uj{zddlmZddlmZddlZddlZddlmZmZm Z ddl Z ddl m Z mZddlmZmZmZmZmZmZmZmZmZmZmZmZmZddlmZddlm Z m!Z!dd l"m#Z#m$Z$m%Z%m&Z&m'Z'm(Z(dd l)m*Z+dd l,m-Z-m.Z.dd l/m0Z0dd l1m2Z2ddl3m4Z4m5Z5m6Z6m7Z7m8Z8m9Z9m:Z:ddl;mZ>ddl?m@Z@ddlAmBZBmCZCddlDmEZEddlFmGZHddlImJZJddlKmLZLddlMmNcmOZPddlQmRZRddlSmTZTerddlUmVZVddlWmXZXmYZYmZZZm[Z[m\Z\m]Z]ddl^m_Z_dZ`d$dZae0dGddeHjZc d% d&d Zdd'd(d!Ze d) d*d"Zfd+d#Zgy),) annotations) timedeltaN) TYPE_CHECKINGSelfcast)libtslibs) DayNaTNaTType ResolutionTick Timedeltaastype_overflowsafeget_resolutionget_supported_dtypeiNaTis_supported_dtypemul_overflowsafeperiods_per_second)cast_from_unit_vectorized)get_timedelta_daysget_timedelta_field)array_to_timedelta64 contains_strfloordiv_object_arrayints_to_pytimedeltaparse_timedelta_unittruediv_object_array)function)OutOfBoundsDatetimeOutOfBoundsTimedelta) set_module)validate_endpoints) TD64NS_DTYPEis_float_dtypeis_integer_dtypeis_object_dtype is_scalaris_string_dtype pandas_dtype) ArrowDtypeBaseMaskedDtypeExtensionDtype)isna)nanops roperator)datetimelike_accumulations) datetimelike)generate_regular_range) IntegerArray) extract_array)unpack_zerodim_and_defer)Callable)AxisIntDateTimeErrorChoicesDtypeObjNpDtypeTimeUnitnpt DataFramecptj|r |dk7r|jr t|S|S)a Return the exact int equivalent of an integral float, otherwise `other`. GH#66551 float64 carries only a 53-bit mantissa, so applying a float operand in floating point rounds results that int64 represents exactly. An integral float has an exact int equivalent, so the int path can be used instead. inf and nan are not integral and so stay in float64. Zero is left alone: it needs no exactness fix, and routing it to the int path would turn division by 0.0 from NaT into a raise. r)ris_float is_integerint)others h/var/www/html/python-recuperauto-other/env/lib/python3.12/site-packages/pandas/core/arrays/timedeltas.py_exact_if_integralrGas0 ||Euze.>.>.@5z LcJdfd }||_d|d|_t|S)Nc|j}dk(rt||j}nt||j}|jr|j |dd}|S)Ndaysresofloat64) fill_valueconvert)asi8r_cresor_hasna_maybe_mask_results)selfvaluesresultaliass rFfz_field_accessor..fssa F?'T[[AF )T[[IF ;;--4.F rH )return np.ndarray)__name____doc__property)namerX docstringrYs ` rF_field_accessorrbrs+ AJYKr"AI A;rHz pandas.arrayscveZdZUdZdZeejefZ dZ de d<e dUdZ e dVdZdZgZd e d <gZd e d <gd Zd e d <eezdgzZd e d<gdZd e d<dWdZe dXdZe dYdZedZeef dZfd Zedddd[dZe d\ d]dZdUdZd^dZd_dZ d`dad Z!dbd!Z"ddddddd"d# dcd$Z#dddd%ddd& ddd'Z$dd(defd)Z%dfdgd*Z&d+dd, dhd-Z'd.Z( did/Z)djd0Z*e+d1dkd2Z,e,Z- dld3Z.d4Z/d5Z0dmd6Z1e+d7d8Z2e+d9d:Z3e+d;d<Z4e+d=d>Z5e+d?d@Z6e+dAdBZ7e+dCdDZ8e+dEdFZ9dndGZ:dndHZ;dndIZe?jdLZAeBdMdMeAZCe?jdNZDeBdOdOeDZEe?jdPZFeBdQdQeFZGe?jdRZHeBdSdSeHZIe dqdTZJxZKS)rTimedeltaArraya Pandas ExtensionArray for timedelta data. .. warning:: TimedeltaArray is currently experimental, and its API may change without warning. In particular, :attr:`TimedeltaArray.dtype` is expected to change to be an instance of an ``ExtensionDtype`` subclass. Parameters ---------- data : array-like The timedelta data. dtype : numpy.dtype Valid ``numpy`` dtypes are ``timedelta64[ns]``, ``timedelta64[us]``, ``timedelta64[ms]``, and ``timedelta64[s]``. freq : Offset, optional Frequency of the data. copy : bool, default False Whether to copy the underlying array of data. Attributes ---------- None Methods ------- None See Also -------- Timedelta : Represents a duration, the difference between two dates or times. TimedeltaIndex : Immutable Index of timedelta64 data. to_timedelta : Convert argument to timedelta. Examples -------- >>> pd.arrays.TimedeltaArray._from_sequence(pd.TimedeltaIndex(["1h", "2h"])) ['0 days 01:00:00', '0 days 02:00:00'] Length: 2, dtype: timedelta64[us] timedeltaarrayc.tj|dSNm)r is_np_dtypexs rFzTimedeltaArray.sQRTWAXrHzCallable[[DtypeObj], bool]_is_recognized_dtypecBtjd|jS)Nr )np timedelta64unitrUs rF_internal_fill_valuez#TimedeltaArray._internal_fill_values~~eTYY//rHctSNrrrs rF _scalar_typezTimedeltaArray._scalar_typesrHiz list[str] _other_ops _bool_ops)rKseconds microseconds nanoseconds _field_opsrq_datetimelike_ops)to_pytimedelta total_secondsroundfloorceilas_unit_datetimelike_methodsc|jd}|tjk(rtStj||j S)Ni8rL)viewr _valuer_from_value_and_resorR)rUrkys rF _box_funczTimedeltaArray._box_funcs6 FF4L  ?J--adkkBBrHc.|jjS)a3 The dtype for the TimedeltaArray. .. warning:: A future version of pandas will change dtype to be an instance of a :class:`pandas.api.extensions.ExtensionDtype` subclass, not a ``numpy.dtype``. 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Timedelta | NaTTyperzCannot divide z by )r_recognized_scalarsrrr roemptyshaperNfillnanrr1rtruediv rfloordivrrr]rGrrBr@rr)rUrErresrWs rF_scalar_divlike_opz!TimedeltaArray._scalar_divlike_op~s eT55 6e$E)51S8hhtzz<  dmmU+ + i(()*=*=>>$T%[%9%9$:$tDz?R?R>ST'u-E||E"..u5 u-F:))& )E ErHct|dstj|}t|t|k7r t d|S)Nrz*Cannot divide vectors with unequal lengths)r(ror)r+rrs rF_cast_divlike_opzTimedeltaArray._cast_divlike_ops:ug&HHUOE u:T "IJ J rHctj|}|jjdk(r3|tj tj fvr|j|||j|}t|jst|jrH|tj tj fvr&t|j||jS|tj tjfvrp|jt|z}|j!rD|j#tj$}tj&||tj(|S)z Shared logic for __truediv__, __floordiv__, and their reversed versions with timedelta64-dtype ndarray other. rYr)ror'rroperatortruedivfloordivr@rr'r&rrr1rIr/rrrNputmaskrG)rUrErr:rWmasks rF_vector_divlike_opz!TimedeltaArray._vector_divlike_ops JJu% ??  3 &2(2B2BHDUDU1V+V  * *9 5DMM9- U[[ )^EKK-Hb      U O :))& )E E (##Y%8%89 999;e,Dxxzrzz2 640 rH __truediv__ctj}t|r|j||S|j |}t j |jds*t|jst|jr|j||St|jrtj|}|jdkDr[t||dDcgc] \}}||z  }}}|Dcgc]}|j!dd}}tj"|d}|St%|j&|}|St(Scc}}wcc}wNrhrT)strictrr)r)rOrPr)rKrMrrirr'r&rTr(ror'r7zipreshape concatenaterrr* rUrErleftrightres_colsrk res_cols2rWs rFrUzTimedeltaArray.__truediv__s)   U **5"5 5%%e, OOEKK - ,ekk***5"5 5 5;; 'JJu%Eyy1}47eD4Q%0T5D5L8@@!QYYq"-@ @ :M.dmmUCM" !As %E ;E __rtruediv__ctj}t|r|j||S|j |}t j |jdr|j||St|jr?tt|Dcgc] }||||z }}tj|StScc}wrg)r1rHr)rKrMrrirrTr(r,r+ror)r*)rUrErr1 result_lists rFrazTimedeltaArray.