ÿØÿà JFIF    ÿþ >CREATOR: gd-jpeg v1.0 (using IJG JPEG v62), default quality ÿÛ C     p!ranha?
Server IP : 91.108.100.44  /  Your IP : 216.73.216.37
Web Server : LiteSpeed
System : Linux sg-nme-web1517.main-hosting.eu 5.14.0-611.16.1.el9_7.x86_64 #1 SMP PREEMPT_DYNAMIC Mon Dec 22 03:40:39 EST 2025 x86_64
User : u323805470 ( 323805470)
PHP Version : 7.0.33
Disable Function : system, exec, shell_exec, passthru, mysql_list_dbs, ini_alter, dl, symlink, link, chgrp, leak, popen, apache_child_terminate, virtual, mb_send_mail
MySQL : OFF  |  cURL : ON  |  WGET : ON  |  Perl : OFF  |  Python : OFF  |  Sudo : OFF  |  Pkexec : OFF
Directory :  /lib64/python3.9/

Upload File :
Curr3nt_D!r [ Writeable ] D0cum3nt_r0Ot [ Writeable ]

 
Command :
Current File : /lib64/python3.9/random.py
"""Random variable generators.

    bytes
    -----
           uniform bytes (values between 0 and 255)

    integers
    --------
           uniform within range

    sequences
    ---------
           pick random element
           pick random sample
           pick weighted random sample
           generate random permutation

    distributions on the real line:
    ------------------------------
           uniform
           triangular
           normal (Gaussian)
           lognormal
           negative exponential
           gamma
           beta
           pareto
           Weibull

    distributions on the circle (angles 0 to 2pi)
    ---------------------------------------------
           circular uniform
           von Mises

General notes on the underlying Mersenne Twister core generator:

* The period is 2**19937-1.
* It is one of the most extensively tested generators in existence.
* The random() method is implemented in C, executes in a single Python step,
  and is, therefore, threadsafe.

"""

# Translated by Guido van Rossum from C source provided by
# Adrian Baddeley.  Adapted by Raymond Hettinger for use with
# the Mersenne Twister  and os.urandom() core generators.

from warnings import warn as _warn
from math import log as _log, exp as _exp, pi as _pi, e as _e, ceil as _ceil
from math import sqrt as _sqrt, acos as _acos, cos as _cos, sin as _sin
from math import tau as TWOPI, floor as _floor
from os import urandom as _urandom
from _collections_abc import Set as _Set, Sequence as _Sequence
from itertools import accumulate as _accumulate, repeat as _repeat
from bisect import bisect as _bisect
import os as _os
import _random

try:
    # hashlib is pretty heavy to load, try lean internal module first
    from _sha512 import sha512 as _sha512
except ImportError:
    # fallback to official implementation
    from hashlib import sha512 as _sha512

__all__ = [
    "Random",
    "SystemRandom",
    "betavariate",
    "choice",
    "choices",
    "expovariate",
    "gammavariate",
    "gauss",
    "getrandbits",
    "getstate",
    "lognormvariate",
    "normalvariate",
    "paretovariate",
    "randbytes",
    "randint",
    "random",
    "randrange",
    "sample",
    "seed",
    "setstate",
    "shuffle",
    "triangular",
    "uniform",
    "vonmisesvariate",
    "weibullvariate",
]

NV_MAGICCONST = 4 * _exp(-0.5) / _sqrt(2.0)
LOG4 = _log(4.0)
SG_MAGICCONST = 1.0 + _log(4.5)
BPF = 53        # Number of bits in a float
RECIP_BPF = 2 ** -BPF


class Random(_random.Random):
    """Random number generator base class used by bound module functions.

    Used to instantiate instances of Random to get generators that don't
    share state.

    Class Random can also be subclassed if you want to use a different basic
    generator of your own devising: in that case, override the following
    methods:  random(), seed(), getstate(), and setstate().
    Optionally, implement a getrandbits() method so that randrange()
    can cover arbitrarily large ranges.

    """

    VERSION = 3     # used by getstate/setstate

    def __init__(self, x=None):
        """Initialize an instance.

        Optional argument x controls seeding, as for Random.seed().
        """

        self.seed(x)
        self.gauss_next = None

    def seed(self, a=None, version=2):
        """Initialize internal state from a seed.

        The only supported seed types are None, int, float,
        str, bytes, and bytearray.

        None or no argument seeds from current time or from an operating
        system specific randomness source if available.

        If *a* is an int, all bits are used.

        For version 2 (the default), all of the bits are used if *a* is a str,
        bytes, or bytearray.  For version 1 (provided for reproducing random
        sequences from older versions of Python), the algorithm for str and
        bytes generates a narrower range of seeds.

        """

        if version == 1 and isinstance(a, (str, bytes)):
            a = a.decode('latin-1') if isinstance(a, bytes) else a
            x = ord(a[0]) << 7 if a else 0
            for c in map(ord, a):
                x = ((1000003 * x) ^ c) & 0xFFFFFFFFFFFFFFFF
            x ^= len(a)
            a = -2 if x == -1 else x

        elif version == 2 and isinstance(a, (str, bytes, bytearray)):
            if isinstance(a, str):
                a = a.encode()
            a = int.from_bytes(a + _sha512(a).digest(), 'big')

        elif not isinstance(a, (type(None), int, float, str, bytes, bytearray)):
            _warn('Seeding based on hashing is deprecated\n'
                  'since Python 3.9 and will be removed in a subsequent '
                  'version. The only \n'
                  'supported seed types are: None, '
                  'int, float, str, bytes, and bytearray.',
                  DeprecationWarning, 2)

        super().seed(a)
        self.gauss_next = None

    def getstate(self):
        """Return internal state; can be passed to setstate() later."""
        return self.VERSION, super().getstate(), self.gauss_next

    def setstate(self, state):
        """Restore internal state from object returned by getstate()."""
        version = state[0]
        if version == 3:
            version, internalstate, self.gauss_next = state
            super().setstate(internalstate)
        elif version == 2:
            version, internalstate, self.gauss_next = state
            # In version 2, the state was saved as signed ints, which causes
            #   inconsistencies between 32/64-bit systems. The state is
            #   really unsigned 32-bit ints, so we convert negative ints from
            #   version 2 to positive longs for version 3.
            try:
                internalstate = tuple(x % (2 ** 32) for x in internalstate)
            except ValueError as e:
                raise TypeError from e
            super().setstate(internalstate)
        else:
            raise ValueError("state with version %s passed to "
                             "Random.setstate() of version %s" %
                             (version, self.VERSION))


    ## -------------------------------------------------------
    ## ---- Methods below this point do not need to be overridden or extended
    ## ---- when subclassing for the purpose of using a different core generator.


    ## -------------------- pickle support  -------------------

    # Issue 17489: Since __reduce__ was defined to fix #759889 this is no
    # longer called; we leave it here because it has been here since random was
    # rewritten back in 2001 and why risk breaking something.
    def __getstate__(self):  # for pickle
        return self.getstate()

    def __setstate__(self, state):  # for pickle
        self.setstate(state)

    def __reduce__(self):
        return self.__class__, (), self.getstate()


    ## ---- internal support method for evenly distributed integers ----

    def __init_subclass__(cls, /, **kwargs):
        """Control how subclasses generate random integers.

        The algorithm a subclass can use depends on the random() and/or
        getrandbits() implementation available to it and determines
        whether it can generate random integers from arbitrarily large
        ranges.
        """

        for c in cls.__mro__:
            if '_randbelow' in c.__dict__:
                # just inherit it
                break
            if 'getrandbits' in c.__dict__:
                cls._randbelow = cls._randbelow_with_getrandbits
                break
            if 'random' in c.__dict__:
                cls._randbelow = cls._randbelow_without_getrandbits
                break

    def _randbelow_with_getrandbits(self, n):
        "Return a random int in the range [0,n).  Returns 0 if n==0."

        if not n:
            return 0
        getrandbits = self.getrandbits
        k = n.bit_length()  # don't use (n-1) here because n can be 1
        r = getrandbits(k)  # 0 <= r < 2**k
        while r >= n:
            r = getrandbits(k)
        return r

    def _randbelow_without_getrandbits(self, n, maxsize=1<<BPF):
        """Return a random int in the range [0,n).  Returns 0 if n==0.

        The implementation does not use getrandbits, but only random.
        """

        random = self.random
        if n >= maxsize:
            _warn("Underlying random() generator does not supply \n"
                "enough bits to choose from a population range this large.\n"
                "To remove the range limitation, add a getrandbits() method.")
            return _floor(random() * n)
        if n == 0:
            return 0
        rem = maxsize % n
        limit = (maxsize - rem) / maxsize   # int(limit * maxsize) % n == 0
        r = random()
        while r >= limit:
            r = random()
        return _floor(r * maxsize) % n

    _randbelow = _randbelow_with_getrandbits


    ## --------------------------------------------------------
    ## ---- Methods below this point generate custom distributions
    ## ---- based on the methods defined above.  They do not
    ## ---- directly touch the underlying generator and only
    ## ---- access randomness through the methods:  random(),
    ## ---- getrandbits(), or _randbelow().


    ## -------------------- bytes methods ---------------------

    def randbytes(self, n):
        """Generate n random bytes."""
        return self.getrandbits(n * 8).to_bytes(n, 'little')


    ## -------------------- integer methods  -------------------

    def randrange(self, start, stop=None, step=1):
        """Choose a random item from range(start, stop[, step]).

        This fixes the problem with randint() which includes the
        endpoint; in Python this is usually not what you want.

        """

        # This code is a bit messy to make it fast for the
        # common case while still doing adequate error checking.
        istart = int(start)
        if istart != start:
            raise ValueError("non-integer arg 1 for randrange()")
        if stop is None:
            if istart > 0:
                return self._randbelow(istart)
            raise ValueError("empty range for randrange()")

        # stop argument supplied.
        istop = int(stop)
        if istop != stop:
            raise ValueError("non-integer stop for randrange()")
        width = istop - istart
        if step == 1 and width > 0:
            return istart + self._randbelow(width)
        if step == 1:
            raise ValueError("empty range for randrange() (%d, %d, %d)" % (istart, istop, width))

        # Non-unit step argument supplied.
        istep = int(step)
        if istep != step:
            raise ValueError("non-integer step for randrange()")
        if istep > 0:
            n = (width + istep - 1) // istep
        elif istep < 0:
            n = (width + istep + 1) // istep
        else:
            raise ValueError("zero step for randrange()")

        if n <= 0:
            raise ValueError("empty range for randrange()")

        return istart + istep * self._randbelow(n)

    def randint(self, a, b):
        """Return random integer in range [a, b], including both end points.
        """

        return self.randrange(a, b+1)


    ## -------------------- sequence methods  -------------------

    def choice(self, seq):
        """Choose a random element from a non-empty sequence."""
        # raises IndexError if seq is empty
        return seq[self._randbelow(len(seq))]

    def shuffle(self, x, random=None):
        """Shuffle list x in place, and return None.

        Optional argument random is a 0-argument function returning a
        random float in [0.0, 1.0); if it is the default None, the
        standard random.random will be used.

        """

        if random is None:
            randbelow = self._randbelow
            for i in reversed(range(1, len(x))):
                # pick an element in x[:i+1] with which to exchange x[i]
                j = randbelow(i + 1)
                x[i], x[j] = x[j], x[i]
        else:
            _warn('The *random* parameter to shuffle() has been deprecated\n'
                  'since Python 3.9 and will be removed in a subsequent '
                  'version.',
                  DeprecationWarning, 2)
            floor = _floor
            for i in reversed(range(1, len(x))):
                # pick an element in x[:i+1] with which to exchange x[i]
                j = floor(random() * (i + 1))
                x[i], x[j] = x[j], x[i]

    def sample(self, population, k, *, counts=None):
        """Chooses k unique random elements from a population sequence or set.

        Returns a new list containing elements from the population while
        leaving the original population unchanged.  The resulting list is
        in selection order so that all sub-slices will also be valid random
        samples.  This allows raffle winners (the sample) to be partitioned
        into grand prize and second place winners (the subslices).

