+ QV-jraRyt0tRt^RIt^RIt^RIt^RIt^RIHtHt^RIH t ^RIH t ^RI H t HtHtHt^RIHtHt^RIHt^RIHtHtHtHtHtHt^RIt^RIt ^RI!t"^RI#H$t%^RI&t!^RI'H(t)^R I'H*t+^R I,H-t-^R I.H/t/H0t0^R I1H2t2^R I3H4t4H5t5^RI6H7t7H8t8^RI9H:t:H;t;Ht>^RI?H@t@HAtA^RIBHCtC^RIDHEtEHFtF^RIGHHtHHItI^RIJHKtKHLtL^RIMHNtN]5P!]P4tQRRltRRRltSRRltTRzRRlltU]!RR 7!R!R"44tV!R#R$4tW]!R%R&]W44tX]!R'R(]W44tY]!R)R*]W44tZ]!R+R,]W44t[!R-R.]"P4t]!R/R0]]4t^!R1R2]]4t_!R3R4]]4t`!R5R6]]4ta]"P!]^!R{R744]"P!]_!R|R744]"P!]`!R}R744]"P!]a!R~R744RR8R9lltc!R:R;]"PP4td!R<R=]+4te!R>R?])4tfR@tg]!RARB44th]!RCRD44ti!RERF4t ]!RR 7!RGRH] 44tj]!RR 7!RIRJ44tk]]l]m]n]V]h]K]L]k]j]X]Y]Z][]>]@]C]N]H]E3,toRRKRLlltpRMRNltqRROltrRRPRQllts/]VP]Vb]hP]hb]KP]Kb]LP]Lb]kP]kb]jP]jb]XP]Xb]YP]Yb]ZP]Zb][P][b]>P]>b]@P]@b]CP]Cb]NP]Nb]HP]Hb]EP]Eb]iP]ibtt]^k]4RRRSl4tuRTRUltvRVRWltwRRXRYlltxRZR[ltyRR\R]lltzR^R_lt{RR`Rallt|RbRclt}RdRelt~]m]]]^,3,,t.3RfRglltRRhRilltRjRkltRlRmltRnt!RoRp]l4tRqRrltRsRtltRuRvltRwRxltR#)z[This class handle features definition in datasets and some utilities to display table type.N)IterableMapping)Sequence)InitVar dataclassfieldfields)reducewraps)mul)AnyCallableClassVarLiteralOptionalUnion)ExtensionArray)ExtensionDtype)config)camelcase_to_snakecasesnakecase_to_camelcase) array_cast) experimentallogging) ujson_dumps ujson_loads)asdictfirst_non_null_valuezip_dict)Audio)Imageencode_pil_image)Mesh)Niftiencode_nibabel_image)Pdfencode_pdfplumber_pdf) TranslationTranslationVariableLanguages)VideocDV^8dQhR\PR\/#) arrow_typereturn)paDataTypestr)formats"k/Volumes/fast/ai/experiments/ui-tars-smoke/.venv/lib/python3.14/site-packages/datasets/features/features.py __annotate__r38s&C`C`C`C`c \PPV4'dR#\PPV4'dR#\PP V4'dR#\PP V4'dR#\PP V4'dR#\PPV4'dR#\PPV4'dR#\PPV4'dR#\PPV4'dR #\PPV4'dR #\PPV4'dR #\PPV4'dR #\PPV4'dR #\PPV4'd.R\ P"!\%V44P& R2#\PP)V4'd.R\ P"!\%V44P& R2#\PP+V4'd]VP,fRVP& R2#VP,'dRVP& RVP, R2#\/RV R24h\PP1V4'dR#\PP3V4'dR#\PP5V4'dRVP& R2#\PP7V4'dRVP8 RVP: R2#\PP=V4'dRVP8 RVP: R2#\PP?V4'dR#\PPAV4'dR#\PPCV4'dR#\PPEV4'dR#\PPGV4'dR #\PPIV4'dR!#\PPKV4'd\MVPN4#\/R"V R#24h)$z _arrow_to_datasets_dtype takes a pyarrow.DataType and converts it to a datasets string dtype. In effect, `dt == string_to_arrow(_arrow_to_datasets_dtype(dt))` nullboolint8int16int32int64uint8uint16uint32uint64float16float32float64ztime32[]ztime64[z timestamp[z, tz=zUnexpected timestamp object .date32date64z duration[z decimal128(, )z decimal256(binary large_binary binary_viewstring large_string string_viewz Arrow type z+ does not have a datasets dtype equivalent.)(pyarrowtypesis_null is_booleanis_int8is_int16is_int32is_int64is_uint8 is_uint16 is_uint32 is_uint64 is_float16 is_float32 is_float64 is_time32r.type_for_aliasr0unit is_time64 is_timestamptz ValueError is_date32 is_date64 is_duration is_decimal128 precisionscale is_decimal256 is_binaryis_large_binaryis_binary_view is_stringis_large_stringis_string_view is_dictionary_arrow_to_datasets_dtype value_type)r,s&r2rsrs8s }}Z((  ! !* - -   z * *    + +    + +    + +    + +   , ,   , ,   , ,  ! !* - -  ! !* - -  ! !* - -   , ,**3z?;@@ACC   , ,**3z?;@@ACC  # #J / / ==  02 2 ]]] 0jmm_AF F;J>;zl2]^__r4cDV^8dQhR\R\P/#)r+datasets_dtyper-)r0r.r/)r1s"r2r3r3~s"TTCTBKKTr4c VR8Xd \R4hR-RlpV\P9d\PV,!4#VR,\P9d$\PVR,,!4#\P!RV4pV'dVP ^4p\P!RV4pVR.9d\P !V4#V'd6\P !VP ^4VP ^44#\V!VRRR .R .R 74h\P!R V4pV'dGVP ^4pVR.9d\P!V4#\V!VR RR.R.R 74h\P!RV4pV'dVP ^4pVR8Xd=VP ^4p V R/9d\P!V 4#\V R24hVR8Xd=VP ^4p V R09d\P!V 4#\V R24h\V!VRRR.RR.R 74h\P!RV4p V 'Ed[V P ^4p V R8Xd\P!RV P ^44p V 'dLV P ^4p V P ^4p\P!\V 4\V44#\V!VRRR .R!.R 74hV R"8Xd\P!RV P ^44p V 'dLV P ^4p V P ^4p\P!\V 4\V44#\V!VR#R$R%.R&.R 74h\V!VR'R(R).R!R&.R 74h\R*V R+VR, R,24h)1a string_to_arrow takes a datasets string dtype and converts it to a pyarrow.DataType. In effect, `dt == string_to_arrow(_arrow_to_datasets_dtype(dt))` This is necessary because the datasets.Value() primitive type is constructed using a string dtype Value(dtype=str) But Features.type (via `get_nested_type()` expects to resolve Features into a pyarrow Schema, which means that each Value() must be able to resolve into a corresponding pyarrow.DataType, which is the purpose of this function. jsonz;'json' is not a valid dtype, use the Json() feature insteadcrV RV R2pV'dP\V4^8d*RPVRR 4R,VR ,,MV^,pVRV R2, pV'dP\V4^8d*RPVRR 4R,VR ,,MV^,pVRV R2, pV#) zA is not a validly formatted string representation of the pyarrow z type.rGNz or z Valid examples include: rD and z For more insformation, see: )lenjoin)dtypepa_dtypeexamplesurlsmsgs&&&& r2_dtype_error_msg)string_to_arrow.._dtype_error_msgsXYaXbbhi KNx=[\K\tyy#2/&88B<GbjklbmH /z; ;C @CD A 499T#2Y''1DH*,,,{{>C/022 "8.I/55a8II&RTgh "9 9<< 34 4 << 1 7 7 :SUdUjUjklUm4n 14AGGJ =CCAF}}S^SZ@@ $&$"57J!Kfg $u ,46II>SUdUjUjklUm4n 14AGGJ =CCAF}}S^SZ@@ $&$"57K!Lfg  "13FG``     >"%(<'=>T U r4c jV^8dQhR\R\R\R\\\3,/#)r+objonly_1d_for_numpyoptimize_list_castingr-)r r7tuple)r1s"r2r3r3s<||||VZ|_dehjnen_o|r4c R\P'dR\P9d^RIp\P 'dR\P9d^RIp\P'dR\P9d^RIH p\P'dR\P9d^RI p\P'dR\P9d^RI p\P'dR\P9d^RIp\P 'dR\P9d ^R IHp Hp \)V\*P,4'd`VP.^8Xd VR,R 3#V'dVP.^8XdVR 3#VU u.uFp \1WVR 7^,NK up R 3#\P 'EdR\P9Edp\)VXP24'EdSVP4VP68Xd[\1VP94P;VP<4P?4P4VVR 7^,R 3#VP.^8Xd6VP94P?4P4R,R 3#V'dVP.^8Xd/VP94P?4P4R 3#VP94P?4P4U u.uFp \1WVR 7^,NK up R 3#\P'dR\P9d\)VXP24'dVP.^8XdVP4R,R 3#V'dVP.^8XdVP4R 3#VP4U u.uFp \1WVR 7^,NK up R 3#\P'dR\P9d\)VXP,4'dVP.^8Xd \*P@!V4R,R 3#V'dVP.