| Server IP : 212.252.79.165 / Your IP : 216.73.217.172 [ Web Server : Apache System : Linux 212-252-79-165.cprapid.com 5.15.0-153-generic #163-Ubuntu SMP Thu Aug 7 16:37:18 UTC 2025 x86_64 User : cehaburo ( 1001) PHP Version : 8.1.33 Disable Function : exec,passthru,shell_exec,system Domains : 48 Domains MySQL : OFF | cURL : ON | WGET : ON | Perl : ON | Python : ON | Sudo : ON | Pkexec : ON Directory : /lib/python3.10/__pycache__/ |
Upload File : |
o
p̦iŁ � @ s d Z ddlmZ ddlmZmZm Z
m Z m
Z ddlmZmZmZmZ ddlmZmZmZ ddl m Z ddl m!Z"m#Z$ ddl%m&Z' dd l(m)Z*m+Z, dd
l-m-Z. dd l Z/dd l0Z0zdd l1m2Z1 W n e3ym dd l4m2Z1 Y nw g d
�Z5ded� ed� Z6ed�Z7ded� Z8dZ9de9 Z:dZ;G dd� de0j<�Z<G dd� de<�Z=e<� Z>e>j?Z?e>j@Z@e>jAZAe>jBZBe>jCZCe>jDZDe>jEZEe>jFZFe>jGZGe>jHZHe>jIZIe>jJZJe>jKZKe>jLZLe>jMZMe>jNZNe>jOZOe>jPZPe>jQZQe>jRZRe>jSZSe>jTZTe>jUZUdd � ZVd#d d �ZWeXe/d �r�e/jYe>j?d!� eZd"k�reW� d S d S )$a� Random variable generators.
bytes
-----
uniform bytes (values between 0 and 255)
integers
--------
uniform within range
sequences
---------
pick random element
pick random sample
pick weighted random sample
generate random permutation
distributions on the real line:
------------------------------
uniform
triangular
normal (Gaussian)
lognormal
negative exponential
gamma
beta
pareto
Weibull
distributions on the circle (angles 0 to 2pi)
---------------------------------------------
circular uniform
von Mises
General notes on the underlying Mersenne Twister core generator:
* The period is 2**19937-1.
* It is one of the most extensively tested generators in existence.
* The random() method is implemented in C, executes in a single Python step,
and is, therefore, threadsafe.
� )�warn)�log�exp�pi�e�ceil)�sqrt�acos�cos�sin)�tau�floor�isfinite)�urandom)�Set�Sequence)�index)�
accumulate�repeat)�bisectN)�sha512)�Random� SystemRandom� betavariate�choice�choices� expovariate� gammavariate�gauss� getrandbits�getstate�lognormvariate�
normalvariate�
paretovariate� randbytes�randint�random� randrange�sample�seed�setstate�shuffle�
triangular�uniform�vonmisesvariate�weibullvariate� g �� @� @� �?� @�5 � � c s. e Zd ZdZdZdBdd�ZdC� fdd� Z� fd d
�Z� fd d �Zd
d� Z dd� Z
dd� Z dd� Z dd� Z
de> fdd�Ze
Zdd� Zdefd d �Zd d � Zd d!� ZdBd"d#�Zdd$�d%d&�ZdBddd'�d(d)�Zd*d+� ZdDd.d/�Zd0d1� Zd2d3� Z d4d5� Z d6d7� Z d8d9� Z d:d;� Z d<d=� Z!d>d?� Z"d@dA� Z#� Z$S )Er a� 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.
� Nc C s | � |� d| _dS )zeInitialize an instance.
Optional argument x controls seeding, as for Random.seed().
N)r) �
gauss_next)�self�x� r< � /usr/lib/python3.10/random.py�__init__w s
zRandom.__init__r6 c s |dkrBt |ttf�rBt |t�r|�d�n|}|r!t|d �d> nd}tt|�D ]
}d| |A d@ }q(|t|�N }|dkr?dn|}n5|d krdt |tttf�rdt |t�rW|�� }t �
|t |�� � d
�}nt |t
d �t ttttf�swtd td � t� �|� d | _d S )
a\ 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.
