U -ejR@sdZddlZddlZddlZddlZddlmZmZddlmZ ddl m Z m Z m Z mZddlmZmZddlmZddlmZmZmZmZmZmZmZmZddlmZddlZddl Z!ddl"Z#ddl$m%Z&ddl'Z"dd l(m)Z*dd l(m+Z,d d l-m.Z.d d l/m0Z0m1Z1d dl2m3Z3d dl4m5Z5d dl6m7Z7m8Z8m9Z9ddl:m;Z;ddlZ>ddl?m@Z@mAZAe5BeCZDe#jEeFdddZGeFe#jEdddZHeeIeIeeeIfdddZJdeed d!d"ZKe Gd#d$d$ZLGd%d&d&ZMe Gd'd(d(eMZNe Gd)d*d*eMZOe Gd+d,d,eMZPe Gd-d.d.eMZQGd/d0d0e#jRZSGd1d2d2eSZTGd3d4d4eSZUGd5d6d6eSZVGd7d8d8eSZWde#jEeIeId9d:d;ZXGdd?d?e,ZZGd@dAdAe*Z[dBdCZ\e GdDdEdEZ]e GdFdGdGZee^e_e`eLe]e@eAeeNeOePeQe;e=fZadeeaeIdHdIdJZbeae#jEdHdKdLZcddMdNZddeeeFeeFeIdffdOdPdQZeedRdSdTZfe#jEeadUdVdWZgdejhe#jEe#jidXdYdZZjeee#jke#jid[d\d]Zldeejhe#jEe#jid^d_d`ZmedadbdcZndeejhefe#jEe#jidddedfZoeeSe#jkdgdhdiZpeaeeageeafeadjdkdlZqdeaeIeIdmdndoZreaeIdpdqdrZseaeIdpdsdtZtdudvZuGdwdxdxe^Zveeveevdydzd{Zweevd|d}d~ZxdS)z\ This class handle features definition in datasets and some utilities to display table type.N)IterableMapping)Sequence)InitVar dataclassfieldfields)reducewraps)mul)AnyCallableClassVarDictListOptionalTupleUnion)ExtensionArray)ExtensionDtype)config)camelcase_to_snakecasesnakecase_to_camelcase) array_cast)logging)asdictfirst_non_null_valuezip_dict)Audio)Imageencode_pil_image) TranslationTranslationVariableLanguages) arrow_typereturncCsZtj|rdStj|r dStj|r0dStj|r@dStj|rPdStj|r`dStj|rpdStj |rdStj |rd Stj |rd Stj |rd Stj |rd Stj|rd Stj|rdtt|jdStj|rdtt|jdStj|rv|jdkrFd|jdS|jrdd|jd|jdStd|dntj|rdStj|rdStj|rd|jdStj|rd|jd|jdStj|rd|jd|jdStj|rdStj |r"dStj!|r4dStj"|rFd Std!|d"dS)#z _arrow_to_datasets_dtype takes a pyarrow.DataType and converts it to a datasets string dtype. 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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. NcSs|d|d}|rVt|dkr>d|ddd|dn|d}|d|d 7}|rt|dkrd|ddd |dn|d}|d |d 7}|S) NzA is not a validly formatted string representation of the pyarrow z type.rr/z or rz Valid examples include: r. and z For more insformation, see: )lenjoin)dtypeZpa_dtypeexamplesurlsmsgr?r?r@_dtype_error_msgs22z)string_to_arrow.._dtype_error_msg_z^timestamp\[(.*)\]$rz)^(s|ms|us|ns),\s*tz=([a-zA-Z0-9/_+\-:]*)$)smsusnsr timestampz timestamp[us]z!timestamp[us, tz=America/New_YorkzEhttps://arrow.apache.org/docs/python/generated/pyarrow.timestamp.html)rHrIz^duration\[(.*)\]$durationz duration[s]z duration[us]zDhttps://arrow.apache.org/docs/python/generated/pyarrow.duration.htmlz^time(.*)\[(.*)\]$Z32)rMrNzc is not a valid unit for the pyarrow time32 type. Supported units: s (second) and ms (millisecond).Z64)rOrPzh is not a valid unit for the pyarrow time64 type. 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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. 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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 rerrf)r^r_r`r?r?r@cast_to_python_objectssrc@sheZdZUdZeed<dZeeed<dZe e ed<e ddddZ eed<d d Z d d Zd dZdS)Valuea The `Value` dtypes 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` - `string` - `large_string` Example: ```py >>> from datasets import Features >>> features = Features({'stars': Value(dtype='int32')}) >>> features {'stars': Value(dtype='int32', id=None)} ``` rGNidpa_typeFdefaultinitrepr_typecCs0|jdkrd|_|jdkr d|_t|j|_dS)Ndoubler,floatr+)rGr]rselfr?r?r@ __post_init__s   zValue.__post_init__cCs|jSNrrr?r?r@__call__szValue.__call__cCs`tj|jrt|Stj|jr,t|Stj|jrBt|Stj |jrXt |S|SdSr) r7r4r6rr( is_integerr\Z is_floatingrr>r8rrnr?r?r@encode_exampleszValue.encode_example)__name__ __module__ __qualname____doc__r8__annotations__rrrrr rrrrrr?r?r?r@rs (rc@s$eZdZddZddZddZdS)_ArrayXDcCst|j|_dSr)rshaperr?r?r@r sz_ArrayXD.__post_init__cCs t|jjd|j|j}|S)NZ ExtensionType)globals __class__rrrG)rrr?r?r@rsz_ArrayXD.