similar.py

# Copyright (c) 2006, 2008-2014 LOGILAB S.A. (Paris, FRANCE) <contact@logilab.fr>
# Copyright (c) 2012 Ry4an Brase <ry4an-hg@ry4an.org>
# Copyright (c) 2012 Google, Inc.
# Copyright (c) 2012 Anthony VEREZ <anthony.verez.external@cassidian.com>
# Copyright (c) 2014-2020 Claudiu Popa <pcmanticore@gmail.com>
# Copyright (c) 2014 Brett Cannon <brett@python.org>
# Copyright (c) 2014 Arun Persaud <arun@nubati.net>
# Copyright (c) 2015 Ionel Cristian Maries <contact@ionelmc.ro>
# Copyright (c) 2017, 2020 Anthony Sottile <asottile@umich.edu>
# Copyright (c) 2017 Mikhail Fesenko <proggga@gmail.com>
# Copyright (c) 2018 Scott Worley <scottworley@scottworley.com>
# Copyright (c) 2018 ssolanki <sushobhitsolanki@gmail.com>
# Copyright (c) 2019, 2021 Pierre Sassoulas <pierre.sassoulas@gmail.com>
# Copyright (c) 2019 Hugo van Kemenade <hugovk@users.noreply.github.com>
# Copyright (c) 2019 Taewon D. Kim <kimt33@mcmaster.ca>
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# Copyright (c) 2020 Shiv Venkatasubrahmanyam <shvenkat@users.noreply.github.com>
# Copyright (c) 2021 Daniël van Noord <13665637+DanielNoord@users.noreply.github.com>
# Copyright (c) 2021 Ville Skyttä <ville.skytta@iki.fi>
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# Copyright (c) 2021 Aditya Gupta <adityagupta1089@users.noreply.github.com>

# 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

"""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 fullfill 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 postprocessed 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 postprocessed the values of association table are the result looked for, i.e start and end lines numbers of common lines in both files.
"""
import copy
import functools
import itertools
import operator
import re
import sys
from collections import defaultdict
from getopt import getopt
from io import BufferedIOBase, BufferedReader, BytesIO
from itertools import chain, groupby
from typing import (
    Any,
    Dict,
    FrozenSet,
    Generator,
    Iterable,
    List,
    NamedTuple,
    NewType,
    Optional,
    Set,
    TextIO,
    Tuple,
    Union,
)

import astroid
from astroid import nodes

from pylint.checkers import BaseChecker, MapReduceMixin, table_lines_from_stats
from pylint.interfaces import IRawChecker
from pylint.reporters.ureports.nodes import Table
from pylint.utils import LinterStats, decoding_stream

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:
    """
    This class holds a couple of SuccessiveLinesLimits objects, one for each file compared,
    and a counter on 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 ot 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) -> 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 index_i, *succ_lines in enumerate(zip(*shifted_lines)):
        start_linenumber = lineset.stripped_lines[index_i].line_number
        try:
            end_linenumber = lineset.stripped_lines[
                index_i + min_common_lines
            ].line_number
        except IndexError:
            end_linenumber = lineset.stripped_lines[-1].line_number + 1

        index = Index(index_i)
        index2lines[index] = SuccessiveLinesLimits(
            start=LineNumber(start_linenumber), end=LineNumber(end_linenumber)
        )

        l_c = LinesChunk(lineset.name, index, *succ_lines)
        hash2index[l_c].append(index)

    return hash2index, index2lines


def remove_successives(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 successives 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 successives 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 successives 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_successives(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 ligne with only an
    ending parathensis)

    :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 successives 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:
        self.min_lines = min_lines
        self.ignore_comments = ignore_comments
        self.ignore_docstrings = ignore_docstrings
        self.ignore_imports = ignore_imports
        self.ignore_signatures = ignore_signatures
        self.linesets: List["LineSet"] = []

    def append_stream(
        self, streamid: str, stream: STREAM_TYPES, encoding: Optional[str] = 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:
            readlines = stream.readlines  # type: ignore[assignment] # hint parameter is incorrectly typed as non-optional
        try:
            self.linesets.append(
                LineSet(
                    streamid,
                    readlines(),
                    self.ignore_comments,
                    self.ignore_docstrings,
                    self.ignore_imports,
                    self.ignore_signatures,
                )
            )
        except UnicodeDecodeError:
            pass

    def run(self) -> None:
        """start looking for similarities and display results on stdout"""
        if self.min_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} duplicates={duplicated_line_number} percent={duplicated_line_number * 100.0 / total_line_number:.2f}\n"
        return report

