EiI{tSSKrSSKJs Jr SSKrSSKrSSKrSSK r SSK r SSK r SSK r SSK r SSKrSSKrSSK Jr SSKJrJrJrJrJrJrJrJrJrJrJrJr SSKJr SSK J!r! SSK"J#r# Sr$\RJ"S \RL\RNS 9 \\\(\)4\\(\)\\)\444r*\\*r+"S S \!5r,g) N) JSONEncoder) AnyCallableDictListLiteralOptionalSequenceSetTupleTypeUnionoverload)UnsupportedFeature) BaseProvider) TypesSpecT faker-csv)quotingc\rSrSrSGS\S\4SjjrS\\4SjrSHS\S\ 4Sjjr \ S\ 4Sj5r \ S \S S\ 4S j5r \ S \S S\ 4S j5r SIS \S\\ \ 44Sjjr \ S\ 4Sj5r\ S \S S\ 4Sj5r\ S \S S\ 4Sj5rSIS \S\\ \ 44Sjjr\ S\ 4Sj5r\ S \S S\ 4Sj5r\ S \S S\ 4Sj5rSIS \S\\ \ 44Sjjr\ S\ 4Sj5r\ SSS\R(4Sj5r\ S\\R(/\ 4S\ 4Sj5r\ S\\R(/\ 4S\ 4Sj5r\ 4S\\\\R(/\ 4\\R(/\ 44S\\ \ \R(44SjjrSJS\S\S\S \S!\S\ 4 S"jjrSKS#\S$\S%\S&\\ S\ 4 S'jjrSKS#\S$\S%\S&\\ S\ 4 S(jjrSLS)\\\4S*\ S+\\\\\\ 4S,\\ S\ 4 S-jjrSMS.\ S/\\\ S0\\ \ 4S1\S2\S3\S\ 4S4jjrSNS/\\\ S0\\ \ 4S1\S2\S\ 4 S5jjrSNS/\\\ S0\\ \ 4S1\S2\S\ 4 S6jjrSNS/\\\ S0\\ \ 4S1\S2\S\ 4 S7jjr SOS0\\!S1\S8\\S9\\"\#S\ 4 S:jjr$SOS0\\!S1\S8\\S9\\"\#S\ 4 S;jjr%SPS<\S=\S>\\&S?\\&S\ 4 S@jjr'SQS0\\(S1\SA\ S\ 4SBjjr)SC\ SD\S\\\ 44SEjr*SFr+g)RProviderchance_of_getting_truereturncTURRRSS5U:*$)zGenerate a random boolean value based on ``chance_of_getting_true``. :sample: chance_of_getting_true=25 :sample: chance_of_getting_true=50 :sample: chance_of_getting_true=75 d generatorrandomrandint)selfrs Z/home/mande/repo/quber/.venv/lib/python3.13/site-packages/faker/providers/misc/__init__.pybooleanProvider.booleans'~~$$,,Q48NNNc\SSSS.URRRSS5$)zGGenerate ``None``, ``True``, or ``False``, each with equal probability.NTF)rrr(rrr"s r# null_booleanProvider.null_boolean's8  ..   ' 'A . 0 0r&lengthcURR(aH[U5Vs/sH'o RRR S5PM) nn[ U5$[ R"U5$s snf)zGenerate a random binary blob of ``length`` bytes. If this faker instance has been seeded, performance will be signficiantly reduced, to conform to the seeding. :sample: length=64 )r _is_seededranger randrangebytesosurandom)r"r,_blobs r#binaryProvider.binary0s] >> $ $BG-P-QNN))33C8-DP; zz&!! Qs.A:cgNr)s r#md5 Provider.md5@sr& raw_outputTcgr:r;r"r>s r#r<r=C7:r&Fcgr:r;r@s r#r<r=Fs69r&c[R"[URRR 55R 55nU(aUR 5$UR5$)a Generate a random MD5 hash. If ``raw_output`` is ``False`` (default), a hexadecimal string representation of the MD5 hash will be returned. If ``True``, a ``bytes`` object representation will be returned instead. :sample: raw_output=False :sample: raw_output=True )hashlibr<strrr encodedigest hexdigestr"r>ress r#r<r=IsN%[[T^^-B-B-I-I-K)L)S)S)UV ::< }}r&cgr:r;r)s r#sha1 Provider.sha1Wsr&cgr:r;r@s r#rLrMZs8;r&cgr:r;r@s r#rLrM]rAr&c[R"[URRR 55R 55nU(aUR 5$UR5$)aGenerate a random SHA-1 hash. If ``raw_output`` is ``False`` (default), a hexadecimal string representation of the SHA-1 hash will be returned. If ``True``, a ``bytes`` object representation will be returned instead. :sample: raw_output=False :sample: raw_output=True )rDrLrErr rFrGrHrIs r#rLrM`sN%\\#dnn.C.C.J.J.L*M*T*T*VW ::< }}r&cgr:r;r)s r#sha256Provider.sha256ns r&cgr:r;r@s r#rRrSqs:=r&cgr:r;r@s r#rRrSts9* *r& special_charsdigits upper_case lower_casecSn/nU(a9URURRRS55 US- nU(aUURURRR[R 55 U[R - nU(aUURURRR[R 55 U[R - nU(aUURURRR[R55 U[R- n[U5oU:*o(GdZ[R"SU 4SX45S[R"5;d[R"[5(a[R"[5OSS[R"5;d[R"U5(a[R"U5OS[R"U5S[R"5;d[R"U5(a[R"U5OSS.