o 沪gB@sUdZddlmZddlZddlZddlZddlZddlZddlm Z m Z m Z m Z ddl mZmZmZmZddlmZmZmZddlmZddlmZmZmZmZmZmZmZm Z dd l!m"Z"m#Z#dd l$m%Z%m&Z&m'Z'm(Z(dd l)m*Z*m+Z+m,Z,m-Z-m.Z.m/Z/m0Z0m1Z1m2Z2m3Z3erddl4Z5ddl6Z7dd l8m9Z9m:Z:m;Z;dd lm?Z?e'@eAZBdeCd<dZDeEdZFdeCd<dZGdeCd<dZHdeCd<dZIdeCd<dZJdeCd<dZKdeCd<dZLdeCd<d ZMdeCd!<d"ZNdeCd#<d$ZOdeCd%<d&ZPdeCd'<d(ZQdeCd)<d*ZRdeCd+<d,ZSdeCd-<d.ZTdeCd/<d0ZUdeCd1<d2ZVdeCd3<d4ZWdeCd5<d6ZXdeCd7<d8ZYdeCd9<d:ZZdeCd;<dZ\deCd?<ed@dAdBZ]e GdCdDdDeZ^GdEdFdFee]Z_GdGdHdHeZ`GdIdJdJeZaeee]e_e]e`eafZbdKeCdL<edMdNdOdPdQdRdSeedTe^e`eae*dfZcdKeCdU<GdVdWdWeZddd\d]Zedd^d_ZfddbdcZgddddeZhddfdgZiddhdiZjddjdkZkddldmZlddndoZmddpdqZnddrdsZoddtduZpddvdwZqddxdyZrddzd{Zsdd|d}Ztdd~dZudddZvdddZwdddZxdddZy ddddZzdddZ{dddZ|eDdfdddZ}dddZ~dddZdddZdddZeDfdddZdddZ ddddZdddZ ddddZdddZddd„ZdddńZdddɄZdS)z8A bunch of useful utilities for dealing with dataframes.) annotationsN)ChainMapUserDictUserListdeque) ItemsViewIterableMappingSequence)EnumEnumMetaauto)MappingProxyType) TYPE_CHECKINGAnyFinalProtocolTypeVarUnioncastruntime_checkable) TypeAlias TypeGuard)configerrorslogger string_util) CustomDict NumpyShapehas_callable_attris_custom_dictis_dataclass_instance is_list_like is_namedtupleis_pydantic_modelis_typeis_version_less_than) DataFrameIndexSeries) _iLocIndexer)Stylerr_LOGGERi'z ^pandas.*$_PANDAS_DATA_OBJECT_TYPE_REzdask.dataframe.core.DataFrame_DASK_DATAFRAMEzdask.dataframe.core.Index _DASK_INDEXzdask.dataframe.core.Series _DASK_SERIESzduckdb.duckdb.DuckDBPyRelation_DUCKDB_RELATIONz modin.pandas.dataframe.DataFrame_MODIN_DF_TYPE_STRzmodin.pandas.series.Series_MODIN_SERIES_TYPE_STRzpandas.io.formats.style.Styler_PANDAS_STYLER_TYPE_STRz polars.dataframe.frame.DataFrame_POLARS_DATAFRAMEz polars.lazyframe.frame.LazyFrame_POLARS_LAZYFRAMEzpolars.series.series.Series_POLARS_SERIESzpyspark.sql.dataframe.DataFrame_PYSPARK_DF_TYPE_STRz'pyspark.sql.connect.dataframe.DataFrame_PYSPARK_CONNECT_DF_TYPE_STRzray.data.dataset.Dataset _RAY_DATASETz$ray.data.dataset.MaterializedDataset_RAY_MATERIALIZED_DATASETz3snowflake.snowpark.modin.pandas.dataframe.DataFrame_SNOWPANDAS_DF_TYPE_STRz6snowflake.snowpark.modin.plugin.extensions.index.Index_SNOWPANDAS_INDEX_TYPE_STRz-snowflake.snowpark.modin.pandas.series.Series_SNOWPANDAS_SERIES_TYPE_STRzsnowflake.snowpark.row.Row_SNOWPARK_DF_ROW_TYPE_STRz&snowflake.snowpark.dataframe.DataFrame_SNOWPARK_DF_TYPE_STRzsnowflake.snowpark.table.Table_SNOWPARK_TABLE_TYPE_STRzxarray.core.dataset.Dataset_XARRAY_DATASET_TYPE_STRzxarray.core.dataarray.DataArray_XARRAY_DATA_ARRAY_TYPE_STRV_coT) covariantc@s4eZdZdZedddZddd d Zdd d ZdS) DBAPICursora`Protocol for DBAPI 2.0 Cursor objects (PEP 249). This is a simplified version of the DBAPI Cursor protocol that only contains the methods that are relevant or used for our DB API Integration. Specification: https://peps.python.org/pep-0249/ Inspired by: https://github.com/python/typeshed/blob/main/stdlib/_typeshed/dbapi.pyi returndSequence[tuple[str, Any | None, int | None, int | None, int | None, int | None, bool | None]] | NonecCdSNselfrKrKT/var/www/html/chatdoc2/venv/lib/python3.10/site-packages/streamlit/dataframe_util.py