^8Xd\*P@!V4R 3#\*P@!V4U u.uFp \1WVR 7^,NK up R 3#\P'dIR\P9d4\)VXPBPB4'd\EV4R 3#\P'dIR\P9d4\)VXPFPH4'd\KV4R 3#\P'dKR\P9d6\)VXPLPN4'd\QVR R 7R 3#\)V\RPT4'd%\1VPW4WR 7^,R 3#\)V\RPX4'dIVP[R4P]4U U u/uFwrV \1WVR 7^,bK up p R 3#\)V\RP^4'dVPa4R 3#\)V\RPb4'dVPe4R 3#\)V\f4'd_\)V\h4'*p/pVP]4F$wpp\1VWR 7wppVV,pVVV&K& V'dW3#TV3#\kVR4'dE\*Pl!V4'dVR 3#\1VPo4WR 7^,R 3#\)V\p\r34'd\uV4^8dVFp\wV4'gKM \1XWR 7wppV'g V'g'VUu.uFp\1VWR 7^,NK upR 3#\)V\p\r34'dVR 3#\qV4R 3#VR 3#\P 'dER\P9d0\)VX 4'd\y4pVP{V4R 3#\P 'dER\P9d0\)VX 4'd\}4pVP{V4R 3#VR 3#uup iuup iuup iuup iuup p iuupi)a Cast pytorch/tensorflow/pandas objects to python numpy array/lists. It works recursively. If `optimize_list_casting` is True, to avoid iterating over possibly long lists, it first checks (recursively) if the first element that is not None or empty (if it is a sequence) has to be casted. If the first element needs to be casted, then all the elements of the list will be casted, otherwise they'll stay the same. This trick allows to cast objects that contain tokenizers outputs without iterating over every single token for example. Args: obj: the object (nested struct) to cast. only_1d_for_numpy (bool): whether to keep the full multi-dim tensors as multi-dim numpy arrays, or convert them to nested lists of 1-dimensional numpy arrays. This can be useful to keep only 1-d arrays to instantiate Arrow arrays. Indeed Arrow only support converting 1-dimensional array values. optimize_list_casting (bool): whether to optimize list casting by checking the first non-null element to see if it needs to be casted and if it doesn't, not checking the rest of the list elements. Returns: casted_obj: the casted object has_changed (bool): True if the object has been changed, False if it is identical tensorflowNtorchjaxPIL pdfplumbernibabel torchcodec) AudioDecoder VideoDecoderTFrr) force_bytesseries __array__)?r TF_AVAILABLEsysmodulesrTORCH_AVAILABLEr JAX_AVAILABLE jax.numpynumpy PIL_AVAILABLE PIL.ImagePDFPLUMBER_AVAILABLErNIBABEL_AVAILABLErTORCHCODEC_AVAILABLEtorchcodec.decodersrr isinstancenpndarrayndim_cast_to_python_objectsTensorr~bfloat16detachtofloatcpuasarrayr r!pdfPDFr&analyze AnalyzeImager$pdSeriestolist DataFrameto_dictitems Timestamp to_pydatetime Timedeltato_pytimedeltardicthasattrisscalarrlistrr|#_check_non_null_non_empty_recursiver)encode_exampler)rrrtfrjnprrnibrrxkeyvalue has_changedoutputkvcasted_v has_changed_v first_elmtcasted_first_elmthas_changed_first_elmtelmtas&&& r2rrs ,|s{{: 'S[["8  4  4 """|s{{'B I$< """|s{{'BB#rzz"" 88q=r7D= "chh!m: ! !,Vk!       Gs{{$:z#u||?\?\ 99 &*  ,00288:"3&;    88q=::<##%++-b147 7"chh!m::<##%++-t3 3!ZZ\--/557 8,Vk8       !<CQSQZQZA[A[ 88q=99;r?D( ("chh!m99;$ $!YY[ ),Vk)       %3;;"6:c3;;;W;W 88q=::c?2&, ,"chh!m::c?D( ( ZZ_ -,Vk-       %3;;"6:c399??;[;[$d**  $ $ $)DTWYcYgYgYkYkIlIl$S)4//  ! ! !i3;;&>:cSVS^S^SkSkClCl#CT:D@@ C # # # 0A      C & & #&++h"7"="="?  #@JC,Vk#@      C & &  "D(( C & &!!#T)) C ! !$S$// IIKDAq&=%6' #Hm = (K F1I  %v::#{:: k " " ;;s  : (MMO7H   C$ ' ' s8a<! 6zBB"9P.?9 5 5&-B %( %(D0 4E%(  cD%=11:%9d?*:   $ $ $)DTWYeIfIf G$d**  $ $ $)DTWYeIfIf G$d**EzU*. Ts$-f  ff;f? f?f$Fc0V^8dQhR\R\/#r+rr-r )r1s"r2r3r3s   ]` r4c*\WVR7^,#)a Cast numpy/pytorch/tensorflow/pandas objects to python lists. It works recursively. If `optimize_list_casting` is True, To avoid iterating over possibly long lists, it first checks (recursively) if the first element that is not None or empty (if it is a sequence) has to be casted. If the first element needs to be casted, then all the elements of the list will be casted, otherwise they'll stay the same. This trick allows to cast objects that contain tokenizers outputs without iterating over every single token for example. Args: obj: the object (nested struct) to cast only_1d_for_numpy (bool, default ``False``): whether to keep the full multi-dim tensors as multi-dim numpy arrays, or convert them to nested lists of 1-dimensional numpy arrays. This can be useful to keep only 1-d arrays to instantiate Arrow arrays. Indeed Arrow only support converting 1-dimensional array values. optimize_list_casting (bool, default ``True``): whether to optimize list casting by checking the first non-null element to see if it needs to be casted and if it doesn't, not checking the rest of the list elements. Returns: casted_obj: the casted object r)r)rrrs&&&r2cast_to_python_objectsrs( # H]   r4)reprcva]tRtRtoRt]!RRR7tRt]!RRRR7tRt Rt R t R t V3R lt R tVtR#) Valueia  Scalar feature value of a particular data type. The possible dtypes of `Value` are as follows: - `null` - `bool` - `int8` - `int16` - `int32` - `int64` - `uint8` - `uint16` - `uint32` - `uint64` - `float16` - `float32` (alias float) - `float64` (alias double) - `time32[(s|ms)]` - `time64[(us|ns)]` - `timestamp[(s|ms|us|ns)]` - `timestamp[(s|ms|us|ns), tz=(tzstring)]` - `date32` - `date64` - `duration[(s|ms|us|ns)]` - `decimal128(precision, scale)` - `decimal256(precision, scale)` - `binary` - `large_binary` - `binary_view` - `string` - `large_string` - `string_view` Args: dtype (`str`): Name of the data type. Example: ```py >>> from datasets import Features >>> features = Features({'stars': Value('int32')}) >>> features {'stars': Value('int32')} ``` NFdefaultrrinitrcVPR8XdRVnVPR8XdRVn\VP4VnR#)doublerBrrAN)r~rpa_typeselfs&r2 __post_init__Value.__post_init__$s8 :: !"DJ :: "DJ&tzz2 r4cVP#Nrrs&r2__call__Value.__call__+ ||r4c\PPVP4'd \ V4#\PP VP4'd \ V4#\PPVP4'd \V4#\PPVP4'd \V4#\PPVP4'd \V4#\PPVP4'd \V4#V#r ) r.rPrRrr7 is_integerr is_floatingrror0rprqr rs&&r2rValue.encode_example.s 88  t|| , ,;  XX  . .u:  XX ! !$,, / /<  XX   - -u:  XX % %dll 3 3u:  XX $ $T\\ 2 2u: Lr4cL\V4P RVP R2#)z('z'))type__name__r~rs&r2__repr__Value.__repr__>s$t*%%&b B77r4cj<V^8dQh/S[;R&S[S[,;R&S[S[,;R&S[;R&#)r+r~idr_type)r0rrr )r1 __classdict__s"r2r3Value.__annotate__sHb Jcd  7ehc]!ij ?kr4)r~r)r __module__ __qualname____firstlineno____doc__rrrrr rrr__annotate_func____static_attributes____classdictcell__rs@r2rrsH-`d7B!GwU?E3 8cr4rc2a]tRtRtoRtRtRtRtVtR#)_ArrayXDiBc:\VP4VnR#r )rshapers&r2r _ArrayXD.__post_init__Cs4::& r4c\4VPPR,,!VPVP4pV#) ExtensionType)globals __class__rr,r~)r rs& r2r_ArrayXD.__call__Fs4)DNN33oEFtzzSWS]S]^r4cV#r rrs&&r2r_ArrayXD.encode_exampleJs r4)r,N) rr!r"r#r rrr&r'r(s@r2r*r*Bs'r4r*cZa]tRtRtoRt]!RRR7t]!RRRR7tV3RltRt Vt R#) Array2DiNa Create a two-dimensional array. Args: shape (`tuple`): Size of each dimension. dtype (`str`): Name of the data type. Example: ```py >>> from datasets import Features >>> features = Features({'x': Array2D(shape=(1, 3), dtype='int32')}) ``` NFrrcZ<V^8dQh/S[;R&S[;R&S[S[,;R&S[;R&#r+r,r~rrrr0r)r1rs"r2r3Array2D.__annotate__N;$ L%& J'(  7), A-r4r rr!r"r#r$rrrr%r&r'r(s@r2r6r6N-$d7Byu5AE-r4r6cZa]tRtRtoRt]!RRR7t]!RRRR7tV3RltRt Vt R#) Array3DigaCreate a three-dimensional array. Args: shape (`tuple`): Size of each dimension. dtype (`str`): Name of the data type. Example: ```py >>> from datasets import Features >>> features = Features({'x': Array3D(shape=(1, 2, 3), dtype='int32')}) ``` NFrrcZ<V^8dQh/S[;R&S[;R&S[S[,;R&S[;R&#r8r9)r1rs"r2r3Array3D.