-n [R"S 5S -S U 0-n [![R""U 55eS =pUR%XaS 9n ['5n [U 5[U5:a[U R)URRR+S[U 5S- 55 [U 5[U5:aM[[-U 5H upX~X'M SR/U 5$)aIGenerate a random password of the specified ``length``. The arguments ``special_chars``, ``digits``, ``upper_case``, and ``lower_case`` control what category of characters will appear in the generated password. If set to ``True`` (default), at least one character from the corresponding category is guaranteed to appear. Special characters are characters from ``!@#$%^&*()_+``, digits are characters from ``0123456789``, and uppercase and lowercase characters are characters from the ASCII set of letters. :sample: length=12 :sample: length=40, special_chars=False, upper_case=False z !@#$%^&*()_+)<=)z0%(py3)s {%(py3)s = %(py0)s(%(py1)s) } <= %(py5)slenrequired_tokensr,)py0py1py3py5z3Required length is shorter than required charactersz >assert %(py7)spy7N)r,rr)appendrr choicestringriascii_uppercaseascii_lowercasero @pytest_ar_call_reprcompare @py_builtinslocals_should_repr_global_name _saferepr_format_assertmsgAssertionError_format_explanationrandom_choicessetaddr! enumeratejoin)r"r,rhrirjrkchoicesrp @py_assert2 @py_assert4 @py_format6 @py_format8charsrandom_indexesiindexs r#passwordProvider.passwords>(   " "4>>#8#8#?#?#O P ~ %G   " "4>>#8#8#?#? #N O v}} $G   " "4>>#8#8#?#?@V@V#W X v-- -G   " "4>>#8#8#?#?@V@V#W X v-- -G?#dv-dddd#ddddddsddddsdddddd?dddd?ddd#ddddddvddddvdddd/ddddddd(((@$'5.!C$88   t~~44<US;a[ RnO'US;a[ RnO[ Rn[R"5nUn[ R"USUS9n[S US -5Hn URR5[!U 5-n XrU - U-- n X:a*URR"R%X;5n X|- nOUn URR'U 5n UR)X5 M S S S 5 UR+5$!,(df  UR+5$=f) aYGenerate a bytes object containing a random valid zip archive file. The number and sizes of files contained inside the resulting archive can be controlled using the following arguments: - ``uncompressed_size`` - the total size of files before compression, 16 KiB by default - ``num_files`` - the number of files archived in resulting zip file, 1 by default - ``min_file_size`` - the minimum size of each file before compression, 4 KiB by default No compression is used by default, but setting ``compression`` to one of the values listed below will use the corresponding compression type. - ``'bzip2'`` or ``'bz2'`` for BZIP2 - ``'lzma'`` or ``'xz'`` for LZMA - ``'deflate'``, ``'gzip'``, or ``'gz'`` for GZIP :sample: uncompressed_size=256, num_files=4, min_file_size=32 :sample: uncompressed_size=256, num_files=32, min_file_size=4, compression='bz2' rO`num_files`, `min_file_size`, and `uncompressed_size` must be positive integersH`uncompressed_size` is smaller than the calculated minimum required sizebzip2bz2lzmaxz)deflategzipgzw)moderrN)any isinstancerb ValueErrorrzipfile ZIP_BZIP2ZIP_LZMA ZIP_DEFLATED ZIP_STOREDioBytesIOZipFiler0rpystrrEr r!r7writestrgetvalue)r"rrrr compression_ zip_bufferremaining_size zip_handle file_numberfilenamemax_allowed_size file_sizedatas r#zip Provider.zips4 y#..@)q.