__rtruediv__s   U **5"5 5%%e, ??5;; ,**5"5 5 U[[ )8=SY7GH!58d1g-HKH88K( (" ! IsC  __floordiv__ctj}t|r|j||S|j |}t j |jds*t|jst|jr|j||St|jrtj|}|jdkDrZt||dDcgc] \}}||z }}}|Dcgc]}|j!dd}}tj"|d}nt%|j&|}|jt(k(sJ|St*Scc}}wcc}wrW)rOrQr)rKrMrrirr'r&rTr(ror'r7rYrZr[rrrr*r\s rFrdzTimedeltaArray.__floordiv__s0    U **5"5 5%%e, OOEKK - ,ekk***5"5 5 U[[ )JJu%Eyy1}58uT5R&1dEDEM8@@!QYYq"-@ @ :.t}}eD<<6) ))M" !As %E!;E' __rfloordiv__ctj}t|r|j||S|j |}t j |jdr|j||St|jrAtt|Dcgc] }||||z}}tj|}|StScc}wrg)r1rIr)rKrMrrirrTr(r,r+ror)r*)rUrErr1rcrWs rFrfzTimedeltaArray.__rfloordiv__s   U **5"5 5%%e, ??5;; ,**5"5 5 U[[ )8=c$i8HI158tAw.IKIXXk*FM" ! JsC __mod__cZt||jr t|}|||z|zz SrurrCrrs rFrhzTimedeltaArray.__mod__)s2 eT55 6e$Etu}---rH__rmod__cZt||jr t|}|||z|zz Srurjrs rFrkzTimedeltaArray.__rmod__0s2 eT55 6e$E ---rH __divmod__cft||jr t|}||z}|||zz }||fSrurjrUrEres1res2s rFrmzTimedeltaArray.__divmod__7s? eT55 6e$Eu}dUl"TzrH __rdivmod__cft||jr t|}||z}|||zz }||fSrurjros rFrrzTimedeltaArray.__rdivmod__As? eT55 6e$E}td{"TzrHcdt|j|j |jSNr)rrrrrrs rF__neg__zTimedeltaArray.__neg__Ks&Dz%%t}}nDJJ%GGrHc~t|j|jj|jSru)rrrrrrrs rF__pos__zTimedeltaArray.__pos__Ns-Dz%%dmm&8&8&:$**%MMrHct|jtj|j|j Sru)rrrorrrrrs rF__abs__zTimedeltaArray.__abs__Qs-Dz%%bffT]]&;4::%NNrHct|j}|j}tj||\}}|||z z}|dkDj s|dkj r|dk7tj |dkz}|jtj}|||k(z}|||dzk(z} |j r.tj|tj||dz|}| j r.tj| tj||dz |}|j|dS)a{ Return total duration of each element expressed in seconds. This method is available directly on TimedeltaArray, TimedeltaIndex and on Series containing timedelta values under the ``.dt`` namespace. Returns ------- ndarray, Index or Series When the calling object is a TimedeltaArray, the return type is ndarray. When the calling object is a TimedeltaIndex, the return type is an Index with a float64 dtype. When the calling object is a Series, the return type is Series of type `float64` whose index is the same as the original. See Also -------- datetime.timedelta.total_seconds : Standard library version of this method. TimedeltaIndex.components : Return a DataFrame with components of each Timedelta. Examples -------- **Series** >>> s = pd.Series(pd.to_timedelta(np.arange(5), unit="D")) >>> s 0 0 days 1 1 days 2 2 days 3 3 days 4 4 days dtype: timedelta64[us] >>> s.dt.total_seconds() 0 0.0 1 86400.0 2 172800.0 3 259200.0 4 345600.0 dtype: float64 **TimedeltaIndex** >>> idx = pd.to_timedelta(np.arange(5), unit="D") >>> idx TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'], dtype='timedelta64[us]', freq=None) >>> idx.total_seconds() Index([0.0, 86400.0, 172800.0, 259200.0, 345600.0], dtype='float64') llrlg?N)rO) rrRrQrodivmodrrrrNr  nextafterrT) rUppsrQfloor_sresidualrWfixablefloor_fon_flooron_ceils rFrzTimedeltaArray.total_secondsXsl!