iaoSSKJrJr SSKJr SSKJr SSKJr SSKJrJ r SSK J r J r J r SSKrSSKrSSKJr SS KJr SS KJr SS KJr SS KJr SS KJrJr SSK J!r!J"r"J#r#J$r$ SSK%J&r&J'r' SSK(J)r)J*r* SSK+J,r, SSK-J.r. Sr0Sr1\S5Sj5r2"SS\Rf5r4Sr5\)\*"5SS\Rl"54S\7S\7S-S\Rf\8S-S\Rf\Rr4Sjj55r:\)\*"5SSSSS\Rl"5S .S!\Rf\&S-S"\ S\7S\7S-S#\;S\Rf\8S-S\Rf\Rx4S$jj55r=\*"5SSSSS%\Rl"5S&.S!\Rf\&S-S"\ S'\RfS(S-S)\Rf\&S-S#\;S\Rf\8S-S\Rf\R|4S*jj5r?\"SS%S+9"S,S-\ \&55r@SSSS%S..S/\7\\8-S"\ S!\Rf\&S-S)\Rf\&S-S#\;S\A\@\&4 S0jjrB\*"5S5SSS1.S2\\@S-S3\Rf\C\\ -S-S'\Rf\&S-S\Rf\R4S4jjj5rEg!\/a Sr.GNKf=f)6) OrderedDictabc)Sequence)copy) dataclass)datetime timedelta)AnyGenericUnionN) strategies)reject)InvalidArgument)numpy)utils)checkcheck_function) check_typecheck_valid_intervalcheck_valid_size try_convert)Excheck_strategy) cacheabledefines_strategy)note_deprecation) IntegerDtypecP[R"URS5SU4S9$)NcD[R"U/5R$N)pandasSeriesdtype)xs [/var/www/html/ai-image-ml/venv/lib/python3.13/site-packages/hypothesis/extra/pandas/impl.py-dtype_for_elements_strategy..-s qc*00z3hypothesis.extra.pandas.dtype_for_elements_strategy)key)stsharedmap)ss r&dtype_for_elements_strategyr/+s( 99 01 BA F r)c<UcU(dU"[U55$U$r!)r/r$valueselementsdraws r&infer_dtype_if_necessaryr52s }V/9:: Lr)c^^^UcSmOUS3mUb[UTS35 O*[S5 Tc[STSTS35eSSS5 [S5 [RR R RT5(a[TS 35eSSS5 [T[5(a%[T[5(a[S T<S 35e[T[5(a[R"T5RS :XaiT[La`S T<S 3n[T[ 5(aUS- n[U5e[T["5(aUS- n[U5e[%US3SSSS9 [T[&R(5(a[S T<S35e[+[S[,5nU"5Vs0sHoUR.U"5_M snR1TT5mSm[T[5(a1Sm[R"TR.R355mOTb[5[RTS5mUc6[6R8"T5nT(a[&R:"5U-nO,Tb)TRS :waUUU4SjnUR=U5nUceUT4$!,(df  GN=f!,(df  GNL=fs snf)N.r3zdtype is not NonezAt least one of z elements or zdtype must be provided.z#isinstance(dtype, CategoricalDtype)z4dtype is categorical, which is currently unsupportedz Passed dtype=zi is a dtype class, please pass in an instance of this class.Otherwise it would be treated as dtype=objectOz is not a valid Pandas dtype.z; To generate valid datetimes, pass `dtype="datetime64[ns]"`z= To generate valid timedeltas, pass `dtype="timedelta64[ns]"`zX We'll treat it as dtype=object for now, but this will be an error in a future version.z 2021-12-31F)since has_codemod stacklevelzx is a strategy, but we require a concrete dtype here. See https://stackoverflow.com/q/74355937 for workaround patterns.__subclasses__Tr$c >T(aUcg[R"U/TS9S$![[[4a= STS3n[ SUSU<S[ U5RSTR35Sef=f) Nr$rzdraw(z elements)zCannot convert = of type z to dtype ) nparray TypeError ValueError OverflowErrorrtype__name__str)valuenamer$ is_na_dtypeprefixs r&convert_element+elements_and_dtype..convert_element|su} xxu5a88z=9 vhi0%%dV1UIYE{++,JuyykC s 'AA9)rrrr"apitypesCategoricalDtypeis_dtype isinstancerH issubclassrrCr$kindobjectrr rr+SearchStrategygetattrlistrLgetlowerrnpst from_dtypenoner-) r3r$sourceerr_msg_get_subclassestrOrMrNs ` @@r&elements_and_dtypere8s ~81xF88!45 & '}%&vhl6(BYZ( 4 5 ::   , , 5 5e < <!(NO  = 6 %:e\#B#BeX< <  %288E?#7#73#>5PVCV"E8#@A eX & & T TG!'* * y ) ) V VG!'* *iS S  %**++eXW W  l,[TU]5 XlX lX0lX@lXPlX`lgr!)super__init__r3r$min_sizemax_sizeuniquerL)selfr3r$rlrmrnrL __class__s r&rkValueIndexStrategy.