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a n�h�� � @ s� U d Z ddlmZ ddlZddlZddlZddlZddlZddlZddl Z ddl Z ddl m Z ddl mZmZmZmZ ddlmZmZmZ ddlmZ ddlmZmZmZmZm Z m Z ddl Z dd l m Z dd l m!Z!m"Z"m#Z# dd l$m%Z%m&Z& dd l'm(Z(m)Z) dd l*m+Z+m,Z, e�r ddl-m.Z. dZ/e�0d�Z1ede2�Z3ede2�Z4G dd� de�Z5e6de7e3 f Z8e6e3df Z9e e eef Z:G dd� d�Z;e6de;f Z<G dd� d�Z=G dd� d�Z>G d d� de�Z?e@d e4e4f ZAe/fd d d d �d!d"�ZBd#d$d%�d&d'�ZCd dd dd d d(�d)d*�ZDG d+d,� d,e�ZEG d-d.� d.�ZFdMd/d0d0d0d0d1d2d3�d4d5�ZGejHG d6d � d ��ZId7d8iZJd9eKd:< d;d<d=d$d>�d?d@�ZLG dAdB� dBe"eF�ZMdCd$dD�dEdF�ZNdNdGdHdI�dJdK�ZOePdLk�r�eO� dS )Oa� 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. � )� annotationsN)� defaultdict)�Callable� Generator�Iterable�Sequence)�BufferedIOBase�BufferedReader�BytesIO)�chain)� TYPE_CHECKING� NamedTuple�NewType�NoReturn�TextIO�Union)�nodes)� BaseChecker�BaseRawFileChecker�table_lines_from_stats)�Section�Table)�MessageDefinitionTuple�Options)� LinterStats�decoding_stream)�PyLinter� z.*\w+�Index� LineNumberc @ s e Zd ZU ded< ded< dS )� LineSpecifsr � line_number�str�textN��__name__� __module__� __qualname__�__annotations__� r) r) ��/root/rpmbuild/BUILDROOT/imh-python39-modules-3.9.7-92.el8.x86_64/opt/imh-python/lib/python3.9/site-packages/pylint/checkers/symilar.pyr E s r � LinesChunk�SuccessiveLinesLimitsc @ s( e Zd ZdZdZddddd�dd�Zd S ) �CplSuccessiveLinesLimitsz�Holds a SuccessiveLinesLimits object for each checked file and counts the number of common lines between both stripped lines collections extracted from both files. �� first_file� second_file�effective_cmn_lines_nbr, �int�None)r/ r0 r1 �returnc C s || _ || _|| _d S �Nr. )�selfr/ r0 r1 r) r) r* �__init__\ s z!CplSuccessiveLinesLimits.__init__N)r% r& r' �__doc__� __slots__r7 r) r) r) r* r- U s r- �LineSetStartCouplec @ sb e Zd ZdZdZddddd�dd �Zd d d �d d�Zdd�dd�Zdd�dd�Zdd�dd�Z dS )r+ zlThe LinesChunk object computes and stores the hash of some consecutive stripped lines of a lineset. ��_fileid�_index�_hashr" r2 � Iterable[str]r3 )�fileid�num_line�linesr4 c G s( || _ t|�| _tdd� |D ��| _d S )Nc s s | ]}t |�V qd S r5 )�hash)�.0Zlinr) r) r* � <genexpr>| � z&LinesChunk.__init__.<locals>.<genexpr>)r< r r= �sumr> )r6 r@ rA rB r) r) r* r7 s s zLinesChunk.__init__�object�bool)�or4 c C s t |t�stS | j|jkS r5 )� isinstancer+ �NotImplementedr> )r6 rJ r) r) r* �__eq__ s zLinesChunk.__eq__�r4 c C s | j S r5 )r> �r6 r) r) r* �__hash__� s zLinesChunk.__hash__c C s d| j � d| j� d| j� d�S )Nz <LinesChunk object for file z (z, z)>r; rO r) r) r* �__repr__� s �zLinesChunk.__repr__c C s d| j � d| j� d| j� �S )NzLinesChunk object for file z, starting at line z Hash is r; rO r) r) r* �__str__� s ��zLinesChunk.__str__N) r% r& r' r8 r9 r7 rM rP rQ rR r) r) r) r* r+ l s c @ sn e Zd ZdZdZdddd�dd�Zedd�d d ��Zedd�d d ��Zej ddd �dd ��Zdd�dd�Z dS )r, zA class to handle the numbering of begin and end of successive lines. :note: Only the end line number can be updated. ��_start�_endr r3 )�start�endr4 c C s || _ || _d S r5 rS )r6 rV rW r) r) r* r7 � s z SuccessiveLinesLimits.