-yyIIdC08c>) 5L   D8O#8#8#: 1}5)@AGnnRZZ0G&G"34H7S=!89G||~(BLL#,NPVW{{}'2<< +MvV''4'@@rHc,t|jS)a Return an ndarray of datetime.timedelta objects. Each element of the :class:`TimedeltaIndex` is converted to the corresponding native Python :class:`datetime.timedelta` object. Returns ------- numpy.ndarray A NumPy ``timedelta64`` object representing the same duration as the original pandas ``Timedelta`` object. The precision of the resulting object is in nanoseconds, which is the default time resolution used by pandas for ``Timedelta`` objects, ensuring high precision for time-based calculations. See Also -------- to_timedelta : Convert argument to timedelta format. Timedelta : Represents a duration between two dates or times. DatetimeIndex: Index of datetime64 data. Timedelta.components : Return a components namedtuple-like of a single timedelta. Examples -------- >>> tdelta_idx = pd.to_timedelta([1, 2, 3], unit="D") >>> tdelta_idx TimedeltaIndex(['1 days', '2 days', '3 days'], dtype='timedelta64[us]', freq=None) >>> tdelta_idx.to_pytimedelta() array([datetime.timedelta(days=1), datetime.timedelta(days=2), datetime.timedelta(days=3)], dtype=object) >>> tidx = pd.TimedeltaIndex(data=["1 days 02:30:45", "3 days 04:15:10"]) >>> tidx TimedeltaIndex(['1 days 02:30:45', '3 days 04:15:10'], dtype='timedelta64[us]', freq=None) >>> tidx.to_pytimedelta() array([datetime.timedelta(days=1, seconds=9045), datetime.timedelta(days=3, seconds=15310)], dtype=object) )rrrrs rFrzTimedeltaArray.to_pytimedeltasT#4==11rHaNumber of days for each element. This attribute returns the number of whole days in each timedelta value. It represents the days component of the duration, not the total duration expressed in days. See Also -------- Series.dt.seconds : Return number of seconds for each element. Series.dt.microseconds : Return number of microseconds for each element. Series.dt.nanoseconds : Return number of nanoseconds for each element. Examples -------- For Series: >>> ser = pd.Series(pd.to_timedelta([1, 2, 3], unit='D')) >>> ser 0 1 days 1 2 days 2 3 days dtype: timedelta64[us] >>> ser.dt.days 0 1 1 2 2 3 dtype: int64 For TimedeltaIndex: >>> tdelta_idx = pd.to_timedelta(["0 days", "10 days", "20 days"]) >>> tdelta_idx TimedeltaIndex(['0 days', '10 days', '20 days'], dtype='timedelta64[us]', freq=None) >>> tdelta_idx.days Index([0, 10, 20], dtype='int64')rKaNumber of seconds (>= 0 and less than 1 day) for each element. This attribute returns the seconds component of each timedelta value, which is the number of seconds remaining after subtracting whole days. Values range from 0 to 86399. See Also -------- Series.dt.seconds : Return number of seconds for each element. Series.dt.nanoseconds : Return number of nanoseconds for each element. Examples -------- For Series: >>> ser = pd.Series(pd.to_timedelta([1, 2, 3], unit='s')) >>> ser 0 0 days 00:00:01 1 0 days 00:00:02 2 0 days 00:00:03 dtype: timedelta64[us] >>> ser.dt.seconds 0 1 1 2 2 3 dtype: int32 For TimedeltaIndex: >>> tdelta_idx = pd.to_timedelta([1, 2, 3], unit='s') >>> tdelta_idx TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:02', '0 days 00:00:03'], dtype='timedelta64[us]', freq=None) >>> tdelta_idx.seconds Index([1, 2, 3], dtype='int32')rzaNumber of microseconds (>= 0 and less than 1 second) for each element. This attribute returns the microseconds component of each timedelta value, which is the number of microseconds remaining