__init__s,       r)cv/n[5n[R"UURURURUR-S- S9nUR 5(a|UR UR5nUR(a(XS;aUR5 MXURU5 URU5 UR 5(aM|[URX RUR S9n[R"X&SUR UR 5S9$)N)rlrm average_sizer1F)r$ tupleize_colsrL)setcumanyrlrmmorer4r3rnraddappendr5r$r"IndexrL)rodataresultseeniteratoreltr$s r&do_drawValueIndexStrategy.do_drawsu77 ]]]]--$--71<  mmoo))DMM*C{{;OO% MM# mmoo)**Vmm$)) || u499TYY;O  r))r$r3rmrlrLrn)rI __module__ __qualname____firstlineno__rkr__static_attributes__ __classcell__)rps@r&rgrgs  r)rg rlrmrLreturnc[US5 [US5 Uc[U[-S/5n[XSS5 [ U5 [ R "[R[ R"X5US9$)aProvides a strategy which generates an :class:`~pandas.Index` whose values are 0, 1, ..., n for some n. Arguments: * min_size is the smallest number of elements the index can have. * max_size is the largest number of elements the index can have. If None it will default to some suitable value based on min_size. * name is the name of the index. If st.none(), the index will have no name. rlrmlrL) rminDEFAULT_MAX_SIZErrr+buildsr" RangeIndexintegers)rlrmrLs r& range_indexesrsi"Xz*Xz*#33Y?@ZD4 99V&& H(Gd SSr)Tr3r$rlrmrnrLr3r$rnc[US5 [US5 [X#SS5 [[US5 [ X5upUc U[ -n[ XX#XE5$)aProvides a strategy for producing a :class:`pandas.Index`. Arguments: * elements is a strategy which will be used to generate the individual values of the index. If None, it will be inferred from the dtype. Note: even if the elements strategy produces tuples, the generated value will not be a MultiIndex, but instead be a normal index whose elements are tuples. * dtype is the dtype of the resulting index. If None, it will be inferred from the elements strategy. At least one of dtype or elements must be provided. * min_size is the minimum number of elements in the index. * max_size is the maximum number of elements in the index. If None then it will default to a suitable small size. If you want larger indexes you should pass a max_size explicitly. * unique specifies whether all of the elements in the resulting index should be distinct. * name is a strategy for strings or ``None``, which will be passed to the :class:`pandas.Index` constructor. rlrmrn)rrrboolrerrgrs r&indexesrs]@Xz*Xz*ZDtVX&(9OH.. hx6 PPr)F)r3r$indexfillrnrLrzUnion[Sequence, pandas.Index]rc^^^^^^Uc [5nO [US5 [TT5umnUmUb'URS:Xa[ T[ 5(dUm[ RUUUUUU4Sj5nU"5$)aProvides a strategy for producing a :class:`pandas.Series`. Arguments: * elements: a strategy that will be used to generate the individual values in the series. If None, we will attempt to infer a suitable default from the dtype. * dtype: the dtype of the resulting series and may be any value that can be passed to :class:`numpy.dtype`. If None, will use pandas's standard behaviour to infer it from the type of the elements values. Note that if the type of values that comes out of your elements strategy varies, then so will the resulting dtype of the series. * index: If not None, a strategy for generating indexes for the resulting Series. This can generate either :class:`pandas.Index` objects or any sequence