r7 zlatin-1r � iCB l ���� ������r6 �bigNz�Seeding based on hashing is deprecated
since Python 3.9 and will be removed in a subsequent version. The only
supported seed types are: None, int, float, str, bytes, and bytearray.)�
isinstance�str�bytes�decode�ord�map�len� bytearray�encode�int�
from_bytes�_sha512�digest�type�float�_warn�DeprecationWarning�superr) r9 )r: �a�versionr; �c�� __class__r< r= r) � s"
�
z Random.seedc s | j t� �� | jfS )z9Return internal state; can be passed to setstate() later.)�VERSIONrT r r9 �r: rX r< r= r � � zRandom.getstatec
s� |d }|dkr|\}}| _ t� �|� dS |dkrC|\}}| _ z tdd� |D ��}W n ty: } zt|�d}~ww t� �|� dS td|| jf ��)z:Restore internal state from object returned by getstate().r r8 r6 c s s � | ]}|d V qdS )l Nr< )�.0r; r< r< r= � <genexpr>� s � z"Random.setstate.<locals>.<genexpr>Nz?state with version %s passed to Random.setstate() of version %s)r9 rT r* �tuple�
ValueError� TypeErrorrZ )r: �staterV �
internalstater rX r< r= r* � s ���zRandom.setstatec C s | � � S �N)r r[ r< r< r= � __getstate__� s zRandom.__getstate__c C s | � |� d S rd )r* )r: rb r< r< r= � __setstate__� s zRandom.__setstate__c C s | j d| �� fS )Nr< )rY r r[ r< r< r= �
__reduce__� s zRandom.__reduce__c K sP | j D ]"}d|jv r
dS d|jv r| j| _ dS d|jv r%| j| _ dS qdS )a 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.
�
_randbelowr r& N)�__mro__�__dict__�_randbelow_with_getrandbitsrh � _randbelow_without_getrandbits)�cls�kwargsrW r< r< r= �__init_subclass__� s
��zRandom.__init_subclass__c C s: |sdS | j }|�� }||�}||kr||�}||ks|S )z;Return a random int in the range [0,n). Returns 0 if n==0.r )r �
bit_length)r: �nr �k�rr< r< r= rk � s �z"Random._randbelow_with_getrandbitsr7 c C sp | j }||krtd� t|� | �S |dkrdS || }|| | }|� }||kr0|� }||ks)t|| �| S )z�Return a random int in the range [0,n). Returns 0 if n==0.
The implementation does not use getrandbits, but only random.
z�Underlying random() generator does not supply
enough bits to choose from a population range this large.
To remove the range limitation, add a getrandbits() method.r )r& rR �_floor)r: rq �maxsizer& �rem�limitrs r< r< r= rl � s �z%Random._randbelow_without_getrandbitsc C s | � |d ��|d�S )�Generate n random bytes.� �little)r �to_bytes�r: rq r< r< r= r$ s zRandom.randbytesc C s� zt |�}W n! ty' t|�}||kr tdtd� td��tdtd� Y nw |du rA|tur4td��|dkr=| �|�S td��zt |�}W n! tyh t|�}||kr`tdtd� td ��tdtd� Y nw || }zt |�}W n! ty� t|�}||kr�tdtd� td
��tdtd� Y nw |d kr�|dkr�|| �|� S td |||f ��|dkr�|| d | }n|dk r�|| d | }ntd
��|dkr�td��||| �|� S )z�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.
z.randrange() will raise TypeError in the futurer6 z!non-integer arg 1 for randrange()zwnon-integer arguments to randrange() have been deprecated since Python 3.10 and will be removed in a subsequent versionNz Missing a non-None stop argumentr zempty range for randrange()z non-integer stop for randrange()z non-integer step for randrange()r7 z(empty range for randrange() (%d, %d, %d)zzero step for randrange())�_indexra rL rR rS r` �_ONErh ) r: �start�stop�step�istart�istop�width�isteprq r< r< r= r' $ st
���
���
��� zRandom.randrangec C s | � ||d �S )zJReturn random integer in range [a, b], including both end points.
r7 )r' �r: rU �br< r< r= r% n s zRandom.randintc C s || � t|�� S )z2Choose a random element from a non-empty sequence.)rh rI )r: �seqr< r< r= r w s z
Random.choicec C s� |du r(| j }ttdt|���D ]}||d �}|| || ||< ||< qdS tdtd� t}ttdt|���D ]}||� |d �}|| || ||< ||< q9dS )z�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.