__call__cCs|Srr?rr?r?r@rsz_ArrayXD.encode_exampleN)rrrrrrr?r?r?r@r src@sHeZdZUdZeed<eed<dZeeed<e ddddZ eed<dS) Array2Da?Create a two-dimensional array. Args: shape (`tuple`): The size of each dimension. dtype (`str`): The value of the data type. Example: ```py >>> from datasets import Features >>> features = Features({'x': Array2D(shape=(1, 3), dtype='int32')}) ``` rrGNrFrr rrrrrrr8rrrrr?r?r?r@rs rc@sHeZdZUdZeed<eed<dZeeed<e ddddZ eed<dS) Array3DaDCreate a three-dimensional array. Args: shape (`tuple`): The size of each dimension. dtype (`str`): The value of the data type. Example: ```py >>> from datasets import Features >>> features = Features({'x': Array3D(shape=(1, 2, 3), dtype='int32')}) ``` rrGNrFrrrr?r?r?r@r/s rc@sHeZdZUdZeed<eed<dZeeed<e ddddZ eed<dS) Array4DaFCreate a four-dimensional array. Args: shape (`tuple`): The size of each dimension. dtype (`str`): The value of the data type. Example: ```py >>> from datasets import Features >>> features = Features({'x': Array4D(shape=(1, 2, 2, 3), dtype='int32')}) ``` rrGNrFrrrr?r?r?r@rHs rc@sHeZdZUdZeed<eed<dZeeed<e ddddZ eed<dS) Array5DaICreate a five-dimensional array. Args: shape (`tuple`): The size of each dimension. dtype (`str`): The value of the data type. Example: ```py >>> from datasets import Features >>> features = Features({'x': Array5D(shape=(1, 2, 2, 3, 3), dtype='int32')}) ``` rrGNrFrrrr?r?r?r@ras rc@sVeZdZUdZeeed<eedddZ ddZ dd Z d d Z d d Z ddZdS)_ArrayXDExtensionTypeNndimsrrGcCs|jdks|jdkrtdt||jkrBtd|d|jdtd|jD]}||dkrNtd|qNt||_||_||j|_t j ||jdS)NrzCYou 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: ) rr;rErangerr value_type_generate_dtype storage_dtyper7PyExtensionType__init__)rrrGdimr?r?r@r}s  z_ArrayXDExtensionType.__init__cCs|j|j|jffSr)rrrrr?r?r@ __reduce__sz _ArrayXDExtensionType.__reduce__cCst|j|j|jfSr)hashrrrrr?r?r@__hash__sz_ArrayXDExtensionType.__hash__cCstSr)ArrayExtensionArrayrr?r?r@__arrow_ext_class__sz)_ArrayXDExtensionType.__arrow_ext_class__cCs&t|}t|jD]}t|}q|Sr)r]reversedrr7list_)rrGdr?r?r@rs z%_ArrayXDExtensionType._generate_dtypecCs t|jSr)PandasArrayExtensionDtyperrr?r?r@to_pandas_dtypesz%_ArrayXDExtensionType.to_pandas_dtype)rrrrrr\rrr8rrrrrrr?r?r?r@rzs  rc@seZdZdZdS)Array2DExtensionTyperNrrrrr?r?r?r@rsrc@seZdZdZdS)Array3DExtensionTypeNrr?r?r?r@rsrc@seZdZdZdS)Array4DExtensionTypeNrr?r?r?r@rsrc@seZdZdZdS)Array5DExtensionTypeNrr?r?r?r@rsr)runnestr&csJtjtjdfdd |r$|}tj|oHtj|pFtj| S)a When converting a pyarrow array to a numpy array, we must know whether this could be done in zero-copy or not. This function returns the value of the ``zero_copy_only`` parameter to pass to ``.to_numpy()``, given the type of the pyarrow array. # zero copy is available for all primitive types except booleans and temporal types (date, time, timestamp or duration) # primitive types are types for which the physical representation in arrow and in numpy # https://github.com/wesm/arrow/blob/c07b9b48cf3e0bbbab493992a492ae47e5b04cad/python/pyarrow/types.pxi#L821 # see https://arrow.apache.org/docs/python/generated/pyarrow.Array.html#pyarrow.Array.to_numpy # and https://issues.apache.org/jira/browse/ARROW-2871?jql=text%20~%20%22boolean%20to_numpy%22 rr&cstj|r|jS|Sr)r7r4Zis_listrr_unnest_pa_typer?r@rs  z+_is_zero_copy_only.._unnest_pa_type)r7DataTyper4Z is_primitiver6Z is_temporal)rrr?rr@_is_zero_copy_onlys rc@s.eZdZddZddZd ddZdd Zd S) rcCst|jjdd}|j|dS)NTrzero_copy_only)rstoragetypeto_numpy)rrr?r?r@rqszArrayExtensionArray.__array__cCs |j|Srr)rir?r?r@ __getitem__szArrayExtensionArray.