}c22Hmq6H0#66P:Kq:P   a   $'8 8 Z  * *",,L N *"++L 5 5"//L"--LZZ\ * __Zc| LPZ$Q A 6 >>//1C 4DD#15LP]4]#] * $ 5 5 = =m ^I%3%?N .I~~,,Y7##H3 7M""$$M L""$$s B$G  G*cZ[[U[5(+=(d US:*[U[5(+=(d US:*[U[5(+=(d US:*/5(a [S5eX2-U:a [ S5eSnUS;aSnOUS;aSnOUS ;aS n[ R "5nUn[R"XVS 9n[S US -5GH n [ R "5n URR5[U 5-n XrU - U-- n X:a*URRRX<5n X}- nOUn [R"U S 9nURR!U 5nU R#U5 [%U R'55UlU R+S5 UR-X5 U R/5 GM SSS5 UR'5$!,(df  UR'5$=f)aAGenerate a bytes object containing a random valid tar file. The number and sizes of files contained inside the resulting archive can be controlled using the following arguments: - ``uncompressed_size`` - the total size of files before compression, 16 KiB by default - ``num_files`` - the number of files archived in resulting zip file, 1 by default - ``min_file_size`` - the minimum size of each file before compression, 4 KiB by default No compression is used by default, but setting ``compression`` to one of the values listed below will use the corresponding compression type. - ``'bzip2'`` or ``'bz2'`` for BZIP2 - ``'lzma'`` or ``'xz'`` for LZMA - ``'gzip'`` or ``'gz'`` for GZIP :sample: uncompressed_size=256, num_files=4, min_file_size=32 :sample: uncompressed_size=256, num_files=32, min_file_size=4, compression='bz2' rrrzw|)rrzw|gzrzw|bz2rzw|xz)rfileobjr)nameN)rrrbrrrrtarfileopenr0rrrEr r!TarInfor7writerorsizeseekaddfileclose)r"rrrrr tar_bufferr tar_handler file_bufferrrrtarinfors r#tar Provider.tars4 y#..@)q.}c22Hmq6H0#66P:Kq:P   a   $'8 8 Z 8< . (D , ,D N *DZZ\ * \\t 8J$Q A 6  jjl >>//1C 4DD#15LP]4]#] * $ 5 5 = =m ^I%3%?N .I!//x8~~,,Y7!!$'";#7#7#9:   #""78!!## 79&""$$'9 8&""$$s DH  H*r image_formathue luminosityc SSKnSSKnUupgURR SXR RX4S95nURRU5n U R[URSS55V s/sH&oRSU5URSU54PM( sn UR RX4S9UR RX4S9S 9 [R"5n URXS 9 U R!S5 U R#5sSSS5 $![a [SS5ef=fs sn f!,(df  g=f) aGenerate an image and draw a random polygon on it using the Python Image Library. Without it installed, this provider won't be functional. Returns the bytes representing the image in a given format. The argument ``size`` must be a 2-tuple containing (width, height) in pixels. Defaults to 256x256. The argument ``image_format`` can be any valid format to the underlying library like ``'tiff'``, ``'jpeg'``, ``'pdf'`` or ``'png'`` (default). Note that some formats need present system libraries prior to building the Python Image Library. Refer to https://pillow.readthedocs.io/en/stable/handbook/image-file-formats.html for details. The arguments ``hue`` and ``luminosity`` are the same as in the color provider and are simply forwarded to it to generate both the background and the shape colors. Therefore, you can ask for a "dark blue" image, etc. :sample: size=(2, 2), hue='purple', luminosity='bright', image_format='pdf' :sample: size=(16, 16), hue=[90,270], image_format='ico' rNz-`image` requires the `Pillow` python library.imageRGB)rr )filloutline)format) PIL.Image PIL.ImageDraw ImportErrorrImagenewrcolor ImageDrawDrawpolygonr0 random_intrrsaverread) r"rrrrPILwidthheightrdrawr5fobjs r#rProvider.image`s30 _    eT>>+?+?C+?+_`}}!!