__annotate__gr;r4rr<r(s@r2r?r?gr=r4r?cZa]tRtRtoRt]!RRR7t]!RRRR7tV3RltRt Vt R#) Array4DiaCreate a four-dimensional array. Args: shape (`tuple`): Size of each dimension. dtype (`str`): Name of the data type. Example: ```py >>> from datasets import Features >>> features = Features({'x': Array4D(shape=(1, 2, 2, 3), dtype='int32')}) ``` NFrrcZ<V^8dQh/S[;R&S[;R&S[S[,;R&S[;R&#r8r9)r1rs"r2r3Array4D.__annotate__r;r4rr<r(s@r2rCrCr=r4rCcZa]tRtRtoRt]!RRR7t]!RRRR7tV3RltRt Vt R#) Array5DiaCreate a five-dimensional array. Args: shape (`tuple`): Size of each dimension. dtype (`str`): Name of the data type. Example: ```py >>> from datasets import Features >>> features = Features({'x': Array5D(shape=(1, 2, 2, 3, 3), dtype='int32')}) ``` NFrrcZ<V^8dQh/S[;R&S[;R&S[S[,;R&S[;R&#r8r9)r1rs"r2r3Array5D.__annotate__r;r4rr<r(s@r2rGrGr=r4rGcva]tRtRtoRtV3RlRltRt]R4tRt Rt R t R t R t V3R ltR tVtR#)_ArrayXDExtensionTypeiNc&<V^8dQhRS[RS[/#)r+r,r~)rr0)r1rs"r2r3"_ArrayXDExtensionType.__annotate__s v ve vC vr4cJVPeVP^8:d \R4h\V4VP8wd\RV RVP R24h\^VP4FpW,eK\RV 24h \ V4VnW nVPVP 4Vn\PPWPVPP RVPP 24R#)NzCYou must instantiate an array type with a value for dim that is > 1zshape=z and ndims=z don't matchz3Support only dynamic size on first dimension. Got: rD)ndimsrdr|rangerr,rt_generate_dtype storage_dtyper.r/__init__r1r!r)r r,r~dims&&& r2rS_ArrayXDExtensionType.__init__s :: qbc c u: #veWK |<PQ QDJJ'Cz! #VW\V]!^__(5\ !11$//B !!$(:(:t~~?X?X>YYZ[_[i[i[r[rZs.__annotate__s"r4cv<\PPV4'dS!VP4#V#r )r.rPis_listrt)r_unnest_pa_types&r2r+_is_zero_copy_only.._unnest_pa_types. 88  G $ $"7#5#56 6r4)r.rP is_primitiverR is_temporal)rrrs&&@r2_is_zero_copy_onlyrsf !'* 88  ) q q2883F3Fw3O3p3pSUS[S[SgSghoSp.qqr4cLa]tRtRtoRtRtR RltR V3RlRlltRtVt R#) rki ch\VPPRR7pVPVR7#T)rzero_copy_only)rstoragerto_numpy)r rs& r2rArrayExtensionArray.__array__s)+DLL,=,=dK}}N};;r4c(VPV,#r )r)r is&&r2 __getitem__ArrayExtensionArray.__getitem__s||Ar4cVPpVP4PRR7pVPP^,Ee\^p\ P !\V44V,\ P !\ P!V44, p\VPP4F6pW@PPV,,pVP4pK8 VPVR7pVP!\V4\V4, .VPPO5!p\V4'dF\ P!VP\ P4V\ P ^R7pV#VPPpVPPp .p \ P"!VP$U u.uFqP'4NK up 4p \)V4FwrmV 'd"V P+\ P 4K.W&V^,pW^,,W,, p\V 4FpVP4pK VPVR7pV P+VP!V.VR,O5!4K \\ P,!\ P.!V 444^8d,\ P0!\V 4\2R7pWR&V#\ P"!V 4pV#uup i)FraxisNNr~NNN)rrQrrr,raranger|sumrPrOflattenreshapeinsertastyperBnanarrayoffsetsas_py enumerateappenduniquediffemptyobject)r rr null_masksize null_indicesr numpy_arrr,rOarraysofffirst_dim_offsetsrQ storage_el first_dimrs&& r2rArrayExtensionArray.to_numpysb $ OO%..e.D 99??1  )D99S\29= "&&QZJ[@\\L499??+ **!//+, (((GI!))#d)c,6G*GZ$))//ZI<  IIi&6&6rzz&BLRTRXRX_`a 61IIOOEIIOOEF ")Q#))+)Q R ' 2 MM"&&)!(QU!3J 1a% 8;L;O OI"5\%/%7%7%9 *!+ 3 3> 3 RIMM)"3"3I"Jb "JK3299RWW%6789A=HHS[? %! HHV, +*Rs4MNc><V^8dQhRS[S[R,,/#)r+maps_as_pydicts)lossystrict)rr)r1rs"r2r3 ArrayExtensionArray.__annotate__As&&':K2L)M&r4cT\VPPRR7pVPVR7pVPP^,fCVP \ 8Xd.VP4Uu.uFqDP4NK up#VP4#uupir)rrrrr,r~rr)r rrrarrs&& r2 to_pylistArrayExtensionArray.to_pylistAs+DLL,=,=dKMMM@ 99??1  %)//V*C,5,<,<,>?,>SJJL,>? ?##% %@s:B%r)Tr ) rr!r"r#rrrrr&r'r(s@r2rkrk s$<*X&&&r4rkca]tRtRtoRtV3RlRltV3RlRlt]R4t] V3RlR l4t ] V3R lR l4t ] V3R lR l4t ] V3RlRl4t RtVtR#)rtiJrtcH<V^8dQhRS[RS[P3,/#)r+rtrt)rrr~)r1rs"r2r3&PandasArrayExtensionDtype.__annotate__Ms#&&5)Dbhh)N#O&r4cWnR#r  _value_type)r rts&&r2rS"PandasArrayExtensionDtype.__init__Ms%r4c^<V^8dQhRS[S[PS[P3,/#)r+r)rr.Array ChunkedArray)r1rs"r2r3rPs&44E"((BOO*C$D4r4cl\V\P4'dSVPP \P !VP Uu.uFq"PNK up44p\VPPRR7pVPVR7p\V4#uupir) rr.rr wrap_array concat_arrayschunksrrrPandasArrayExtensionArray)r rchunkrrs&& r2__from_arrow__(PandasArrayExtensionDtype.__from_arrow__Ps eR__ - -JJ))"*:*:W\WcWc;dWceMMWc;d*efE+EMM,>,>tLNN.NA (33 / ; r4Nc lV\P!\48Xdl\P!\ VP 4\R7p\ \ VP 44FpVP V,W#&K V#Vf VP #VP PV4#)ax Convert to NumPy Array. Note that Pandas expects a 1D array when dtype is set to object. But for other dtypes, the returned shape is the same as the one of ``data``. More info about pandas 1D requirement for PandasExtensionArray here: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.api.extensions.ExtensionArray.html#pandas.api.extensions.ExtensionArray r)rr~rrr|rrPr)r r~outrs&& r2r#PandasArrayExtensionArray.__array__qs| BHHV$ $((3tzz?&9C3tzz?+A,J =:: ::$$U+ +r4c$<V^8dQhRS[RR/#)r+deepr-rr7)r1rs"r2r3rs @@@*E@r4c0\VPRR7#)Tr)rr)r rs&&r2rPandasArrayExtensionArray.copys($??r4c:<V^8dQhRS[S[,RS[RR/#)r+r~rr-r)rrtr7)r1rs"r2r3rs+ $ $%&?@ $OS $ $ $r4c^a\S4^8dk\;QJdV3RlS4F 'dK RM RM!V3RlS44'd+\P!SVfTM VPVR7pM*\P !\S4\ R7pSVR&V!WCR7#)rc3<"TFop\V\P4;'dIVPS^,P8H;'d!VPS^,P8HxKq R#5iN)rrrr,r~).0rscalarss& r2 ;PandasArrayExtensionArray._from_sequence..s]$ ovjkJq"** % e e!''WQZ5E5E*E e e!''U\]^U_UeUeJe eovs%A:'A:)A:FT)r~rrrr)r|allrrrtrr)r]rr~rrs&f&& r2_from_sequence(PandasArrayExtensionArray._from_sequences wPandasArrayExtensionArray._concat_same_type..s^&  HHNNil0066 6 e e288>>YWX\M_M_MeMe;e es ?B=BFTrrr)r|r rvstackrrr)r]rrrs&f r2_concat_same_type+PandasArrayExtensionArray._concat_same_types y>A ##& & ###& & # # 99;2hh;GGc5"**&< =G% ToHoBpGr4c\V\4'dVPV,#\VPV,RR7#)Fr)rrrr)r r,s&&r2r%PandasArrayExtensionArray.__getitem__s4 dC ::d# #(D)9FFr4c@<V^8dQhRS[S[,RS[RS[RR/#)r+indices allow_fill fill_valuer-r)rrr7)r1rs"r2r3rs1;; ~;37;MQ; $;r4c\P!V\R7pV'dVfVPPM*\P!W0PP R7pVR8HpVR8P 4'd \R4h\V4^8dMq\P!V4'g \R4h\P!V.\V4,VPP R7p\VRR7#VPPV^R7pV'd7XP 4'd!V.\P!V4,Wd&\VRR7#)rzFInvalid value in `indices`, must be all >= -1 for `allow_fill` is TruezAInvalid take for empty PandasArrayExtensionArray, must be all -1.Frrr{)rrrr~na_valuertr#rdr|r  IndexErrorrrrtaker)r r0r1r2maskrtooks&&&& r2r6PandasArrayExtensionArray.takes!jj< '1'9 ##rzz*\f\f\q\q?r b=D" !!## !ijjTQVVD\\ !deexx s7| ;4::CXCXY0EBBzzwQ/ $((**$t 4DJ(E::r4c <V^8dQhRS[/#rr)r1rs"r2r3rsr4c,\VP4#r )r|rrs&r2__len__!PandasArrayExtensionArray.__len__s4::r4c4<V^8dQhRS[P/#rr)r1rs"r2r3rs11rzz1r4c\V\4'g\R\V4 24hVPVP8HP 4#)zInvalid type to compare to: )rrr(rrr )r others&&r2__eq__ PandasArrayExtensionArray.__eq__sC%!:;;%(DT%[M&RS S ekk)..00r4)rrFr )NF)FN)rr!r"r#rSrrrxr rrr~rr"r)rr6r<rAr&r'r(s@r2rrls<<,(@@ $ $ $ % %!!DD$$GG ;;.11r4rc\\V\4'd\VP4#R#r )rrKrtrtrs&r2pandas_types_mapperrEs&%.