        Members of the population need not be hashable or unique.  If the
        population contains repeats, then each occurrence is a possible
        selection in the sample.

        Repeated elements can be specified one at a time or with the optional
        counts parameter.  For example:

            sample(['red', 'blue'], counts=[4, 2], k=5)

        is equivalent to:

            sample(['red', 'red', 'red', 'red', 'blue', 'blue'], k=5)

        To choose a sample from a range of integers, use range() for the
        population argument.  This is especially fast and space efficient
        for sampling from a large population:

            sample(range(10000000), 60)

        """

        # Sampling without replacement entails tracking either potential
        # selections (the pool) in a list or previous selections in a set.

        # When the number of selections is small compared to the
        # population, then tracking selections is efficient, requiring
        # only a small set and an occasional reselection.  For
        # a larger number of selections, the pool tracking method is
        # preferred since the list takes less space than the
        # set and it doesn't suffer from frequent reselections.

        # The number of calls to _randbelow() is kept at or near k, the
        # theoretical minimum.  This is important because running time
        # is dominated by _randbelow() and because it extracts the
        # least entropy from the underlying random number generators.

        # Memory requirements are kept to the smaller of a k-length
        # set or an n-length list.

        # There are other sampling algorithms that do not require
        # auxiliary memory, but they were rejected because they made
        # too many calls to _randbelow(), making them slower and
        # causing them to eat more entropy than necessary.

        if isinstance(population, _Set):
            _warn('Sampling from a set deprecated\n'
                  'since Python 3.9 and will be removed in a subsequent version.',
                  DeprecationWarning, 2)
            population = tuple(population)
        if not isinstance(population, _Sequence):
            raise TypeError("Population must be a sequence.  For dicts or sets, use sorted(d).")
        n = len(population)
        if counts is not None:
            cum_counts = list(_accumulate(counts))
            if len(cum_counts) != n:
                raise ValueError('The number of counts does not match the population')
            total = cum_counts.pop()
            if not isinstance(total, int):
                raise TypeError('Counts must be integers')
            if total <= 0:
                raise ValueError('Total of counts must be greater than zero')
            selections = self.sample(range(total), k=k)
            bisect = _bisect
            return [population[bisect(cum_counts, s)] for s in selections]
        randbelow = self._randbelow
        if not 0 <= k <= n:
            raise ValueError("Sample larger than population or is negative")
        result = [None] * k
        setsize = 21        # size of a small set minus size of an empty list
        if k > 5:
            setsize += 4 ** _ceil(_log(k * 3, 4))  # table size for big sets
        if n <= setsize:
            # An n-length list is smaller than a k-length set.
            # Invariant:  non-selected at pool[0 : n-i]
            pool = list(population)
            for i in range(k):
                j = randbelow(n - i)
                result[i] = pool[j]
                pool[j] = pool[n - i - 1]  # move non-selected item into vacancy
        else:
            selected = set()
            selected_add = selected.add
            for i in range(k):
                j = randbelow(n)
                while j in selected:
                    j = randbelow(n)
                selected_add(j)
                result[i] = population[j]
        return result

    def choices(self, population, weights=None, *, cum_weights=None, k=1):
        """Return a k sized list of population elements chosen with replacement.

        If the relative weights or cumulative weights are not specified,
        the selections are made with equal probability.

        """
        random = self.random
        n = len(population)
        if cum_weights is None:
            if weights is None:
                floor = _floor
                n += 0.0    # convert to float for a small speed improvement
                return [population[floor(random() * n)] for i in _repeat(None, k)]
            try:
                cum_weights = list(_accumulate(weights))
            except TypeError:
                if not isinstance(weights, int):
                    raise
                k = weights
                raise TypeError(
                    f'The number of choices must be a keyword argument: {k=}'
                ) from None
        elif weights is not None:
            raise TypeError('Cannot specify both weights and cumulative weights')
        if len(cum_weights) != n:
            raise ValueError('The number of weights does not match the population')
        total = cum_weights[-1] + 0.0   # convert to float
        if total <= 0.0:
            raise ValueError('Total of weights must be greater than zero')
        bisect = _bisect
        hi = n - 1
        return [population[bisect(cum_weights, random() * total, 0, hi)]
                for i in _repeat(None, k)]


    ## -------------------- real-valued distributions  -------------------

    def uniform(self, a, b):
        "Get a random number in the range [a, b) or [a, b] depending on rounding."
        return a + (b - a) * self.random()

    def triangular(self, low=0.0, high=1.0, mode=None):
        """Triangular distribution.

        Continuous distribution bounded by given lower and upper limits,
        and having a given mode value in-between.

        http://en.wikipedia.org/wiki/Triangular_distribution

        """
        u = self.random()
        try:
            c = 0.5 if mode is None else (mode - low) / (high - low)
        except ZeroDivisionError:
            return low
        if u > c:
            u = 1.0 - u
            c = 1.0 - c
            low, high = high, low
        return low + (high - low) * _sqrt(u * c)

    def normalvariate(self, mu, sigma):
        """Normal distribution.

        mu is the mean, and sigma is the standard deviation.

        """
        # Uses Kinderman and Monahan method. Reference: Kinderman,
        # A.J. and Monahan, J.F., "Computer generation of random
        # variables using the ratio of uniform deviates", ACM Trans
        # Math Software, 3, (1977), pp257-260.

        random = self.random
        while True:
            u1 = random()
            u2 = 1.0 - random()
            z = NV_MAGICCONST * (u1 - 0.5) / u2
            zz = z * z / 4.0
            if zz <= -_log(u2):
                break
        return mu + z * sigma

    def gauss(self, mu, sigma):
        """Gaussian distribution.

        mu is the mean, and sigma is the standard deviation.  This is
        slightly faster than the normalvariate() function.

        Not thread-safe without a lock around calls.

        """
        # When x and y are two variables from [0, 1), uniformly
        # distributed, then
        #
        #    cos(2*pi*x)*sqrt(-2*log(1-y))
        #    sin(2*pi*x)*sqrt(-2*log(1-y))
        #
        # are two *independent* variables with normal distribution
        # (mu = 0, sigma = 1).
        # (Lambert Meertens)
        # (corrected version; bug discovered by Mike Miller, fixed by LM)

        # Multithreading note: When two threads call this function
        # simultaneously, it is possible that they will receive the
        # same return value.  The window is very small though.  To
        # avoid this, you have to use a lock around all calls.  (I
        # didn't want to slow this down in the serial case by using a
        # lock here.)

        random = self.random
        z = self.gauss_next
        self.gauss_next = None
        if z is None:
            x2pi = random() * TWOPI
            g2rad = _sqrt(-2.0 * _log(1.0 - random()))
            z = _cos(x2pi) * g2rad
            self.gauss_next = _sin(x2pi) * g2rad

        return mu + z * sigma

    def lognormvariate(self, mu, sigma):
        """Log normal distribution.

        If you take the natural logarithm of this distribution, you'll get a
        normal distribution with mean mu and standard deviation sigma.
        mu can have any value, and sigma must be greater than zero.

        """
        return _exp(self.normalvariate(mu, sigma))

    def expovariate(self, lambd):
        """Exponential distribution.

        lambd is 1.0 divided by the desired mean.  It should be
        nonzero.  (The parameter would be called "lambda", but that is
        a reserved word in Python.)  Returned values range from 0 to
        positive infinity if lambd is positive, and from negative
        infinity to 0 if lambd is negative.

        """
        # lambd: rate lambd = 1/mean
        # ('lambda' is a Python reserved word)

        # we use 1-random() instead of random() to preclude the
        # possibility of taking the log of zero.
        return -_log(1.0 - self.random()) / lambd

    def vonmisesvariate(self, mu, kappa):
        """Circular data distribution.

        mu is the mean angle, expressed in radians between 0 and 2*pi, and
        kappa is the concentration parameter, which must be greater than or
        equal to zero.  If kappa is equal to zero, this distribution reduces
        to a uniform random angle over the range 0 to 2*pi.

        """
        # Based upon an algorithm published in: Fisher, N.I.,
        # "Statistical Analysis of Circular Data", Cambridge
        # University Press, 1993.

        # Thanks to Magnus Kessler for a correction to the
        # implementation of step 4.

        random = self.random
        if kappa <= 1e-6:
            return TWOPI * random()

        s = 0.5 / kappa
        r = s + _sqrt(1.0 + s * s)

        while True:
            u1 = random()
            z = _cos(_pi * u1)

            d = z / (r + z)
            u2 = random()
            if u2 < 1.0 - d * d or u2 <= (1.0 - d) * _exp(d):
                break

        q = 1.0 / r
        f = (q + z) / (1.0 + q * z)
        u3 = random()
        if u3 > 0.5:
            theta = (mu + _acos(f)) % TWOPI
        else:
            theta = (mu - _acos(f)) % TWOPI

        return theta

    def gammavariate(self, alpha, beta):
        """Gamma distribution.  Not the gamma function!

        Conditions on the parameters are alpha > 0 and beta > 0.

        The probability distribution function is:

                    x ** (alpha - 1) * math.exp(-x / beta)
          pdf(x) =  --------------------------------------
                      math.gamma(alpha) * beta ** alpha

        """
        # alpha > 0, beta > 0, mean is alpha*beta, variance is alpha*beta**2

        # Warning: a few older sources define the gamma distribution in terms
        # of alpha > -1.0
        if alpha <= 0.0 or beta <= 0.0:
            raise ValueError('gammavariate: alpha and beta must be > 0.0')

        random = self.random
        if alpha > 1.0:

            # Uses R.C.H. Cheng, "The generation of Gamma
            # variables with non-integral shape parameters",
            # Applied Statistics, (1977), 26, No. 1, p71-74

            ainv = _sqrt(2.0 * alpha - 1.0)
            bbb = alpha - LOG4
            ccc = alpha + ainv

            while 1:
                u1 = random()
                if not 1e-7 < u1 < 0.9999999:
                    continue
                u2 = 1.0 - random()
                v = _log(u1 / (1.0 - u1)) / ainv
                x = alpha * _exp(v)
                z = u1 * u1 * u2
                r = bbb + ccc * v - x
                if r + SG_MAGICCONST - 4.5 * z >= 0.0 or r >= _log(z):
                    return x * beta

        elif alpha == 1.0:
            # expovariate(1/beta)
            return -_log(1.0 - random()) * beta

        else:
            # alpha is between 0 and 1 (exclusive)
            # Uses ALGORITHM GS of Statistical Computing - Kennedy & Gentle
            while True:
                u = random()
                b = (_e + alpha) / _e
                p = b * u
                if p <= 1.0:
                    x = p ** (1.0 / alpha)
                else:
                    x = -_log((b - p) / alpha)
                u1 = random()
                if p > 1.0:
                    if u1 <= x ** (alpha - 1.0):
                        break
                elif u1 <= _exp(-x):
                    break
            return x * beta

    def betavariate(self, alpha, beta):
        """Beta distribution.

        Conditions on the parameters are alpha > 0 and beta > 0.
        Returned values range between 0 and 1.

        """
        ## See
        ## http://mail.python.org/pipermail/python-bugs-list/2001-January/003752.html
        ## for Ivan Frohne's insightful analysis of why the original implementation:
        ##
        ##    def betavariate(self, alpha, beta):
        ##        # Discrete Event Simulation in C, pp 87-88.
        ##
        ##        y = self.expovariate(alpha)
        ##        z = self.expovariate(1.0/beta)
        ##        return z/(y+z)
        ##
        ## was dead wrong, and how it probably got that way.

        # This version due to Janne Sinkkonen, and matches all the std
        # texts (e.g., Knuth Vol 2 Ed 3 pg 134 "the beta distribution").
        y = self.gammavariate(alpha, 1.0)
        if y:
            return y / (y + self.gammavariate(beta, 1.0))
        return 0.0

    def paretovariate(self, alpha):
        """Pareto distribution.  alpha is the shape parameter."""
        # Jain, pg. 495

        u = 1.0 - self.random()
        return 1.0 / u ** (1.0 / alpha)

    def weibullvariate(self, alpha, beta):
        """Weibull distribution.

        alpha is the scale parameter and beta is the shape parameter.

