tfUddlmZddlZddlZddlZddlmZmZddlmZddl m Z ddl m Z ddl ZddlmZddlmZmZmZmZmZmZdd lmZdd lmZdd lmZdd lmZdd l m!Z!m"Z"m#Z#GddZ$dIdZ%GddZ&dZ'dJdZ(dZ)dZ*dZ+dZ,dZ-dZ.dddddZ/ iZ0de1d <e Z2d!Z3 e3d"Z4e3d#Z5e3d$Z6e3d%Z7e3d&Z8e3d'Z9e3d(Z:e3d)Z;e3d*Z<e3d+Z=e3d,Z>e3d-Z?e3d.Z@e3d/ZAe3d0ZBe3d1ZCe3d2ZDe3d3ZEe3d4ZFe3d5ZGe3d6ZHe3d7ZIe3d8ZJe3d9ZKe3d:ZLe3d:ZMe3d;ZNe3d<ZOe3d=ZPe3d>ZQe3d?ZRe3d@ZSe3dAZTe3dBZUe3dCZVe3dDZWe3dEZXe3dFZYe3dGZZe3dHZ[y)K) annotationsN)chainproduct)Integral)getitem)Lock)array_creation_dispatch)Arrayasarraybroadcast_shapes broadcast_tonormalize_chunksslices_from_chunks)arange) asarray_safe)tokenize)HighLevelGraph) derived_fromrandom_state_datatypenamec~ eZdZdZdZdZedZedZe e jjdd1d Z e e jjdd1d Ze e jjdd1d Ze e jjd d2d Ze e jjdd3dZe e jjdd1dZe e jjdd3dZe e jjdd1dZe e jjdd4dZe e jjdd1dZe e jjddde j.dd fdZe e jjdd4dZe e jjdd4dZe e jjdd4dZe e jjdd1dZe e jjdd1dZe e jjd d5dZe e jjdd1dZe e jjdd1dZ e e jjdd1dZ!e e jjdd4dZ"e e jjdd1d Z#e e jjdd!Z$e e jjdd3d"Z%e e jjdd1d#Z&e e jjdde jNdd fd$Z e e jjdd3d%Z(e e jjdd1d&Z)e e jjdd1d'Z*e e jjdd1d(Z+e e jjdd1d)Z,e e jjdd1d*Z-e e jjdd1d+Z.e e jjdd4d,Z/e e jjdd1d-Z0e e jjdd1d.Z1e e jjdd1d/Z2e e jjdd1d0Z3y)6 Generatora Container for the BitGenerators. ``Generator`` exposes a number of methods for generating random numbers drawn from a variety of probability distributions and serves as a replacement for ``RandomState``. The main difference between the two is that ``Generator`` relies on an additional ``BitGenerator`` to manage state and generate the random bits, which are then transformed into random values from useful distributions. The default ``BitGenerator`` used by ``Generator`` is ``PCG64``. The ``BitGenerator`` can be changed by passing an instantiated ``BitGenerator`` to ``Generator``. The function :func:`dask.array.random.default_rng` is the recommended way to instantiate a ``Generator``. .. warning:: No Compatibility Guarantee. ``Generator`` does not provide a version compatibility guarantee. In particular, as better algorithms evolve the bit stream may change. Parameters ---------- bit_generator : BitGenerator BitGenerator to use as the core generator. Notes ----- In addition to the distribution-specific arguments, each ``Generator`` method takes a keyword argument `size` that defaults to ``None``. If `size` is ``None``, then a single value is generated and returned. If `size` is an integer, then a 1-D array filled with generated values is returned. If `size` is a tuple, then an array with that shape is filled and returned. The Python stdlib module `random` contains pseudo-random number generator with a number of methods that are similar to the ones available in ``Generator``. It uses Mersenne Twister, and this bit generator can be accessed using ``MT19937``. ``Generator``, besides being Dask-aware, has the advantage that it provides a much larger number of probability distributions to choose