%( NSTXTcTcdegiTjNk lNkooa'F)C DNk l%%#%ENN((S(H  ZZ\T JJtJ 1 IIaL99;\ _$%TV]^ ^ _ m\sD5-E;0E5E  E"dialectheader data_columnsnum_rowsinclude_row_ids fmtparamsc [U[5(aUS::a [S5e[U[[45(d [ S5eUbI[U[[45(d [ S5e[ U5[ U5:wa [S5e[R"5n[R"U4SU0UD6nU(a5U(a[U5nURSS5 URU5 [SUS-5H`n UV s/sHoRRU 5PM n n U(aU RS[!U 55 URU 5 Mb UR#5$s sn f) aGenerate random delimiter-separated values. This method's behavior share some similarities with ``csv.writer``. The ``dialect`` and ``**fmtparams`` arguments are the same arguments expected by ``csv.writer`` to control its behavior, and instead of expecting a file-like object to where output will be written, the output is controlled by additional keyword arguments and is returned as a string. The ``dialect`` argument defaults to ``'faker-csv'`` which is the name of a ``csv.excel`` subclass with full quoting enabled. The ``header`` argument expects a list or a tuple of strings that will serve as the header row if supplied. The ``data_columns`` argument expects a list or a tuple of string tokens, and these string tokens will be passed to :meth:`pystr_format() ` for data generation. Argument Groups are used to pass arguments to the provider methods. Both ``header`` and ``data_columns`` must be of the same length. Example: fake.set_arguments('top_half', {'min_value': 50, 'max_value': 100}) fake.dsv(data_columns=('{{ name }}', '{{ pyint:top_half }}')) The ``num_rows`` argument controls how many rows of data to generate, and the ``include_row_ids`` argument may be set to ``True`` to include a sequential row ID column. :sample: dialect='excel', data_columns=('{{name}}', '{{address}}') :sample: dialect='excel-tab', data_columns=('{{name}}', '{{address}}'), include_row_ids=True :sample: data_columns=('{{name}}', '{{address}}'), num_rows=5, delimiter='$' rz%`num_rows` must be a positive integerz(`data_columns` must be a tuple or a listz"`header` must be a tuple or a listz6`header` and `data_columns` must have matching lengthsrIDr)rrbrlisttuple TypeErrorrorStringIOcsvwriterinsertwriterowr0r pystr_formatrEr) r"rrrrrr dsv_bufferrrow_numcolumnrows r#dsv Provider.dsvs;J(C((HMDE E,u 66FG G  ftUm44 DEE6{c,// !YZZ[[] JEE9E f a& OOF #Q1 -GEQR\6>>..v6\CR 1c'l+ OOC .""$$ Ss$Fc(URUUUUSS9$)aGenerate random comma-separated values. For more information on the different arguments of this method, please refer to :meth:`dsv() ` which is used under the hood. :sample: data_columns=('{{name}}', '{{address}}'), num_rows=10, include_row_ids=False :sample: header=('Name', 'Address', 'Favorite Color'), data_columns=('{{name}}', '{{address}}', '{{safe_color_name}}'), num_rows=10, include_row_ids=True ,rrrr delimiterrr"rrrrs r#r Provider.csv)"xx%+   r&c(URUUUUSS9$)aGenerate random tab-separated values. For more information on the different arguments of this method, please refer to :meth:`dsv() ` which is used under the hood. :sample: data_columns=('{{name}}', '{{address}}'), num_rows=10, include_row_ids=False :sample: header=('Name', 'Address', 'Favorite Color'), data_columns=('{{name}}', '{{address}}', '{{safe_color_name}}'), num_rows=10, include_row_ids=True  rrr s r#tsv Provider.tsvs)"xx%+   r&c(URUUUUSS9$)aGenerate random pipe-separated values. For more information on the different arguments of this method, please refer to :meth:`dsv() ` which is used under the hood. :sample: data_columns=('{{name}}', '{{address}}'), num_rows=10, include_row_ids=False :sample: header=('Name', 'Address', 'Favorite Color'), data_columns=('{{name}}', '{{address}}', '{{safe_color_name}}'), num_rows=10, include_row_ids=True |rrr s r#psv Provider.psvr r&indentclsc>URXX4S9R5$)z