//()9)9::0r4ca]tRtRtoRtRtRtRt]!RRR7t Rt ] P!4t RtRt]!RRRR7tRtR tV3R lR ltV3R lR ltV3RlRltRtV3RlRlt]R4tV3RltRtVtR#) ClassLabeliaFeature type for integer class labels. There are 3 ways to define a `ClassLabel`, which correspond to the 3 arguments: * `num_classes`: Create 0 to (num_classes-1) labels. * `names`: List of label strings. * `names_file`: File containing the list of labels. Under the hood the labels are stored as integers. You can use -1 to represent unknown/missing labels. Args: num_classes (`int`, *optional*): Number of classes. All labels must be < `num_classes`. names (`list` of `str`, *optional*): String names for the integer classes. The order in which the names are provided is kept. names_file (`str`, *optional*): Path to a file with names for the integer classes, one per line. Example: ```py >>> from datasets import Features, ClassLabel >>> features = Features({'label': ClassLabel(num_classes=3, names=['bad', 'ok', 'good'])}) >>> features {'label': ClassLabel(names=['bad', 'ok', 'good'])} ``` NFrr;rc:WnW nVPeVPe \R4hVPf}VPe"VP VP4VnMVPe5\ VP4Uu.uFp\ V4NK upVnML\R4h\VP\4'g"\R\VP4 24hVPf\VP4VnMSVP\VP48wd0\R\VP4 RVP R24hVPUu.uFp\ V4NK upVn \VP4UUu/uFwr4WCbK uppVn\VP4\VP48wd \R4hR#uupiuupiuuppi)Nz7Please provide either names or names_file but not both.z7Please provide either num_classes, names or names_file.z#Please provide names as a list, is zEClassLabel number of names do not match the defined num_classes. Got z names VS z num_classeszBSome label names are duplicated. Each label name should be unique.) num_classes names_filenamesrd_load_names_from_filerPr0r SequenceABC TypeErrorrr|_int2strr_str2int)r rIrJrrs&&& r2r ClassLabel.__post_init__s&$ ?? &4::+AVW W :: *!77H !!-.3D4D4D.EF.Ec!f.EF  !Z[[DJJ 44A$tzzBRASTU U    #"4::D    TZZ 04::'z$2B2B1C<Q  04zz:ztTz: 09$--0HI0HWQ0HI t}} T]]!3 3ab b 4!G;IsH  H= HcVP#r rrs&r2rClassLabel.__call__!rr4cR<V^8dQhRS[S[S[3,RS[S[S[3,/#r+valuesr-)rr0rr)r1rs"r2r3ClassLabel.__annotate__$s-44eCM24uS(]7K4r4c \V\4'g&\V\4'g\RV R24hRp\V\4'dV.pRpVUu.uFq0P V4NK ppV'dV#V^,#uupi)zConversion class name `string` => `integer`. Example: ```py >>> from datasets import load_dataset >>> ds = load_dataset("cornell-movie-review-data/rotten_tomatoes", split="train") >>> ds.features["label"].str2int('neg') 0 ``` Values zS should be a string or an Iterable (list, numpy array, pytorch, tensorflow tensors)TF)rr0rrd _strval2int)r rV return_listrrs&& r2str2intClassLabel.str2int$s&#&&z&(/K/K&!tu  fc " "XFK7=>ve""5)v>$v3&)3?sB c&<V^8dQhRS[RS[/#)r+rr-)r0r)r1rs"r2r3rW<sr4cTRp\V4pVPPV4pVfSVPPVP44pVf&\ V4pVR8gW0P 8dRpV'd\ RV 24hV# \ dRpL'i;i)FTzInvalid string class label r{)r0rPgetstriprrIrd)r r failed_parse int_values&& r2rZClassLabel._strval2int<s E MM%%e,   ))%++-8I , #E I!2~6F6F)F'+ :5'BC C"(#'L(s B B'&B'cR<V^8dQhRS[S[S[3,RS[S[S[3,/#rU)rrrr0)r1rs"r2r3rWQs-44eCM24uS(]7K4r4c \V\4'g&\V\4'g\RV R24hRp\V\4'dV.pRpVF-p^Tu;8:dVP8dKM\RVR 24h VUu.uFq0P \V4,NK ppV'dV#V^,#uupi)a6Conversion `integer` => class name `string`. Regarding unknown/missing labels: passing negative integers raises `ValueError`. Example: ```py >>> from datasets import load_dataset >>> ds = load_dataset("cornell-movie-review-data/rotten_tomatoes", split="train") >>> ds.features["label"].int2str(0) 'neg' ``` rYzU should be an integer or an Iterable (list, numpy array, pytorch, tensorflow tensors)TFzInvalid integer class label rq)rrrrdrIrO)r rVr[rrs&& r2int2strClassLabel.int2strQs&#&&z&(/K/K&!vw  fc " "XFKA,D,,,, #?!u!EFF288A--A''8$v3&)39s$C cVPf \R4h\V\4'dVP V4pRTu;8:dVP8gM\RVR RVP 24hV#)NzlTrying to use ClassLabel feature with undefined number of class. Please set ClassLabel.names or num_classes. Class label rq% greater than configured num_classes r{)rIrdrr0r\r  example_datas&&r2rClassLabel.encode_exampleos|    #>  lC ( (<< 5L\4D$4$44|L+;;`aeaqaq`rst tr4cx<V^8dQhRS[S[PS[P3,RS[P/#r+rr-)rr. StringArray IntegerArray Int64Array)r1rs"r2r3rWs111E".."//*I$J1r}}1r4c :\V\P4'd{\V4^8dk\P !V4P 4pVR,e;VR,VP8d#\RVR, RVP 24hMh\V\P4'dI\P!VP4Uu.uFq3eVPV4MRNK up4p\WP4#uupi)aYCast an Arrow array to the `ClassLabel` arrow storage type. The Arrow types that can be converted to the `ClassLabel` pyarrow storage type are: - `pa.string()` - `pa.int()` Args: storage (`Union[pa.StringArray, pa.IntegerArray]`): PyArrow array to cast. Returns: `pa.Int64Array`: Array in the `ClassLabel` arrow storage type. maxNrjrk)rr.rrr|pcmin_maxrrIrdrqrrrZrr)r rrwlabels&& r2 cast_storageClassLabel.cast_storages gr / /CL14Djj)//1Gu~)gen@P@P.P "75>"22WX\XhXhWij 0 0hhU\UfUfUhiUhE,=!!%(4GUhiG'<<00jsDc\VRR7;_uu_4pVP4PR4Uu.uF*q"P4'gKVP4NK, upuuRRR4#uupi +'giR#;i)zutf-8)encoding N)openreadsplitra)names_filepathfrs& r2rL ClassLabel._load_names_from_filesY .7 3 3q-.VVX^^D-AR-ATZZ\LDJJL-AR4 3R4 3 3s""A:A5A5(A:5A:: B cR<V^8dQh/S[S[S[,,;R&S[S[,;R&S[S[S[,,;R&S[S[,;R&S[S[,;R&S[S[,;R&S[S[S[S[3,,;R&S[S[S[S[3,,;R&S[;R &#) r+rIrKrJrr~rrPrOr)rrrrr0rr r)r1rs"r2r3rWs@#'.AB 9CD &-EF  7GJ C="KLc]'MNtCH~&-OPtCH~&-QR DSr4)rOrPrKrJrI)rr!r"r#r$rIrKrJrrr~r.r;rrPrOrr rr\rZrgrry staticmethodrLr%r&r'r(s@r2rGrGs<+/KE)-Jd7B"EXXZG)-H)-H|%eDEc:440*44< 114SSCr4rGca]tRtRtoRtRt]!RRR7t]P!4t ]!RRRR7t Rt R t RV3R lR lltV3R lR ltV3RltRtVtR#)JsoniaFeature type for JSON objects. Under the hood the objects are stored as JSON-encoded strings. Example: ```py >>> from datasets import Features, Json >>> features = Features({'json': Json()}) >>> features {'json': Json()} ``` ```py >>> from datasets import Dataset, Features, Json, List >>> features = Features({"a": List(Json())}) >>> ds = Dataset.from_dict({"a": [[{"b": 0}, {"c": 0}]]}, features=features) >>> # OR >>> ds = Dataset.from_dict({"a": [[{"b": 0}, {"c": 0}]]}, on_mixed_types="use_json") >>> ds.features {'a': List(Json())} >>> ds[0] {'a': [{'b': 0}, {'c': 0}]} >>> def f(x): ... for y in x["a"]: ... y["d"] = "foo" ... return x >>> ds = ds.map(f) >>> ds.features >>> ds[0] {'a': [{'b': 0, 'd': 'foo'}, {'c': 0, 'd': 'foo'}]} ``` TNFrrcVP#r rrs&r2r Json.__call__rr4cVfR#\V\4'g\V4pV#\V4V# \d\T4pT#i;ir )rr0rr Exceptionrls&&r2rJson.encode_examples`  ,,,&|4L   9L) 9*<8  9s 8AAc^<V^8dQhRS[S[S[S[S[S[R3,3,,/#r+token_per_repo_idNrrr0rr7)r1rs"r2r3Json.