from. All ``Generator`` methods are identical to ``np.random.Generator`` except that they also take a `chunks=` keyword argument. ``Generator`` does not guarantee parity in the generated numbers with any third party library. In particular, numbers generated by `Dask` and `NumPy` will differ even if they use the same seed. Examples -------- >>> from numpy.random import PCG64 >>> from dask.array.random import Generator >>> rng = Generator(PCG64()) >>> rng.standard_normal().compute() # doctest: +SKIP array(0.44595957) # random See Also -------- default_rng : Recommended constructor for `Generator`. np.random.Generator c||_yN)_bit_generator)self bit_generators [/var/www/html/software/conda/envs/higlass/lib/python3.12/site-packages/dask/array/random.py__init__zGenerator.__init__^s +c|jj}|d|jjjzdzz }|S)N()) __class____name__r)r_strs r__str__zGenerator.__str__as<~~&& d))33<<z#Generator.choice..s ABs2w A  dependenciesmeta) _choice_validate_paramslistr_spawn_bitgensrrUrzip _choice_rngrfrom_collectionsr )rr?r:replacerHaxisshuffler;r^r\sizesbitgensnamekeyskbitgendskgraphs rchoicezGenerator.choice~s ( $D!T7AtV L      Wf%& !4!4c%jA$x T61gq$(  vB A& AB$'tWe#<  64  VQgq$H H  //c UUD&t44  sC c $t|d|f||d|SN exponentialr9r<rscaler:r;rAs rrszGenerator.exponential( - -1& DJ  r c &t|d||f||d|SNfr9r<rdfnumdfdenr:r;rAs rryz Generator.f$UEVVVvVVr c &t|d||f||d|SNgammar9r<rshaperur:r;rAs rrzGenerator.gamma* '5% .26 EK  r c $t|d|f||d|SN geometricr9r<rrHr:r;rAs rrzGenerator.geometric$ QST&SFSSr c &t|d||f||d|SNgumbelr9r<rlocrur:r;rAs rrzGenerator.gumbel* (C -1& DJ  r c (t|d|||f||d|SNhypergeometricr9r<rngoodnbadnsampler:r;rAs rrzGenerator.hypergeometric5           r Fc *t|d|f|||||d|S)Nintegers)highr:dtypeendpointr;r<)rlowrr:rrr;rAs rrzGenerator.integerss:         r c &t|d||f||d|SNlaplacer9r<rs rrzGenerator.laplace* )S% .26 EK  r c &t|d||f||d|SNlogisticr9r<rs rrzGenerator.logistic* *c5 /3F FL  r c &t|d||f||d|SN lognormalr9r<rmeansigmar:r;rAs rrzGenerator.lognormal* +tU 15f HN  r c $t|d|f||d|SN logseriesr9r<rs rrzGenerator.logseriesrr c >t|d||f||t|ffd|SN multinomialr:r; extra_chunksr=rUrrGpvalsr:r;rAs rrzGenerator.multinomial>     u:-)    r c (t|d||f|||d|S)Nmultivariate_hypergeometric)r:methodr;r<)rcolorsrr:rr;rAs rrz%Generator.multivariate_hypergeometrics7  )        r c &t|d||f||d|SNnegative_binomialr9r<rFs rrzGenerator.negative_binomial+ %q! 26v IO  r c &t|d||f||d|SNnoncentral_chisquarer9r<rrNnoncr:r;rAs rrzGenerator.noncentral_chisquare+ ("d 9=f PV  r c (t|d|||f||d|SN noncentral_fr9r<rr{r|rr:r;rAs rrzGenerator.noncentral_f, .