__getitem__TcCs|j}|jdd}|jjddk rd}tt||tt|}t |jj D]}||jj|9}| }qX|j|d}|j t|t|f|jj}t|rtj |tj|tjdd}n|jj}|jj } g} tdd|jD} t|D]\}} | r| tjq|||d} | |d| |}t | D]}| } qB| j|d}| |j |f|ddqttt| dkrtjt| td}| |dd<n t| }|S) NFrrrZaxiscSsg|] }|qSr?)as_py)rioffr?r?r@rksz0ArrayExtensionArray.to_numpy..rG)rr5rrrrvarangerEsumrrflattenZreshapeinsertastyper,nanarrayoffsets enumerateappenduniquediffemptyobject)rrr null_masksize null_indicesr numpy_arrrrZarraysZfirst_dim_offsetsr5Z storage_elZ first_dimrLr?r?r@rs<"        zArrayExtensionArray.to_numpycCsTt|jjdd}|j|d}|jjddkrH|jtkrHdd|DS|SdS)NTrrrcSsg|] }|qSr?)r}riarrr?r?r@rksz1ArrayExtensionArray.to_pylist..)rrrrrrGrr})rrrr?r?r@ to_pylists  zArrayExtensionArray.to_pylistN)T)rrrrqrrrr?r?r?r@rs ,rc@seZdZdZedejfdddZeej ej fdddZ e dd Z eed d d Zeed d dZeed ddZeejd ddZdS)rrrcCs ||_dSrZ _value_type)rrr?r?r@rsz"PandasArrayExtensionDtype.__init__)rcCsNt|tjr*|jtdd|jD}t|jjdd}|j |d}t |S)NcSsg|] }|jqSr?r)richunkr?r?r@rk sz.Trr) rur7 ChunkedArrayrZ wrap_array concat_arrayschunksrrrPandasArrayExtensionArray)rrrrr?r?r@__from_arrow__ s   z(PandasArrayExtensionDtype.__from_arrow__cCstSr)r)clsr?r?r@construct_array_typesz.PandasArrayExtensionDtype.construct_array_typer&cCstjSr)rvrwrr?r?r@rszPandasArrayExtensionDtype.typecCsdS)NOr?rr?r?r@kindszPandasArrayExtensionDtype.kindcCsd|jdS)Nzarray[r-rrr?r?r@nameszPandasArrayExtensionDtype.namecCs|jSrrrr?r?r@r"sz$PandasArrayExtensionDtype.value_typeN)rrr _metadatarrvrGrr7Arrayrr classmethodrpropertyrr8rrrr?r?r?r@rs rc@s eZdZd%ejedddZd&ddZd'eddd d Ze d(e e edd d d Z e e dddddZee dddZeedddZejdddZeeeejfeddddZeeeejfeejdfdddZd)e eeedddd Zedd!d"Zejdd#d$ZdS)*rF)datacopycCs$|s|nt||_t|j|_dSr)rvr_datarrG_dtype)rrrr?r?r@r(sz"PandasArrayExtensionArray.__init__NcCs`|tkrBtjt|jtd}tt|jD]}|j|||<q*|S|dkrP|jS|j|SdS)a 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 rN)rrvrrErrr)rrGoutrr?r?r@rq,s z#PandasArrayExtensionArray.__array__)deepr&cCst|jddS)NTr)rr)rrr?r?r@r@szPandasArrayExtensionArray.copy)rGrr&csltdkrBtfddDrBtj|dkr4|n|j|d}ntjttd}|dd<|||dS)Nrc3s:|]2}t|tjo0|jdjko0|jdjkVqdSrN)rurvrwrrGrhscalarsr?r@ Gsz;PandasArrayExtensionArray._from_sequence..)rGrrr )rEallrvrrrr)rr rGrrr?r r@_from_sequenceCs  z(PandasArrayExtensionArray._from_sequence) to_concatr&csltdkr8tfddDr8tddD}n(tjttd}ddD|dd<||dd S) Nrc3s6|].}|jjdjjko,|jjdjjkVqdSr )rrrGrivarr?r@r Rsz>PandasArrayExtensionArray._concat_same_type..cSsg|] }|jqSr?rrr?r?r@rkVsz?PandasArrayExtensionArray._concat_same_type..rcSsg|] }|jqSr?rrr?r?r@rkYsFr )rErrvZvstackrr)rrrr?rr@_concat_same_typePsz+PandasArrayExtensionArray._concat_same_typercCs|jSr)rrr?r?r@rG\szPandasArrayExtensionArray.dtypecCs|jjSr)rnbytesrr?r?r@r`sz PandasArrayExtensionArray.nbytescCstdd|jDS)NcSsg|]}t|qSr?)r|isnaanyrr?r?r@rkesz2PandasArrayExtensionArray.isna..)rvrrrr?r?r@rdszPandasArrayExtensionArray.isna)rmrnr&cCs tdSr)NotImplementedError)rrmrnr?r?r@ __setitem__gsz%PandasArrayExtensionArray.__setitem__)itemr&cCs&t|tr|j|St|j|ddS)NFr )rur\rr)rrr?r?r@rjs  z%PandasArrayExtensionArray.__getitem__)indices allow_fill fill_valuer&cCstj|td}|r|dkr"|jjntj||jjd}|dk}|dkrRtdnJt|dkr`n= -1 for `allow_fill` is TruerzAInvalid take for empty PandasArrayExtensionArray, must be all -1.Fr r)rvr{r\rGZna_valuerrr;rEr IndexErrorrrrtaker)rrrrmaskrZtookr?r?r@r os"        zPandasArrayExtensionArray.takecCs t|jSr)rErrr?r?r@__len__sz!PandasArrayExtensionArray.__len__cCs,t|tstdt||j|jkS)NzInvalid type to compare to: )rurrrrrrotherr?r?r@__eq__s z PandasArrayExtensionArray.__eq__)F)N)F)NF)FN)rrrrvrwr(rrqrrrrr Sequence_rrrGr\rrrslicer rrr r"r%r?r?r?r@r's<   & rcCst|trt|jSdSr)rurrrrr?r?r@pandas_types_mappers r(c@sVeZdZUdZdZeeeed<dZ e e ed<dZ eee ed<dZ ee ed<dZee ed<eZeeed <dZeee efed <dZeeeefed <edd d d Ze ed<ddZddZee efeeefdddZe edddZeeefee efdddZddZ eej!ej"fej#dddZ$e%d d!Z&dS)" ClassLabela)Feature type for integer class labels. There are 3 ways to define a `ClassLabel`, which correspond to the 3 arguments: * `num_classes`: Create 0 to (num_classes-1) labels. * `names`: List of label strings. * `names_file`: File containing the list of labels. Under the hood the labels are stored as integers. You can use negative integers to represent unknown/missing labels. Args: num_classes (`int`, *optional*): Number of