__init__rN c C s | j S r5 )rT rO r) r) r* rV � s zSuccessiveLinesLimits.startc C s | j S r5 �rU rO r) r) r* rW � s zSuccessiveLinesLimits.end��valuer4 c C s || _ d S r5 rX �r6 rZ r) r) r* rW � s r" c C s d| j � d| j� d�S )Nz<SuccessiveLinesLimits <�;�>>rS rO r) r) r* rQ � s z SuccessiveLinesLimits.__repr__N) r% r& r' r8 r9 r7 �propertyrV rW �setterrQ r) r) r) r* r, � s c @ s^ e Zd ZU dZded< ded< dd�dd�Zd d d �d d �Zdd�dd�Zdd d�dd�ZdS )r: zEIndices in both linesets that mark the beginning of successive lines.r �fst_lineset_index�snd_lineset_indexr" rN c C s d| j � d| j� d�S )Nz<LineSetStartCouple <r\ r] )r` ra rO r) r) r* rQ � s �zLineSetStartCouple.__repr__rH rI ��otherr4 c C s& t |t�stS | j|jko$| j|jkS r5 )rK r: rL r` ra �r6 rc r) r) r* rM � s �zLineSetStartCouple.__eq__r2 c C s t | j�t | j� S r5 )rC r` ra rO r) r) r* rP � s zLineSetStartCouple.__hash__rY c C s t t| j| �t| j| ��S r5 )r: r r` ra r[ r) r) r* � increment� s �z LineSetStartCouple.incrementN) r% r& r' r8 r( rQ rM rP re r) r) r) r* r: � s �LineSetr2 z$tuple[HashToIndex_T, IndexToLines_T])�lineset�min_common_linesr4 c s� t t�}i }tdd� | jD ��� � fdd�t|�D �}tt|� �D ]�^}}t| j| j�}z| j|| j}W n& t y� t| jd jd �}Y n0 t |�} t ||d�|| < t | j | g|�R � } || �| � qB||fS )a` 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 c s s | ] }|j V qd S r5 )r# )rD �xr) r) r* rE � rF z hash_lineset.<locals>.<genexpr>c s g | ]}t � |d � ��qS r5 )�iter)rD �i�rB r) r* � <listcomp>� rF z hash_lineset.<locals>.<listcomp>���� )rV rW )r �list�tuple�stripped_lines�range� enumerate�zipr r! � IndexErrorr r, r+ �name�append) rg rh Z hash2indexZ index2linesZ shifted_linesrk Z succ_linesZstart_linenumberZend_linenumber�indexZl_cr) rl r* � hash_lineset� s" � rz �CplIndexToCplLines_Tr3 )� all_couplesr4 c C s� t | �� �D ]�}g }|�td��}|| v r~| | jj| | j_| | jj| | j_| | jd7 _|�|� |�td��}q"|D ]&}z| � |� W q� t y� Y q�0 q�q dS )a 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])} ro N) rq �keysre r r/ rW r0 r1 rx �pop�KeyError)r| �couple� to_remove�test�targetr) r) r* �remove_successive� s r� )�ls_1� stindex_1�ls_2� stindex_2�common_lines_nbr4 c C sP dd� | j ||| � D �}dd� |j ||| � D �}tdd� t||�D ��S )a| 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 c S s g | ]}t �|j�r|j�qS r) �� REGEX_FOR_LINES_WITH_CONTENT�matchr# �rD Zlspecifr) r) r* rm 8 s �z(filter_noncode_lines.<locals>.<listcomp>c S s g | ]}t �|j�r|j�qS r) r� r� r) r) r* rm = s �c s s | ]\}}||kV qd S r5 r) )rD Zsline_1Zsline_2r) r) r* rE B rF z'filter_noncode_lines.<locals>.<genexpr>)rr rG ru )r� r� r� r� r� Z stripped_l1Z stripped_l2r) r) r* �filter_noncode_lines# s ��r� c @ sF e Zd ZU ded< ded< ded< ded< ded< ded < ded < d S ) � Commonalityr2 � cmn_lines_nbrf �fst_lsetr �fst_file_start� fst_file_end�snd_lset�snd_file_start� snd_file_endNr$ r) r) r) r* r� E s r� c @ s� e Zd ZdZeddddfddddddd�dd�Zd)d d d dd �dd�Zdd�dd�Zdd�dd�Zddd�dd�Z dd d�dd�Z ddd d �d d �Z d d�d d!