Generate random JSON structure and return as bytes. For more information on the different arguments of this method, refer to :meth:`json() ` which is used under the hood. )rrrr)jsonrF)r"rrrrs r# json_bytesProvider.json_bytess yylfy^eeggr&c^^ ^ SSS.nU(aUOUnS[[S[4U U4Sjjm S[[[[ [ [[44S[4U U4Sjjm S[[ [4S[4U U 4S jjnUS :Xa[R"U"U5X4S 9$[U5Vs/sH ov"U5PM nn[R"XUS 9$s snf) a Generate random JSON structure values. Using a dictionary or list of records that is passed as ``data_columns``, define the structure that is used to build JSON structures. For complex data structures it is recommended to use the dictionary format. Data Column Dictionary format: {'key name': 'definition'} The definition can be 'provider', 'provider:argument_group', tokenized 'string {{ provider:argument_group }}' that is passed to the python provider method pystr_format() for generation, or a fixed '@word'. Using Lists, Tuples, and Dicts as a definition for structure. Example: fake.set_arguments('top_half', {'min_value': 50, 'max_value': 100}) fake.json(data_columns={'Name': 'name', 'Score': 'pyint:top_half'}) Data Column List format: [('key name', 'definition', {'arguments'})] With the list format the definition can be a list of records, to create a list within the structure data. For literal entries within the list, set the 'field_name' to None. :param data_columns: specification for the data structure :type data_columns: dict :param num_rows: number of rows the returned :type num_rows: int :param indent: number of spaces to indent the fields :type indent: int :param cls: optional json encoder to use for non-standard objects such as datetimes :type cls: json.JSONEncoder :return: Serialized JSON data :rtype: str :sample: data_columns={'Spec': '@1.0.1', 'ID': 'pyint', 'Details': {'Name': 'name', 'Address': 'address'}}, num_rows=2 :sample: data_columns={'Candidates': ['name', 'name', 'name']}, num_rows=1 :sample: data_columns=[('Name', 'name'), ('Points', 'pyint', {'min_value': 50, 'max_value': 100})], num_rows=1 {{name}} {{address}})r residencyrrc>0nUHtp#nU(aUSO0n[U[5(d [S5eUcTR"U40UD6s $[U[5(a T"U5X'Ml[U[ [ 45(aUVs/sH nT"U/5PM snX'MTR"U40UD6X'M U$s snf)Nr,Invalid arguments type. Must be a dictionary)rdictr_value_format_selectionrrr) rentryr definition argumentskwargsitemprocess_list_structurer"s r#r'-Provider.json..process_list_structure_s$&E04,9)21!&$//#$RSS<77 MfMMj%00"8"DEK T3K88NX"Yjd#94&#Aj"YEK"&">">z"TV"TEK15L#ZsCc>0n[U[5(aTRU5$[U[5(aUR 5Hup#[U[ [ [45(aUVs/sH nT"U5PM snX'MB[U[[[[45(a T"U5X'MsTRU5X'M U$U$s snfr:) rrEr!r itemsrrrrbfloatbool)rr"rr#r&process_dict_structurer"s r#r--Provider.json..process_dict_structuress$&E$$$33D99$%%(, $D!*udC.@AAPZ&[PZ'=d'CPZ&[ #JsE40HII&[U[5(aT"U5$[U[5(aT"U5$[S5e)Nz7Invalid data_columns type. Must be a dictionary or list)rr rr)rr-r's r#create_json_structure,Provider.json..create_json_structures?,---l;;,---l;;UV Vr&r)rr) r rrrbr+r,rrErr rdumpsr0) r"rrrrdefault_data_columnsr0r5rr-r's ` @@r#r Provider.json&sh& ;G,L`  # 3  ( sE4c3h/O)P UX  $ WdDj0A Wd W W q=::3LA&Z Z=B8_M_%l3_Mzz$377Ns9C! nb_elementsvariable_nb_elements value_types allowed_typescSSKnURR UUUUS9nURR 5U0nUR U5$![a [SS5ef=f)z Returns some XML. :nb_elements: number of elements for dictionary :variable_nb_elements: is use variable number of elements for dictionary :value_types: type of dictionary values Note: this provider required xmltodict library installed rNz.