after subtracting whole seconds. Values range from 0 to 999999. See Also -------- pd.Timedelta.microseconds : Number of microseconds (>= 0 and less than 1 second). pd.Timedelta.to_pytimedelta.microseconds : Number of microseconds (>= 0 and less than 1 second) of a datetime.timedelta. Examples -------- For Series: >>> ser = pd.Series(pd.to_timedelta([1, 2, 3], unit='us')) >>> ser 0 0 days 00:00:00.000001 1 0 days 00:00:00.000002 2 0 days 00:00:00.000003 dtype: timedelta64[us] >>> ser.dt.microseconds 0 1 1 2 2 3 dtype: int32 For TimedeltaIndex: >>> tdelta_idx = pd.to_timedelta([1, 2, 3], unit='us') >>> tdelta_idx TimedeltaIndex(['0 days 00:00:00.000001', '0 days 00:00:00.000002', '0 days 00:00:00.000003'], dtype='timedelta64[us]', freq=None) >>> tdelta_idx.microseconds Index([1, 2, 3], dtype='int32')r{abNumber of nanoseconds (>= 0 and less than 1 microsecond) for each element. This attribute returns the nanoseconds component of each timedelta value, which is the number of nanoseconds remaining after subtracting whole microseconds. Values range from 0 to 999. See Also -------- Series.dt.seconds : Return number of seconds for each element. Series.dt.microseconds : Return number of microseconds for each element. Examples -------- For Series: >>> ser = pd.Series(pd.to_timedelta([1, 2, 3], unit='ns')) >>> ser 0 0 days 00:00:00.000000001 1 0 days 00:00:00.000000002 2 0 days 00:00:00.000000003 dtype: timedelta64[ns] >>> ser.dt.nanoseconds 0 1 1 2 2 3 dtype: int32 For TimedeltaIndex: >>> tdelta_idx = pd.to_timedelta([1, 2, 3], unit='ns') >>> tdelta_idx TimedeltaIndex(['0 days 00:00:00.000000001', '0 days 00:00:00.000000002', '0 days 00:00:00.000000003'], dtype='timedelta64[ns]', freq=None) >>> tdelta_idx.nanoseconds Index([1, 2, 3], dtype='int32')r|cddlm}gd|j}|rfd}nd}||Dcgc] }|| c}}|s|jd}|Scc}w)a Return a DataFrame of the individual resolution components of the Timedeltas. The components (days, hours, minutes seconds, milliseconds, microseconds, nanoseconds) are returned as columns in a DataFrame. Returns ------- DataFrame See Also -------- TimedeltaIndex.total_seconds : Return total duration expressed in seconds. Timedelta.components : Return a components namedtuple-like of a single timedelta. Examples -------- >>> tdelta_idx = pd.to_timedelta(["1 day 3 min 2 us 42 ns"]) >>> tdelta_idx TimedeltaIndex(['1 days 00:03:00.000002042'], dtype='timedelta64[ns]', freq=None) >>> tdelta_idx.components days hours minutes seconds milliseconds microseconds nanoseconds 0 1 0 3 0 0 2 42 rr?)rKhoursminutesrz millisecondsr{r|clt|rtjgtzS|jSru)r/rorGr+ components)rkcolumnss rFrYz$TimedeltaArray.components..fs)7FF8c'l22||#rHc|jSru)rrjs rFrYz$TimedeltaArray.components..fs ||#rH)rint64)pandasr@rSr)rUr@hasnansrYrkrWrs @rFrzTimedeltaArray.componentss_8 % ++  $ $$/QAaD/A]]7+F 0sA)r[np.timedelta64)r[ztype[Timedelta])rkrr[rB)r[np.dtype[np.timedelta64])r[r )rVznpt.NDArray[np.timedelta64]rrr[r)rboolr[rru)rqr=r[r)r[rB)r[None)T)rr)rr\r[r\) rAxisInt | NonerNpDtype | NonerrrrrrD) rrrrrrDrrrr)r`strrr)F)rr)rz