descriptionqszDBAPICursor.description.sizeintSequence[Sequence[Any]]cCrIrJrK)rMrPrKrKrN fetchmanyzDBAPICursor.fetchmanycCrIrJrKrLrKrKrNfetchallrTzDBAPICursor.fetchallN)rGrH).)rPrQrGrR)rGrR)__name__ __module__ __qualname____doc__propertyrOrSrUrKrKrKrNrFes   rFc@seZdZdZedddZdS)DataFrameGenericAliasatTechnically not a GenericAlias, but serves the same purpose in OptionSequence below, in that it is a type which admits DataFrame, but is generic. This allows OptionSequence to be a fully generic type, significantly increasing its usefulness. We can't use types.GenericAlias, as it is only available from python>=3.9, and isn't easily back-ported. rGr*cCrIrJrKrLrKrKrNilocszDataFrameGenericAlias.ilocN)rGr*)rVrWrXrYrZr\rKrKrKrNr[s r[c@seZdZdZdddZdS)PandasCompatiblezFProtocol for Pandas compatible objects that have a `to_pandas` method.rGDataFrame | SeriescCrIrJrKrLrKrKrN to_pandasrTzPandasCompatible.to_pandasN)rGr^)rVrWrXrYr_rKrKrKrNr]sr]c@seZdZdZd ddZdS) DataframeInterchangeCompatiblezProtocol for objects support the dataframe-interchange protocol. https://data-apis.org/dataframe-protocol/latest/index.html allow_copyboolrGrcCrIrJrK)rMrarKrKrN __dataframe__rTz,DataframeInterchangeCompatible.__dataframe__N)rarbrGr)rVrWrXrYrcrKrKrKrNr`sr`rOptionSequencer'r)r+r(pa.Tablezpa.Arrayznp.ndarray[Any, np.dtype[Any]]zMapping[Any, Any]Datac@seZdZdZeZeZeZeZeZ eZ eZ eZ eZ eZeZeZeZeZeZeZeZeZeZeZeZeZeZeZeZeZeZeZ eZ!eZ"eZ#eZ$eZ%dS) DataFormatz7DataFormat is used to determine the format of the data.N)&rVrWrXrYr UNKNOWNEMPTYCOLUMN_INDEX_MAPPINGCOLUMN_SERIES_MAPPINGCOLUMN_VALUE_MAPPING DASK_OBJECT DBAPI_CURSORDUCKDB_RELATIONKEY_VALUE_DICTLIST_OF_RECORDS LIST_OF_ROWSLIST_OF_VALUES MODIN_OBJECT NUMPY_LIST NUMPY_MATRIX PANDAS_ARRAYPANDAS_DATAFRAME PANDAS_INDEX PANDAS_SERIES PANDAS_STYLERPOLARS_DATAFRAMEPOLARS_LAZYFRAME POLARS_SERIES PYARROW_ARRAY PYARROW_TABLEPYSPARK_OBJECT RAY_DATASET SET_OF_VALUESSNOWPANDAS_OBJECTSNOWPARK_OBJECTTUPLE_OF_VALUESXARRAY_DATASETXARRAY_DATA_ARRAYrKrKrKrNrgsF rgvstrrGrbcCddl}t|j|S)aReturn True if the current Pyarrow version is less than the input version. Parameters ---------- v : str Version string, e.g. "0.25.0" Returns ------- bool Raises ------ InvalidVersion If the version strings are not valid. rN)pyarrowr& __version__)rparKrKrNis_pyarrow_version_less_thans rcCr)aReturn True if the current Pandas version is less than the input version. Parameters ---------- v : str Version string, e.g. "0.25.0" Returns ------- bool Raises ------ InvalidVersion If the version strings are not valid. rN)pandasr&r)rpdrKrKrNis_pandas_version_less_thans robjobjectcCs|dust|tttttttfrdSt|t j t j t j t j t jt jt jt jt jt jt jt jt jt jt jt jt jt jt jt jt jt jhvS)zTrue if the object is a dataframe-like object. This does not include basic collection types like list, dict, tuple, etc. NF) isinstancetuplesetrbytesrQfloatrbdetermine_data_formatrgrkrmrnrtrurvrwrxryrzr{r|r}r~rrrrrrrrrrKrKrNis_dataframe_likes4 rcCsRt|p(t|p(t|p(t|p(t|p(t|p(t|p(t|p(t|p(t |S)aTrue if the object is one of the supported unevaluated data objects: Currently supported