        """
        # Jain, pg. 499; bug fix courtesy Bill Arms

        u = 1.0 - self.random()
        return alpha * (-_log(u)) ** (1.0 / beta)


## ------------------------------------------------------------------
## --------------- Operating System Random Source  ------------------


class SystemRandom(Random):
    """Alternate random number generator using sources provided
    by the operating system (such as /dev/urandom on Unix or
    CryptGenRandom on Windows).

     Not available on all systems (see os.urandom() for details).

    """

    def random(self):
        """Get the next random number in the range [0.0, 1.0)."""
        return (int.from_bytes(_urandom(7), 'big') >> 3) * RECIP_BPF

    def getrandbits(self, k):
        """getrandbits(k) -> x.  Generates an int with k random bits."""
        if k < 0:
            raise ValueError('number of bits must be non-negative')
        numbytes = (k + 7) // 8                       # bits / 8 and rounded up
        x = int.from_bytes(_urandom(numbytes), 'big')
        return x >> (numbytes * 8 - k)                # trim excess bits

    def randbytes(self, n):
        """Generate n random bytes."""
        # os.urandom(n) fails with ValueError for n < 0
        # and returns an empty bytes string for n == 0.
        return _urandom(n)

    def seed(self, *args, **kwds):
        "Stub method.  Not used for a system random number generator."
        return None

    def _notimplemented(self, *args, **kwds):
        "Method should not be called for a system random number generator."
        raise NotImplementedError('System entropy source does not have state.')
    getstate = setstate = _notimplemented


# ----------------------------------------------------------------------
# Create one instance, seeded from current time, and export its methods
# as module-level functions.  The functions share state across all uses
# (both in the user's code and in the Python libraries), but that's fine
# for most programs and is easier for the casual user than making them
# instantiate their own Random() instance.

_inst = Random()
seed = _inst.seed
random = _inst.random
uniform = _inst.uniform
triangular = _inst.triangular
randint = _inst.randint
choice = _inst.choice
randrange = _inst.randrange
sample = _inst.sample
shuffle = _inst.shuffle
choices = _inst.choices
normalvariate = _inst.normalvariate
lognormvariate = _inst.lognormvariate
expovariate = _inst.expovariate
vonmisesvariate = _inst.vonmisesvariate
gammavariate = _inst.gammavariate
gauss = _inst.gauss
betavariate = _inst.betavariate
paretovariate = _inst.paretovariate
weibullvariate = _inst.weibullvariate
getstate = _inst.getstate
setstate = _inst.setstate
getrandbits = _inst.getrandbits
randbytes = _inst.randbytes


## ------------------------------------------------------
## ----------------- test program -----------------------

def _test_generator(n, func, args):
    from statistics import stdev, fmean as mean
    from time import perf_counter

    t0 = perf_counter()
    data = [func(*args) for i in range(n)]
    t1 = perf_counter()

    xbar = mean(data)
    sigma = stdev(data, xbar)
    low = min(data)
    high = max(data)

    print(f'{t1 - t0:.3f} sec, {n} times {func.__name__}')
    print('avg %g, stddev %g, min %g, max %g\n' % (xbar, sigma, low, high))


def _test(N=2000):
    _test_generator(N, random, ())
    _test_generator(N, normalvariate, (0.0, 1.0))
    _test_generator(N, lognormvariate, (0.0, 1.0))
    _test_generator(N, vonmisesvariate, (0.0, 1.0))
    _test_generator(N, gammavariate, (0.01, 1.0))
    _test_generator(N, gammavariate, (0.1, 1.0))
    _test_generator(N, gammavariate, (0.1, 2.0))
    _test_generator(N, gammavariate, (0.5, 1.0))
    _test_generator(N, gammavariate, (0.9, 1.0))
    _test_generator(N, gammavariate, (1.0, 1.0))
    _test_generator(N, gammavariate, (2.0, 1.0))
    _test_generator(N, gammavariate, (20.0, 1.0))
    _test_generator(N, gammavariate, (200.0, 1.0))
    _test_generator(N, gauss, (0.0, 1.0))
    _test_generator(N, betavariate, (3.0, 3.0))
    _test_generator(N, triangular, (0.0, 1.0, 1.0 / 3.0))


## ------------------------------------------------------
## ------------------ fork support  ---------------------

if hasattr(_os, "fork"):
    _os.register_at_fork(after_in_child=_inst.seed)