`xml` requires the `xmltodict` Python library.xml)r5r6r7r8) xmltodictrrrpydictwordunparse)r"r5r6r7r8r;_dicts r#r: Provider.xmls~  ^ %%#!5#' &  $$&.  '' ^$%UW\] ] ^s AA&alignc SSSSS04/nUcUnSSS S .n/n[U5Hn/nUHotpn U (aU S O0n [U [5(d [S 5eUR"U 40U D6n UR XR US5U 3SU 5 Mq UR S RU55 M SRU5$)a, Generate random fixed width values. Using a list of tuple records that is passed as ``data_columns``, that defines the structure that will be generated. Arguments within the record are provider specific, and should be a dictionary that will be passed to the provider method. Data Column List format [('field width', 'definition', {'arguments'})] The definition can be 'provider', 'provider:argument_group', tokenized 'string {{ provider:argument_group }}' that is passed to the python provider method pystr_format() for generation, or a fixed '@word'. Using Lists, Tuples, and Dicts as a definition for structure. Argument Groups can be used to pass arguments to the provider methods, but will override the arguments supplied in the tuple record. Example: fake.set_arguments('top_half', {'min_value': 50, 'max_value': 100}) fake.fixed_width(data_columns=[(20, 'name'), (3, 'pyint:top_half')]) :param data_columns: specification for the data structure :type data_columns: list :param num_rows: number of rows the generator will yield :type num_rows: int :param align: positioning of the value. (left, middle, right) :type align: str :return: Serialized Fixed Width data :rtype: str :sample: data_columns=[(20, 'name'), (3, 'pyint', {'min_value': 50, 'max_value': 100})], align='right', num_rows=2 )rrpyint max_valuerCN<^>)leftmiddlerightrrrm )r0rr rr!rvgetr)r"rrrAr3 align_maprr5rrr#r$r%results r# fixed_widthProvider.fixed_widthsJ  +r* +   ( KK %!yyr&r#r%c [R"SU5(aURRU5$[R"SU5(aUR S5$[R"SU5(agUR S5upURR UR55nURR"UR540UD6$URR"U40UD6$)aa Formats the string in different ways depending on its contents. The return can be the '@word' itself, a '{{ token }}' passed to PyStr, or a 'provider:argument_group' format field that returns potentially a non-string type. This ensures that Numbers, Boolean types that are generated in the JSON structures in there proper type, and not just strings. z.*\{\{.*\}\}.*z^@.*@z^[a-zA-Z0-9_-]*:\w:) rematchrrlstripsplit get_argumentsstripr)r"r#r%argument_groupr$s r#r! Provider._value_format_selections 88%z 2 2>>..z: : 88GZ ( ($$S) ) 88): 6 6)3)9)9#)> &J44^5I5I5KLI>>(()9)9);IyI I~~$$Z:6::r&r;)2)i)F) TTTT)iriN))r.r.pngNN)rNrrr^F)Nr`r^F)Nr^NN)r^TNN)Nr^rI),__name__ __module__ __qualname____firstlineno__rbr,r$r r*r2r7rrEr<rrrLrRrXrdrerrrrr r rrrrrrrr rrrrr: DataColumnsrPr!__static_attributes__r;r&r#rrs`OcO4O0htn0"S"" S :gdm::: 9gen999 d uUCZ/@ c ;wt};;; :wu~:#:: t eSj0A     ==5== <(=>PS>ad>ru>@;#;;sTWx;r&r)-builtinsr}_pytest.assertion.rewrite assertionrewriter{rrDrrr3rUrxrrdrrtypingrrrrrr r r r r rrfaker.exceptionsrrmrpythonr localizedregister_dialectexcel QUOTE_ALLrbrE ColumnSpecrerr;r&r#rss   llll/  [#))S]]C5c?E#sDcN*B$CC D : p ;|p ;r&