of values (which will be passed to the Index constructor). You will probably find it most convenient to use the :func:`~hypothesis.extra.pandas.indexes` or :func:`~hypothesis.extra.pandas.range_indexes` function to produce values for this argument. * name: is a strategy for strings or ``None``, which will be passed to the :class:`pandas.Series` constructor. Usage: .. code-block:: pycon >>> series(dtype=int).example() 0 -2001747478 1 1153062837 rr9c >U"T5n[U5S:aTb:U"[R"[T[U5TTS95R 5nOB[ U"[R"[T[U5TTS95R 55n[ R"X!TU"T5S9$[ R"SUTbTOU"[T55U"T5S9$)Nr)r$r3shaperrn)rr$rLr) lenr^arraysrXtolistr[r"r#r/) r4r result_datar$r3rindex_strategyrLrns r&r~series..resultCs^$ u:> "KK$!)!%j!% &(# "(%-"%e*!%#) fh  ==TRVZX X==(9(CD$Z  r))rrrerWrUrr+ composite) r3r$rrrnrLnp_dtyper~rs `` ``` @r&seriesrs}f }ug&+He<HhN  MMS 5,//\\%%%N 8Or))slotsfrozenc\rSrSr%SrSr\\-S-\S'Sr \ R\ S-\S'Sr \\S'Sr\ R\ S-\S'Sr\\S 'S rg) columnina}Data object for describing a column in a DataFrame. Arguments: * name: the column name, or None to default to the column position. Must be hashable, but can otherwise be any value supported as a pandas column name. * elements: the strategy for generating values in this column, or None to infer it from the dtype. * dtype: the dtype of the column, or None to infer it from the element strategy. At least one of dtype or elements must be provided. * fill: A default value for elements of the column. See :func:`~hypothesis.extra.numpy.arrays` for a full explanation. * unique: If all values in this column should be distinct. NrLr3r$rFrnr)rIrrr__doc__rLrJint__annotations__r3r+rYrr$r rrnrrrr)r&rrnsd "D#)d !-1Hb#d*1E3)-D"  B $ &-FDr)r)r$r3rrnnames_or_numberc [U[[45(aS/U-nO [U5nUVs/sHn[ XaX#US9PM sn$s snf)a{A convenience function for producing a list of :class:`column` objects of the same general shape. The names_or_number argument is either a sequence of values, the elements of which will be used as the name for individual column objects, or a number, in which case that many unnamed columns will be created. All other arguments are passed through verbatim to create the columns. N)rLr$r3rrn)rUrfloatr[r)rr$r3rrnnamesns r&columnsrs["/C<00)-(@_% A AXP  sA )rowsrrrc ^^ ^ ^ Uc [5nO [US5 Um Uc/Tc [S5e[RU U4Sj5nU"5$Uce[ [ US5n/m [5m [U5GH5upV[[USUS35 [U5nURc SUS3nXVl O"URn[UR5 URT ;a[S UR<35eT R#UR5 [%UR&UR(U5uUlnUR(cTb [S 5e[*R,"UR.UR&UR0US9UlT R3U5 GM8 Tc![RU U 4Sj5n U "5$[RU U U U4Sj5n U "5$![a? [SUS UR<S [UR5R S 35Sef=f)aProvides a strategy for producing a :class:`pandas.DataFrame`. Arguments: * columns: An iterable of :class:`column` objects describing the shape of the generated DataFrame. * rows: A strategy for generating a row object. Should generate either dicts mapping column names to values or a sequence mapping column position to the value in that position (note that unlike the :class:`pandas.DataFrame` constructor, single values are not allowed here. Passing e.g. an integer is an error, even if there is only one column). At least one of rows and columns