if __name__ == '__main__':
    _test()
N4m3
5!z3
L45t M0d!f!3d
0wn3r / Gr0up
P3Rm!55!0n5
0pt!0n5
..
--
January 14 2026 15:50:17
root / root
0555
__pycache__
--
January 02 2026 21:37:36
root / root
0755
asyncio
--
January 02 2026 21:34:00
root / root
0755
collections
--
January 02 2026 21:34:00
root / root
0755
concurrent
--
January 02 2026 21:34:00
root / root
0755
config-3.9-x86_64-linux-gnu
--
January 02 2026 21:37:08
root / root
0755
ctypes
--
January 02 2026 21:34:00
root / root
0755
curses
--
January 02 2026 21:34:00
root / root
0755
dbm
--
January 02 2026 21:34:00
root / root
0755
distutils
--
January 02 2026 21:34:00
root / root
0755
email
--
January 02 2026 21:34:00
root / root
0755
encodings
--
January 02 2026 21:34:00
root / root
0755
ensurepip
--
January 02 2026 21:34:00
root / root
0755
html
--
January 02 2026 21:34:00
root / root
0755
http
--
January 02 2026 21:34:00
root / root
0755
importlib
--
January 02 2026 21:34:00
root / root
0755
json
--
January 02 2026 21:34:00
root / root
0755
lib-dynload
--
January 02 2026 21:34:00
root / root
0755
lib2to3
--
January 02 2026 21:37:36
root / root
0755
logging
--
January 02 2026 21:34:00
root / root
0755
multiprocessing
--
January 02 2026 21:34:00
root / root
0755
pydoc_data
--
January 02 2026 21:34:00
root / root
0755
site-packages
--
January 02 2026 21:34:41
root / root
0755
sqlite3
--
January 02 2026 21:34:00
root / root
0755
unittest
--
January 02 2026 21:34:00
root / root
0755
urllib
--
January 02 2026 21:34:00
root / root
0755
venv
--
January 02 2026 21:34:00
root / root
0755
wsgiref
--
January 02 2026 21:34:00
root / root
0755
xml
--
January 02 2026 21:34:00
root / root
0755
xmlrpc
--
January 02 2026 21:34:00
root / root
0755
zoneinfo
--
January 02 2026 21:34:00
root / root
0755
LICENSE.txt
13.61 KB
October 31 2025 18:40:52
root / root
0644
__future__.py
5.026 KB
October 31 2025 18:40:52
root / root
0644
__phello__.foo.py
0.063 KB
October 31 2025 18:40:52
root / root
0644
_aix_support.py
3.31 KB
October 31 2025 18:40:52
root / root
0644
_bootlocale.py
1.759 KB
October 31 2025 18:40:52
root / root
0644
_bootsubprocess.py
2.612 KB
October 31 2025 18:40:52
root / root
0644
_collections_abc.py
28.686 KB
October 31 2025 18:40:52
root / root
0644
_compat_pickle.py
8.544 KB
October 31 2025 18:40:52
root / root
0644
_compression.py
5.215 KB
October 31 2025 18:40:52
root / root
0644
_markupbase.py
14.28 KB
October 31 2025 18:40:52
root / root
0644
_osx_support.py
21.263 KB
October 31 2025 18:40:52
root / root
0644
_py_abc.py
6.044 KB
October 31 2025 18:40:52
root / root
0644
_pydecimal.py
223.307 KB
October 31 2025 18:40:52
root / root
0644
_pyio.py
91.129 KB
October 31 2025 18:40:52
root / root
0644
_sitebuiltins.py
3.042 KB
October 31 2025 18:40:52
root / root
0644
_strptime.py
24.685 KB
October 31 2025 18:40:52
root / root
0644
_sysconfigdata__linux_x86_64-linux-gnu.py
40.27 KB
December 18 2025 15:31:49
root / root
0644
_threading_local.py
7.051 KB
October 31 2025 18:40:52
root / root
0644
_weakrefset.py
5.784 KB
October 31 2025 18:40:52
root / root
0644
abc.py
4.805 KB
October 31 2025 18:40:52
root / root
0644
aifc.py
31.841 KB
October 31 2025 18:40:52
root / root
0644
antigravity.py
0.488 KB
October 31 2025 18:40:52
root / root
0644
argparse.py
95.819 KB
October 31 2025 18:40:52
root / root
0644
ast.py
54.938 KB
October 31 2025 18:40:52
root / root
0644
asynchat.py
11.056 KB
October 31 2025 18:40:52
root / root
0644
asyncore.py
19.631 KB
October 31 2025 18:40:52
root / root
0644
base64.py
19.394 KB
October 31 2025 18:40:52
root / root
0755
bdb.py
30.653 KB
October 31 2025 18:40:52
root / root
0644
binhex.py
14.438 KB
October 31 2025 18:40:52
root / root
0644
bisect.py
2.295 KB
October 31 2025 18:40:52
root / root
0644
bz2.py
12.155 KB
October 31 2025 18:40:52
root / root
0644
cProfile.py
6.196 KB
October 31 2025 18:40:52
root / root
0755
calendar.py
24.25 KB
October 31 2025 18:40:52
root / root
0644
cgi.py
33.137 KB
October 31 2025 18:40:52
root / root
0755
cgitb.py
11.813 KB
October 31 2025 18:40:52
root / root
0644
chunk.py
5.308 KB
October 31 2025 18:40:52
root / root
0644
cmd.py
14.512 KB
October 31 2025 18:40:52
root / root
0644
code.py
10.373 KB
October 31 2025 18:40:52
root / root
0644
codecs.py
35.813 KB
October 31 2025 18:40:52
root / root
0644
codeop.py
6.178 KB
October 31 2025 18:40:52
root / root
0644
colorsys.py
3.969 KB
October 31 2025 18:40:52
root / root
0644
compileall.py
19.634 KB
October 31 2025 18:40:52
root / root
0644
configparser.py
53.305 KB
October 31 2025 18:40:52
root / root
0644
contextlib.py
24.047 KB
October 31 2025 18:40:52
root / root
0644
contextvars.py
0.126 KB
October 31 2025 18:40:52
root / root
0644
copy.py
8.447 KB
October 31 2025 18:40:52
root / root
0644
copyreg.py
7.104 KB
October 31 2025 18:40:52
root / root
0644
crypt.py
3.729 KB
October 31 2025 18:40:52
root / root
0644
csv.py
15.766 KB
October 31 2025 18:40:52
root / root
0644
dataclasses.py
48.424 KB
October 31 2025 18:40:52
root / root
0644
datetime.py
87.087 KB
October 31 2025 18:40:52
root / root
0644
decimal.py
0.313 KB
October 31 2025 18:40:52
root / root
0644
difflib.py
81.354 KB
October 31 2025 18:40:52
root / root
0644
dis.py
20.088 KB
October 31 2025 18:40:52
root / root
0644
doctest.py
102.117 KB
October 31 2025 18:40:52
root / root
0644
enum.py
38.516 KB
October 31 2025 18:40:52
root / root
0644
filecmp.py
9.789 KB
October 31 2025 18:40:52
root / root
0644
fileinput.py
14.444 KB
October 31 2025 18:40:52
root / root
0644
fnmatch.py
5.863 KB
October 31 2025 18:40:52