__annotate__s3))htCQVWZ\`bfWfQgLgGh>i)r4c^VP'g \R4hVfR#\V4#)zLDecoding is disabled for this feature. Please use Json(decode=True) instead.N)decode RuntimeErrorr)r rmrs&&&r2decode_exampleJson.decode_examples+{{{mn n  <((r4c^<V^8dQhRS[S[P,RS[P/#rp)rr.r JsonArray)r1rs"r2r3rs'11E"((O1 1r4c  \V\P4'dV#\V\P4'd4VR,P 4pVFpVfK \ V4K MV\P!VP 4Uu.uFqDe \V4MRNK up\P!44p\WP4# \ d`\P!TP 4Uu.uFqDe \T4MRNK Muupiup\P!44pL~i;iuupi)zCast an Arrow array to the `Json` arrow storage type. Args: storage (`Union[pa.StringArray, pa.IntegerArray]`): PyArrow array to cast. Returns: `pa.JsonArray`: Array in the `Json` arrow storage type. :NNN) rr.rrqrrrrrjson_rr)r rrr,rs&& r2ryJson.cast_storages gr|| , ,N ".. 2 2BK))+E !D'#D)"hhSZSdSdSfgSfa- AT ISfgikiqiqistG'<<00  ((HOHYHYH[\H[1}[^$>H[\^`^f^f^h  hs*C!&C!E!-E D( '!E  E cj<V^8dQh/S[;R&S[S[,;R&S[S[,;R&S[;R&#)r+rrrr)r7rr0rr )r1rs"r2r3rsIH IJ  7KNc]'OP >Qr4rr )rr!r"r#r$rrrr.rrrrrrryr%r&r'r(s@r2rrs] DFd7BXXZGvE>E ))11Ar4rc:aa]tRtRtoRtRV3RlltRtVtV;t#)ria A `Sequence` is a utility that automatically converts internal dictionary feature into a dictionary of lists. This behavior is implemented to have a compatibility layer with the TensorFlow Datasets library but may be un-wanted in some cases. If you don't want this behavior, you can use a [`List`] or a [`LargeList`] instead of the [`Sequence`]. Args: feature ([`FeatureType`]): Child feature data type of each item within the large list. length (optional `int`, default to -1): Length of the list if it is fixed. Defaults to -1 which means an arbitrary length. Returns: [`List`] of the specified feature, except `dict` of sub-features which are converted to `dict` of lists of sub-features for compatibility with TFDS. c <V\Jd\V\4'd\;QJd*RVP 44F 'gK RM RM!RVP 444'd3VP 4UUu/uFwrEV\ V3RV/VBbK pppV#\SV`!\ 4pV#uuppi)c3L"TFp\V\4'*xK R#5ir )rList)r subfeatures& r2r #Sequence.__new__..sXGW :t444GWs"$TFlength) rrrr#rVrrsuper__new__)r]featurerkwargsrrrr1s&&&, r2rSequence.__new__s 8O7D))Xw~~GWXXw~~GWXXXOV}}_3U<64;;-qQ Q4j))*!DLL>; ;r4cv<V^8dQh/S[;R&S[;R&S[S[,;R&S[S[,;R&S[;R&#)r+rrrrr)r rrr0r)r1rs"r2r3List.__annotate__sML   7!$c]!%& >'r4rr{)rr!r"r#r$rrrrrrr%r&r'r(s@r2rrs< Fd7B!GvE>E<+r4rcda]tRtRtoRt]!RRR7tRt]!RRRR7tRt V3Rlt R t Vt R#) LargeListi7a'Feature type for large list data composed of child feature data type. It is backed by `pyarrow.LargeListType`, which is like `pyarrow.ListType` but with 64-bit rather than 32-bit offsets. Args: feature ([`FeatureType`]): Child feature data type of each item within the large list. NFrrcL\V4P RVP R2#)rrH)rrrrs&r2rLargeList.__repr__Hs$t*%%&a ~Q77r4cj<V^8dQh/S[;R&S[S[,;R&S[S[,;R&S[;R&#)r+rrrr)r rr0r)r1rs"r2r3LargeList.__annotate__7s@L  7c]! Cr4r) rr!r"r#r$rrrrrr%r&r'r(s@r2rr7s7d7B!G{UCE8#r4rcFV^8dQhR\\,R\/#r+schemar-)r FeatureTyper7)r1s"r2r3r3cs Xk5JVZr4cVVfR#\V\\34'dVe'\V\\\\34'd]\ V4^8dKVfM2\V\\34'd V^,pM VP p\V^,V4#R#R#)z Check if the object is not None. If the object is a list or a tuple, recursively check the first element of the sequence and stop if at any point the first element is not a sequence or is an empty sequence. FT)rrrrrr|rr)rrs&&r2rrcs  { C$ ' 'V^z&SWY^`ikoRp?q?q s8a<~FT5M226s1vvF Fr4cDV^8dQhR\R\P/#r)rr.r/)r1s"r2r3r3ysKBKKr4c &\V\4'd6\P!VUu/uFq\ W,4bK up4#\V\ 4'd6\P!VUu/uFq\ W,4bK up4#\V\ \34'dD\V4^8wd \R4h\ V^,4p\P!V4#\V\4'd,\ VP4p\P!V4#\V\4'd6\ VP4p\P!W P4#V!4#uupiuupi)a get_nested_type() converts a datasets.FeatureType into a pyarrow.DataType, and acts as the inverse of generate_from_arrow_type(). It performs double-duty as the implementation of Features.type and handles the conversion of datasets.Feature->pa.struct zQWhen defining list feature, you should just provide one example of the inner type)rFeaturesr.structget_nested_typerrrr|rdrprr large_listrr)rrrts& r2rrys/&(##yy:@ A&3/&+. .& A   FD ! !yy:@ A&3/&+. .& A   FT5M * * v;! pq q$VAY/ xx ## FI & &$V^^4 }}Z(( FD ! !$V^^4 xx MM22 8O' B Bs F 6Fc \V\4'dWV^8XdVf \R4hVe;VUu/uF-q3\W,VP V4V^,R7bK/ up#R#\V\ \ 34'dVfR#\V4^8dwVPpVFp\WT4'gKM \\VXV^,R7V8g4pV'd%VUu.uFp\WGV^,R7NK up#\V4#\VR4'dVeVPV4#R#V#uupi \dRpLvi;iuupi)aEncode a nested example. This is used since some features (in particular ClassLabel) have some logic during encoding. To avoid iterating over possibly long lists, it first checks (recursively) if the first element that is not None or empty (if it is a sequence) has to be encoded. If the first element needs to be encoded, then all the elements of the list will be encoded, otherwise they'll stay the same. Nz*Got None but expected a dictionary insteadlevelFr)rrrdencode_nested_exampler`rrr|rrr7rrr)rrrr sub_schemarchangedos&&& r2rrsc&$ A:#+IJ JX^ ^W]RS%fi519M MW] ^  FY- . . ;3x!|#^^ "%J::RR#&$"#8ZW\_`W`#aeo#opG[^_[^VW1*uqyQ[^__9  ) * *-0_v$$S)F$F J3 _"$#G$`s3E !E5E# E E cV^8dQhR\\\\\\R3,3,,/#rr)r1s"r2r3r3s1,,(4USVX\^bSbMcHcCd:e,r4c \V\4'd5Ve/\W4UUUu/uFwpwrEV\WE4bK uppp#R#\V\\ 34'dsV^,pVfR#\ V4^8dIVFp\Wd4'gKM \VX4V8wdVUu.uFp\WG4NK up#\ V4#\V\\34'dVfR#VPp\V\4'dVUu.uFp\WG4NK up#\ V4^8dIVFp\Wd4'gKM \VX4V8wdVUu.uFp\WG4NK up#\ V4#\VR4'd,\VRR4'dVeVPWR7#R#V#uupppiuupiuupiuupi)aDecode a nested example. This is used since some features (in particular Audio and Image) have some logic during decoding. To avoid iterating over possibly long lists, it first checks (recursively) if the first element that is not None or empty (if it is a sequence) has to be decoded. If the first element needs to be decoded, then all the elements of the list will be decoded, otherwise they'll stay the same. NrrTr)rrrdecode_nested_examplerrr|rrrrrgetattrr)rrrrrsub_objrrs&&& r2rrs&$\ddj[p q[p?WqBW:Q%j: :[p q  FT5M * *AY ;3x!