classes. All labels must be < `num_classes`. names (`list` of `str`, *optional*): String names for the integer classes. The order in which the names are provided is kept. names_file (`str`, *optional*): Path to a file with names for the integer classes, one per line. Example: ```py >>> from datasets import Features >>> features = Features({'label': ClassLabel(num_classes=3, names=['bad', 'ok', 'good'])}) >>> features {'label': ClassLabel(num_classes=3, names=['bad', 'ok', 'good'], id=None)} ``` N num_classesnames names_filerr*rGr_str2int_int2strFrrcCs(||_||_|jdk r(|jdk r(td|jdkrx|jdk rL||j|_q|jdk rnddt|jD|_qtdn t|jtstdt |j|jdkrt |j|_n.|jt |jkrtdt |jd|jdd d|jD|_ d d t |j D|_ t |j t |j kr$td dS) Nz7Please provide either names or names_file but not both.cSsg|] }t|qSr?r8rirr?r?r@rksz,ClassLabel.__post_init__..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_classescSsg|] }t|qSr?r/rirr?r?r@rkscSsi|]\}}||qSr?r?)rirrr?r?r@rosz,ClassLabel.__post_init__..zBSome label names are duplicated. Each label name should be unique.)r*r,r+r;_load_names_from_filerru SequenceABC TypeErrorrrEr.rr-)rr*r,r?r?r@rs,      zClassLabel.__post_init__cCs|jSrrrr?r?r@rszClassLabel.__call__)valuesr&cs^t|ts$t|ts$td|dd}t|tr<|g}d}fdd|D}|rV|S|dS)a Conversion class name `string` => `integer`. Example: ```py >>> from datasets import load_dataset >>> ds = load_dataset("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)TFcsg|]}|qSr? _strval2intrirnrr?r@rksz&ClassLabel.str2int..r)rur8rr;)rr5 return_listrr?rr@str2ints   zClassLabel.str2int)rnr&cCsd}t|}|j|}|dkrt|j|}|dkrtz t|}Wntk r\d}YnX|dksp||jkrtd}|rtd||S)NFTrCzInvalid string class label )r8r-getstripr\r;r*)rrnZ failed_parseZ int_valuer?r?r@r8s   zClassLabel._strval2intcst|ts$t|ts$td|dd}t|tr<|g}d}|D],}d|krZjks@ntd|dq@fdd |D}|r|S|dS) adConversion `integer` => class name `string`. Regarding unknown/missing labels: passing negative integers raises `ValueError`. Example: ```py >>> from datasets import load_dataset >>> ds = load_dataset("rotten_tomatoes", split="train") >>> ds.features["label"].int2str(0) 'neg' ``` r6zU should be an integer or an Iterable (list, numpy array, pytorch, tensorflow tensors)TFrzInvalid integer class label rcsg|]}jt|qSr?)r.r\)rirrr?r@rk'sz&ClassLabel.int2str..)rur\rr;r*)rr5r:rrr?rr@int2str s  zClassLabel.int2strcCsZ|jdkrtdt|tr&||}d|kr<|jksVntd|dd|j|S)NzlTrying to use ClassLabel feature with undefined number of class. Please set ClassLabel.names or num_classes.rC Class label r% greater than configured num_classes )r*r;rur8r;)rZ example_datar?r?r@r*s   zClassLabel.encode_example)rr&cst|tjr\t|dkr\t|}|ddk r|djkrtd|ddjn(t|tj rt fdd| D}t |j S)aCast 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. rmaxNr?r@cs"g|]}|dk r|ndqSrr7)rilabelrr?r@rkPsz+ClassLabel.cast_storage..)rur7 IntegerArrayrEpcmin_maxrr*r; StringArrayrrrr)rrrEr?rr@ cast_storage:s zClassLabel.cast_storagec Cs<t|dd&}dd|dDW5QRSQRXdS)Nutf-8)encodingcSsg|]}|r|qSr?)r=r1r?r?r@rkWsz4ClassLabel._load_names_from_file.. )openreadsplit)Znames_filepathfr?r?r@r2Tsz ClassLabel._load_names_from_file)'rrrrr*rrr\rr+rr8r,rrGrr7r*rr r-rr.rrrrrrr;r8r>rrFrCZ Int64ArrayrG staticmethodr2r?r?r?r@r)s&   r)c@sleZdZUdZeed<dZeed<dZe e ed<dZ e e ed<dZ e eed <edd d d Ze ed <dS) ra Construct a list of feature from a single type or a dict of types. Mostly here for compatiblity with tfds. Args: feature: A list of features of a single type or a dictionary of types. length (`int`): Length of the sequence. Example: ```py >>> from datasets import Features, Sequence, Value, ClassLabel >>> features = Features({'post': Sequence(feature={'text': Value(dtype='string'), 'upvotes': Value(dtype='int32'), 'label': ClassLabel(num_classes=2, names=['hot', 'cold'])})}) >>> features {'post': Sequence(feature={'text': Value(dtype='string', id=None), 