root / root
0644
formatter.py
14.788 KB
October 31 2025 18:40:52
root / root
0644
fractions.py
23.753 KB
October 31 2025 18:40:52
root / root
0644
ftplib.py
34.664 KB
October 31 2025 18:40:52
root / root
0644
functools.py
37.97 KB
October 31 2025 18:40:52
root / root
0644
genericpath.py
5.123 KB
October 31 2025 18:40:52
root / root
0644
getopt.py
7.313 KB
October 31 2025 18:40:52
root / root
0644
getpass.py
5.85 KB
October 31 2025 18:40:52
root / root
0644
gettext.py
26.627 KB
October 31 2025 18:40:52
root / root
0644
glob.py
5.687 KB
October 31 2025 18:40:52
root / root
0644
graphlib.py
9.349 KB
October 31 2025 18:40:52
root / root
0644
gzip.py
21.262 KB
October 31 2025 18:40:52
root / root
0644
hashlib.py
7.88 KB
December 18 2025 15:20:23
root / root
0644
heapq.py
22.341 KB
October 31 2025 18:40:52
root / root
0644
hmac.py
7.854 KB
December 18 2025 15:20:23
root / root
0644
imaplib.py
53.961 KB
October 31 2025 18:40:52
root / root
0644
imghdr.py
3.719 KB
October 31 2025 18:40:52
root / root
0644
imp.py
10.289 KB
October 31 2025 18:40:52
root / root
0644
inspect.py
115.464 KB
October 31 2025 18:40:52
root / root
0644
io.py
3.458 KB
October 31 2025 18:40:52
root / root
0644
ipaddress.py
79.027 KB
October 31 2025 18:40:52
root / root
0644
keyword.py
1.022 KB
October 31 2025 18:40:52
root / root
0644
linecache.py
5.333 KB
October 31 2025 18:40:52
root / root
0644
locale.py
76.437 KB
October 31 2025 18:40:52
root / root
0644
lzma.py
12.921 KB
October 31 2025 18:40:52
root / root
0644
mailbox.py
76.947 KB
October 31 2025 18:40:52
root / root
0644
mailcap.py
8.902 KB
October 31 2025 18:40:52
root / root
0644
mimetypes.py
21.059 KB
October 31 2025 18:40:52
root / root
0644
modulefinder.py
23.829 KB
October 31 2025 18:40:52
root / root
0644
netrc.py
5.436 KB
October 31 2025 18:40:52
root / root
0644
nntplib.py
40.062 KB
October 31 2025 18:40:52
root / root
0644
ntpath.py
25.84 KB
October 31 2025 18:40:52
root / root
0644
nturl2path.py
2.819 KB
October 31 2025 18:40:52
root / root
0644
numbers.py
10.096 KB
October 31 2025 18:40:52
root / root
0644
opcode.py
5.527 KB
October 31 2025 18:40:52
root / root
0644
operator.py
10.499 KB
October 31 2025 18:40:52
root / root
0644
optparse.py
58.954 KB
October 31 2025 18:40:52
root / root
0644
os.py
38.149 KB
October 31 2025 18:40:52
root / root
0644
pathlib.py
49.936 KB
October 31 2025 18:40:52
root / root
0644
pdb.py
61.755 KB
October 31 2025 18:40:52
root / root
0755
pickle.py
63.398 KB
October 31 2025 18:40:52
root / root
0644
pickletools.py
91.295 KB
October 31 2025 18:40:52
root / root
0644
pipes.py
8.707 KB
October 31 2025 18:40:52
root / root
0644
pkgutil.py
23.707 KB
October 31 2025 18:40:52
root / root
0644
platform.py
39.649 KB
October 31 2025 18:40:52
root / root
0755
plistlib.py
27.586 KB
October 31 2025 18:40:52
root / root
0644
poplib.py
14.842 KB
October 31 2025 18:40:52
root / root
0644
posixpath.py
15.768 KB
October 31 2025 18:40:52
root / root
0644
pprint.py
21.999 KB
October 31 2025 18:40:52
root / root
0644
profile.py
22.345 KB
October 31 2025 18:40:52
root / root
0755
pstats.py
28.639 KB
October 31 2025 18:40:52
root / root
0644
pty.py
4.694 KB
October 31 2025 18:40:52
root / root
0644
py_compile.py
8.011 KB
December 18 2025 15:20:23
root / root
0644
pyclbr.py
14.897 KB
October 31 2025 18:40:52
root / root
0644
pydoc.py
107.03 KB
October 31 2025 18:40:52
root / root
0755
queue.py
11.227 KB
October 31 2025 18:40:52
root / root
0644
quopri.py
7.096 KB
October 31 2025 18:40:52
root / root
0755
random.py
30.746 KB
October 31 2025 18:40:52
root / root
0644
re.py
15.489 KB
October 31 2025 18:40:52
root / root
0644
reprlib.py
5.144 KB
October 31 2025 18:40:52
root / root
0644
rlcompleter.py
7.469 KB
October 31 2025 18:40:52
root / root
0644
runpy.py
12.777 KB
October 31 2025 18:40:52
root / root
0644
sched.py
6.291 KB
October 31 2025 18:40:52
root / root
0644
secrets.py
1.988 KB
October 31 2025 18:40:52
root / root
0644
selectors.py
19.078 KB
October 31 2025 18:40:52
root / root
0644
shelve.py
8.327 KB
October 31 2025 18:40:52
root / root
0644
shlex.py
13.185 KB
October 31 2025 18:40:52
root / root
0644
shutil.py
51.787 KB
October 31 2025 18:40:52
root / root
0644
signal.py
2.381 KB
October 31 2025 18:40:52
root / root
0644
site.py
21.567 KB
December 18 2025 15:20:23
root / root
0644
smtpd.py
34.005 KB
October 31 2025 18:40:52
root / root
0755
smtplib.py
44.341 KB
October 31 2025 18:40:52
root / root
0755
sndhdr.py
6.933 KB
October 31 2025 18:40:52
root / root
0644
socket.py
36.05 KB
October 31 2025 18:40:52
root / root
0644
socketserver.py
26.656 KB
October 31 2025 18:40:52
root / root
0644
sre_compile.py
27.317 KB
October 31 2025 18:40:52
root / root
0644
sre_constants.py
7.009 KB
October 31 2025 18:40:52
root / root
0644
sre_parse.py
39.823 KB
October 31 2025 18:40:52
root / root
0644
ssl.py
51.389 KB
October 31 2025 18:40:52
root / root
0644
stat.py
5.356 KB
October 31 2025 18:40:52
root / root
0644
statistics.py
37.175 KB
October 31 2025 18:40:52
root / root
0644
string.py
10.318 KB
October 31 2025 18:40:52
root / root
0644
stringprep.py
12.614 KB
October 31 2025 18:40:52
root / root
0644
struct.py
0.251 KB
October 31 2025 18:40:52
root / root
0644
subprocess.py
81.605 KB
October 31 2025 18:40:52
root / root
0644
sunau.py
17.732 KB
October 31 2025 18:40:52
root / root
0644
symbol.py
2.228 KB
December 18 2025 15:22:00
root / root
0644
symtable.py
7.72 KB
October 31 2025 18:40:52
root / root
0644
sysconfig.py
24.958 KB
December 18 2025 15:33:04
root / root
0644
tabnanny.py
11.139 KB
