+ JV-jJ pa0t$^RIHt^RIHtHt^RIHtHtH t ^RI t ^RI t ^RI Ht^RIHt]t]t]t]P(^] P*!4^] P,!4^ ] P.!4^@] P0!4/]P2^] P4!4^] P6!4^ ] P8!4^@] P:!4/]P<^] P>!4^ ] P@!4^@] PB!4/]PD^] PF!4^] P4!4/]PH^] PJ!4//t&R]'R&RRR llt(RR R llt)RR R llt*RRRllt+RRRllt,RRRllt-Rt.Rt/RRRllt0RRRllt1RRRllt2R#)) annotations)AnyTuple) DtypeKind ColumnBuffersColumnNullTypeN)Dtypezdict[DtypeKind, dict[int, Any]]_PYARROW_DTYPESc V^8dQhRRRR/#)dfDataFrameObjectreturnzpa.Table)formats"s/Volumes/fast/ai/experiments/ui-tars-smoke/.venv/lib/python3.14/site-packages/pyarrow/interchange/from_dataframe.py __annotate__r?s222H2c:\V\P4'dV#\V\P4'd!\PP V.4#\ VR4'g \ R4h\VPVR7VR7#)a} Build a ``pa.Table`` from any DataFrame supporting the interchange protocol. Parameters ---------- df : DataFrameObject Object supporting the interchange protocol, i.e. `__dataframe__` method. allow_copy : bool, default: True Whether to allow copying the memory to perform the conversion (if false then zero-copy approach is requested). Returns ------- pa.Table __dataframe__z#`df` does not support __dataframe__) allow_copy) isinstancepaTable RecordBatch from_batcheshasattr ValueError_from_dataframer)r rs&&rfrom_dataframer ?s{""bhh B ' 'xx$$bT** 2 ' '>?? 2++z+B&0 22rcV^8dQhRR/#)r r rr)rs"rrr\s***rc.pVP4Fp\W14pVPV4K! V'g\V4pVPV4\PP V4#)al Build a ``pa.Table`` from the DataFrame interchange object. Parameters ---------- df : DataFrameObject Object supporting the interchange protocol, i.e. `__dataframe__` method. allow_copy : bool, default: True Whether to allow copying the memory to perform the conversion (if false then zero-copy approach is requested). Returns ------- pa.Table ) get_chunksprotocol_df_chunk_to_pyarrowappendrrr)r rbatcheschunkbatchs&& rrr\s_"G,U?u! ,R0u 88  ))rc$V^8dQhRRRRRR/#)r r rrboolrzpa.RecordBatchr)rs"rrrys&,/,/,/,/,/rc/pVP4EFp\V\4'g\RV R24hW29d\RV R24hVP V4pVP ^,pV\ P\ P\ P\ P\ P39d\WA4W#&KV\ P8Xd\WA4W#&KV\ P8Xd\!WA4W#&EK \#RV R24h \$P&P)V4#)ap Convert interchange protocol chunk to ``pa.RecordBatch``. Parameters ---------- df : DataFrameObject Object supporting the interchange protocol, i.e. `__dataframe__` method. allow_copy : bool, default: True Whether to allow copying the memory to perform the conversion (if false then zero-copy approach is requested). Returns ------- pa.RecordBatch zColumn z is not a stringz is not uniquez Data type z not handled yet) column_namesrstrrget_column_by_namedtyperINTUINTFLOATSTRINGDATETIMEcolumn_to_arrayBOOLbool_column_to_array CATEGORICAL categorical_column_to_dictionaryNotImplementedErrorrr from_pydict)r rcolumnsnamecolr/s&& rr$r$ys*$&G!$$$wtf,<=> > ?wtfN;< <##D) !   MM NN OO         ,C> % %g ..rc$V^8dQhRRRRRR/#r r> ColumnObjectrr*rpa.Arrayr)rs"rrrs& rcVP4pVPp\W#VP4VPVP V4pV#)ab Convert a column holding one of the primitive dtypes to a PyArrow array. A primitive type is one of: int, uint, float, bool (1 bit). Parameters ---------- col : ColumnObject allow_copy : bool, default: True Whether to allow copying the memory to perform the conversion (if false then zero-copy approach is requested). Returns ------- pa.Array ) get_buffersr/buffers_to_arraysize describe_nulloffset)r>rbuffers data_typedatas&& rr5r5sH&ooG I GHHJ--JJ&  (D Krc$V^8dQhRRRRRR/#r@r)rs"rrrs&$$ $$$rcbVP4pVR,^,^,pV^8XdV'g \R4hVPp\W$VP 4VP VP 4pV^8Xd+\P!V\P!44pV#)a Convert a column holding boolean dtype to a PyArrow array. Parameters ---------- col : ColumnObject allow_copy : bool, default: True Whether to allow copying the memory to perform the conversion (if false then zero-copy approach is requested). Returns ------- pa.Array rKzfBoolean column will be casted from uint8 and a copy is required which is forbidden by allow_copy=False) rD RuntimeErrorr/rErFrGrHpccastrbool_)r>rrIrFrJrKs&& rr7r7s$ooG 6?1 a D qy A   I GHHJ--JJ (D qywwtRXXZ( Krc$V^8dQhRRRRRR/#)r r>rArr*rzpa.DictionaryArrayr)rs"rrrs&-- ---rctV'g \R4hVPpVR,'g \R4hVR,p\V4pVP 4pVR,wrg\ WWVP 4VPVP4p\PPW4p V #)a* Convert a column holding categorical data to a pa.DictionaryArray. Parameters ---------- col : ColumnObject allow_copy : bool, default: True Whether to allow copying the memory to perform the conversion (if false then zero-copy approach is requested). Returns ------- pa.DictionaryArray zjCategorical column will be casted from uint8 and a copy is required which is forbidden by allow_copy=False is_dictionaryz-Non-dictionary categoricals not supported yet categoriesrK) rNdescribe_categoricalr:r5rDrErFrGrHrDictionaryArray from_arrays) r>r categorical cat_column dictionaryrI_rJindices dict_arrays && rr9r9s$  A  **K  ' '! ;= =\*J ,JooG6?LAw"xxz"00"zz+G ##//DJ rc\P!RV4pV'd5VP^4VP^4r2VR8wd VR, pW#3#\RV 24h)z4Parse datetime `format_str` to interpret the `data`.zts([smun]):(.*)sz DateTime kind is not supported: )rematchgroupr:) format_strtimestamp_metaunittzs& rparse_datetime_format_strrhsaXX0*=N!''*N,@,@,Cb 3; CKDx  @ M NNrcVwrr4V\P8Xd%\V4wrV\P!WVR7#\ P V/4P VR4pV'dV#\RV R24h)z+Map column date type to pyarrow date type. )rgNzConversion for  is not yet supported.)rr4rhr timestampr getr:)rJkind bit_widthf_stringr\rfrgpa_dtypes& r map_date_typerq.sz#, DX y!!!,X6||D(("&&tR044YE O%!),BCE Erc4V^8dQhRRRRRRRRR RR R R R /#)r rIrrJzTuple[DtypeKind, int, str, str]lengthintrGrrHrr*rrBr)rs"rrr@sWff f.f f" f  f  ffrcVR,wrgVR,wrVR,wr\P!VPVPVR7p V'd\ VX VVVV4p M\ V VVVVV4p \V4pV 'dX wrp\P!V PV PV R7pV^,R8Xd\P!4pM2V^@8Xd\P!4pM\P!4p\PPVVV VV .VR7pV#\PPVVW.VR7pV# \dRpEL\i;i \dRp ELdi;i)a Build a PyArrow array from the passed buffer. Parameters ---------- buffer : ColumnBuffers Dictionary containing tuples of underlying buffers and their associated dtype. data_type : Tuple[DtypeKind, int, str, str], Dtype description of the column as a tuple ``(kind, bit-width, format string, endianness)``. length : int The number of values in the array. describe_null: ColumnNullType Null representation the column dtype uses, as a tuple ``(kind, value)`` offset : int, default: 0 Number of elements to offset from the start of the buffer. allow_copy : bool, default: True Whether to allow copying the memory to perform the conversion (if false then zero-copy approach is requested). Returns ------- pa.Array Notes ----- The returned array doesn't own the memory. The caller of this function is responsible for keeping the memory owner object alive as long as the returned PyArrow array is being used. rKvalidityNoffsetsbaseUrH) TypeErrorrforeign_bufferptrbufsizevalidity_buffer_from_maskvalidity_buffer_nan_sentinelrq large_stringstringArray from_buffers)rIrJrsrGrHr