�Z d"d�d#d$�Z d%dd&�d'd(�Zd S )*�Symilarz-Finds copy-pasted lines of code in a project.Fr2 rI r3 )� min_lines�ignore_comments�ignore_docstrings�ignore_imports�ignore_signaturesr4 c C sR t | t�r| jj| _n t�� | _|| j_|| j_|| j_ || j_ || j_ g | _ d S r5 ) rK r �linter�config� namespace�argparse� Namespace�min_similarity_linesr� r� r� r� �linesets)r6 r� r� r� r� r� r) r) r* r7 R s zSymilar.__init__Nr" � STREAM_TYPESz str | None)�streamid�stream�encodingr4 c C s� t |t�r$|du rt�t||�j}n|j}z |� }W n tyJ g }Y n0 | j�t||| j j | j j | j j | j j t| d�r�| jjndd�� dS )z)Append a file to search for similarities.Nr� ��line_enabled_callback)rK r � ValueErrorr � readlines�UnicodeDecodeErrorr� rx rf r� r� r� r� r� �hasattrr� Z_is_one_message_enabled)r6 r� r� r� r� rB r) r) r* � append_streamg s, � ��zSymilar.append_streamrN c C s" | j jdkrdS | �| �� � dS )z=Start looking for similarities and display results on stdout.r N)r� r� � _display_sims� _compute_simsrO r) r) r* �run� s z Symilar.runz)list[tuple[int, set[LinesChunkLimits_T]]]c C s� t t�}| �� D ]z}|j}|j}|j}|j}|j}|j}|j } || } | D ]$} |||f| v sj||| f| v rJ qqJ| � |||f||| fh� qg } |� � D ] \}} | D ]}| � ||f� q�q�| � � | � � | S )z'Compute similarities in appended files.)r rp � _iter_simsr� r� r� r� r� r� r� rx �items�sort�reverse)r6 Z no_duplicatesZ commonality�num�lineset1Z start_line_1Z end_line_1�lineset2Z start_line_2Z end_line_2Z duplicate�couplesZsimsZ ensemblesZcplsr) r) r* r� � s> ����zSymilar._compute_sims)� similaritiesr4 c C s | � |�}t|� dS )z(Display computed similarities on stdout.N)�_get_similarity_report�print)r6 r� �reportr) r) r* r� � s zSymilar._display_simsc C s� d}d}|D ]�\}}|d|� dt |�� d�7 }t|�}d } }} |D ](\}}} |d|j� d|� d | � d �7 }qF|r�|j|| � D ]$} || �� r�d | �� � d�nd7 }q�||t |�d 7 }q td d� | jD ��} |d| � d|� d|d | d�d�7 }|S )z"Create a report from similarities.� r � z similar lines in z files N�==�:[�:z] z ro c s s | ]}t |�V qd S r5 ��len�rD rg r) r) r* rE � rF z1Symilar._get_similarity_report.<locals>.<genexpr>z TOTAL lines=z duplicates=z percent=� Y@z.2f)r� �sortedrw � _real_lines�rstriprG r� ) r6 r� r� Zduplicated_line_number�numberr� Z couples_lZline_set� start_line�end_line�lineZtotal_line_numberr) r) r* r� � s* "� ��z Symilar._get_similarity_reportrf zGenerator[Commonality])r� r� r4 c # sB t || jj�\� }t || jj�\}}t� �� �}t|�� �}t||@ � fdd�d�}i } t|t�d�d�D ]^} t� � | || �D ]D} | d } | d } t t � || �t � || �| jjd�| t | | �< q�qjt | � | �� D ]b\}}|j}|j}|j}t|||jj|jj||jj|jjd�}t|||||�}|| jjkr�|V q�d S ) at 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. c s � | d S )Nr r) )�m�Zhash_to_index_1r) r* �<lambda>� rF z&Symilar._find_common.<locals>.