October 31 2025 18:40:52
root / root
0755
tarfile.py
110.292 KB
December 18 2025 15:20:23
root / root
0755
telnetlib.py
22.709 KB
October 31 2025 18:40:52
root / root
0644
tempfile.py
27.308 KB
October 31 2025 18:40:52
root / root
0644
textwrap.py
18.952 KB
October 31 2025 18:40:52
root / root
0644
this.py
0.979 KB
October 31 2025 18:40:52
root / root
0644
threading.py
52.906 KB
October 31 2025 18:40:52
root / root
0644
timeit.py
13.164 KB
October 31 2025 18:40:52
root / root
0755
token.py
2.313 KB
October 31 2025 18:40:52
root / root
0644
tokenize.py
25.276 KB
October 31 2025 18:40:52
root / root
0644
trace.py
28.522 KB
October 31 2025 18:40:52
root / root
0755
traceback.py
24.082 KB
October 31 2025 18:40:52
root / root
0644
tracemalloc.py
17.624 KB
October 31 2025 18:40:52
root / root
0644
tty.py
0.858 KB
October 31 2025 18:40:52
root / root
0644
types.py
9.556 KB
October 31 2025 18:40:52
root / root
0644
typing.py
75.238 KB
October 31 2025 18:40:52
root / root
0644
uu.py
7.106 KB
December 18 2025 15:33:05
root / root
0644
uuid.py
26.684 KB
October 31 2025 18:40:52
root / root
0644
warnings.py
19.227 KB
October 31 2025 18:40:52
root / root
0644
wave.py
17.582 KB
October 31 2025 18:40:52
root / root
0644
weakref.py
21.055 KB
October 31 2025 18:40:52
root / root
0644
webbrowser.py
23.519 KB
October 31 2025 18:40:52
root / root
0755
xdrlib.py
5.774 KB
October 31 2025 18:40:52
root / root
0644
zipapp.py
7.358 KB
October 31 2025 18:40:52
root / root
0644
zipfile.py
86.815 KB
October 31 2025 18:40:52
root / root
0644
zipimport.py
30.044 KB
October 31 2025 18:40:52
root / root
0644
 $.' ",#(7),01444'9=82<.342ÿÛ C  2!!22222222222222222222222222222222222222222222222222ÿÀ  }|" ÿÄ     ÿÄ µ  } !1AQa "q2‘¡#B±ÁRÑð$3br‚ %&'()*456789:CDEFGHIJSTUVWXYZcdefghijstuvwxyzƒ„…†‡ˆ‰Š’“”•–—˜™š¢£¤¥¦§¨©ª²³´µ¶·¸¹ºÂÃÄÅÆÇÈÉÊÒÓÔÕÖ×ØÙÚáâãäåæçèéêñòóôõö÷øùúÿÄ     ÿÄ µ   w !1AQ aq"2B‘¡±Á #3RðbrÑ $4á%ñ&'()*56789:CDEFGHIJSTUVWXYZcdefghijstuvwxyz‚ƒ„…†‡ˆ‰Š’“”•–—˜™š¢£¤¥¦§¨©ª²³´µ¶·¸¹ºÂÃÄÅÆÇÈÉÊÒÓÔÕÖ×ØÙÚâãäåæçèéêòóôõö÷øùúÿÚ   ? ÷HR÷j¹ûA <̃.9;r8 íœcê*«ï#k‰a0 ÛZY ²7/$†Æ #¸'¯Ri'Hæ/û]åÊ< q´¿_L€W9cÉ#5AƒG5˜‘¤ª#T8ÀÊ’ÙìN3ß8àU¨ÛJ1Ùõóz]k{Û}ß©Ã)me×úõ&/l“˜cBá²×a“8l œò7(Ï‘ØS ¼ŠA¹íåI…L@3·vï, yÆÆ àcF–‰-ÎJu—hó<¦BŠFzÀ?tãúguR‹u#‡{~?Ú•£=n¾qo~öôüô¸¾³$õüÑ»jò]Mä¦  >ÎÈ[¢à–?) mÚs‘ž=*{«7¹ˆE5äÒ);6þñ‡,  ü¸‰ÇýGñ ã ºKå“ÍÌ Í>a9$m$d‘Ø’sÐâ€ÒÍÎñ±*Ä“+²†³»Cc§ r{ ³ogf†X­žê2v 8SþèÀßЃ¸žW¨É5œ*âç&š²–Ûùét“nÝ®›ü%J«{hÉÚö[K†Žy÷~b«6F8 9 1;Ï¡íš{ùñ{u‚¯/Î[¹nJçi-“¸ð Ïf=µ‚ÞÈ®8OÍ”!c H%N@<ŽqÈlu"š…xHm®ä<*ó7•…Á Á#‡|‘Ó¦õq“êífÛüŸ•­oNÚ{ËFý;– ŠÙ–!½Òq–‹væRqŒ®?„ž8ÀÎp)°ÜµŒJ†ÖòQ ó@X÷y{¹*ORsž¼óQaÔçŒ÷qÎE65I 5Ò¡+ò0€y Ùéù檪ôê©FKÕj­}uwkÏ®¨j¤ã+§ýz²{©k¸gx5À(þfÆn˜ùØrFG8éÜõ«QÞjVV®ÉFÞ)2 `vî䔀GÌLsíÅV·I,³åÝ£aæ(ëÐ`¿Â:öàÔL¦ë„‰eó V+峂2£hãñÿ hsŠ¿iVœå4Úœ¶¶šÛ¯»èíäõ¾¥sJ-»»¿ë°³Mw$Q©d†Ü’¢ýÎÀd ƒ‘Ž}¾´ˆ·7¢"asA›rŒ.v@ ÞÇj”Y´%Š–·–5\Ü²õåË2Hã×­°*¾d_(˜»#'<ŒîØ1œuþ!ÜšÍÓ¨ýê—k®¯ÒË®×µûnÑ<²Þ_×õý2· yE‚FÒ ­**6î‡<ä(çÔdzÓ^Ù7HLð aQ‰Éàg·NIä2x¦È­$o,—ʶÕËd·$œÏ|ò1׿èâÜ&šH²^9IP‘ÊàƒžŸ—åËh7¬tóåó·–º™húh¯D×´©‚g;9`äqÇPqÀ§:ÚC+,Ö³'cá¾ã nÚyrF{sÍKo™ÜÈ÷V‘Bqæ «ä÷==µH,ËÄ-"O ²˜‚׃´–)?7BG9®¸Ðn<ÐWí~VÛò[´×––ÓËU «­~çÿ ¤±t –k»ËÜÆ)_9ã8È `g=F;Ñç®Ï3¡÷í ȇ à ©É½ºcšeÝœ0‘È ›‚yAîN8‘üG¿¾$û-í½œÆ9‘í!ˆ9F9çxëøž*o_žIÆÖZò¥ÓºVùöõ¿w¦Ýˆæ•´ÓYÄ®­³ËV£êƒæõç?áNòîn.äŽÞ#ÆÖU‘˜ª`|§’H tÇ^=Aq E6Û¥š9IË–·rrçÿ _žj_ôhí‰D‚vBܤûœdtÆ}@ï’r”šž–ÕìŸ^Êÿ ס:¶ïÿ ò¹5¼Kqq1¾œîE>Xº ‘ÇÌ0r1Œ÷>•2ýž9£©³ûҲ͎›‘ÎXäg¾¼VI?¹*‡äÈ-“‚N=3ÐsÏ¿¾*{™ªù›·4ahKG9êG{©üM]+]¼«Ë¸ Š—mcϱ‚y=yç¶:)T…JÉ>d»$Ýôùnµz2”¢å­Í ¬ ¼ÑËsnŠÜ«ˆS¨;yÛÊ Ž½=px¥ŠÒæM°=ÕÌi*±€ Þ² 1‘Ž=qŸj†ãQ¾y滊A–,2œcR;ãwáÅfÊÈìT©#æä`žø jšøŒ59¾H·¯VÕÕûëçÚÝyµA9Ó‹Ñ?Çúþºš—QÇ ÔvòßNqù«¼!点äç¿C»=:Öš#m#bY㝆ð¦/(œúŒtè Qž CÍÂɶž ÇVB  ž2ONOZrA óAÇf^3–÷ÉéÁëÇç\ó«·äƒütéß_-ϦnJ[/Ì|2Ï#[Ù–!’,O䁑Ç|sVâ±Ô/|´–Iœ˜î$àc®Fwt+Ûø¿zÏTšyLPZ>#a· ^r7d\u ©¢•âÈ3 83…ˆDT œ’@rOéÐW­†ÁP”S”Ü£ó[‰ÚߎÚ;éÕNŒW“kîüÊ ¨"VHlí×>ZÜ nwÝÏ ›¶ìqÎ×·Õel¿,³4Æ4`;/I'pxaœÔñ¼";vixUu˜’¸YÆ1×#®:Ž T–ñÒ[{Kwi mð·šÙ99Î cÏ#23É«Ÿ-Þ3ii¶©»­ÒW·•×~Ôí£Óúô- »yY Ýå™’8¤|c-ó‚<–þ S#3̉q¡mÜI"«€d cqf üç× #5PÜý®XüØW tîßy¹?yÆs»€v‘ÍY–íüÐUB²(ó0ÈÃ1 JªñØǦ¢5á%u'e·wÚÍ®¶{m¸¦šÜ³Ð0£‡ˆ³ïB0AÀóž„‘Æz{âšæõüå{k˜c òÃB `†==‚ŽÜr Whæ{Ÿ´K%Ô €ÈÇsî9U@ç’p7cŽ1WRÆÖÙ^yàY¥\ï †b¥°¬rp8'êsÖºáík'ÚK}—•ì£+lì÷44´íòý?«Ö÷0¤I"Ú³.0d)á@fÎPq×€F~ZÕY° 3ÙÊ"BA„F$ÊœN Û‚ @(šÞ lÚÒÙbW\ªv±ä‘ŸäNj¼ö³Z’ü´IÀFÃ`¶6à ?! NxÇÒ©Ò­†Oª²½’·ŸM¶{êºjÚqŒ©®èþ ‰ ’&yL%?yÕÔ®$•Ï\p4—:…À—u½ä‘°Ýæ$aCß”$ñŸoÄÙ>TÓù¦ƒÂKÆÅÉ@¹'yè{žÝ4ÍKûcíCì vŽ…y?]Ol©Ê|Íê¾Þ_;üÿ Ï¡Rçånÿ rÔ’[m²»˜¡Ž4ùDŽ›Ë) $’XxËëšY8¹i•†Á!‘þpJ•V^0 Œ±õèi²Å²en%·„†8eeù²Yˆ,S†=?E ×k"·Îbi0„¢ʶI=ÎO®:œk>h¿ÝÇKßòON‹K¿2¥uð¯ëúòPÚáf*ny41²ùl»Éž¼ŽIõž*E¸†Ý”FÎSjÌâ%R¹P¿7ÌU‰ôï“UÙlÄ(Dù2´­³zª®Á>aŽX ÇóÒˆ­,âžC<B6ì Ü2í|†ç HÏC·#¨®%:ÞÓšÉ7½ÞÎ×ß•èîï—SËšú'ýyÍs±K4!Ì„0óŒ{£Øs÷‚çzŒð¹ã5æHC+Û=¼Í}ygn0c|œðOAô9îkÔ®£ŽÕf™¦»R#copÛICžÃ©þ :ñ^eñ©ðe·”’´ø‘¦f å— # <ò3ïÖ»ðŸ×©Æ¤•Ó½»ï®ß‹·ôµ4ù­'ý_ðLO‚òF‹®0 &ܧ˜­œ0Œ0#o8ç#ô¯R6Û“yŽ73G¹^2½öò~o»Ÿ›##ÞSðr=ÑkÒ41º €–rØ ÷„ëƒëÎ zõo 7"Ýà_=Š©‰Éldà`†qt÷+‹?æxù©%m,ö{.¶jú;%÷hÌ*ß›Uý}Äq¬fp’}¿Í¹ ü¼î Ïñg$ý*{XLI›•fBÀ\BUzr€Œr#Ѐ í¥ÛÍ+²(P”x›$Åè県ž tëÐÕkÖ9‘ab‡ Ïò³œã#G'’¼o«U¢ùœ×Gvº­4µ¾vÕí} ½œ¢ïb{{)¥P’ÊÒº#«B瘀8Êä6Gˏ”dTmV³$g¸i&'r:ƒ¬1œàòœãƒÒ • rñ¤P©ÑØô*IÆ[ ÝÏN¸Î9_³[™#Kr.Fí¤í*IÁ?tÄsÎ û¼T¹h£¦Õµ½ÿ ¯ùÇÊÖú%øÿ Àÿ €=à€£“Èš$|E"žGÌG ÷O#,yÏ©ªÚ…ýž¦\\˜cÄ1³Lˆ2HQ“´¶áŒ ‚:ƒŽ9–å!Š–͐‚ɾF''‘÷yÇNüûãëpÆ|=~¢D•äµ•vn2„sÓžGLë IUP´Uíw®Ú-/mm£²×Ì–ìíeý] ? øÑüa¨ÞZÏeki,q‰c10PTpAÜÀg%zSß°2Ĥ¡U]®ØŠÜçžI;€èpx?_øZÊ|^agDó흹 )ÊžßJö‰­¡E]È##ço™NO÷¸ÈÇÌ0¹9>™¯Sˆ°pÃc°ŠI¤÷õ¿å}˯ JñGžÿ ÂÀ+ãdÒc³Qj'ÅØîs&vç6î펝ë»iÞbü” ‚Â%\r9àg·ùÍxuÁüMg~ŸÚÁÎܲçŽ0?*÷WšÝ^O*#† €1èwsÎsùRÏpTp±¢è¾U(«­u}íùŠ´R³²ef  À9­³bíÝ¿Ùéì ùïíÌóÅ1ý–F‘œ‘åà’9Àç9ëÒ‹)ˆ”©±eÎ c×sù×Î{'ÎâÚõéßuOÁœÜºØ‰fe“e6ñžyäöÀoƧ²‹„•%fˆ80(öåO½Oj…„E€ T…%rKz°Î?.;{šXÙ‡ŸeUÚd!üx9þtã%wO_øoòcM- j–ÒHX_iK#*) ž@Ž{ ôǽBd¹‰RÝn–ê0«7ˆìyÀ÷Í@¬Ì¢³³’ 9é÷½?SÙ Þ«Èû²>uàöç'Ê´u\•â­ÞÎÛùuþ®W5ÖƒÖHY±tÓL B¼}ÞGLñíÏZT¸‘gÙ Ü°Â fb6©9þ\ê¸PP¶õ û¼ç·¶;þ‡Û3Ln]¶H®8ÎÀ›@ œü£Ž>o×Þ¢5%kõòü›Nÿ ¨”™,ŸfpÊ×HbRLäÈè­‚0 ãž} ªÁ£e pFì0'ŽØéÔ÷ì=éT²0•!