(other, LineSet):
return False
return self.__dict__ == other.__dict__
@property
def stripped_lines(self) -> list[LineSpecifs]:
return self._stripped_lines
@property
def real_lines(self) -> list[str]:
return self._real_lines
MSGS: dict[str, MessageDefinitionTuple] = {
"R0801": (
"Similar lines in %s files\n%s",
"duplicate-code",
"Indicates that a set of similar lines has been detected "
"among multiple file. This usually means that the code should "
"be refactored to avoid this duplication.",
)
}
def report_similarities(
sect: Section,
stats: LinterStats,
old_stats: LinterStats | None,
) -> None:
"""Make a layout with some stats about duplication."""
lines = ["", "now", "previous", "difference"]
lines += table_lines_from_stats(stats, old_stats, "duplicated_lines")
sect.append(Table(children=lines, cols=4, rheaders=1, cheaders=1))
# wrapper to get a pylint checker from the similar class
class SimilarChecker(BaseRawFileChecker, Similar):
"""Checks for similarities and duplicated code.
This computation may be memory / CPU intensive, so you
should disable it if you experience some problems.
"""
# configuration section name
name = "similarities"
# messages
msgs = MSGS
# configuration options
# for available dict keys/values see the optik parser 'add_option' method
options: Options = (
(
"min-similarity-lines",
{
"default": DEFAULT_MIN_SIMILARITY_LINE,
"type": "int",
"metavar": "<int>",
"help": "Minimum lines number of a similarity.",
},
),
(
"ignore-comments",
{
"default": True,
"type": "yn",
"metavar": "<y or n>",
"help": "Comments are removed from the similarity computation",
},
),
(
"ignore-docstrings",
{
"default": True,
"type": "yn",
"metavar": "<y or n>",
"help": "Docstrings are removed from the similarity computation",
},
),
(
"ignore-imports",
{
"default": True,
"type": "yn",
"metavar": "<y or n>",
"help": "Imports are removed from the similarity computation",
},
),
(
"ignore-signatures",
{
"default": True,
"type": "yn",
"metavar": "<y or n>",
"help": "Signatures are removed from the similarity computation",
},
),
)
# reports
reports = (("RP0801", "Duplication", report_similarities),)
def __init__(self, linter: PyLinter) -> None:
BaseRawFileChecker.__init__(self, linter)
Similar.__init__(
self,
min_lines=self.linter.config.min_similarity_lines,
ignore_comments=self.linter.config.ignore_comments,
ignore_docstrings=self.linter.config.ignore_docstrings,
ignore_imports=self.linter.config.ignore_imports,
ignore_signatures=self.linter.config.ignore_signatures,
)
def open(self) -> None:
"""Init the checkers: reset linesets and statistics information."""
self.linesets = []
self.linter.stats.reset_duplicated_lines()
def process_module(self, node: nodes.Module) -> None:
"""Process a module.
the module's content is accessible via the stream object
stream must implement the readlines method
"""
if self.linter.current_name is None:
warnings.warn(
(
"In pylint 3.0 the current_name attribute of the linter object should be a string. "
"If unknown it should be initialized as an empty string."
),
DeprecationWarning,
)
with node.stream() as stream:
self.append_stream(self.linter.current_name, stream, node.file_encoding) # type: ignore[arg-type]
def close(self) -> None:
"""Compute and display similarities on closing (i.e. end of parsing)."""
total = sum(len(lineset) for lineset in self.linesets)
duplicated = 0
stats = self.linter.stats
for num, couples in self._compute_sims():
msg = []
lineset = start_line = end_line = None
for lineset, start_line, end_line in couples:
msg.append(f"=={lineset.name}:[{start_line}:{end_line}]")
msg.sort()
if lineset:
for line in lineset.real_lines[start_line:end_line]:
msg.append(line.rstrip())
self.add_message("R0801", args=(len(couples), "\n".join(msg)))
duplicated += num * (len(couples) - 1)
stats.nb_duplicated_lines += int(duplicated)
stats.percent_duplicated_lines += float(total and duplicated * 100.0 / total)
def get_map_data(self) -> list[LineSet]:
"""Passthru override."""
return Similar.get_map_data(self)
def reduce_map_data(self, linter: PyLinter, data: list[list[LineSet]]) -> None:
"""Reduces and recombines data into a format that we can report on.
The partner function of get_map_data()
"""
Similar.combine_mapreduce_data(self, linesets_collection=data)
def register(linter: PyLinter) -> None:
linter.register_checker(SimilarChecker(linter))
def usage(status: int = 0) -> NoReturn:
"""Display command line usage information."""
print("finds copy pasted blocks in a set of files")
print()
print(
"Usage: symilar [-d|--duplicates min_duplicated_lines] \
[-i|--ignore-comments] [--ignore-docstrings] [--ignore-imports] [--ignore-signatures] file1..."
)
sys.exit(status)
def Run(argv: Sequence[str] | None = None) -> NoReturn:
"""Standalone command line access point."""
if argv is None:
argv = sys.argv[1:]
s_opts = "hdi"
l_opts = [
"help",
"duplicates=",
"ignore-comments",
"ignore-imports",
"ignore-docstrings",
"ignore-signatures",
]
min_lines = DEFAULT_MIN_SIMILARITY_LINE
ignore_comments = False
ignore_docstrings = False
ignore_imports = False
ignore_signatures = False
opts, args = getopt(list(argv), s_opts, l_opts)
for opt, val in opts:
if opt in {"-d", "--duplicates"}:
min_lines = int(val)
elif opt in {"-h", "--help"}:
usage()
elif opt in {"-i", "--ignore-comments"}:
ignore_comments = True
elif opt in {"--ignore-docstrings"}:
ignore_docstrings = True
elif opt in {"--ignore-imports"}:
ignore_imports = True
elif opt in {"--ignore-signatures"}:
ignore_signatures = True
if not args:
usage(1)
sim = Similar(
min_lines, ignore_comments, ignore_docstrings, ignore_imports, ignore_signatures
)
for filename in args:
with open(filename, encoding="utf-8") as stream:
sim.append_stream(filename, stream)
sim.run()
sys.exit(0)
if __name__ == "__main__":
Run()
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