must be provided. If both are provided then the generated rows will be validated against the columns and an error will be raised if they don't match. Caveats on using rows: * In general you should prefer using columns to rows, and only use rows if the columns interface is insufficiently flexible to describe what you need - you will get better performance and example quality that way. * If you provide rows and not columns, then the shape and dtype of the resulting DataFrame may vary. e.g. if you have a mix of int and float in the values for one column in your row entries, the column will sometimes have an integral dtype and sometimes a float. * index: If not None, a strategy for generating indexes for the resulting DataFrame. This can generate either :class:`pandas.Index` objects or any sequence of values (which will be passed to the Index constructor). You will probably find it most convenient to use the :func:`~hypothesis.extra.pandas.indexes` or :func:`~hypothesis.extra.pandas.range_indexes` function to produce values for this argument. Usage: The expected usage pattern is that you use :class:`column` and :func:`columns` to specify a fixed shape of the DataFrame you want as follows. For example the following gives a two column data frame: .. code-block:: pycon >>> from hypothesis.extra.pandas import column, data_frames >>> data_frames([ ... column('A', dtype=int), column('B', dtype=float)]).example() A B 0 2021915903 1.793898e+232 1 1146643993 inf 2 -2096165693 1.000000e+07 If you want the values in different columns to interact in some way you can use the rows argument. For example the following gives a two column DataFrame where the value in the first column is always at most the value in the second: .. code-block:: pycon >>> from hypothesis.extra.pandas import column, data_frames >>> import hypothesis.strategies as st >>> data_frames( ... rows=st.tuples(st.floats(allow_nan=False), ... st.floats(allow_nan=False)).map(sorted) ... ).example() 0 1 0 -3.402823e+38 9.007199e+15 1 -1.562796e-298 5.000000e-01 You can also combine the two: .. code-block:: pycon >>> from hypothesis.extra.pandas import columns, data_frames >>> import hypothesis.strategies as st >>> data_frames( ... columns=columns(["lo", "hi"], dtype=float), ... rows=st.tuples(st.floats(allow_nan=False), ... st.floats(allow_nan=False)).map(sorted) ... ).example() lo hi 0 9.314723e-49 4.353037e+45 1 -9.999900e-01 1.000000e+07 2 -2.152861e+134 -1.069317e-73 (Note that the column dtype must still be specified and will not be inferred from the rows. This restriction may be lifted in future). Combining rows and columns has the following behaviour: * The column names and dtypes will be used. * If the column is required to be unique, this will be enforced. * Any values missing from the generated rows will be provided using the column's fill. * Any values in the row not present in the column specification (if dicts are passed, if there are keys with no corresponding column name, if sequences are passed if there are too many items) will result in InvalidArgument being raised. Nrz1At least one of rows and columns must be providedc>^T"T5nUU4Sjn[U5S:a*[R"UVs/sH o2"5PM snUS9$[R"U"5/5nURS5$s snf)NcN>T"T5n[[RUS5 U$)Nz draw(row))rrIterable)r~r4rs r&row+data_frames..rows_only..rows!!