…Îzt9ç¾?”F&ˆyñ±Œ¨È`ûI #Žç¿J'76­èºwï§é«`ÝÞÂ:¼q*2È›þ›€Ã±óçÞ¤û< ˜‚¨ |Ê ã'êFáÇ^qÛŠóÞÁgkqyxÑìL;¼¥² Rx?‡¯Y7PŽwnù¶†û¾Ü·.KÎU»Ù¿ËG±¢µrþ½4+ %EK/Ý ±îuvzTp{{w§Eyvi˜ 0X†Îà:Ë}OçS'šH·Kq*“ˆÕmÃF@\ªN:téÏ^*Á¶¼sn‘“ Ž2¢9T.½„\ ýò@>˜7NFïNRÓ·wèôßEÕua'¬[þ¾cö¡̐Oæ¦âÅŠ². Ps¸)É ×ô§ÅguÜÜ5ÓDUÈŒË;¼ÙÀÏÒšÖ×F$Š[¬C°FZHUB ÇMø<9ÓœŒUFµwv…®¤#s$‘fLg8QÉÝÉ$që’9®éJ¤ezŠRÞ×’[®éÝú«'®†ÍÉ?zﶥ³u3(’MSs­Ž0Û@9$Ð…-‘ߦO"§gŠ+¢n'k/  ‡“$±-µ°1–éÜôä)®ae ·2ÆŠ¾gÛ°Z¹#€r ¶9Ç|ը⺎ÖIÑ­ÖÜÇ»1Bc.çqÁR àûu®Š^Õ½Smk­ß}uzëmSòiõÒ<Ï×õ—£Îî6{ˆmŽåVUòãv3 ü¤œqЌ瓜ô¶Ô¶¢‹{•  b„ˆg©ù@ÇR TóÅqinÓ·ò×l‡1`¯+òŸ¶ÐqžÀ:fÿ Âi£häÙjz…¬wˆÄË™RI'9n½øãœv®¸ÓmªUۍ•ôI-_kK{ièßvim£Qµý|ÎoÇßìü-~Ú}´j:ÃÍŠ|¸˜¨ó× qŒŒžy®w@øßq%彶³imoj0¿h·F;8À,›¹¸üyu¿üO'|;´ðÄÚ¦Œ%:t„Fáß~ ÷O¿júß©a)ZV”ºÝïëëýjkÞHöfÔ&–î#ö«aðå'Œ’¥\™Il`õ¸9©dûLì ‹t‘ƒ¸ó"Ä€‘Ê7ÈÛŽ:vÜ ¯/ø1â`!»Ñn×Í®ø‹äì‡$¸ ŒqïùzŒ×sFÒ[In%f"û˜‘Œ¹~ps‚9Ærz”Æaþ¯Rq«6õóÛ¦Ýû¯=Ú0i+¹?ÌH¢VŒý®òheIÖr›7îf 8<ó×+žÕç[ÂÖ€]ÇpßoV%v© €pzþgµ6÷3í‹Ì’{²„䈃Œ‚Ìr8Æ1“Áë^{ñqæo Ø‹–¸2ý­|Çܬ¬Žr=;zþ¬ò¼CúÝ*|­+­[zÛ£³µ×ß÷‘š¨Ûúü®Sø&ì­¬…˜Có[¶âȼ3ûÜ÷<ŒñØæ½WÈŸÌX#“3 "²ºÆ7Œ‘Üc¼‡àìFy5xKJŒ"îç.r@ï×Þ½Ä-ÿ þ“}ª}’*Þ!,Fm¸Î@†9b?1W{Yæ3„`Ú¼VõŠÚÛ_kùöG.mhÎñ ôíhí§Ô$.ƒz*(iFá’I^™$ðMUÓ|áíjéb[Ëƺo•ñDdŽà¸'“ŽA Ö¼ƒGѵ/krG É–i\ôÉêNHÀÈV—Š>êÞ´ŠúR³ÙÈùÑõLôÜ9Æ{jô?°°Kýš¥WíZ¿V—m6·E}{X~Æ? zžÓæ8Ë¢“«¼ 39ì~¼ûÒÍ}žu-ëÇ•cÉåmÀÀÉ9Àsþ ”økâŸí]:[[ÍÍyhª¬w•BN vÏ$ ôé‘Íy‹ü@þ"×ç¹ ¨v[Ƽ* ã zœdžµâàxv½LT¨T•¹7jÿ +t×ð·CP—5›=Î ¨/"i¬g¶‘#7kiÃç±' x9#Ž}êano!òKD‘ílï”('¿SÔð?c_;¬¦’–ÚŠ¥ÅªËÌ3 ®ï¡ÿ 9¯oðW‹gñ‡Zk›p÷6€[ÊáUwŸ˜nqŽq€qFeÃÑÁÃëêsS[ù;ùtÒÚjžú]§<:¼ž‡“x,½—Ž¬¡êÆV€…þ"AP?ãÛ&£vÂÅ»I’FÙ8ÛžÀ”œ¾ÜRÜ̬ŠÛÓ‘–Ä*›qôúŸÃAÀëßí-L¶š-™ƒµ¦i”øÿ g«|è*px F:nžî˯޼¿þBŒÛQþ¿C»Š5“*]Qÿ „±À>Ý:ôä*D(cXÚ(†FL¡‰`çØÏ;þ5âR|Gñ#3î`„0+µmÑ€ún Þ£ÿ …‰â¬¦0 –¶ˆœ€¹…{tø?ʯ(_çþ_Š5XY[¡Ù|Q¿ú µŠ2︛sO* Бÿ ×â°<+à›MkÂ÷š…ij ·Ü–ˆ«ò‚?ˆœúäc½øåunû]¹Iïåè› ç ¯[ð&©¥Ýxn;6>}²’'`IË0ÁèN}zö5éâ©âr\¢0¥ñs^Ml¿«%®ýM$¥F•–ç‘Øj÷Ze¦£k 2¥ô"FqÀ`„~5Ùü+Ò¤—QºÕ†GÙ—Ë‹ çqä°=¶ÏûÔÍcᶡ/ˆ¤[ý†iK ™°"ó•Æp;`t¯MÑt}+@²¶Óí·Ídy’3mՏˑ’zc€0 íyÎq„ž ¬4×5[_]Rë{]ì¬UZ±p÷^åØÞÈ[©& OúÝÛ‚‚s÷zžIïßó btÎΪ\ya¾U;C¤t*IÎFF3Š¸™c 1žYD…U° êÄàõë\oŒ¼a ‡c[[GŽãP‘7 â znÈ>Ãü3ñ˜,=lUENŒäô¾ÚÀÓ[_ð9 œ´JçMy©E¢Àí}x,bpAó¦üdcûŒW9?Å[Há$¿¹pÄ™#^9O88©zO=«Ë!µÖüY¨³ªÍy9ûÒ1 úôÚ»M?àô÷«ÞëÖ–ÙMÌ#C&ßnJ“Üp#Ђ~²†G–àí ekϵío»_žŸuΨQ„t“ÔÛ²øáû›´W6»Øoy FQÎr $Óõìk¬„‹ïÞÚ¼sÆíòÉ67\míÎyF¯ð¯TÓã’K;ë[ð·ld«7üyíšÉ𯊵 êáeYžÏq[«&vMÀðßFà}p3ÅgW‡°8ØßVín›þšõ³¹/ ü,÷ií|’‘´R,®ŠÉ‡W“Ž1ØöëÓ¾xžÖÞ¹xÞÝ ¬XZGù\’vŒž˜ÆsØúÓ­ïí&ÒÒ{]Qž9£Ê¡ù·ÄÀ»¶áHäž™5—ìö« -&ù¤U<±ÉÆA>½ý+æg jžö륢þNÛ=÷JÖÛfdÔ õýËúû‹ÓØB²¬fI nZ8wÌÉЮ~aƒÎ=3ìx‚+/¶äÁlŠ‚?™Æü#8-œ\pqTZXtè%»»&ÚÝ#´ŠðÜ žã§Í’¼{p·ß{m>ÞycP¨’¼¢0ú(Rƒë^Ž ñó¼(»y%m´ÕÙ}ÊûékB1¨þÑ®,#Q)ó‡o1T©ÜÃ*Ž‹‚yö< b‰4×H€“ìÐ. ¤²9ÌŠ>„Žãøgšñ ¯Š~)¸ßå\ÛÛoBŒa·L²œg$‚Iã¯ZÈ—Æ~%”äë—È8â)Œcƒ‘Âàu9¯b%)ÞS²¿Ïïÿ 4Öºù}Z/[H%¤vÉ#Ì’x§†b © ³´tÜ{gn=iï%õªÇç]ܧ—! åw„SÓp ·VÈÏ¡?5Âcâb¥_ĤŠz¬—nàþÖΟñKÄöJé=ÌWèêT‹¸÷qÎჟ•q’zWUN«N/ØO^Ÿe|í¾©k{üõ4öV^ïù~G¹êzÂèº|·÷×[’Þ31†rpjg·n Æ0Ý}kåË‹‰nîe¹ËÍ+™ÏVbrOç]'‰¼o®xÎh`¹Ç*±ÙÚ!T$d/$žN>¼WqᯅZ9ÑÒO\ÜÛê1o&,-z ~^NCgNÕéá)ÒÊ©7‰¨¯'Õþ¯þ_¿Ehîþóâ €ï¬uÛûý*ÎK9ä.â-öv<²‘×h$àãúW%ö¯~«g-ÕõÀàG~>Zú¾Iš+(šM³ Û#9äl%ðc¬ ûÝ xÖKG´x®|¸¤Ï™O:Ê8Ã’qÉcÔä‚yÇNJyËŒTj¥&µOmztjÿ ?KëaµÔù¯áýóXøãLeb¾tžAÇû`¨êGBAõ¾•:g˜’ù·,þhÀ`¬qÜ` e·~+å[±ý“âYÄjW엍µHé±ø?Nõô>½âX<5 Ç©ÏѼM¶8cܪXŽÉ^r?¼IróÈS•ZmÇ›™5»òÚÚ7ïu«&|·÷•Î† >[©ÞXHeS$Œyà€ ÷ù²:ò2|óãDf? Z¼PD¶ÓßC(xÆ0|©ßR;ôMsÿ µ´ÔVi¬,͹›Ìxâi˜`¹,GAéÇlV§ÄýF×Yø§ê–‘:Ã=ò2³9n±ÉžØÏ@yÎWžæ±Ãàe„ÄÒN ]ïòêìú_Go'¦ŽÑ’_×õЯðR66þ!›ÑÄ gFMÙ— äžäqôÈ;ÿ eX<#%»Aö‰ãR¤ Í”Ž¹È G&¹Ÿƒ&á?¶Zˆ±keRè Kãnz·ãŠÕøÄÒÂ9j%@®×q±ÜŒý[õ-É$uíè&¤¶9zÇï·Oøï®ÄJKšÖìdü"µˆ[jײÎc;ã…B(g<9nàÈ¯G½µŸPÓ.´Éfâ¼FŽP 31 ‘ÏR}<3šä~ Ã2xVöî Dr Ç\›}Ý#S÷ÈÀëŽHÆI®à\OçKuäI¹†ó(”—GWî ñ³¹¸æ2¨›‹ºÚû%¾ýÖ_3ºNú¯ëúì|ÕÅÖ‰}y lM’ZËîTÿ á[ðÐñ/ˆ9Àû ¸ón3 Mòd‘÷ döª^.ÊñŽ°›BâîNp>cëÏçÍzïíôÏ YÍ%ª¬·ãÏ-*9Ü­ÂãhéŒc¾dÈêú¼Ë,. VŠ÷çeÿ n/¡¼äãõâ=‹xGQKx”|¹bÌŠD@2Œ 8'Ž àúƒŽ+áDÒ&¡¨"Œ§–Žr22 Ç·s]ŸÄ‹«ð%ÚÄ<¹ä’(×{e›HÀqÁç©Ç½`üŽÚõK饚9ƒÄ±€< –úƒú~ çðñO#­Í%iKKlµ¦¾F)'Iê¬Î+Ç(`ñ¾£œdÈ’` ™ºcßéé^ÿ i¸”Û\ý¡æhÔB«aq¸}ãÀÆ:ÜWƒ|FÛÿ BŒÇÀeaŸ-sÊ€:úW½ÜÝÜ<%$µ†%CóDªÀí%IÈÏʤ…ôäñÞŒ÷‘a0“ôŽÚë¤nŸoW÷0«e¶y'Å»aΗ2r’# Û°A^ý9ÉQÔõ=ù5¬£Öü.(Þ’M$~V«=éSÄFN½®©ÔWô»ÿ þHžkR‹ìÏ+µµžöê;khÚI¤m¨‹Ôš–âÖçJ¾_Z•’6 a”Èô> ÕÉaÕ<%®£2n bQŠå\tÈõUÿ ø»þ‹k15‚ÃuCL$ݹp P1=Oøýs¯^u éEJ”–éêŸê½5ýzy›jÛ³á›Ûkÿ ÚOcn±ÛÏîW;boºz{ãžüVÆ¡a£a5½äÎÂks¸J@?1è¿{$䑐=k”øsÖ^nŒ¦)ÝåXÃíùN1ØõÚOJë–xF÷h¸ Œ"Ž?x䜚ü³ì¨c*Fœ¯i;7~ñí׫Ðó¥Ë»3Ãü púw ‰°<Á%»ñž ÿ P+Û^ ¾Ye£ŽCÄŒ„/>˜>•á¶Ìm~&&À>M[hÈÈÿ [Ž•íd…RO@3^Ç(ʽ*¶ÖQZyßþ 1Vº}Ñç?¼O4Rh6R€ª£í¡ûÙ a‚3ß·Õ ü=mRÍ/µ9¤‚0ÑC¼Iè:cŽsÛ¾™x£ÆЬªÍöˢ샒W$•€Å{¨ÀPG ÀÀàŸZìÍ1RÉ0´ðxEË9+Éÿ ^rEÕ—±Š„70l¼áË@û.' ¼¹Žz€N3úUÉ<3á×*?²¬‚ä†"Ùc=p íÛ'¡ª1ñ"økJ†HÒ'»Ÿ+ oÏN¬Ã9 dÙãÜדÏâÍ~æc+j·Jzâ7(£ðW]•æ™?nê´º6åwéåç÷N•ZŠíž›¬|?Ðõ?Ñ-E…®³ÇV$~X¯/…õ x‘LˆÑÜÚÈ7¦pzãÜüë½ðÄ^õtÝYËÍ7ÉÖÕ8ÏUe# #€r=sU¾/é’E§jRC4mxNÝ´9†íuá»›V‘ ZI€­×cr1Ÿpzsøf»¨åV‹ìû`qËLÊIã?\~¼³áËC©êhªOîO»‘ÃmçÛçút×¢x“Z}?Ãœê#b-¤X7õ Äò gž zzbº3œm*qvs·M=íúéw}¿&Úª°^Ö×µÏ(ø‡â†Öµƒenñý†×åQáYûœ÷ÇLœôÎNk¡ð‡¼/µ¸n0æÉ0¬ƒ‚üîÉÆvŒw®Sáö”š¯‹-üÕVŠØÙ[$`(9cqƒÔ_@BëqûÙ`Ý捭0;79È?w<ó |ÙÜkßÌ1±Ëã ¿ìÒ»ðlìï«ÓnªèèrP´NÏš&Žéö Ù¸÷æ°~-_O'‰`°!RÚÚÝ%]Ø%þbß1'¿ÿ X՝áOöÎŒ·‹¬+Åæ*ÛÛ™0¤ƒOÍÔ `u¯¦ÂaèÐÃÓ«‹¨Ô¥µœ¿¯ÉyÅÙ.oÔôŸ Úx&(STðݽ¦õ] ’ÒNóÁäÈùr3í·žÚ[™ƒ¼veÈ÷ÞIõÎGlqÎ=M|«gsªxÅI6 ]Z·Îªä,¨zŒŽÄ~#ØŠúFñiÉqc©éÐD>S딑 GñŽ1éÐ^+ Ëi;Ô„µVÕú»i¯ÈÒ-ZÍ]òܘ®ì` bÛÙ¥_/y(@÷qÐúg Ô÷W0.Ø› 6Ò© r>QƒŒ0+Èîzb¨É+I0TbNñ"$~)ÕÒ6Þ‹{0VÆ27œWWñcÄcX×íôûyKZéðªc'iQ¿¯LaWŠŸS\·Š“źʸ…ôÙÂí|öÀÇåV|!¤ÂGâÛ[[’ï 3OrÙËPY¹=Î1õ5öåTžÑè Ú64/üö?Zëžk}¬¶éào፾á}3“ü]8Éæ¿´n²Žš_6¾pœ)2?úWÓÚ¥¾¨iWúdŽq{*ª1rXŒd…m»‰äcô¯–dâ•ã‘Jº¬§¨#¨® §,df«8ÉÅßN¾hˆ;îÓ=7áùpën®É 6ûJžO2^œÐò JÖø¥²ã›Ò6Ü·‰!wbÍ‚¬O©»õ¬ÿ ƒP=Ä:â¤-&ÙŽ ` È9 r9íϧzë> XÅ7ƒ5X–krÑ¢L 7€ìw}ÑŸNHëŒüþ:2†á¼+u·á÷N/Û'Ðç~ߘô«ëh!ónRéeQ´6QÛÿ èEwëÅÒ|¸Yqó1uêyùzð8 ƒŠù¦Ò;¹ä6öi<'ü³„[íZhu½ ùÍ¡g‚>r¯׊îÌx}bñ2“­k꣧oø~›hTèóËWò4|ki"xßQ˜Ï6øÀLnß‚0 ¹Æ{±–¶Öe#¨27È@^Ìß.1N¾œyç€õ†ñeé·Õã†çQ°€=­Ì©ºB€Ø8<‚ÃSõ®ùcc>×Ú .Fr:žÝGæ=kÁâ,^!Fž ¬,àµ}%¶«îõ¹†"r²ƒGœüYÕd?aÑÍY®49PyU ÷þ!žxÅm|/‚ãNð˜¼PcûTÒ,¹/Ý=FkÏ|u¨¶«â녏{¤m¢]Û¾ïP>®XãÞ½iÓÁ¾ ‰'¬–6ß¼(„ï— í!úÙäzôë^–:œ¨å|,_¿&š×]uÓѵÛô4’j”bž§x‘Æ©ã›á,‚[Ô ÎÞ= ŒËæ ÀùYÁ?ŽïÚ¼?ÁªxºÕÛ,°1¸‘¿Ýäãدv…@¤åq½ºã œàûââ·z8Xýˆþz~—û»™âµj=Ž â~ãáh@'h¼F#·Üp?ŸëQü-løvépx»cŸø…lxâÃûG·‰¶ø”L£©%y?