    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.min_lines)
        hash_to_index_2, index_to_lines_2 = hash_lineset(lineset2, self.min_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.min_lines,
                )

        remove_successives(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.min_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):
        """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):
        """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,
) -> List[LineSpecifs]:
    """
    Return tuples of line/line number/line type with leading/trailing whitespace 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
    :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]
            )
        }
        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):
        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,
    ) -> None:
        self.name = name
        self._real_lines = lines
        self._stripped_lines = stripped_lines(
            lines, ignore_comments, ignore_docstrings, ignore_imports, ignore_signatures
        )

    def __str__(self):
        return f"<Lineset for {self.name}>"

    def __len__(self):
        return len(self._real_lines)

    def __getitem__(self, index):
        return self._stripped_lines[index]

    def __lt__(self, other):
        return self.name < other.name

    def __hash__(self):
        return id(self)

    def __eq__(self, other):
        if not isinstance(other, LineSet):
            return False
        return self.__dict__ == other.__dict__

    @property
    def stripped_lines(self):
        return self._stripped_lines

    @property
    def real_lines(self):
        return self._real_lines


MSGS = {
    "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,
    stats: LinterStats,
    old_stats: Optional[LinterStats],
) -> 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(BaseChecker, Similar, MapReduceMixin):
    """checks for similarities and duplicated code. This computation may be
    memory / CPU intensive, so you should disable it if you experiment some
    problems.
    """

    __implements__ = (IRawChecker,)
    # configuration section name
    name = "similarities"
    # messages
    msgs = MSGS
    # configuration options
    # for available dict keys/values see the optik parser 'add_option' method
    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": False,
                "type": "yn",
                "metavar": "<y or n>",
                "help": "Imports are removed from the similarity computation",
            },
        ),
        (
            "ignore-signatures",
            {
                "default": False,
                "type": "yn",
                "metavar": "<y or n>",
                "help": "Signatures are removed from the similarity computation",
            },
        ),
    )
    # reports
    reports = (("RP0801", "Duplication", report_similarities),)

    def __init__(self, linter=None) -> None:
        BaseChecker.__init__(self, linter)
        Similar.__init__(
            self,
            min_lines=self.config.min_similarity_lines,
            ignore_comments=self.config.ignore_comments,
            ignore_docstrings=self.config.ignore_docstrings,
            ignore_imports=self.config.ignore_imports,
            ignore_signatures=self.config.ignore_signatures,
        )

    def set_option(self, optname, value, action=None, optdict=None):
        """method called to set an option (registered in the options list)

        Overridden to report options setting to Similar
        """
        BaseChecker.set_option(self, optname, value, action, optdict)
        if optname == "min-similarity-lines":
            self.min_lines = self.config.min_similarity_lines
        elif optname == "ignore-comments":
            self.ignore_comments = self.config.ignore_comments
        elif optname == "ignore-docstrings":
            self.ignore_docstrings = self.config.ignore_docstrings
        elif optname == "ignore-imports":
            self.ignore_imports = self.config.ignore_imports
        elif optname == "ignore-signatures":
            self.ignore_signatures = self.config.ignore_signatures

    def open(self):
        """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
        """
        with node.stream() as stream:
            self.append_stream(self.linter.current_name, stream, node.file_encoding)

    def close(self):
        """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):
        """Passthru override"""
        return Similar.get_map_data(self)

    def reduce_map_data(self, linter, data):
        """Reduces and recombines data into a format that we can report on

        The partner function of get_map_data()"""
        recombined = SimilarChecker(linter)
        recombined.min_lines = self.min_lines
        recombined.ignore_comments = self.ignore_comments
        recombined.ignore_docstrings = self.ignore_docstrings
        recombined.ignore_imports = self.ignore_imports
        recombined.ignore_signatures = self.ignore_signatures
        recombined.open()
        Similar.combine_mapreduce_data(recombined, linesets_collection=data)
        recombined.close()


def register(linter):
    """required method to auto register this checker"""
    linter.register_checker(SimilarChecker(linter))


def usage(status=0):
    """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=None):
    """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(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()