objects are: - Snowpark DataFrame / Table - PySpark DataFrame - Modin DataFrame / Series - Snowpandas DataFrame / Series / Index - Dask DataFrame / Series / Index - Ray Dataset - Polars LazyFrame - Generator functions - DB API 2.0 Cursor (PEP 249) - DuckDB Relation (Relational API) Unevaluated means that the data is not yet in the local memory. Unevaluated data objects are treated differently from other data objects by only requesting a subset of the data instead of loading all data into th memory ) is_snowpark_data_objectis_pyspark_data_objectis_snowpandas_data_objectis_modin_data_objectis_ray_datasetis_polars_lazyframeis_dask_objectis_duckdb_relationis_dbapi_cursorinspectisgeneratorfunctionrrKrKrNis_unevaluated_data_object<s& rcC t|tS)zLTrue if obj is a Pandas object (e.g. DataFrame, Series, Index, Styler, ...).)r%r-rrKrKrNis_pandas_data_object] rcCt|tp t|tS)z-True if obj is a Snowpark DataFrame or Table.)r%rAr@rrKrKrNrbrcCs2t|tot|dkot|dtot|ddS)z4True if obj is a list of snowflake.snowpark.row.Row.ras_dict)rlistlenr%r?rrrKrKrNis_snowpark_row_listgs    rcCst|tp t|tot|dS)z5True if obj is a PySpark or PySpark Connect dataframetoPandas)r%r8r9rrrKrKrNrqsrcCt|tpt|tpt|tS)z2True if obj is a Dask DataFrame, Series, or Index.)r%r.r0r/rrKrKrNrx rcCr)z+True if obj is of Modin Dataframe or Series)r%r2r3rrKrKrNrrrcCr)z5True if obj is a Snowpark Pandas DataFrame or Series.)r%r<r>r=rrKrKrNrrrcCr)z"True if obj is a Polars Dataframe.)r%r5rrKrKrNis_polars_dataframerrcCr)z True if obj is a Xarray Dataset.)r%rBrrKrKrNis_xarray_datasetrrcCr)z"True if obj is a Xarray DataArray.)r%rCrrKrKrNis_xarray_data_arrayrrcCr)zTrue if obj is a Polars Series.)r%r7rrKrKrNis_polars_seriesrrcCr)z"True if obj is a Polars Lazyframe.)r%r6rrKrKrNrrrcCr)zTrue if obj is a Ray Dataset.)r%r:r;rrKrKrNrrrTypeGuard[Styler]cCr)zTrue if obj is a pandas Styler.)r%r4rrKrKrNis_pandas_stylerrrTypeGuard[DBAPICursor]cCr)zWTrue if obj looks like a DB API 2.0 Cursor. https://peps.python.org/pep-0249/ )rrFrrKrKrNrs rcCr)z]True if obj is a DuckDB relation. https://duckdb.org/docs/api/python/relational_api )r%r1rrKrKrNrs rdata Iterable[Any]cCsddlm}||dddvS)z.Check if the list only contains scalar values.r) infer_dtypeTskipna)mixedz unknown-array)pandas.api.typesr)rrrKrKrN_is_list_of_scalarss riterablemax_iterations int | None list[Any]cCsB|durt|Sg}t|D]\}}||kr|S||q|S)aAConvert an iterable to a list. Parameters ---------- iterable : Iterable The iterable to convert to a list. max_iterations : int or None The maximum number of iterations to perform. If None, all iterations are performed. Returns ------- list The converted list. N)r enumerateappend)rrresultiitemrKrKrN_iterable_to_lists rdata_dfcCs2t|jdkr|jddkr|jddidd|S)zRename the first column to "value" if it is not named and if there is only one column in the dataframe. The default name of the first column is 0 if it is not named which is not very descriptive. rvalueT)columnsinplace)rrrename)rrKrKrN_fix_column_namingsrdict[Any, Any]cCsddl}t|jj|ddS)zConvert a key-value dict to a Pandas DataFrame. Parameters ---------- data : dict The dict to convert to a Pandas DataFrame. Returns ------- pandas.DataFrame The converted Pandas DataFrame. rNindexorient)rrr' from_dict)rrrKrKrN_dict_to_pandas_dfs rFrmax_unevaluated_rowsrQ ensure_copyc CsJddl}ddl}ddl}t||jr|r|St|j|St||j|j|j j j fr0||St |rCt|j|r?