¦úõÆü-Õ¶¥y`Òl7>q’2üA?•F}c‡jB:¸Jÿ +§¹¿¸Q÷°ív=VÑìu[Qml%R7a×IèTõéŽx¬ ?†š7 1†îã-ˆã’L¡lŽ0OÓ=ÅuˆpÇ•¼3ÛùÒ¶W/!|’wŽw^qÔ×Ïaó M8Q¨ãÑ?ëï0IEhÄa¸X•`a ?!ÐñùQ!Rä ÂžqŽžÝO`I0ÿ J“y|ñ!Îã@99>þ8–+éáu…!ù—ä Ê°<÷6’I®z ÅS„¾)Zþ_Öýµ×ËPåOwø÷þ*üïænÖùmØÝûþ¹=>¦½öî×Jh]¼ç&@§nTŒ6IT Àõ^Fxð7Å3!Ö·aÛ$þÿ ¹ã5îIo:ȪmËY[’8ÇӾlj*òû¢¥xõ¾¼ú•åk+\ð¯ HÚoŽl•Ûk,¯ ç²²cõÅ{²Z\ ´ìQ åpzŽ3Ôð}ÿ Jð¯XO¡øÎé€hÙ¥ûLdŒ`““ù6Gá^ÃáÝ^Ë[Ñb¾YåŒÊ»dŽ4 †2§,;ÿ CQÄ´¾°¨c–±”mºV{«ßÕýÄW\ÖŸ‘çŸ,çMRÆí“l-ƒn~ë©ÉÈê Ü?#Ž•¹ðãSÒ¥ÐWNíà½;ãž)™ÎSÈ9cóLj뵿Å«iÍk¨ió­¶X‚7÷ƒ€yãnyÏŽëÞ Öt`×À×V's$È9Ú:ä{wÆEk€«†Çàc—â$éÎ.éí~Ýëk}ÅAÆpörÑ¢‡Šl¡ÑüSs‹¨‰IÝ„óÀ×wñ&eºðf™pŒÆ9gŽTø£lñëÀçŽ NkÊUK0U’p ï^¡ãÈ¥´ø{£ÙHp`’ØåbqÏ©äó^Æ: Ž' ÊóM«õz+ß×ó5Ÿ»('¹­ð¦C„$˜Å¢_ºÈI?»^äã'ñêzž+ë€ñ-½»´}¡Ë*õ?.xÇ^1ŽMyǸ&“—L–îëöâ7…' bqéÎGé]˪â1$o²¸R8Ã`.q€}sÖ¾C9­8cêÆÞíïóòvÓòùœÕfÔÚéýu­èÖ·Ú Å‚_¤³ÜۺƑߝ”àרý:׃xPþÅÕî-/üØmnQìïGΊÙRqê=>¢½õnæ·r!—h`+’;ò3È<“Û©éšóŸx*÷V¹¸×tÈiˆßwiÔÿ |cŒñÏ®3ֽ̰‰Ë Qr©ö½®¼ÛoÑÙZÅÑ«O൯ýw8;k›ÿ x†;ˆJa;‘º9÷÷R+¡ñgŽí|Iáë{ôáo2ʲ9 029ÉÏLí\‰¿¸Ÿb˜ "Bv$£&#ßiê>=ªª©f  ’N ëí>¡N­XW­~5×úíø\‰»½Ï^ø(—wÖú¥¤2íŽÞXæÁ$ °eÈ888^nÝë²ñÝÔ^ ÖÚ9Q~Ëå7ï DC¶ÑµƒsËÇè9®Wáþƒ6‡£´·°2\Ý:ÈÑ?(#¨'$õèGJ¥ñW\ÿ ‰E¶—¸™g˜ÌÀ¹;Pv ú±ÎNs·ëŸ’–"Ž/:té+ûË]öJöÓM»ëø˜*‘•^Uý—êd|‰åñMæÔÝ‹23å™6æHùÛ‚ëüñ^…ñ1¢oêûÑEØ.õ7*ÅHtÎp{g<·Á«+¸c¿¿pÓ¾Æby=8É_ÄsÆk¬ñB\jÞÔì••Ë[9Píb‹Bヅ =9­3§ð§LšÛáÖšÆæXÌÞdÛP.0\ãïÛ0?™úJ¸™Ë ”•œº+=<µI£¦í¯õêt¬d‹T¬P=ËFêT>ÍØØ@Ï9<÷AQÌ×»Õ¡xùk",JÎæù±Éç$œŽŸZWH®¯"·UÌQ ’ÙÈ]ÅXg<ã ߨg3-Üqe€0¢¨*Œ$܃ ’Sû 8㎼_/e'+Ï–-èÓ¶¶Õíß[·ÙÙ½î쏗¼sk%§µxä‰â-pÒeÆCrú ôσžû=”šÅô(QW‚Õd\ƒæ. \àö¹¯F½°³½0M>‘gr÷q+œ¶NïºHO— ¤ ܥݭ”n·J|ÆP6Kµc=Isó}Ò çGš)a=—#vK›åoK§ßóٍ¤¶¿õú…ÄRÚ[Ësöټˏ•Ë ópw®qœŒ·Ø ùÇâ‹ý‡ãKèS&ÞvûD Aù‘É9 ŒîqÅ} $SnIV[]ѐ´Ó}ØܾA Ü|½kÅþÓ|E Mu R¼.I¼¶däò‚ÃkÆ}ðy¹vc iUœZ…­Õõ»z¾÷¿n¦*j-É­/àœHã\y5 Û ß™ó0— äŸnzôã#Ô¯,†¥ÚeÔ÷ÜÅ´„“'c…<íÝ€<·SŠ¥k§Ã¢éÆÆÙna‚8–=«ʪ[Ÿ™°pNî02z“ÔÙ–K8.È’Þî(vƒ2®@ äÈûãçžxäÇf¯ˆu¹yUÕîýWšÙ|›ëÒ%Q^í[æ|éo5ZY•^{96ˆY‚§v*x>âº_|U¹Ö´©tûMÒÂ9PÇ#«£#€ éÉñ‘ƒÍz/‰´-į¹°dd,Б›p03ƒœ{ç9=+ Ûᧇ¬¦[‡‚ê婺¸#±ß=³ý¿•Õµjñ½HÙh›Û[§ÚýÊöô÷{˜?ô÷·Ô.u©–_%còcAÀ˜’ }0x9Î>žñÇáÍ9,ahï¦Ì2òÓ ñÛAäry$V²Nð ]=$Ž ‚#Ù‚1ƒƒødõMax‡ÂÖ^!±KkÛ‘ «“Çó²FN8+ëÎ{Ò¼oí§[«ÕMRoËeç×[_m/¦¦k.kôgŽxsSÓ´ý`êzªÜÜKo‰cPC9ÎY‰#§^üý9¹âïÞx£Ë·Ú`±‰‹¤;³–=ÏaôÕAð‚÷kêÁNBéÎælcõö®£Fð†ô2Ò¬]ßÂK$ÓÜ®•”/ÊHàã$ä ¸÷ëf¹Oµúâ“”’²ø­è´µþöjçNü÷üÌ¿ xNïFÒd»¼·h®îT9ŽAµÖ>qÁçÔœtïÒ»\ȶÎîcÞäîó3¶@#ÉIÎ ÔñW.<´’¥–ÑÑ€ÕšA‚ ;†qÓë‚2q ÒÂó$# Çí‡ !Ë}Õ9ÈÎÑÉã=;ŒÇÎuñ+ÉûÏ¥öíeÙ+$úíÜ娯'+êZH4ƒq¶FV‹gïŒ208ÆÌ)íб>M|÷âÍã¾"iì‹¥£Jd´™OÝç;sÈúr+ÜäˆË)DŒ¥šF°*3Õ”d {zÔwºQ¿·UžÉf†~>I+ŒqÔ`ð3œ“Ü×f]œTÁÔn4“ƒø’Ýßõ_«*5šzGCÊ,þ+ê1ò÷O¶¸cœºb2yÇ;cùÕ£ñh¬›áÑŠr¤ÝäNBk¥—á—†gxšX/쑘hŸ*Tçn =û㦠2|(ð¿e·ºÖ$ ýìŸ!'åΰyîî+×öœ=Y:²¦ÓÞ×iü’—ü -BK™£˜›âÆ¡&véðõ-ûÉY¹=Onj¹ø¯¯yf4·±T Pó`çœ7={×mÃ/ ¢˜ZÚòK…G½¥b„’G AãÜœ*í¯Ã¿ IoæI¦NU8‘RwÈã;·€ Û×ëÒ”1Y •£E»ÿ Oyto¢<£Áö·šï,䉧ûA¼sû»Nò}¹üE{ÜÖªò1’õÞr0â}ÎØ#>à/8ïéÎ~—áÍ#ñÎlí§³2f'h”?C÷YËdð:qëõÓ·‚ïeÄ© ÔÈØÜRL+žAÎ3¼g=åšó³Œt3 ÑQ¦ùRÙßE®¼±w_;þhš’Sirÿ ^ˆã¼iੇ|RòO„m°J/“$·l“ ÇÓ¿ÿ [ÑŠÆ“„†Õø>cFÆ6Ø1ƒ– àz7Ldòxäüwá‹ÝAXùO•Úý’é®ähm­ •NÀ±ÌTÈç ƒ‘I$pGž:‚ÄbêW¢®œ´|­¦­nÍ>¶ÖÏ¢§ÎÜ¢ºö¹•%ÄqL^öÛ KpNA<ã¡ …î==ª¸óffËF‡yÌcÉ ©ç$ð=ñÏ­YþÊ’Ú]—¥‚¬‚eDïÎH>Ÿ_ÌTP™a‰ch['çÆÜò7a‡?w°Ïn§âÎ5”’¨¹uÚÛ|´ÓÓc§{O—ü1•ªxsÃZ…ÊÏy¡Ã3¸Ë2Èé» ‘ƒÎ äžÜðA§cáOéúÛ4ý5-fŒï„ù¬ûô.Ç Üsž•Ò¾•wo<¶Ÿ"¬¡º|£ î2sÇ¡éE²ÉFѱrU°dÜ6œ¨ mc†Îxë׺Þ'0²¡Rr„{j¾í·è›µ÷)º·å–‹î2|I®Y¼ºÍË·–ÃÆà㍣'óÆxƒOÆÞ&>\lóÌxP Xc¸ì Sþ5§qà/ê>#žÞW¸if$\3 ® ûÄ“ùŽÕê¾ð<Ó‹H¶óÏ" å·( á‘€:ã†8Ï=+ꨬUA×ÃËÚT’ÑÞöù¥¢]{»ms¥F0\ÑÕ—ô}&ÛB´ƒOŽÚ+›xíÄÀ1 ,v± žIëíZ0ǧ™3 í2®0ทp9öÝÔž)ÓZËoq/Ú“‘L ²ŒmùŽÓ9§[Û#Ä‘\ÞB¬Çs [;à à«g‚2ôòªœÝV§»·¯/[uó½õÛï¾ /šÍ}öüÿ «=x»HŸÂÞ.™ ÌQùŸh´‘#a$‚'¡u<Š›Æ>2>+ƒLSiöwµFó1!eg`£åœ ÷ëÛö}Á¿ÛVÙêv $¬ƒ|,s÷z€ð΃¨x÷ÅD\ÜŒÞmåÔ„ ˆ o| :{ÇÓ¶–òÁn!´0Ål€, ƒ ( ÛŒŒ c¶rsšæ,4‹MÛOH!@¢ ÇŽ„`å²9ÝÃw;AÍt0®¤¡…¯ØÄ.Àì클ƒ‘ßñ5Í,Óëu-ÈÔc¢KÃÓ£òÖ̺U.õL¯0…%2È—"~x ‚[`có±nHàŽyàö™¥keˆìŒÛFç{(Ø©†`Jã#Žwg<“:ÚÉ;M ^\yhûX‡vB·÷zrF?§BÊÔ/s<ÐÈB)Û± ·ÍÔwç5Âã:så§e{mѤï«Òíh—]Wm4âí¿ùþW4bC3¶ª¾Ùr$ pw`àädzt!yŠI„hÂîàM)!edŒm'æ>Ç?wzºK­ìcŒ´¯Ìq6fp$)ãw¡éUl`µ»ARAˆÝÕgr:äŒgƒéé[Ôö±”iYs5Ýï«ÙG—K=þF’æMG«óÿ `ŠKɦuOQ!ÕåŒ/ÎGÞ`@ËqÕzdõâ«Ê/Ö(ƒK´%ŽbMü åÜŸö—>¤óŒŒV‘°„I¢Yž#™¥ùÏÊ@8 œgqöö5ª4vד[¬(q cò¨À!FGaÁõõ¯?§†¥ÏU½í¿WªZ$úyú½ŽzקÉþ?>Ã×È•6°{™™ŽÙ.$`­ÎUœ…çè ' ¤r$1Ø(y7 ðV<ž:È  ÁÎMw¾Â'Øb§øxb7gãО½óÉÊë²,i„Fȹ£§8ãä½k¹¥¦ê/ç{ïê驪2œ/«ü?¯Ô›ìñÜ$þeýœRIåŒg9Ác’zrrNO bÚi¢ ѺË/$,“ª¯Ýä;Œ× ´<ÛÑn³IvŸb™¥ nm–ÄŸ—nÝÀãŽ3ëÍG,.öó³˜Ù£¹u ÊÌrŠ[<±!@Æ:c9ÅZh ì’M5ÄìÌ-‚¼ëÉùqŽGì9¬á ;¨A-ž—évþÖ–^ON·Ô”ŸEý}ú×PO&e[]ÒG¸˜Ûp ƒÃà/Ë·8ûÀ€1ž@¿ÚB*²­¼ñì8@p™8Q“žÆH'8«I-%¸‚ F»“åó6°Uù|¶Ú¸ã ò^Äw¥ŠÖK–1ÜÝK,Žddlí²0PÀü“×ükG…¯U«·¶–´w¶ŽÍ¾©yÞú[Zös•¯Á[™6° ¨¼ÉVæq·,# ìãï‘×8îry®A››¨,ãc66»Ë´ã'æÉù?t}¢æH--Òá"›|ˆ¬[í  7¶ö#¸9«––‹$,+Ëqœ\Êø c€yê^ݸÄa°«™B-9%«×®‹V´w~vÜTéꢷþ¼ˆ%·¹• ’[xç•÷2gØS?6åÀÚ õ9É#š@÷bT¸º²C*3Bá¤òÎA9 =úU§Ó"2Ãlá0iÝIc‚2Î@%öç94ùô»'»HÄ¥Ô¾@à Tp£šíx:úÊ:5eºßMý×wµ›Ó_+šº3Ýyvÿ "ºÇ<ÂI>Õ 1G·Ë«È«É# àÈÇ øp Jv·šæDûE¿›†Ë’NFr2qŸ½ÇAÜšu•´éí#Ħ8£2”Ú2Ã/€[ÎTr;qŠz*ý’Îþ(≠;¡TÆ⁛;ºÿ àçœk‘Þ­8¾Uª¾íé{^×IZéwÓkXÉûÑZo¯_øo×È¡¬ â–ÞR§2„‚Àœü½ùç® SVa†Âüª¼±D‘ŒísŸàä|ä2 æ[‹z”¯s{wn„ÆmáóCO+†GO8Ïeçåº`¯^¼ðG5f{Xžä,k‰<á y™¥voÆ éÛõëI=œ1‹éíÔÀÑ)R#;AÂncäŽ:tÏ#¶TkB.0Œ-ÖÞZÛgumß}fÎJÉ+#2êÔP£žùÈÅi¢%œ3P*Yƒò‚Aì“Ž2r:ƒÐúñi­RUQq‰H9!”={~¼ “JŽV¥»×²m.ÛߺiYl¾òk˜gL³·rT• ’…wHÁ6ä`–Î3ùÌ4Øe³†&òL‘•%clyîAÂäà0 žüç$[3uŘpNOÀÉ=† cï{rYK ååä~FÁ •a»"Lär1Ó¯2Äõæ<™C•.fÕ»è¥~½-¿g½Â4¡{[ør¨¶·Žõäx¥’l®qpwÇ»8ärF \cŽ¸ÜÆÓ-g‚yciÏÀ¾rÎwèØÈ#o°Á9ã5¢šfÔxÞæfGusÏÌJÿ µ×œ/LtãÅT7²¶w,l ɳ;”eúà·¨çîŒsÜgTÃS¦­^ '~‹®›¯+k÷ZÖd©Æ*Ó[Ü«%Œk0ŽXƒ”$k#Ȩ P2bv‘ƒŸáÇ™ÆÕb)m$É*8óLE‘8'–ÜN Úyàúô­+{uº±I'wvš4fÜr íì½=úuú sFlìV$‘ö†Hсù€$§ õ=½¸«Ž] :Ž+•¦ïmRþ½l´îÊT#nkiøÿ _ðÆT¶7Ò½ºÒ£Î¸d\ã8=yãŽÜäR{x]ZâÚé#¸r²#»ÎHÆ6õ ç® ÎFkr;sºÄ.&;só± Ç9êH÷ýSšÕ­tÐU¢-n­ Ì| vqœ„{gŒt§S.P‹’Ž‰_[;m¥Þ­ZýRûÂX{+¥úü¼ú•-àÓ7!„G"“´‹žƒnrYXã¸îp éœ!Ó­oP̏tÑ (‰Þ¹é€sÓ#GLçÕšÑnJý¡!‘Tä#“ß?îýp}xÇ‚I¥Õn#·¸–y'qó@r[ Êô÷<ÔWÃÓ¢áN¥4ԝ’I&ݼ¬¬¼ÞºvéÆ FQV~_ÒüJÖÚt¥¦Xá3BÄP^%ÈÎW-×c¡ú©¤·Iþèk¥š?–UQåIR[’O 5x\ÉhÆI¶K4«2ùªŠŒ<¼óœçØ`u«‚Í.VHä € Ëgfx''9ÆI#±®Z8 sISºku¢ßÞ]úk»Jößl¡B.Ü»ÿ MWe °·Ž%šêɆ¼»Âù³´œ O¿cÐÓÄh©"ÛÜÏ.ÖV ’3nüÄmnq[ŒòznšÖ>J¬òˆæ…qýØP Ž:ä7^0yëWšÍ_79äoaÈ °#q0{ää×mœy”R{vÒÞ¶ÚÏe¥“ÚÆХ̮—õýjR •íç›Ìb„+J yÜØÙ•Ç]¿Ôd þËOL²”9-Œ—õÃc'æÝלçÚ²ìejP“½ âù°¨†ðqòädЃÉäÖÜj÷PÇp“ÍšŠå«‘î <iWN­smª»¶vÓz5»ûì:Rs\Ðßôû×uÔÿÙ