$ZFs||V[A!Mr)rr)rr" DataFramedrop)r4rr_baserrs` r& rows_onlydata_frames..rows_onlysm^," u:>!++E,BEqSUE,B%PP "++SUG4D99Q<' -CsA8rzcolumns[]z+Column names must be hashable, but columns[z ].name was rBz, which cannot be hashed.z$duplicate definition of column name zFMust specify a dtype for all columns when combining rows with columns.)rr3rnrLc ^>U"T 5n[R"U5n[ST 55nT Vs/sH!oDRR(dMUPM# nnU(GaOUHOn[ R "[R"[U5[S9UURS9X4R'MQ UVs0sH*oDR(dMUR[5_M, nn[[U55HnUHnUR(a`[S5HFnU"UR 5n XUR;dM)XdRR#U 5 O [%5 OU"UR 5n W X4RR&U'M M T HgnURR(aM U"[9UURUR URURS95X4R'Mi [ R:"X1S 9$s snfs snf![(a`n URcM[+W [,[.[0[2[4[645(d[)SU <SURS35U eeSn A ff=f) Nc3<# UHoRS4v M g7fr!r.0cs r& 9data_frames..just_draw_columns..]sI7H!~7Hs)rr$)rr$zFailed to add value=z to column z9 with dtype=None. Maybe passing dtype=object would help?)rr$r3rrnr)r+justrris_emptyr"r#rCzerosrrXr$rLrnrvranger3rzrilocrFrUrrrJrrr rr) r4rlocal_index_strategyr}rcolumns_without_fillrirrKerrrrewritten_columnss r&just_draw_columns&data_frames..just_draw_columnsXs)(E#%775> I7HIID0A#T/@!FFOOA/@ #T#-A#)==s5z@#gg$DL. 0DP/C!xx  /CPs5z*A188%*1X(,QZZ(8#(QVV #<$(L$4$4U$;$) &. !'$($4E"38DL--a02+:'vv#'"6"#''%&ZZ!"#$88 $DL'##D6 6g$UQ" * " !wwz %sCx'S88'1&;UHK'(vvh/?%?'"(+ !+ " "s0H8H8H=H=I J, AJ''J,cL>^U"T5m[R"[U4SjT55TS9n0n[ST55nU(aJTVs/sHoDR(a [ 5OSPM! nnUScUR 5 UScM[[T55GHn[S5GHnU"T5nUn [U [5(awS/[T5-n [T5HupXRX'M U H;n U T;dM [!SU <S U <S TVs/sHoDRPM sn<S 35e U n U(a>S n [#WU S S 9H$upUcM X;aSn OUR%U5 M& U (aM['[)[*U S55n [U 5[T5:a'[!SU<S[U 5S[T535e[U 5[T5:awT[U 5nURR(a[!SU<SU<35eU R-U"UR55 [U 5[T5:aMwXR.U' GM [15 GM U$s snf![ah X+X'GM![aO URR(a[!SU<SU<35SeU"UR5X+'X+X'GM!f=ff=fs snf)Nc 3># UHVnUR[R"[R"UR [ T5S9UR S94v MX g7f))r$rr@N)rLr"r#rCrr$r)rrrs r&r3data_frames..assign_rows..sM/  HH177#e*EQWW /sAA!rc38# UHoRv M g7fr!)rnrs r&rrsA/@!XX/@srzEmpty fill strategy in z cannot complete row zRow z contains column z not in columns )F)strictTz draw(rows)z contains too many entries. 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R  R h}s %A!Kvvj $qvv,*?*?)@@Y[ s HA I r!)F collectionsrrcollections.abcrr dataclassesrrr typingr r r rrCr" hypothesisr r+hypothesis.controlrhypothesis.errorsrhypothesis.extrar^hypothesis.internal.conjecturerrwhypothesis.internal.coveragerrhypothesis.internal.validationrrrr*hypothesis.strategies._internal.strategiesrr%hypothesis.strategies._internal.utilsrrhypothesis.utils.deprecationrpandas.core.arrays.integerr ImportErrorr/r5rerYrgrr`rrJrrrr|rr#rrr[rrrrrr)r&rs)$!(&& '%-*6> JM97  SSl$ **$ N *,'')TTDjT  C$J 'Tv(() T T2 .2*,'')'Q#d*'Q 'Q 'Q Dj 'Q  'Q  C$J ''Qv||$'Q 'QT.2 )-*,'')j#d*j j 9:TAj  B $ &j j  C$J 'jv}}%jjZ e$WR[%6-1)- 8C=( #d*   B $ &    &*6'+V<@*. V f  $V  D8C=0 1D 8V   R 4 ' V v''( VVy Ls>KKK