|"%J::RR#&)Z@JNJMN#Q1*@#NN9  FY- . . ;J*d++FIJc-j rHN)_FEATURE_TYPESloggerwarningr)rrs&&r2register_featurersR~%(c.:V:_:_9``depeyeydzz{ | $/N< r4c$V^8dQhR\/#)r+rr)r1s"r2r3r3s#L#LC#Lr4c \V\4'dVUu.uFp\V4NK up#RV9g\VR,\4'd/VP 4UUu/uFwr!V\V4bK upp#\V4pVP R4p\ PVR4;'g\4PVR4pVf-\RV R\\ P44 24hV\8Xd'VP R4p\\V43/VB#V\8Xd'VP R4p\\V43/VB#V\8Xd(VP R4p\RR\V4/VB#\V4Uu0uFqfPkK ppT!R/VP 4UU u/uFwrW9gK WbK up pB#uupiuuppiuupiuup pi)aRegenerate the nested feature object from a deserialized dict. We use the '_type' fields to get the dataclass name to load. generate_from_dict is the recursive helper for Features.from_dict, and allows for a convenient constructor syntax to define features from deserialized JSON dictionaries. This function is used in particular when deserializing a :class:`DatasetInfo` that was dumped to a JSON object. This acts as an analogue to :meth:`Features.from_arrow_schema` and handles the recursive field-by-field instantiation, but doesn't require any mapping to/from pyarrow, except for the fact that it takes advantage of the mapping of pyarrow primitive dtypes that :class:`Value` automatically performs. rNzFeature type 'z&' not found. Available feature types: rr)rrgenerate_from_dictrrpoprr`r0rdkeysrrrrr) rrrr class_typerr field_namesrrs & r2rrs#t7:;se"5)s;;cZG d;;ADM:3'..MM s)C GGG E##E40NNGIMM%4NJ>%0VW[\j\o\o\qWrVstuuY'')$+G4<<<T'')$&w/7377X'')$C 27 ;CsCC#)*#56#5a66#5K6  K#))+J+$!9I+J KK-<N$7JsG1)G6)G< H%HcDV^8dQhR\PR\/#r)r.r/r)r1s"r2r3r3<s"IIbkkIkIr4c\V\P4'd0VUu/uF"qP\ VP 4bK$ up#\V\P 4'd+\\ VP4VPR7#\V\P4'd\\ VP44#\V\P4'd\\ VP44#\V\4'dHRR\\\ \".VP$,pV!VP&VPR7#\V\P(4'd \+4#\V\P,4'd\/\1V4R7#\3RV R24huupi)a generate_from_arrow_type accepts an arrow DataType and returns a datasets FeatureType to be used as the type for a single field. This is the high-level arrow->datasets type conversion and is inverted by get_nested_type(). This operates at the individual *field* level, whereas Features.from_arrow_schema() operates at the full schema level and holds the methods that represent the bijection from Features<->pyarrow.Schema rN)r,r~rzCannot convert z to a Feature type.)rr. StructTypergenerate_from_arrow_typerFixedSizeListTyperrt list_sizeListType LargeListTyperrKr6r?rCrGrOr,JsonTyperr/rrsrd)rr array_features& r2rr<sT'2==))NUVgU 4UZZ@@gVV GR11 2 2,W-?-?@IZIZ[[ GR[[ ) ),W-?-?@AA GR-- . .1'2D2DEFF G2 3 3tWgwHW 7==8J8JKK GR[[ ) )v GR[[ ) )3G<==?7)3FGHHWs(G!cxV^8dQhR\PR\PR\P/#)r+rrr-)rrr.r/ ListArray)r1s"r2r3r3Ys-  BJJ bkk R\\ r4c^\P!V4p\P!VP4VR7p\ VP ^, 4Fp\ \VPRVP V, ^, ^4pVPVP V, ^, ,p\P!\P!V^,4V,\P!4R7p\PPWb4pK V#)z=Build a PyArrow ListArray from a multidimensional NumPy arrayrN) rrr.rrPrr r r,rr:r from_arrays)rrrVr n_offsets step_offsetsrs&& r2numpy_to_pyarrow_listarrayrYs ((3-C XXckkm$ /F 388a< 3 *$ ## D$  $%9$%?%BCCr4cV^8dQhR\\P\3,R\P R\P /#)r+rrr-)rrrrr.r/r)r1s"r2r3r3s? w wE"**d2B,C w2;; wbdbnbn wr4c \V\P4'd \WR7#\V\4'd&\ VUu.uFp\ W!R7NK up4#R#uupi)aConvert to PyArrow ListArray either a NumPy ndarray or (recursively) a list that may contain any NumPy ndarray. Args: data (Union[np.ndarray, List]): Data. type (pa.DataType): Explicit PyArrow DataType passed to coerce the ListArray data type. Returns: pa.ListArray rN)rrrrrr!any_np_array_to_pyarrow_listarray)rrrs&& r2rrsZ$ ##)$:: D$  5pt6uptkl7XYZ7fpt6uvv 6us A*cPV^8dQhR\R\R\P/#)r+rrr-)r rKr.r)r1s"r2r3r3s& 5 5s 5-B 5rxx 5r4c\V4'd\WPR7#\P!WP 4#)zConvert to PyArrow ListArray. Args: data (Any): List, iterable, np.ndarray or pd.Series. pa_type (_ArrayXDExtensionType): Any of the ArrayNDExtensionType. Returns: pyarrow.Array r)rrrtr.rrR)rrs&&r2to_pyarrow_listarrayrs5T""0.sA0@1#A&&0@TFrr) rrr#rVrrrrrrrr)rr s&&r2rrs'4  sA0@AssAsAsA0@AAA GdE] + + ++ GY ' '00 GT " "00w 01  4KGGXt , QU r4c0V^8dQhR\R\/#r+rr-r )r1s"r2r3r3s00+0$0r4c\V\4'dQ\;QJd*RVP44F 'gK R# R#!RVP444#\V\4'd\ VP 4#\V\4'd\ VP 4#\VR4#)zCheck if a (possibly nested) feature requires storage casting. Args: feature (FeatureType): the feature type to be checked Returns: :obj:`bool` c38"TFp\V4xK R#5ir require_storage_castrs& r2r 'require_storage_cast..E4Dq'**4DrTFry rrr#rVrrrrrrs&r2rrs'4  sEGNN4DEssEsEsEGNN4DEEE GY ' '#GOO44 GT " "#GOO44w//r4c0V^8dQhR\R\/#rr )r1s"r2r3r3s11;141r4c\V\4'dQ\;QJd*RVP44F 'gK R# R#!RVP444#\V\4'd\ VP 4#\V\4'd\ VP 4#\VR4#)zCheck if a (possibly nested) feature requires embedding data into storage. Args: feature (FeatureType): the feature type to be checked Returns: :obj:`bool` c38"TFp\V4xK R#5ir rrs& r2r (require_storage_embed..rrTF embed_storagerrs&r2require_storage_embedr#s'4  sEGNN4DEssEsEsEGNN4DEEE GY ' '#GOO44 GT " "#GOO44w00r4c>a\S4V3Rl4pRVnV#)z Wrapper to keep the secondary dictionary, which tracks whether keys are decodable, of the :class:`datasets.Features` object in sync with the main dictionary. c<V'dV^,pVR,pMVPR4pS!V.VO5/VBp\VR4'gQhVP4UUu/uFwrEV\V4bK uppVnV#uuppi)rrr _column_requires_decoding)rrrrr&)r`rr rcolrrs*, r2wrapper+keep_features_dicts_synced..wrapper's #AwD8D%zz&1D4)$)&)t89999]a]g]g]i)j]i\S#/?/H*H]i)j& *ks!B_keep_dicts_synced)r _decorator_name_)rr(sf r2keep_features_dicts_syncedr,!s*  4[   4G Nr4c^aa]tRtRtoRtV3Rlt]!]P4t]!]P4t ]!]P4t ]!]P4t ]!]P4t ]!]P4t ]!]P4tRt]R4t]R4t]V3RlRl4t]V3R lR l4tR tV3R lR lt]V3RlRl4tRtV3RlRltRtR!V3RlRlltR!V3RlRlltR!V3RlRlltV3RlRltV3RlRlt R"V3RlRllt!R t"Vt#V;t$#)#ri7a A special dictionary that defines the internal structure of a dataset. Instantiated with a dictionary of type `dict[str, FieldType]`, where keys are the desired column names, and values are the type of that column. `FieldType` can be one of the following: - [`Value`] feature specifies a single data type value, e.g. `int64` or `string`. - [`ClassLabel`] feature specifies a predefined set of classes which can have labels associated to them and will be stored as integers in the dataset. - Python `dict` specifies a composite feature containing a mapping of sub-fields to sub-features. It's possible to have nested fields of nested fields in an arbitrary manner. - [`List`] or [`LargeList`] specifies a composite feature containing a sequence of sub-features, all of the same feature type. - [`Array2D`], [`Array3D`], [`Array4D`] or [`Array5D`] feature for multidimensional arrays. - [`Audio`] feature to store the absolute path to an audio file or a dictionary with the relative path to an audio file ("path" key) and its bytes content ("bytes" key). This feature loads the audio lazily with a decoder. - [`Image`] feature to store the absolute path to an image file, an `np.ndarray` object, a `PIL.Image.Image` object or a dictionary with the relative path to an image file ("path" key) and its bytes content ("bytes" key). This feature extracts the image data. - [`Video`] feature to store the absolute