<lambda>)�keyr= r ro )r1 )r� r� r� r� r� r� r� N)rz r� r� � frozensetr} r� �operator� attrgetter� itertools�productr- �copyr: r� r� r` ra r1 r� r/ rV rW r0 r� )r6 r� r� Zindex_to_lines_1Zhash_to_index_2Zindex_to_lines_2Zhash_1Zhash_2Z common_hashesr| Zc_hashZindices_in_linesetsZindex_1Zindex_2Zcml_stripped_lZcmn_lZ start_index_1Z start_index_2Znb_common_lines�comZ eff_cmn_nbr) r� r* � _find_common� sV �� � � ��� � zSymilar._find_commonc c sJ t | jdd� �D ]2\}}| j|d d� D ]}| �||�E dH q,qdS )zXIterate on similarities among all files, by making a Cartesian product. Nrn ro )rt r� r� )r6 �idxrg r� r) r) r* r� s zSymilar._iter_sims� list[LineSet]c C s | j S )z�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. �r� rO r) r) r* � get_map_data( s zSymilar.get_map_data�list[list[LineSet]])�linesets_collectionr4 c C s dd� |D �| _ dS )zyReduces and recombines data into a format that we can report on. The partner function of get_map_data() c S s g | ]}|D ]}|�q qS r) r) )rD rg r� r) r) r* rm 5 rF z2Symilar.combine_mapreduce_data.<locals>.<listcomp>Nr� )r6 r� r) r) r* �combine_mapreduce_data0 s z Symilar.combine_mapreduce_data)N)r% r& r' r8 �DEFAULT_MIN_SIMILARITY_LINEr7 r� r� r� r� r� r� r� r� r� r) r) r) r* r� O s" � �!'Kr� r? rI �!Callable[[str, int], bool] | None�list[LineSpecifs])rB r� r� r� r� r� r4 c sz t � }|s|r�t�d�| ��}|rJ|�t�dd� |�tj tj f�D ��� |r�dddd�� fdd� � � g |�}|�t�d d� |D ��� g } d } t | d d �D ]�\} } |d ur�|d | �s�q�| � � } |�r.| �s| � d�r�| d d� } | dd � } n$| � d��r| d d� } | dd � } | �r.| �| ��r*d } d} |�rH| �dd �d � � } | |v �rVd} | r�| �t| t| d �d�� q�| S )a� Return tuples of line/line number/line type with leading/trailing white-space and any ignored code features removed. :param lines: a collection of lines :param ignore_comments: if true, any comment in the lines collection is removed from the result :param ignore_docstrings: if true, any line that is a docstring is removed from the result :param ignore_imports: if true, any line that is an import is removed from the result :param ignore_signatures: if true, any line that is part of a function signature is removed from the result :param line_enabled_callback: If called with "R0801" and a line number, a return value of False will disregard the line :return: the collection of line/line number/line type tuples r� c s s& | ] }t |j|jp|jd �V qdS )ro N)rs �lineno� end_lineno)rD �noder) r) r* rE Q s �z!stripped_lines.<locals>.<genexpr>zlist[nodes.NodeNG]z nodes.NodeNG)� functions�treer4 c sL |j D ]@}t|tjtjf�r&| �|� t|tjtjtjf�r� | |� q| S )zRecursively get all functions including nested in the classes from the. tree. )�bodyrK r � FunctionDef�AsyncFunctionDefrx �ClassDef)r� r� r� ��_get_functionsr) r* r� X s � z&stripped_lines.