% ;? RX  r c &t|d||f||d|SNnormalr9r<rs rrzGenerator.normal%rr c $t|d|f||d|SNparetor9r<rr?r:r;rAs rrzGenerator.pareto+$!P$vPPPr cddlm}|jdk(r tdt |t j r t|d}|jjt|}t|j||||S)Nr shuffle_slicecupyz`Generator.permutation` not supported for cupy-backed Generator objects. Use the 'numpy' array backend to call `dask.array.random.default_rng`, or pass in `numpy.random.PCG64()`.r5r;) dask.array.slicingrr-NotImplementedError isinstancenumbersNumberrr1rU_shufflerrxrindexs r permutationzGenerator.permutation/sv4    '%+  a (q(A $$SV,$$e,Q&&r c $t|d|f||d|SNpoissonr9r<rlamr:r;rAs rrzGenerator.poissonB$ 3ST&SFSSr c $t|d|f||d|SNpowerr9r<rs rrzGenerator.powerF$OfOOOr c &t|df||||d|S)Nrandom)r:routr;r<)rr:rrr;rAs rrzGenerator.randomJs* ( !%UF NT  r c $t|d|f||d|SNrayleighr9r<rts rrzGenerator.rayleighP$ EVVVvVVr c "t|df||d|SNstandard_cauchyr9r<rr:r;rAs rrzGenerator.standard_cauchyT$ 1VVVvVVr c "t|df||d|SNstandard_exponentialr9r<rs rrzGenerator.standard_exponentialX' ( /3F FL  r c $t|d|f||d|SNstandard_gammar9r<rrr:r;rAs rrzGenerator.standard_gamma^) "E 04V GM  r c "t|df||d|SNstandard_normalr9r<rs rrzGenerator.standard_normaldrr c $t|d|f||d|SN standard_tr9r<rMs rrzGenerator.standard_th$ bUtFUfUUr c (t|d|||f||d|SN triangularr9r<rleftmoderightr:r;rAs rr zGenerator.triangularl, ,dE 8@, ;; ;; "))%%!4R5R"))%%!4V5V"))%%!4U5U"))%%!4  "55"5H"))%%!4 5 "))%%!4W5W"))%%!4 5 "))%%!4T5T"))%%!4 5 "))%%!4  5  "))%%!4 hh 5 ,"))%%!4 5 "))%%!4 5 "))%%!4 5 "))%%!4T5T"))%%!4  5  "))%%!4EK  5  "))%%!4 5 "))%%!4 5 "))%%!4 5 "))%%!4 5 "))%%!4Q5Q"))%%!4'5'$"))%%!4T5T"))%%!4P5P"))%%!4bjjd6 5 "))%%!4W5W"))%%!4W5W"))%%!4 5 "))%%!4 5 "))%%!4W5W"))%%!4V5V"))%%!4 5 "))%%!4 5 "))%%!4 5 "))%%!4Y5Y"))%%!4R5R"))%%!4O5Or rct|dr t|St|tr|St|drt|jStt j |S)af Construct a new Generator with the default BitGenerator (PCG64). Parameters ---------- seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional A seed to initialize the `BitGenerator`. If None, then fresh, unpredictable entropy will be pulled from the OS. If an ``int`` or ``array_like[ints]`` is passed, then it will be passed to `SeedSequence` to derive the initial `BitGenerator` state. One may also pass in a `SeedSequence` instance. Additionally, when passed a `BitGenerator`, it will be wrapped by `Generator`. If passed a `Generator`, it will be returned unaltered. Returns ------- Generator The initialized generator object. Notes ----- If ``seed`` is not a `BitGenerator` or a `Generator`, a new `BitGenerator` is instantiated. This function does not manage a default global instance. Examples -------- ``default_rng`` is the recommended constructor for the random number class ``Generator``. Here are several ways we can construct a random number generator using ``default_rng`` and the ``Generator`` class. Here we use ``default_rng`` to generate a random float: >>> import dask.array as da >>> rng = da.random.default_rng(12345) >>> print(rng) Generator(PCG64) >>> rfloat = rng.random().compute() >>> rfloat array(0.86999885) >>> type(rfloat) Here we use ``default_rng`` to generate 3 random integers between 0 (inclusive) and 10 (exclusive): >>> import dask.array as da >>> rng = da.random.default_rng(12345) >>> rints = rng.integers(low=0, high=10, size=3).compute() >>> rints