'upvotes': Value(dtype='int32', id=None), 'label': ClassLabel(num_classes=2, names=['hot', 'cold'], id=None)}, length=-1, id=None)} ``` featurerClengthNrrprGrFrr)rrrrr rrQr\rrr8rGrrrrr?r?r?r@rZs  r)schemar&cCs|dkr dSt|ttfrz|dks2t|tttfrzt|dkrt|dkrHnt|ttfr`|d}n|j}t|d|SdSndSdS)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. NFrT)rurprrrErPrr^rRr?r?r@rs&  rcsttr"tfddDSttrDtfddDStttfr|tdkrftdt d}t |Stt rt j }tj trtfdd|DSt |j SS)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 csi|]}|t|qSr?get_nested_typerirmrRr?r@rosz#get_nested_type..csi|]}|t|qSr?rTrVrWr?r@rosrzQWhen defining list feature, you should just provide one example of the inner typercs i|]}|jt|jjqSr?)rr7rrrQrirNrWr?r@ros)ruFeaturesr7structrrprrEr;rUrrrPrQ)rRrr?rWr@rUs&        rUcs8ttrFdkr"|dkr"td|dk rBfddt|DSdStttfrˆd|dkrhdSt|dkr|D]}t|rxqqxt|dd|krfdd |DSt|Snrtt r|dkrdStj trti}t|ttfr.rr\csg|]}t|ddqSr[r^rior]rar?r@rksz)encode_nested_example..cs"g|]}td|ddqS)rrr\r^rb) dict_tuplesr]r?r@rkscsg|]}t|ddqSr[r^rbrdr?r@rksz+Got a string but expected a list instead: ''cs g|]}tj|ddqSr[)r_rPrb)r]rRr?r@rks)rurr;rrprrErr_rrPr8r r!r)r$rrr)rRr^r]rZ list_dictrZsub_objsr?)rer]rRrar@r_s^       $   r_token_per_repo_idcs ttr*dk r&ddtDSdStttfrddkrLdStdkrD]}t|r\qpq\t||krfddDStSnlttrtj trƇfddj DStj gSn0tt t frdk rj rj |dSS) 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. NcSs i|]\}\}}|t||qSr?decode_nested_exampler`r?r?r@ros z)decode_nested_example..rcsg|]}t|qSr?rirb)rar?r@rk!sz)decode_nested_example..cs$i|]}|tj|g|qSr?)rjrPrirrSr?r@ro&srg)rurrrprrErrjrrPr r!decodedecode_example)rRr^rhrr?)r^rRrar@rj s0      rj)r^cst|trdd|DSd|ks.t|dtr@dd|DSt|}t|d}|tkr|tt|d|ddd Sd d t |D|ffd d|DS) 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. cSsg|] }t|qSr?generate_from_dictr9r?r?r@rk>sz&generate_from_dict..rcSsi|]\}}|t|qSr?rnrlr?r?r@roAsz&generate_from_dict..rPrQrCrPrQcSsh|] }|jqSr?rrXr?r?r@ Hsz%generate_from_dict..csi|]\}}|kr||qSr?r?rirr field_namesr?r@roIs) rurprrrpoprror<r)r^Z class_typer?rtr@ro1s rorcCst|tjrdd|DSt|tjr:tt|j|jdSt|tjrpt|j}t|t t t frf|gSt|dSt|t rddt tttg|j}||j|jdSt|tjrtn*t|tjrtt|dStd|d dS) a generate_from_arrow_type accepts an arrow DataType and returns a datasets FeatureType to be used as the type for a single field. 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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 cSsi|]}|jt|jqSr?rgenerate_from_arrow_typerrirr?r?r@roWsz,generate_from_arrow_type..rprPNrrzCannot convert z to a Feature type.)rur7Z StructTypeZFixedSizeListTyperrxrZ list_sizeZListTyperrrprrrrrrrZDictionaryTyperrrrAr;)rrPZ array_featurer?r?r@rxLs"        rx)rrr&cCst|}tj||d}t|jdD]f}tt|jd|j|dd}|j|j|d}tjt |d|t d}tj ||}q*|S)z=Build a PyArrow ListArray from a multidimensional NumPy arrayrrN) rvrr7rrrxr r rrr) ListArray from_arrays)rrr5rZ n_offsetsZ step_offsetsrr?r?r@numpy_to_pyarrow_listarrayjs   r~)l_arrr&cCstdd|D}tt||tt|}dd|D}tjdgdd|Dtd}t||d}tj|t d}t |}tj ||S)NcSsg|] }|dkqSrr?rr?r?r@rkwsz:list_of_pa_arrays_to_pyarrow_listarray..cSsg|]}|dk r|qSrr?rr?r?r@rkysrcSsg|] }t|qSr?)rErr?r?r@rk{srr{) rvrrrErZcumsumrrr7r)rr|r})rrrrr5r?r?r@&list_of_pa_arrays_to_pyarrow_listarrayvs" r)rrr&cs4t|dkr"tfdd|DStjgdSdS)zEBuild a PyArrow ListArray from a possibly nested list of NumPy arraysrcs$g|]}|dk rt|dndqS)Nr{)r~rr{r?r@rksz9list_of_np_array_to_pyarrow_listarray..r{N)rErr7r)rrr?r{r@%list_of_np_array_to_pyarrow_listarrays  rrcCs2t|tjrdSt|tr*tt|dSdSdS)zReturn `True` if data is a NumPy ndarray or (recursively) if first non-null value in list is a NumPy ndarray. Args: data (Any): Data. Returns: bool TrFN)rurvrwrpcontains_any_np_arrayrrr?r?r@rs  r)rrr&cs<t|tjrt|dSt|tr8tfdd|DSdS)a0Convert 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 r{csg|]}t|dqS)r{)!any_np_array_to_pyarrow_listarrayr0r{r?r@rksz5any_np_array_to_pyarrow_listarray..N)rurvrwr~rpr)rrr?r{r@rs   r)rrr&cCs(t|rt||jdSt||jSdS)zConvert to PyArrow ListArray. Args: data (Any): Sequence, iterable, np.ndarray or pd.Series. pa_type (_ArrayXDExtensionType): Any of the ArrayNDExtensionType. Returns: pyarrow.Array r{N)rrrr7rr)rrr?r?r@to_pyarrow_listarrays r)rPfuncr&cst|tr&fdd|D}nRt|ttfrJt|dg}n.t|trptt|j|jd}n|}|dkr|S|S)zVisit a (possibly nested) feature. Args: feature (FeatureType): the feature type to be checked Returns: visited feature (FeatureType) csi|]\}}|t|qSr?)