|j S|j St||jr\t|jdkrU|gSt||St|rl|rf|n|}|St|r~|rv|n|}|St|r||}|jd|krtdt|dt|j|St|r|r|jdd}|St|r|r|jdd}| St!|r|j"|dd}t||j|jfr|}|jd|krtdt|dt|j|St#|r||}|jd|krtdt|d t|j|St$|rM|"|%}t||j|jfr4|}|jd|krGtdt|d t|j|St&|rr||'}|jd|krltdt|d t|j|St(|r|d|}t||j|jfr|}|jd|krtdt|d t|j|St)|r||}|jd|krtdt|d t|j|St*|r||+}|jd|krtdt|d |St,|r|j-rdd|j-Dnd}|j|.||d}|jd|krtdt|d|St/|r-|dd|DSt0|dr:||St0|drWt1ddurW|j j23|}|rU|S|St45|r~t|t6||d}|jd|kr|tdt|d|St|t7rt|dd|DSt|t8t9|j:t;frt|t<|St=|rt>|?St@|rt>|AStB|rt>tCD|St|tEtFtGfstH|rt>tI|Szt||WStJy$}z5t|tIrtKLtJt>|WdWYd}~S1s wYtMNdtO|d|d|d}~ww)aUTry to convert different formats to a Pandas Dataframe. Parameters ---------- data : dataframe-, array-, or collections-like object The data to convert to a Pandas DataFrame. max_unevaluated_rows: int If unevaluated data is detected this func will evaluate it, taking max_unevaluated_rows, defaults to 10k. ensure_copy: bool If True, make sure to always return a copy of the data. If False, it depends on the type of the data. For example, a Pandas DataFrame will be returned as-is. Returns ------- pandas.DataFrame rNu⚠️ Showing only z6 rows. Call `collect()` on the dataframe to show more.T)deep)computez8 rows. Call `compute()` on the data object to show more.z6 rows. Call `to_pandas()` on the dataset to show more.z; rows. Call `_to_pandas()` on the data object to show more.z9 rows. Call `toPandas()` on the data object to show more.z: rows. Call `to_pandas()` on the data object to show more.z0 rows. Call `df()` on the relation to show more.cSsg|] }|r |dndqS)rrK).0drKrKrN sz1convert_anything_to_pandas_df..)rz4 rows. Call `fetchall()` on the Cursor to show more.cSsg|]}|qSrK)r)rrowrKrKrNrsr_rcz1.5.0F)rz/ rows. Convert the data to a list to show more.cSsg|]}|jqSrK)r)rcrKrKrNrsz# Unable to convert object of type `z1` to `pandas.DataFrame`. Offending object: ```py z ```)Parraynumpyrrr'copyrr)r(api extensionsExtensionArrayrrndarrayrshaperrcloner_rto_framerlimitcollect_show_data_informationrsimplify_numberr to_dataframer to_seriesrheadrr _to_pandasrrrrrdfrrOrSrrr interchangefrom_dataframerrrr rmap ArrayTyperrr rto_dictr#_asdictr! dataclassesasdictrrrr$dict ValueError contextlibsuppressrStreamlitAPIExceptiontype) rrrrnprrrexrKrKrNconvert_anything_to_pandas_df s,                                  .rtablerc Cszt|}Wnty}z tjd|dWYd}~nd}~wwddl}|}|||j}||| t t | S)zSerialize pyarrow.Table to Arrow IPC bytes. Parameters ---------- table : pyarrow.Table A table to convert. Returns ------- bytes The serialized Arrow IPC bytes. zMRecursion error while truncating Arrow table. This is not supposed to happen.exc_infoNr)_maybe_truncate_tableRecursionErrorr,warningrBufferOutputStreamRecordBatchStreamWriterschema write_tablecloserrgetvalue to_pybytes)r errrsinkwriterrKrKrN"convert_arrow_table_to_arrow_bytess   rrc Csxddl}z |j|}Wt |S|j|j|jfy;}ztjd|dt|}|j|}WYd}~t |Sd}~ww)zSerialize