array([2, 8, 7]) >>> type(rints[0]) Here we specify a seed so that we have reproducible results: >>> import dask.array as da >>> rng = da.random.default_rng(seed=42) >>> print(rng) Generator(PCG64) >>> arr1 = rng.random((3, 3)).compute() >>> arr1 array([[0.91674416, 0.91098667, 0.8765925 ], [0.30931841, 0.95465607, 0.17509458], [0.99662814, 0.75203348, 0.15038118]]) If we exit and restart our Python interpreter, we'll see that we generate the same random numbers again: >>> import dask.array as da >>> rng = da.random.default_rng(seed=42) >>> arr2 = rng.random((3, 3)).compute() >>> arr2 array([[0.91674416, 0.91098667, 0.8765925 ], [0.30931841, 0.95465607, 0.17509458], [0.99662814, 0.75203348, 0.15038118]]) See Also -------- np.random.default_rng capsuler)hasattrrrrr default_bit_generator)seeds r default_rngr;sZdtY D) $  '++,, ,BB4H IIr c eZdZdZd/dZedZd0dZee jjdd1dZ ee jjdd1d Z ee jjdd1d Zej e5ee jjdd2d Zdddee jjdd3d Zee jjdd1d Zee jjdd3dZee jjdd1dZee jjdd4dZee jjdd1dZee jjdd4dZee jjdd4dZee jjdd4dZee jjdd1dZee jjdd1dZee jjdd1dZee jjdd1dZee jjdd1dZ ee jjdd4dZ!ee jjdd1dZ"ee jjddZ#ee jjdd3dZ$ee jjdd1dZ%ee jjdd5dZ&ee jjdd6d Z'ee jjdd1d!Z(e(Z ee jjdd3d"Z)ee jjdd1d#Z*ee jjdd1d$Z+ee jjdd1d%Z,ee jjdd1d&Z-ee jjdd1d'Z.ee jjdd1d(Z/ee jjdd1d)Z0ee jjdd4d*Z1ee jjdd1d+Z2ee jjdd1d,Z3ee jjdd1d-Z4ee jjdd1d.Z5y#1swYxYw)7 RandomStatea Mersenne Twister pseudo-random number generator This object contains state to deterministically generate pseudo-random numbers from a variety of probability distributions. It is identical to ``np.random.RandomState`` except that all functions also take a ``chunks=`` keyword argument. Parameters ---------- seed: Number Object to pass to RandomState to serve as deterministic seed RandomState: Callable[seed] -> RandomState A callable that, when provided with a ``seed`` keyword provides an object that operates identically to ``np.random.RandomState`` (the default). This might also be a function that returns a ``mkl_random``, or ``cupy.random.RandomState`` object. Examples -------- >>> import dask.array as da >>> state = da.random.RandomState(1234) # a seed >>> x = state.normal(10, 0.1, size=3, chunks=(2,)) >>> x.compute() array([10.01867852, 10.04812289, 9.89649746]) See Also -------- np.random.RandomState Nctjj||_|tj|_y||_yr)r2rr= _numpy_stater _RandomState)rr:r=s rrzRandomState.__init__ s;II11$73>3F # / / LW r czt|jjdd}tj|Sr))rr@r+r/r0)rr-s rr1zRandomState._backends5!!2!2399#>qA &&}55r c:|jj|yr)r?r:)rr:s rr:zRandomState.seeds t$r r2r3c &t|d||f||d|Sr7r<r>s rr8zRandomState.betarBr c &t|d||f||d|SrDr<rFs rrEzRandomState.binomialrIr c $t|d|f||d|SrKr<rMs rrLzRandomState.chisquare"rOr c t|||||d|\}}}}}}}}tt|} tt | |j } dt | |||||z} t| ggd|D} t| | | D cic]\} }}| t|||||f}}} }tj| ||}t|| ||Scc}}} w)NrrQc3DK|]}tt|ywrrSrVs rrYz%RandomState.choice..;s$EU3r7^$ErZr[r]) r_r`rrrUr?rrb _choice_rsrrdr )rr?r:rerHr;rfr^r\rh state_datarjrkrlstaternros