_visit)rirrNrr?r@rosz_visit..r)rQN) rurrrprrrrPrQ)rPrrr?rr@rs  r)rPignore_decode_attributer&cCsjt|tr tdd|DSt|ttfr:t|dSt|trNt|jSt |dod|sb|j SdSdS)aRCheck if a (possibly nested) feature requires decoding. Args: feature (FeatureType): the feature type to be checked ignore_decode_attribute (:obj:`bool`, default ``False``): Whether to ignore the current value of the `decode` attribute of the decodable feature types. Returns: :obj:`bool` css|]}t|VqdSrrequire_decodingrXr?r?r@r sz#require_decoding..rrmTN) rurrr5rprrrrPrrl)rPrr?r?r@rs    rrPr&cCs\t|tr tdd|DSt|ttfr:t|dSt|trNt|jSt |dSdS)zCheck if a (possibly nested) feature requires storage casting. Args: feature (FeatureType): the feature type to be checked Returns: :obj:`bool` css|]}t|VqdSrrequire_storage_castrXr?r?r@r sz'require_storage_cast..rrGN rurrr5rprrrrPrrzr?r?r@rs    rcCs\t|tr tdd|DSt|ttfr:t|dSt|trNt|jSt |dSdS)zCheck if a (possibly nested) feature requires embedding data into storage. Args: feature (FeatureType): the feature type to be checked Returns: :obj:`bool` css|]}t|VqdSrrrXr?r?r@r sz(require_storage_embed..rZ embed_storageNrrzr?r?r@require_storage_embeds    rcstfdd}d|_|S)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. csZ|r|d}|dd}n |d}|f||}t|dsBtdd|D|_|S)Nrrr_column_requires_decodingcSsi|]\}}|t|qSr?rricolrPr?r?r@rosz?keep_features_dicts_synced..wrapper..)rvrAssertionErrorrr)argskwargsrrrr?r@wrappers z+keep_features_dicts_synced..wrapperZ_keep_dicts_synced)r Z_decorator_name_)rrr?rr@keep_features_dicts_synced s rc seZdZdZfddZeejZeejZeej Z eej Z eej Z eej Z eej Z ddZeddZedd Zeejdd d d Zedd ddZddZed ddZeeddddZddZedddZddZd0eee ee!ee"dffdd d!Z#eed"d#d$Z$d1eee ee!ee"dffd%d&d'Z%dd d(d)Z&ddd*d+d,Z'd2dd d.d/Z(Z)S)3rYaA 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: - a [`~datasets.Value`] feature specifies a single typed value, e.g. `int64` or `string`. - a [`~datasets.ClassLabel`] feature specifies a field with a predefined set of classes which can have labels associated to them and will be stored as integers in the dataset. - a python `dict` which specifies that the field is a nested field containing a mapping of sub-fields to sub-fields features. It's possible to have nested fields of nested fields in an arbitrary manner. - a python `list` or a [`~datasets.Sequence`] specifies that the field contains a list of objects. The python `list` or [`~datasets.Sequence`] should be provided with a single sub-feature as an example of the feature type hosted in this list. A [`~datasets.Sequence`] with a internal dictionary feature will be automatically converted into a dictionary of lists. This behavior is implemented to have a compatilbity 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 python `list` instead of the [`~datasets.Sequence`]. - a [`Array2D`], [`Array3D`], [`Array4D`] or [`Array5D`] feature for multidimensional arrays. - an [`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 extracts the audio data. - an [`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. - [`~datasets.Translation`] and [`~datasets.TranslationVariableLanguages`], the two features specific to Machine Translation. cs>|s td|^}}tt|j||dd|D|_dS)Nz.)r4superrYrrr)rrrrr?r@r@szFeatures.__init__cCstt|ffSr)rYrrr?r?r@rRszFeatures.