data_buffr\ validity_buffvalidity_dtype offset_buff offset_dtypedata_pa_buffervalidity_pa_buff data_dtypeoffset_bit_widthoffset_pa_buffer string_typearrays&&&&&& rrErE@sP6?LI(/ (;% $+I$6! &&y}}i6G6G,57N4]5C5B5;5;5? A88A8E8>8>8B Dy)J$0!Q,,[__-8-@-@2=? Q<3 //+K2% oo/  iik %%   / @ &  L%%    . &  Lw    s" E$ E8$ E54E58 F F c4V^8dQhRRRRRRRRR RR R R R /#)r r BufferObjectrr rGrrsrtrHrr*r pa.Bufferr)rs"rrrs`LJLJLJLJ"LJ  LJ  LJ  LJLJrcVwrgVwpp V\P8XgQhV\P8XdR#V\P8XgV\P 8XEd-V^8XEd%\ P!VPVPVR7p V\P8XdwV'g \R4h\ PP\ P!4VRV .VR7p \P!V \ P !44p M8\ PP\ P !4VRV .VR7p V^8Xd\P"!V 4p V P%4^,#V\P 8Xd5V^8Xd.\ P!VPVPVR7#\'V R24h)a Build a PyArrow buffer from the passed mask buffer. Parameters ---------- validity_buff : BufferObject Tuple of underlying validity buffer and associated dtype. validity_dtype : Dtype Dtype description as a tuple ``(kind, bit-width, format string, endianness)``. describe_null : ColumnNullType Null representation the column dtype uses, as a tuple ``(kind, value)`` length : int The number of values in the array. offset : int, default: 0 Number of elements to offset from the start of the buffer. allow_copy : bool, default: True Whether to allow copying the memory to perform the conversion (if false then zero-copy approach is requested). Returns ------- pa.Buffer NrxYTo create a bitmask a copy of the data is required which is forbidden by allow_copy=Falser{* null representation is not yet supported.)rr6r NON_NULLABLE USE_BYTEMASK USE_BITMASKrr}r~rrNrrint8rOrPrQinvertrIr:) rrrGrsrHr null_kind sentinel_val validity_kindr\buffmask mask_bools &&&&&& rrrsB,I+M1a INN ** *N/// n11 1^///LA4E  !2!2!.!6!6&35 33 3"F88((F*.06)8Dbhhj1I--bhhj&/3Tl5;.=I 1  ),I  "1%% n00 0\Q5F  !2!2!.!6!6&35 5"oG HJ Jrc4V^8dQhRRRRRRRRR RR R R R /#)r rrrJr rGrrsrtrHrr*rrr)rs"rrrs`UJUJ UJUJ"UJ  UJ  UJ  UJUJrcjVwrgp\V4p VwrV \P8XdV'g \R4hV\P 8XdV^8Xd\ V RV R24h\PPV VRV.VR7p \P!V 4p \P!V 4p V P4^,#V \P8XdV'g \R4hV\P8Xd\P !4pMT p\PPVVRV.VR7p \P"!W4p\P!V4pVP4^,#V \P$8XdR#\ V R24h)a Build a PyArrow buffer from NaN or sentinel values. Parameters ---------- data_pa_buffer : pa.Buffer PyArrow buffer for the column data. data_type : Dtype Dtype description as a tuple ``(kind, bit-width, format string, endianness)``. describe_null : ColumnNullType Null representation the column dtype uses, as a tuple ``(kind, value)`` length : int The number of values in the array. offset : int, default: 0 Number of elements to offset from the start of the buffer. allow_copy : bool, default: True Whether to allow copying the memory to perform the conversion (if false then zero-copy approach is requested). Returns ------- pa.Buffer rz with rjNr{r)rqrUSE_NANrNrr2r:rrrrOis_nanrrI USE_SENTINELr4int64equalr)rrJrGrsrHrrmrnr\rrr pyarrow_datarsentinel_dtype sentinel_arrrs&&&&&& rrrsB&DQy)J+IN***B  9?? "yB&+VI;.DEG G8800~& 1L 99\*D99T?D<<>!$ $ n11 1B  9%% %XXZN'Nxx,,^-3.2N-C4:-< xx ; IIl+   "1%% n11 1 !oG HJ Jr)T)rT)3__conditional_annotations__ __future__rtypingrrpyarrow.interchange.columnrrrpyarrowrrapyarrow.computecomputerOr rrArr0rint16int32rr1uint8uint16uint32uint64r2float16float32float64r6rQr3rr __annotations__r rr$r5r7r9rhrqrErr)rs@rrsn$#"   ,   MMArwwy   $NNQ &OOb"**,"**,"**,(NNQ  $ q"))+&40$2:*:,/^:$N-`O$E$fRLJ^UJUJr