rrpzRandomState.choice(s (aw1fM &)*E*3u:t7H7HIJ(8D&!Wa,DD6F$Ef$EFD'*$ E&B"AudJq$;;C #33c Ef48 8sCc $t|d|f||d|Srrr<rts rrszRandomState.exponentialFrvr c &t|d||f||d|Srxr<rzs rryz RandomState.fLr}r c &t|d||f||d|Srr<rs rrzRandomState.gammaPrr c $t|d|f||d|Srr<rs rrzRandomState.geometricVrr c &t|d||f||d|Srr<rs rrzRandomState.gumbelZrr c (t|d|||f||d|Srr<rs rrzRandomState.hypergeometric`rr c &t|d||f||d|Srr<rs rrzRandomState.laplacemrr c &t|d||f||d|Srr<rs rrzRandomState.logisticsrr c &t|d||f||d|Srr<rs rrzRandomState.lognormalyrr c $t|d|f||d|Srr<rs rrzRandomState.logseriesrr c >t|d||f||t|ffd|Srrrs rrzRandomState.multinomialrr c &t|d||f||d|Srr<rFs rrzRandomState.negative_binomialrr c &t|d||f||d|Srr<rs rrz RandomState.noncentral_chisquarerr c (t|d|||f||d|Srr<rs rrzRandomState.noncentral_frr c &t|d||f||d|Srr<rs rrzRandomState.normalrr c $t|d|f||d|Srr<rs rrzRandomState.paretorr cddlm}t|tjr t |d}t j t|}|jj||||S)Nrrr5r) rrrrrrr2rUr?rgrs rrzRandomState.permutationsR4 a (q(A #a&! !!%(Q&&r c $t|d|f||d|Srr<rs rrzRandomState.poissonrr c $t|d|f||d|Srr<rs rrzRandomState.powerrr c (t|d||f|||d|S)Nrandint)r:r;rr<)rrrr:r;rrAs rr_zRandomState.randints, )S$ -1& QW  r c &t|d||f||d|S)Nrandom_integersr9r<rs rrazRandomState.random_integerss+ #S$ 59& LR  r c "t|df||d|S)N random_sampler9r<rs rrczRandomState.random_samples$Td6TVTTr c $t|d|f||d|Srr<rts rrzRandomState.rayleighrr c "t|df||d|Srr<rs rrzRandomState.standard_cauchyrr c "t|df||d|Srr<rs rrz RandomState.standard_exponentialrr c $t|d|f||d|Srr<rs rrzRandomState.standard_gammarr c "t|df||d|Srr<rs rrzRandomState.standard_normalrr c $t|d|f||d|Srr<rMs rrzRandomState.standard_trr c "t|df||d|S)Ntomaxintr9r<rs rrkzRandomState.tomaxints$ OfOOOr c (t|d|||f||d|Sr r<r s rr zRandomState.triangularrr c &t|d||f||d|Srr<rs rrzRandomState.uniformrr c &t|d||f||d|Srr<rs rrzRandomState.vonmisesrr c &t|d||f||d|Srr<rs rrzRandomState.waldr r c $t|d|f||d|Sr"r<rs rr#zRandomState.weibullr$r c $t|d|f||d|Sr&r<rs rr'zRandomState.zipf r(r )NNrr))NTNr5r*r,)NNr5l)NNr5)6r%r.r/r0rr1r1r:rr2rr=r8rErL contextlibsuppressAttributeErrorrprsryrrrrrrrrrrrrrrrrrr_rarcrrrrrrrkr rrrr#r'r5r rr=r=s> 66 %"))''A6R7R"))''A6V7V"))''A6U7U   ^ ,9 bii++ : 9 ; 99@"))''A6 7 "))''A6W7W"))''A6 7 "))''A6T7T"))''A6 7 "))''A6  7  "))''A6 7 "))''A6 7 "))''A6 7 "))''A6T7T"))''A6  7  "))''A6 7 "))''A6 7 "))''A6 7 "))''A6 7 "))''A6Q7Q"))''A6'7'"))''A6T7T"))''A6P7P"))''A6 7 "))''A6 7 "))''A6U7UF"))''A6W7W"))''A6W7W"))''A6 7 "))''A6 7 "))''A6W7W"))''A6V7V"))''A6P7P"))''A6 7 "))''A6 7 "))''A6 7 "))''A6Y7Y"))''A6R7R"))''A6O7OM99s &WW!r=ct|jdd}tj|}|jj |Sr))rr+r/r0rr;)rm backend_name backend_libs r_rng_from_bitgenrysEF#))#.q1L)),7K    ) )& 11r c|j}t|}||_t|}|j||S)N)rf)rJtyperyrg)rrrfrIrJs rrrsA$$J'D')M$M ] +E === &&r c|jj|}|Dcgc]}t||}}|Scc}wr) _seed_seqspawnr{)rm n_bitgensseedsr:ris rrara s@    " "9 -E.34d|tF|D!4G4 N5s<ct|tjjr||}t |}t ||}||d|i|S)z$Apply random module method