<locals>._get_functionsc s s2 | ]*}t |j|jr |jd jn|jd �V qdS )r ro N)rs r� r� Ztolineno)rD �funcr) r) r* rE n s ��Nro )rV �R0801)z"""z'''� )zr"""zr'''r �#r )r# r! )�set�astroid�parse�join�updater � from_iterableZnodes_of_classr �Import� ImportFromrt �strip� startswith�endswith�splitrx r r ) rB r� r� r� r� r� Z ignore_linesr� r� Z strippedlines� docstringr� r� r) r� r* rr 8 s\ �� �� � �rr c @ s� e Zd ZdZd#ddddddddd �d d �Zdd �d d�Zdd �dd�Zddd�dd�Zd dd�dd�Zdd �dd�Z ddd�d d �Z e d d �d d ��Z e dd �d!d"��Z dS )$rf z�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. FNr" z list[str]rI r� r3 )rw rB r� r� r� r� r� r4 c C s& || _ || _t||||||d�| _d S )Nr� )rw r� rr �_stripped_lines)r6 rw rB r� r� r� r� r� r) r) r* r7 � s �zLineSet.__init__rN c C s d| j � d�S )Nz <Lineset for �>�rw rO r) r) r* rR � s zLineSet.__str__r2 c C s t | j�S r5 )r� r� rO r) r) r* �__len__� s zLineSet.__len__r )ry r4 c C s | j | S r5 �r )r6 ry r) r) r* � __getitem__� s zLineSet.__getitem__rb c C s | j |j k S r5 r rd r) r) r* �__lt__� s zLineSet.__lt__c C s t | �S r5 )�idrO r) r) r* rP � s zLineSet.__hash__rH c C s t |t�sdS | j|jkS )NF)rK rf �__dict__rd r) r) r* rM � s zLineSet.__eq__r� c C s | j S r5 r rO r) r) r* rr � s zLineSet.stripped_linesc C s | j S r5 )r� rO r) r) r* � real_lines� s zLineSet.real_lines)FFFFN)r% r& r' r8 r7 rR r r r rP rM r^ rr r r) r) r) r* rf � s" � r� )z Similar lines in %s files %szduplicate-codez�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.z!dict[str, MessageDefinitionTuple]�MSGSr r zLinterStats | None)�sect�stats� old_statsr4 c C s2 g d�}|t ||d�7 }| �t|dddd�� dS )z0Make a layout with some stats about duplication.)r� �now�previous� differenceZduplicated_linesr ro )�children�colsZrheadersZcheadersN)r rx r )r r r rB r) r) r* �report_similarities� s r c @ s� e Zd ZU dZdZeZdZdZdZ dZ dZ de d d ed �fd d dded �fdd dde d �fdd dde d �fdd dde d �ffZ ded< ddeffZddd�dd�Zdd �d d �Zd dd �d!d"�Zdd �d#d$�Zd%d �d&d'�Zdd(dd)�d*d+�Zd,S )-�SimilaritiesCheckerz�Checks for similarities and duplicated code. This computation may be memory / CPU intensive, so you should disable it if you experience some problems. r� z%Minimum lines number of a similarity.z4Comments are removed from the similarity computationz6Docstrings are removed from the similarity computationz3Imports are removed from the similarity computationz6Signatures are removed from the similarity computationzmin-similarity-linesr2 z<int>)�default�type�metavar�helpzignore-commentsTZynz<y or n>zignore-docstringszignore-importszignore-signaturesr �optionsZRP0801Z Duplicationr r3 �r� r4 c C sD t �| |� tj| | jjj| jjj| jjj| jjj| jjj d� d S )N�r� r� r� r� r� ) r r7 r� r� r� r� r� r� r� r� )r6 r� r) r) r* r7 ' s �z SimilaritiesChecker.__init__rN c C s g | _ | jj�� dS )z=Init the checkers: reset linesets and statistics information.N)r� r� r Zreset_duplicated_linesrO r) r) r* �open2 s zSimilaritiesChecker.openz nodes.Module)r� r4 c C s\ | j jdu r tjdtdd� |�� �$}| �| j j||j� W d � n1 sN0 Y dS )z�Process a module. the module's content is accessible via the stream object stream must implement the readlines method Nz�In pylint 3.0 the current_name attribute of the linter object should be a string. 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