pandas.DataFrame to Arrow IPC bytes. Parameters ---------- df : pandas.DataFrame A dataframe to convert. Returns ------- bytes The serialized Arrow IPC bytes. rNzSerialization of dataframe to Arrow table was unsuccessful. Applying automatic fixes for column types to make the dataframe Arrow-compatible.r ) rTable from_pandasArrowTypeError ArrowInvalidArrowNotImplementedErrorr,info#fix_arrow_incompatible_column_typesr)rrr rrKrKrN convert_pandas_df_to_arrow_bytess  r!sourcecCsddl}||}|S)aConvert Arrow bytes (IPC format) to pandas.DataFrame. Using this function in production needs to make sure that the pyarrow version >= 14.0.1, because of a critical security vulnerability in pyarrow < 14.0.1. Parameters ---------- source : bytes A bytes object to convert. Returns ------- pandas.DataFrame The converted dataframe. rN)rRecordBatchStreamReader read_pandas)r"rreaderrKrKrN convert_arrow_bytes_to_pandas_df;s r&msgNonecCsddlm}|j|dS)zVShow a message to the user with important information about the processed dataset.r)get_dg_singleton_instanceN)$streamlit.delta_generator_singletonsr)main_dgcaption)r'r)rKrKrNrRs rcCs.ddl}t||jrt|St||}t|S)adTry to convert different formats to Arrow IPC format (bytes). This method tries to directly convert the input data to Arrow bytes for some supported formats, but falls back to conversion to a Pandas DataFrame and then to Arrow bytes. Parameters ---------- data : dataframe-, array-, or collections-like object The data to convert to Arrow bytes. max_unevaluated_rows: int If unevaluated data is detected this func will evaluate it, taking max_unevaluated_rows, defaults to 10k. Returns ------- bytes The serialized Arrow IPC bytes. rN)rrrrrr!)rrrrrKrKrNconvert_anything_to_arrow_bytesZs   r-OptionSequence[V_co] list[V_co]cCs|durgSt|ttttfr|gSt|trdd|DSt|tr)t|St |r5t |s5t|Szt |dd}|j rBgWSt ttt|jdddfWStjya|gYSw)aTry to convert different formats to a list. If the input is a dataframe-like object, we just select the first column to iterate over. Non sequence-like objects and scalar types, will just be wrapped into a list. Parameters ---------- obj : dataframe-, array-, or collections-like object The object to convert to a list. Returns ------- list The converted list. NcSs g|] }t|tr |jn|qSrK)rrr)rmemberrKrKrNrs z,convert_anything_to_list..T)rr)rrrQrrbr r rkeysr"rremptyrrDr\to_listrr)rrrKrKrNconvert_anything_to_lists(    " r4truncated_rowsc Cstdrttdd}t|jd}|j}|dkrU||krUt|||}ttt |t||d||d|dd}| d|}t ||pOd||S|rt |j}t |j|}||krut|j}t|j|}td |d |d |S) aExperimental feature to automatically truncate tables that are larger than the maximum allowed message size. It needs to be enabled via the server.enableArrowTruncation config option. Parameters ---------- table : pyarrow.Table A table to truncate. truncated_rows : int or None The number of rows that have been truncated so far. This is used by the recursion logic to keep track of the total number of truncated rows. zserver.enableArrowTruncationzserver.maxMessageSizeg.Arg?g{Gz?ru⚠️ Showing z out of z# rows due to data size limitations.)r get_optionrQnbytesnum_rowsmathceilfloormaxminslicer