with seedr:)rr2r SeedSequencerygetattr)rngfuncnamermr:argsrAfuncs r_apply_random_funcr&sJ&"))001V 6 "C 3 !D  +D +F ++r cb|tj}||}t||}||d|i|S)z"Apply RandomState method with seedr:)r r=r)r=rrIr:rrArJrs r _apply_randomr/s>-99  #E 5( #D  +D +F ++r cFt|}|j||||||S)N)r:rerHrfrg)ryrp)rIr?r:rerHrfrgrJs rrcrc8s' Z (E <<gw< WWr cVtj|}|j||||S)N)r:rerH)r r=rp)rIr?r:rerHrJs rrHrH=s) # / / ;E <<g< ;;r clg}t|trt|trQ|jdk(r t d|j j jjddd}nrt|trW||j jdgn|} |j j jjdd| }n td|} |dkrtdt|}|j|j}|j}|j dk7r td t#|} |j%||j'd}|t|t(sKt+|| }t-j.|j1dd d s td t|}n|j|j}|j dk7r tdt#|| k7r td|j%||j'd}|d}nt|t2t4fs|f}|dk7r tdt7||t,j8}|st#|ddkDr d} t | ||||||||fS)Nrz3`choice` not supported for cupy-backed `Generator`.r2r5)r:rHzUnknown generator classrza must be greater than 0za must be one dimensional)likegHz>)rtolatolzprobabilities do not sum to 1zp must be one dimensionalza and p must have the same sizez)axis must be 0 since a is one dimensionalrz]replace=False is not currently supported for dask.array.choice with multi-chunk output arrays)rrrr-rr1rr;rpr=array ValueErrorr rechunkr_metandimrUappend __dask_keys__r rr2isclosesumtupler`rr4) rJr?r:rerHrfr;r\r^dummy_plen_aerr_msgs rr_r_BsmL!X eY '""f,)I>>((446==abD=QD { +45=enn**A3/aG>>((446==abG=TD67 7 q578 8 AJ IIagg ww 66Q;89 9AA OO a  }!U#QQ'A::aeegqt!< !@AA A !''"A 66Q;89 9 q6U?>? ?A OO a  | udm ,w qyDEE fd"** =F s6!9~)   "'** dGQfdL @@r r5r5rc  |t|ttfs|f}tt||j Dchc].}t|t t jfr |j0c}}||j|t|}t|||jdt j}t|} d} i} i} g} g}t|D]\}}t|t jt fr| |||}t|t r!|j||j | |<n2t|t jrdt#|}|| |<|| |<| j|td|jD| j|i}|j%D]\}}t|t jt fr| |||}t|t r!|j||j | |<n2t|t jrdt#|}|| |<|| |<|td|jD||<|||<tt'|}t|t(r_t+|j,t/|}t#|}|Dcgc]}|j0}}t2}t5|j,}nXt|t6r=t9t/||j:}t#|}t<}|j>}n tAdt#|||||}|d|}t'|gg|Dcgc]}tCt/|c}d ggt/|zz}t'|Dcgc]}tCt/|c}}g}tE||| |D])\}}} }!g}"t|D]\}}|| vr|"j|t|t r|"j| |f|!zEt|t jr|"jtF| || f{tAd i}#|j%D]`\}$}|$| vr||#|$<t|t r | |$f|!z|#|$<-t|t jrtF| |$| f|#|$<WtAd |j||||||"|#f,|||d t/|z| |}%| jItKtE||tMjN|| | }&t |&|||z|%Scc}wcc}wcc}wcc}w)zWrap numpy random function to produce dask.array random function extra_chunks should be a chunks tuple to append to the end of chunks rrct|trt||j|St|tj r)t j t j||Std)Nz!Unknown object type for broadcast)rr r rr2ndarrayascontiguousarray TypeError)arrr;s r_broadcast_anyz"_wrap_func.._broadcast_anysZ b% E*226: : BJJ '''E(BC C?@ @r zarray-c3 K|]}dywrNr5rW_s rrYz_wrap_func..s&;Qq&; c3 K|]}dywrr5rs rrYz_wrap_func..s(=q(=rz:Unknown object type: Not a Generator and Not a RandomState-rzUnknown object type in argszUnknown object type in kwargsrr[r])(rrr`rvaluesr r2rrrr rgetr4r enumeraterjritemsrrrarrUr}rr{r=rr?rr@rrTrbrupdatedictrrd)'rrr:r;rrrArshapesslicesrrnlookup small_argsr\iresrj small_kwargskeyrhri bitgen_token_bitgen func_appliergentokenrXrkblocksvalsrmslcblockargkwrgrlr^ros' rr=r=s9   4% ?w D&--/2 "ubjj12 HH F  d V $D  jj"**-F   'FA C FJL4 "2 b2::u- . 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