__reduce__cCst|S)z] Features field types. Returns: :obj:`pyarrow.DataType` rTrr?r?r@rUsz Features.typecCs,dd|ii}t|jdt|iS)zV Features schema. Returns: :obj:`pyarrow.Schema` infofeatures huggingface)r~r7rRrZ with_metadatajsondumps)rZ hf_metadatar?r?r@ arrow_schema_szFeatures.arrow_schema) pa_schemar&cCs|jdk rjdd|jkrjt|jdd}d|krjd|dkrj|dddk rjt|ddSdd|D}|f|S)aO 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. Args: pa_schema (`pyarrow.Schema`): Arrow Schema. Returns: [`Features`] NrrHrrcSsi|]}|jt|jqSr?rwryr?r?r@ro}sz.Features.from_arrow_schema..)metadataencoderloadsrlrY from_dict)rrrr^r?r?r@from_arrow_schemajs $zFeatures.from_arrow_schemarcCst|}|f|S)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(dtype='string', id=None)} rn)rZdicr^r?r?r@rszFeatures.from_dictcCst|Sr)rrr?r?r@r~szFeatures.to_dictcsB|}ttdddtttftdfdd |dS)NrcSs8t|ts"tdt|d|t|dtrTt|ddgkrT|dd|d<t|dtrt|ddgkr|dd|d<t|dtrt|ddgkr|dd|d<t|dtrt|ddgkr|dd|d<t|dtr4t|ddtr4d d t|ddD|dd<|S) NExpected a dict but got a : sequencerGrZrp class_labelr+cSsi|]\}}t||qSr?r/)rilabel_idZ label_namer?r?r@rosz.simplify..)rurr4rr<rprrzr?r?r@simplifys """"(z(Features._to_yaml_list..simplifyrcst|tr|dd}|dkr<|d}d|i|S|dkrH|S|r\|s\dt|iS|rtdt||iiSdfdd |DiSn:t|trd |d iStd t|d |dS)NrrrPrrrGrZcs g|]\}}d|i|qSrqr?rir_feature) to_yaml_innerr?r@rkszAFeatures._to_yaml_list..to_yaml_inner..rpr"Expected a dict or a list but got r)rurrvrrrpr4r)r^rrrrr?r@rs     z-Features._to_yaml_list..to_yaml_innerrZ)r~rrrp)r yaml_datar?rr@ _to_yaml_lists1zFeatures._to_yaml_list)rr&csNt|}ttdddtttftttfdfdd ||S)Nrcstts"tdtdtdtrBddid<tdtrbddid<tdtrtddtrtddtd}|rd d |Dtt t|d d krt d t|d d dfdd |Ddd<S)NrrrrGrprr+)rmcSsg|] }t|qSr?)r\rirr?r?r@rksz@Features._from_yaml_list..unsimplify..rCrz1ClassLabel expected a value for all label ids [0:z] but some ids are missing.csg|]}dd|qS)rr+r?rrzr?r@rk s) rurr4rr<r8sortedr\rprr;)rPZ label_idsr?rzr@ unsimplifys $*z,Features._from_yaml_list..unsimplifyrcsHt|tr|siStt|}|dkrL||}d|i|ddiS|dkrf||gS|dkrz|dS|dkrt|dtrzt|d|ddiWStk rdt|diYSXn |dSndt|i||SnJt|t r,d d |D}fd d t ||DSt d t |d|dS)NrrPrrrprZrGrcSsg|]}|dqSrq)rv)rirr?r?r@rk)szEFeatures._from_yaml_list..from_yaml_inner..csi|]\}}||qSr?r?r)from_yaml_innerr?r@ro*szEFeatures._from_yaml_list..from_yaml_inner..rr) rurnextiterrvr8rr;rrpzipr4r)r^rrr+rrr?r@rs0     z1Features._from_yaml_list..from_yaml_inner)rdeepcopyrrrpr)rrr?rr@_from_yaml_lists &zFeatures._from_yaml_listcCst|}t||S)z Encode example into a format for Arrow. Args: example (`dict[str, Any]`): Data in a Dataset row. Returns: `dict[str, Any]` )rr_)rexampler?r?r@r0s zFeatures.encode_example column_namecst|}fdd|DS)a 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]` csg|]}t|qSr?r^rir^rrr?r@rkLsz*Features.encode_column..)rrcolumnrr?rr@ encode_column>s zFeatures.encode_columncsfi}t|tkr0tdt|dt|D](\}t|}fdd|D|<q8|S)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 csg|]}t|qSr?r^rrmrr?r@rk^sz)Features.encode_batch..)setr;rr)rbatchZ encoded_batchrr?rr@ encode_batchNs zFeatures.encode_batchN)rrhcs,fddtfddDDS)aDecode 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]` cs2i|]*\}\}}|j|r*t||dn|qS)rg)rrj)rirrPrn)rrhr?r@roos  z+Features.decode_example..csi|]\}}|kr||qSr?r?rl)rr?r@rots)rr)rrrhr?)rrrhr@rmas  zFeatures.decode_example)rrcs"jrfdd|DS|S)zDecode column with custom feature decoding. Args: column (`list[Any]`): Dataset column data. column_name (`str`): Dataset column name. Returns: `list[Any]` cs&g|]}|dk rt|ndqSrrir9rr?r@rksz*Features.decode_column..)rrr?rr@ decode_columnxszFeatures.decode_column)rrhcsBi}|D]0\}jr4fdd|Dn||<q |S)aDecode 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]]` cs*g|]"}|dk r"t|dndqS)Nrgrir9rrrhr?r@rksz)Features.decode_batch..)rr)rrrhZ decoded_batchrr?rr@ decode_batchs  zFeatures.decode_batchcCs t|S)a Make a deep copy of [`Features`]. Returns: [`Features`] Example: ```py >>> from datasets import load_dataset >>> ds = load_dataset("rotten_tomatoes", split="train") >>> copy_of_features = ds.features.copy() >>> copy_of_features {'label': ClassLabel(num_classes=2, names=['neg', 'pos'], id=None), 'text': Value(dtype='string', id=None)} ``` )rrrr?r?r@rsz Features.copy)r$r&csdfdd t||S)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, Sequence, Value >>> # let's say we have to features with a different order of nested fields (for a and b for example) >>> f1 = Features({"root": Sequence({"a": Value("string"), "b": Value("string")})}) >>> f2 = Features({"root": {"b": Sequence(Value("string")), "a": Sequence(Value("string"))}}) >>> assert f1.type != f2.type >>> # re-ordering keeps the base structure (here Sequence is defined at the root level), but make the fields order match >>> f1.reorder_fields_as(f2) {'root': Sequence(feature={'b': Value(dtype='string', id=None), 'a': Value(dtype='string', id=None)}, length=-1, id=None)} >>> assert f1.reorder_fields_as(f2).type == f2.type c srdddnd}ttrLjttrFddDngttr؈jjj}}ttrddD}tdd|D||dSg}t|d ||dSnttrtttstd d |ttkr\d d d  d d |}t|fddDStt rtt std d |t t krtdd |fddt t DSSdS)Nz at rrcSsi|]\}}||gqSr?r?rsr?r?r@roszIFeatures.reorder_fields_as..recursive_reorder..cSsi|]\}}||gqSr?r?rsr?r?r@roscSsi|]\}}||dqS)rr?rsr?r?r@ros)rrQrzType mismatch: between rDzKeys mismatch: between z (source) and z (target). z are missing from target and z are missing from sourcec s,i|]$}|||d|qSr.r?rVrecursive_reordersourcestacktargetr?r@roszLength mismatch: between cs$g|]}||dqS)z.r?r0rr?r@rkszIFeatures.reorder_fields_as..recursive_reorder..) rurrPrrrrQr;rkeysrprEr)rrrZstack_positionZid_rQ reorderedmessager)rrrr@rs@        4   z5Features.reorder_fields_as..recursive_reorder)r)rYr#r?rr@reorder_fields_ass&zFeatures.reorder_fields_ascstd|D]}d}|}|D]\}t|tr\d}|fdd|D|=q"t|trt|jtrd}|fdd|jD|=q"t|dr"| |kr"d}|fdd| D|=q"|}|r qq |S) aXFlatten 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("squad", split="train") >>> ds.features.flatten() {'answers.answer_start': Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None), 'answers.text': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'context': Value(dtype='string', id=None), 'id': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None)} ``` rTFcs i|]\}}d||qSrr?rsrr?r@ros z$Features.flatten..cs4i|],\}}d|t|ts*t|n|gqSr)rurrrsrr?r@ro!s rcs i|]\}}d||qSrr?rsrr?r@ro)s ) rrrrurupdaterrPrr)r max_depthdepthZ no_changeZ flattenedZ subfeaturer?rr@rs0   zFeatures.flatten)N)N)r)*rrrrrrrr __delitem__r setdefaultrvpopitemclearrrrrrr7ZSchemarrr~rprrrr8rrrrrr(rmrrrrr __classcell__r?r?rr@rYs>         JE((CrY) features_listr&cs^i|D]B}|D]4\}}|ks@t|tr|jdkr||<qqfdd|DS)zoAlign dictionaries of features so that the keys that are found in multiple dictionaries share the same feature.r'cs&g|]}tfdd|DqS)csi|]}||qSr?r?rk name2featurer?r@ro9sz._align_features...)rYr)rirrr?r@rk9sz#_align_features..)rrurrG)rrrrr?rr@_align_features1s $ r)rc Csi}|D]B}|D]4\}}||ks@t||tr||jdkr|||<qq|D]\}|D]N\}}t|trx|jdks\|||kr\td|d|d|d||d q\qPdS)zCheck if the dictionaries of features can be aligned. Two dictonaries of features can be aligned if the keys they share have the same type or some of them is of type `Value("null")`. r'z.The features can't be aligned because the key z of features z has unexpected type - z (expected either z or Value("null").N)rrurrGr;)rrrrrr?r?r@!_check_if_features_can_be_aligned<s$   r)FT)F)N)r)N)N)N)N)F)yrrrrYrrcollections.abcrrrr3 dataclassesrrrr functoolsr r operatorr typingr r rrrrrrr&rtrvZpandasr|r3r7Zpyarrow.computeZcomputerDZ pyarrow.typesZpandas.api.extensionsrZPandasExtensionArrayrZPandasExtensionDtyperrZnamingrrtablerutilsrZutils.py_utilsrrrZaudior imager!r" translationr#r$Z get_loggerrloggerrr8rAr]r(rgrrrrrrrrrrrrrrrrrr(r)rrprZ FeatureTyperrUr_rjrorxrwr|r~rrrrrrrrrrrrYrrr?r?r?r@s  (         @$F (="hF! H&(   "