rrrr) r r5max_message_size table_size table_rows targeted_rows sliced_tabledisplayed_rows total_rowsrKrKrNr s<      r columnSeries[Any] | IndexcCsddlm}m}m}|jjdvrdSt|jdvrdS|jdkrS||dd}|dvr,dS|d krSt|dks;t|d s=dS|j d}||rO||sOt |t rQdSd Sd S) zTReturn True if the column type is known to cause issues during Arrow conversion.r)r is_dict_liker")rT> period[B] period[N] period[U] period[ns] period[us]geometryrr)z mixed-integercomplexrr\F) rrrIr"dtypekindrrhasattrr\r frozenset)rGrrIr" inferred_type first_valuerKrKrN is_colum_type_arrow_incompatibles,     rWselected_columnslist[str] | NonecCsddl}d}|p |jD]}t||r$|dur|}||d||<q |sBt|j|jsBt|jrB|dur;|}|jd|_|durH|S|S)aFix column types that are not supported by Arrow table. This includes mixed types (e.g. mix of integers and strings) as well as complex numbers (complex128 type). These types will cause errors during conversion of the dataframe to an Arrow table. It is fixed by converting all values of the column to strings This is sufficient for displaying the data on the frontend. Parameters ---------- df : pandas.DataFrame A dataframe to fix. selected_columns: List[str] or None A list of columns to fix. If None, all columns are evaluated. Returns ------- The fixed dataframe. rNstring)rrrWrastyperr MultiIndex)rrXrdf_copycolrKrKrNr @s( r  input_datacCsddl}ddl}ddl}|durtjSt||jrtjSt||jr2t t t |j dkr/tj StjSt||jr;tjSt||jrDtjSt||jrMtjSt||jrVtjSt|r]tjSt||jjjrhtjSt|rotjSt|rvtj St!|r}tj"St#|rtj$St%|rtj&St'|rtj(St)|rtj*St+|rtj,St-|rtj.St/|rtj0St1|st2|rtj3St4|rtj5St6|rtj7St|t8t9t:fst;|st<|st=|st>|rtj?St|t@tAfrtjBSt|tCtDtEtFfr1tG|rt|tDrtjHSt|tEtFfrtjIStjJStKtL|}t|tMr!tjNSt|tCtDtEtFfr.tjBStjUSt|tMtOfrr|s?tj?St |dkrotKtL|P}t|tMrWtjQSt|tCtDfrbtjRSt||jrltjSStj?StjUStT|rztjJStjUS)zDetermine the data format of the input data. Parameters ---------- input_data : Any The input data to determine the data format of. Returns ------- DataFormat The data format of the input data. rNr)Vrrrrgrirr'rxrrrrrrurvrrArrayrr)rzr(ryrr{rrrrwrr~rr|rr}rrtrrrrrrrrrrrrmrrrrrorrnrrrr!r#r r$rprrrrrrrrTrrrrsnextiterrrqr valuesrjrlrkr"rh)r_rrr first_elementrVrKrKrNrrs             rcCs&ddl}||j|jgdgS)a Unify all missing values in a DataFrame to None. Pandas uses a variety of values to represent missing values, including np.nan, NaT, None, and pd.NA. This function replaces all of these values with None, which is the only missing value type that is supported by all data rN)rfillnananreplace infer_objects)rrrKrKrN_unify_missing_valuessri Series[Any]cCs2t|jdkrtdt|jd||jdS)zConvert a Pandas DataFrame to a Pandas Series by selecting the first column. Raises ------ ValueError If the DataFrame has more than one column. r6DataFrame is expected to have a single column but has .r)rrr)rrKrKrN_pandas_df_to_seriess  rm data_formatDataFrame | Series[Any] | pa.Table | pa.Array | np.ndarray[Any, np.dtype[Any]] | tuple[Any] | list[Any] | set[Any] | dict[str, Any]cCs|tjtjtjtjtjtjtjtjtj tj tj tj tj h vr |S|tjkrr?r@rArBrCrDrFr[r]r`rdrfrgrrrrrrrrrrrrrrrrrrrrrrrrrrr!r&rr-r4r rWr rrirmr|rKrKrKrNs  ( 0                              '   ' !                 p $   &: K= 2 u