path to a video file, a `torchcodec.decoders.VideoDecoder` object or a dictionary with the relative path to a video file ("path" key) and its bytes content ("bytes" key). This feature loads the video lazily with a decoder. - [`Pdf`] feature to store the absolute path to a PDF file, a `pdfplumber.pdf.PDF` object or a dictionary with the relative path to a PDF file ("path" key) and its bytes content ("bytes" key). This feature loads the PDF lazily with a PDF reader. - [`Nifti`] feature to store the absolute path to a NIfTI neuroimaging file, a `nibabel.Nifti1Image` object or a dictionary with the relative path to a NIfTI file ("path" key) and its bytes content ("bytes" key). This feature loads the NIfTI file lazily with nibabel. - [`Translation`] or [`TranslationVariableLanguages`] feature specific to Machine Translation. - [`Json`] feature to store unstructred data, e.g. containing mixed/abritrary types. Under the hood c*<aV'g \R4hVvr \\V`!V/VBVP 4UUu/uFwr4V\ V4bK uppVnV3RloVP 4FwrT\VS4W%&K R#uuppi)z._check_old_listes+'4((F71:?@@Nr4N)rNrrrSrrr&r)r`rr r'r column_namer1r1s*, @r2rSFeatures.__init__YsZ[ [  h&77@Dzz|; ?K|sC!'* *|; &   %)JJL K &w @D %1; sBc&\\V433#r )rrrs&r2rdFeatures.__reduce__us$t*&&r4c \V4#)z= Features field types. Returns: :obj:`pyarrow.DataType` )rrs&r2r Features.typexst$$r4c RRVP4//p\P!VP4P R\ P !V4/4#)z6 Features schema. Returns: :obj:`pyarrow.Schema` infofeatures huggingface)rr.rr with_metadatarxrW)r  hf_metadatas& r2 arrow_schemaFeatures.arrow_schemasE DLLN;< yy#11=$**[BY2Z[[r4c8<V^8dQhRS[PRR/#)r+ pa_schemar-r)r.Schema)r1rs"r2r3Features.__annotate__s")) r4c h\4pVPeRVP9d\P!VPR,P 44pRV9dDRVR,9d6VR,R,e$\P VR,R,4pVP pVUu/uFfpVPVPV9d3VPVP4V8XdW%P,M\VP4bKh ppV!R/VB#uupi)a Construct [`Features`] from Arrow Schema. It also checks the schema metadata for Hugging Face Datasets features. Non-nullable fields are not supported and set to nullable. Also, pa.dictionary is not supported and it uses its underlying type instead. Therefore datasets convert DictionaryArray objects to their actual values. Args: pa_schema (`pyarrow.Schema`): Arrow Schema. Returns: [`Features`] s huggingfacer:r;r) rmetadatarxr\r from_dictr?rrrr)r]rBmetadata_featuresrFmetadata_features_schemarrs&& r2from_arrow_schemaFeatures.from_arrow_schemas$%J    )n @R@R.Rzz)"4"4^"D"K"K"MNH!jHV4D&DRXIYZdIeIq$,$6$6x7G 7S$T!#4#A#A #  # JJ::!227O7U7UV[V`V`7aej7j"**--ejj9 : #  zSz s9A,D/c<V^8dQhRR/#r+r-rr)r1rs"r2r3rDszr4c (\V4pV!R/VB#)a Construct [`Features`] from dict. Regenerate the nested feature object from a deserialized dict. We use the `_type` key to infer the dataclass name of the feature `FieldType`. It allows for a convenient constructor syntax to define features from deserialized JSON dictionaries. This function is used in particular when deserializing a [`DatasetInfo`] that was dumped to a JSON object. This acts as an analogue to [`Features.from_arrow_schema`] and handles the recursive field-by-field instantiation, but doesn't require any mapping to/from pyarrow, except for the fact that it takes advantage of the mapping of pyarrow primitive dtypes that [`Value`] automatically performs. Args: dic (`dict[str, Any]`): Python dictionary. Returns: `Features` Example:: >>> Features.from_dict({'_type': {'dtype': 'string', 'id': None, '_type': 'Value'}}) {'_type': Value('string')} r)r)r]dicrs&& r2rGFeatures.from_dicts4!%zSzr4c\V4#r )rrs&r2rFeatures.to_dicts d|r4c <V^8dQhRS[/#rr)r1rs"r2r3rDsHAHAtHAr4caaaVP4pRRloRVV3RlloRV3RlloS!S!V4R,4#)c0V^8dQhR\R\/#rr)r1s"r2r3,Features._to_yaml_list..__annotate__s  d t r4c\V\4'g\R\V4 RV 24hRFp\VP V4\4'd)\ W,4R.8XdW,R,W&\VP V4\4'gKw\ W,4R.8XgKW,R,W&K \VP R4\4'dn\VR,P R4\ 4'dB\ VR,R,4UUu/uFwr#\V4VbK uppVR,R&V#uuppi)Expected a dict but got a : r~r class_labelrKrrsequence)rrrNrr`rrr0)r list_typelabel_id label_names& r2simplify(Features._to_yaml_list..simplifys6gt,,".to_yaml_innerse#t$$.K'"wwy1H#\=3J$Rc$RSSf_"wwy1H#V]8-D$L$LMMg%J3#%;E%BCC#X/Ee/Lc.R%STT$gjgpgpgr&sgrUcUY'P h8O'Pgr&sttC&&-A2G)H IJJC''$T#Y//"DT#YKrRUQV WXX 'tsE%c0V^8dQhR\R\/#rrW)r1s"r2r3rX s  t  r4cJ<\V\4'd,VP4UUu/uFwrVS!V4bK upp#\V\4'dVUu.uF pS!V4NK up#\V\4'dS!\V44#V#uuppiuupir )rrrrr)rrr to_yaml_typess& r2rm-Features._to_yaml_list..to_yaml_types s#t$$8; D =++ DDC&&256#Q a(#66C''$T#Y// E6s BB r)r)r  yaml_datarbrirms& @@@r2 _to_yaml_listFeatures._to_yaml_listsALLN  D Y Y0  ]95h?@@r4c$<V^8dQhRS[RR/#)r+ror-rrT)r1rs"r2r3rDsJ9J9J9J9r4caa\P!V4pRRloRVV3RlloVPS!V44#)c0V^8dQhR\R\/#rrW)r1s"r2r3.Features._from_yaml_list..__annotate__s    r4c  \V\4'g\R\V4 RV 24hRF6p\VP V4\ 4'gK*RW,/W&K8 \VP R4\4'd\VR,P R4\4'd\ VR,R,\R7pV'dpVUu.uFp\V4NK up\\\VR ,4^,448wd'\R\VR ,4^, R24hVUu.uFq0R,R,V,NK upVR,R&V#uupiuupi) rZr[r~r\rK)rz1ClassLabel expected a value for all label ids [0:z] but some ids are missing.r]r{) rrrNrr`r0sortedrrrPrd)rr_ label_idsr`s& r2 unsimplify,Features._from_yaml_list..unsimplifys[gt,,".from_yaml_inner:s`#t$$IT#YL(.z#/B/B5/IJH%xM3MMMJ&!#*d33#2:c?3F3Fu3M#N6>^^5E 5E 1!9k"RS"R'6"RR5E  $3:c?3F3Fu3M#N )8LsLGVLLF?.z#/B/B5/IJH%xH3HHHH$*3x=99g%!#g,44S!#g,/#ABc(f-cBNQRWo^oNDoh77o^^"DT#YKrRUQV WXX; $ *S$+-CCL-Q#RRSC^s$8H"H"I*I "II)rdeepcopyrG)r]rorrys&&@@r2_from_yaml_listFeatures._from_yaml_lists9MM),  6( Y( YT}}_Y788r4c .\V4p\W4#)z Encode example into a format for Arrow. Args: example (`dict[str, Any]`): Data in a Dataset row. Returns: `dict[str, Any]` rr)r examples&&r2rFeatures.encode_examplefs)1$T33r4c <V^8dQhRS[/#)r+r3r)r1rs"r2r3rDtsZZZr4c  j\V4pVUu.uFp\W,V^R7NK up#uupi)z Encode column into a format for Arrow. Args: column (`list[Any]`): Data in a Dataset column. column_name (`str`): Dataset column name. Returns: `list[Any]` rr)r columnr3rs&&& r2 encode_columnFeatures.encode_columnts5(/RXYRX3%d&7AFRXYYYs0c  /p\V4\V48wd$\R\V4 R\V4 24hVP4F6wr4\V4pVUu.uFp\ W,V^R7NK upW#&K8 V#uupi)z Encode batch into a format for Arrow. Args: batch (`dict[str, list[Any]]`): Data in a Dataset batch. Returns: `dict[str, list[Any]]` zColumn mismatch between batch z and features r)setrdrrr)r batch encoded_batchrrrs&& r2 encode_batchFeatures.encode_batchs u:T "=c%j\X[\`XaWbcd d ;;=KC+F3F\b!c\bUX"7 3a"P\b!cM )"ds#B c d<V^8dQhRS[RS[S[S[S[S[S[R3,3,,/#)r+rrNrrr0rr7)r1rs"r2r3rDs:  