str | floatr[npt.NDArray[np.object_])rE.float | np.floating | npt.NDArray[np.floating]r[r)rznpt.NDArray[np.int64]r[r)r[r)rErr[r)r[znp.ndarray | Self)r[rd)r[znpt.NDArray[np.float64])r[r)r[r@)Lr] __module__ __qualname__r^_typrrorprrCrm__annotations__r_rsrw__array_priority__rxryr}r~rrrr classmethodrr%rrrrrrrrrrrrrr rrr7r__rmul__r@rKrMrTrUrardrfrhrkrmrrrvrxrzrrtextwrapdedentdays_docstringrbrKseconds_docstringrzmicroseconds_docstringr{nanoseconds_docstringr|r __classcell__)rs@rFrdrdsZ*X D$bnnd;7X4X 00J IyNJ N#- #9VH#DyD(9C ## DD +7 ?+ ?( ?  ? ?+/e776/3(C>F(C (C(CZ A  I23 $ $ 99 9  999. $ $ 99 9  999(8< F6 (-$ =$ =  = BCB B8Bi(ZB)ZBxHICI I< FD6m,"-"<n-"."(n-"."<o."/""i(.). j).*. l+,m,-HNOTAl*2X%X__#&)#*NH 66> :D'" '$JG -X__$ '&N#L ,HOO# '%L"K 66rHrdc|dvsJ| t|}t|d}d}t|tr=|jr |j nd}|j }||j}d||<tj||d\}}|jtk(st|jrt|||}d }nt|jr( t|| \}}| t ||<d} |xr| }nHt'|jryt|jt(r|j }|j }nt+j,|}||d k7rt+j.d 5|j%t*j0}dddt+j2t*j0}|t+j4|j6k\|t+j4|j8kz} || |k(zzj;} | rt=|d ||} t | |<| S|j%t*j4d } t?||xsd } t | |<| jAd}d }ntKjL|jdr|jjNdk(r,|j%|jjQd}d }tS|js?tU|j}tW||d }d }ntYd|jd|st+jZ|}nt+j\||}|jj^dk(sJ|jdk7sJ|S#t"$r6|d k(r|j%t}|d||<t|||}d}YwxYw#1swYNxYw#tB$ri} |d k(rt#| jD| t+jF|t*jH|}t|j%t||}Yd} ~ d} ~ wwxYw)a Parameters ---------- data : list-like copy : bool, default False unit : str, optional The timedelta unit to treat integers as multiples of. For numeric data this defaults to ``'ns'``. Must be un-specified if the data contains a str and ``errors=="raise"``. errors : {"raise", "coerce", "ignore"}, default "raise" How to handle elements that cannot be converted to timedelta64[ns]. See ``pandas.to_timedelta`` for details. Returns ------- converted : numpy.ndarray The sequence converted to a numpy array with dtype ``timedelta64[ns]``. Raises ------ ValueError : Data cannot be converted to timedelta64[ns]. Notes ----- Unlike `pandas.to_timedelta`, if setting ``errors=ignore`` will not cause errors to be ignored; they are caught and subsequently ignored at a higher level. )YrMNT) extract_numpyrrdrrqerrorsFrraiserr4)r6)rrqrrzm8[ns]rh>OD)#!%X %I   # djj. 1::D::D88D>D   X. 1;;rxx0 1 ((288$C"bjj&99rzz#''**N483C!DEJJLI+5tF $t  {{2::E{2 ,1$ EI#IdO>>(+D S ) ::  3 &;;tzz66s;88D"&&$/D%dkk&&9VTD  Us7N'= O)$O6';O&%O&)O36 Q(?AQ##Q(c$d}||nd}|jtjk7r|jtjdk(rM|tjtjjkDj rt d|d|jtj}d}|dk7rBd|d}|j|}|d k7r"t|tjd  }d}||fS|jd }||fS) a Convert an ndarray with integer-dtype to timedelta64[ns] dtype, treating the integers as multiples of the given timedelta unit. Parameters ---------- data : numpy.ndarray with integer-dtype unit : str, default "ns" The timedelta unit to treat integers as multiples of. Returns ------- numpy.ndarray : timedelta64[ns] array converted from data bool : whether a copy was made Fruint64z Cannot convert input with unit ''Tz timedelta64[rrztimedelta64[us]rztimedelta64[ns]) rrorrrrr"rrr)rrqr dtype_strs rFrr^s I#4D zzRXX ::(+ +8J8N8N1N0S0S0U&)I$q'QR R{{288$  t|"4&* yy# 4<&t288rr@rGrb TimelikeOpsrdrrrrrrHrFrs;" E//6 ,?2=3 2;(!", OrS__rrv! #* Y Y ! Y  Yx*\5<#1##LrH