d xSRWX[]acgXgRhMhHi?j r4c  \VP4UUu/uFwr4W19gK W4bK uppV4UUUu/uF1wpwrdTVPV,'d\WdVR7MTbK3 uppp#uuppiuupppi)a[Decode example with custom feature decoding. Args: example (`dict[str, Any]`): Dataset row data. token_per_repo_id (`dict`, *optional*): To access and decode audio or image files from private repositories on the Hub, you can pass a dictionary `repo_id (str) -> token (bool or str)`. Returns: `dict[str, Any]` r)rrr&r)r rrrrr3rs&&& r2rFeatures.decode_examples$2:.2jjlMl cnlMw2  2- -g --k::/wQbc 2   N  s A7 A7 7A=c j<V^8dQhRS[RS[RS[S[S[S[S[S[R3,3,,/#)r+rr3rN)rr0rrrr7)r1rs"r2r3rDsD   ), AI$sTYZ]_ceiZiTjOjJkAl r4c  VPV,'d*VUu.uFpVe\W,WCR7MRNK up#T#uupi)zDecode column with custom feature decoding. Args: column (`list[Any]`): Dataset column data. column_name (`str`): Dataset column name. Returns: `list[Any]` Nr)r&r)r rr3rrs&&&& r2 decode_columnFeatures.decode_columnsd*--k::$  $E$&d&7d$     s"Ac d<V^8dQhRS[RS[S[S[S[S[S[R3,3,,/#)r+rrNr)r1rs"r2r3rDs9$8DeTWY]_cTcNdIdDe;fr4c  /pVP4FKwrEVPV,'d*VUu.uFpVe\W,WbR7MRNK upMTW4&KM V#uupi)abDecode batch with custom feature decoding. Args: batch (`dict[str, list[Any]]`): Dataset batch data. token_per_repo_id (`dict`, *optional*): To access and decode audio or image files from private repositories on the Hub, you can pass a dictionary repo_id (str) -> token (bool or str) Returns: `dict[str, list[Any]]` Nr)rr&r)r rr decoded_batchr3rrs&&& r2 decode_batchFeatures.decode_batchs #(;;= K11+>>"( "((*$*;Uh"(   &$1s"A$c<V^8dQhRR/#rMr)r1rs"r2r3rDs##j#r4c .\P!V4#)aP Make a deep copy of [`Features`]. Returns: [`Features`] Example: ```py >>> from datasets import load_dataset >>> ds = load_dataset("cornell-movie-review-data/rotten_tomatoes", split="train") >>> copy_of_features = ds.features.copy() >>> copy_of_features {'label': ClassLabel(names=['neg', 'pos']), 'text': Value('string')} ``` )rrrs&r2r Features.copys$}}T""r4c"<V^8dQhRRRR/#)r+r@rr-r)r1rs"r2r3rDs3838z38j38r4c 6aRV3Rllo\S!W44#)a Reorder Features fields to match the field order of other [`Features`]. The order of the fields is important since it matters for the underlying arrow data. Re-ordering the fields allows to make the underlying arrow data type match. Args: other ([`Features`]): The other [`Features`] to align with. Returns: [`Features`] Example:: >>> from datasets import Features, List, Value >>> # let's say we have two features with a different order of nested fields (for a and b for example) >>> f1 = Features({"root": {"a": Value("string"), "b": Value("string")}}) >>> f2 = Features({"root": {"b": Value("string"), "a": Value("string")}}) >>> assert f1.type != f2.type >>> # re-ordering keeps the base structure (here List is defined at the root level), but makes the fields order match >>> f1.reorder_fields_as(f2) {'root': List({'b': Value('string'), 'a': Value('string')})} >>> assert f1.reorder_fields_as(f2).type == f2.type c <V'dRVR,,MRp\V\4'd\V\4'g\RV RV 2V,4h\V4\V48wdhRV RV RVP 4VP 4, RVP 4VP 4, R 2 V,p\V4hVUu/uF#qUS!W,W,VR V 2,4bK% up#\V\ 4'di\V\ 4'g\RV RV 2V,4h\ S!VP VP VR ,4VPR 7#\V\4'd]\V\4'g\RV RV 2V,4h\S!VP VP VR ,44#V#uupi) z at rzType mismatch: between rzzKeys mismatch: between z (source) and z (target). z are missing from target and z are missing from sourcerDz.r) rrrdrwrrrrr)sourcetargetstackstack_positionmessagerrecursive_reorders&&& r2r5Features.reorder_fields_as..recursive_reorder s38VeBi/bN&$''!&$//$'>vheF8%TWe%eff&>VF^31&x|!;;=6;;=89:%{{}v{{}<==UWYgh %W--gmngm`c.v{FKSTUXTYQZIZ[[gmnnFD))!&$//$'>vheF8%TWe%eff-fnnfnneV_N_`ioivivwwFI..!&)44$'>vheF8%TWe%eff !26>>6>>SX[dSd!eff os$)H)r)r)r r@rs&&@r2reorder_fields_asFeatures.reorder_fields_ass6 0)$677r4c<V^8dQhRR/#rMr)r1rs"r2r3rD+ s))z)r4c  N\^V4EFpRpVP4pVP4FwrV\V\4'd@RpTP VP4UUu/uF wrxV RV 2VbK upp4WEKZ\ VR4'gKnVP4V8wgKRpTP VP4P4UUu/uF wrxV RV 2VbK upp4WEK TpV'gEKV# V#uuppiuuppi)a:Flatten the features. Every dictionary column is removed and is replaced by all the subfields it contains. The new fields are named by concatenating the name of the original column and the subfield name like this: `.`. If a column contains nested dictionaries, then all the lower-level subfields names are also concatenated to form new columns: `..`, etc. Returns: [`Features`]: The flattened features. Example: ```py >>> from datasets import load_dataset >>> ds = load_dataset("rajpurkar/squad", split="train") >>> ds.features.flatten() {'answers.answer_start': List(Value('int32'), id=None), 'answers.text': List(Value('string'), id=None), 'context': Value('string'), 'id': Value('string'), 'question': Value('string'), 'title': Value('string')} ``` TFrDr)rPrrrrupdaterr) r  max_depthdepth no_change flattenedr3rrrs && r2rFeatures.flatten+ s41i(EI I+/::<' j$// %I$$*JZJZJ\%]J\$! Qqc&:A&=J\%]^!.Z33 8J8J8LPZ8Z %I$$*J\J\J^JdJdJf%gJf$! Qqc&:A&=Jf%gh!.,8Dy ) &^&hs .D&D!)r&r ))%rr!r"r#r$rSr,rr) __delitem__r setdefaultrpopitemclearrdrrr?rxrJrGrrprrrrrrrrrrr&r'rrs@@r2rr7sTBA(-T-=-=>K,T-=-=>K ' 4F+DOO cd8n cr4c /pVFypVP4Fbwr4W19d5\V\4'd\W,V.4^,W&K?W19g\ W,4'gK^WAV&Kd K{ VUUu.uF2p\ VP 4Uu/uF q3W,bK up4NK4 upp#uupiuuppi)zoAlign dictionaries of features so that the keys that are found in multiple dictionaries share the same feature.)rrr_align_featuresrrrr name2featurer;rrs& r2rrf sL!NN$DA Z4%8%8"1._fix_old_list s) gt $ $=gajIJ Jr4)r)rrs& r2rr s ' ))r4cbV^8dQh/^\9d\\\3,;R&#)r+r)__conditional_annotations__rr0r)r1s"r2r3r3s+aaB/S+%&C/br4)FT)rr+)rr+)rr+r)rr+rrrrCr r0)rr$rrxrrcollections.abcrrrrMr dataclassesrrrr functoolsr r operatorr typingr r rrrrrrpandasrrOr.pyarrow.computecomputerv pyarrow.typespandas.api.extensionsrPandasExtensionArrayrPandasExtensionDtyperrnamingrrtablerutilsrr utils.jsonrrutils.py_utilsrrraudiorimager r!meshr"niftir#r$rr%r& translationr'r(videor) get_loggerrrrsrrrrr*r6r?rCrGr/rKrzr~rrregister_extension_typerrkrtrrErGrrrrrrrrrrrrrrrrrrrrrrr0rr rrr#r,rrrrrr3)rs@r2rs b -3199#DDHHC)1CC*.+B   H %C`LTn|~ 2 Q8Q8Q8h   BhB B0 BhB B0 BhB B0 BhB B0+:B,,+:\0000 /@A/ 7CD/ gFG/IJr,:&"++:&z 4 De1 4e1P;  BSBS BSJ W1W1 W1tB <8<<6 888(            %  .,B%P,^* NNE**+*!))+G *  *  MM4 * g* g* g* g* NNE* NNE* MM4* NNE*LL#*  NNE!*" MM4#** / /#LLI:  5'" w 5 +*%WQZ( ) vx+@ 00$1$,]t]@  c.*r4