Nr7 zuThe *random* parameter to shuffle() has been deprecated
since Python 3.9 and will be removed in a subsequent version.r6 )rh �reversed�rangerI rR rS rt )r: r; r& � randbelow�i�jr
r< r< r= r+ | s �� �zRandom.shuffle)�countsc s� t �t�st �t�rtdtd� t���ntd��t��}|dur\tt |���t��|kr1t
d���� � }t |t �s>td��|dkrFt
d��| j
t|�|d �}t� � ��fd
d �|D �S | j}d| krl|ksqt
d �� t
d ��dg| }d
} |dkr�| dtt|d d�� 7 } || kr�t��}
t|�D ]} ||| �} |
| || < |
|| d |
| < q�|S t� }
|
j}t|�D ] } ||�} | |
v r�||�} | |
v s�|| � �| || < q�|S )am 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)
z\Sampling from a set deprecated
since Python 3.9 and will be removed in a subsequent version.r6 zAPopulation must be a sequence. For dicts or sets, use sorted(d).Nz2The number of counts does not match the populationzCounts must be integersr z)Total of counts must be greater than zero)rr c s g | ] }�� �|� �qS r< r< )r] �s�r �
cum_counts�
populationr< r= �
<listcomp>� s z!Random.sample.<locals>.<listcomp>z,Sample larger than population or is negative� � r0 r8 r7 )rC � _Sequence�_SetrR rS r_ ra rI �list� _accumulater` �poprL r( r� �_bisectrh �_ceil�_log�set�add)r: r� rr r� rq �total�
selectionsr� �result�setsize�poolr� r� �selected� selected_addr< r� r= r( � s\
5
�
�
� �z
Random.sample)� cum_weightsrr c s | j �t����du rD|du r$t��d7 �����fdd�td|�D �S ztt|���W n tyC t|t�s9� |}td|���d�w |durLtd��t���krVt d���d d ��dkrdt d ��t
��slt d
��t � �d �� �����fd d�td|�D �S )
z�Return a k sized list of population elements chosen with replacement.
If the relative weights or cumulative weights are not specified,
the selections are made with equal probability.
N� c s g | ] }�� �� � � �qS r< r< �r] r� )r
rq r� r& r< r= r� s z"Random.choices.<locals>.<listcomp>z4The number of choices must be a keyword argument: k=z2Cannot specify both weights and cumulative weightsz3The number of weights does not match the populationr@ z*Total of weights must be greater than zeroz Total of weights must be finiter7 c s$ g | ]}�� ��� � d �� �qS )r r< r� )r r� �hir� r& r� r< r= r� s �) r& rI rt �_repeatr� r� ra rC rL r` � _isfiniter� )r: r� �weightsr� rr r< )r r� r
r� rq r� r& r� r= r � sB
��� �zRandom.choicesc C s ||| | � � S )zHGet a random number in the range [a, b) or [a, b] depending on rounding.�r& r� r< r< r= r- " r\ zRandom.uniformr� r3 c C sz | � � }z|du r dn|| || }W n ty | Y S w ||kr1d| }d| }||}}||| t|| � S )z�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
N� �?r3 )r& �ZeroDivisionError�_sqrt)r: �low�high�mode�urW r< r< r= r, & s �
zRandom.triangularc C sR | j } |� }d|� }t|d | }|| d }|t|� kr"nq||| S )z\Normal distribution.
mu is the mean, and sigma is the standard deviation.
Tr3 r� r2 )r& �
NV_MAGICCONSTr� )r: �mu�sigmar& �u1�u2�z�zzr< r< r= r" : s
� zRandom.normalvariatec C s` | j }| j}d| _|du r*|� t }tdtd|� � �}t|�| }t|�| | _||| S )z�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.
Ng �r3 )r& r9 �TWOPIr� r� �_cos�_sin)r: r� r� r& r� �x2pi�g2radr< r< r= r O s
z Random.gaussc C s t | �||��S )z�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.
)�_expr" )r: r� r� r< r< r= r! u s zRandom.lognormvariatec C s t d| �� � | S )a^ 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.
r3 )r� r& )r: �lambdr< r< r= r s zRandom.expovariatec C s� | j }|dkr t|� S d| }|td|| � } |� }tt| �}||| }|� } | d|| k s?| d| t|� kr@nqd| }
|
| d|
| } |� } | dkr`|t| � t }
|
S |t| � t }
|
S )aF 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.
g���ư>r� r3 )r& r� r� r� �_pir� �_acos)r: r� �kappar& r� rs r� r� �dr� �q�f�u3�thetar< r< r= r. � s*
$� �zRandom.vonmisesvariatec C sz |dks|dkr t d��| j}|dkrktd| d �}|t }|| } |� }d| k r1dk s3n q#d|� }t|d| �| } |t| � }
|| | } ||| |
} | t d| dksf| t| �krj|
| S q$|dkrytd|� � | S |� }
t| t }||
}|dkr�|d| }
n t|| | � }
|� }|dkr�||
|d kr� |
| S n |t|
�kr� |
| S qz) aZ 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
r� z*gammavariate: alpha and beta must be > 0.0r3 r1 TgH�����z>g�P���?r4 )r` r& r� �LOG4r� r� �
SG_MAGICCONST�_e)r: �alpha�betar& �ainv�bbb�cccr� r� �vr; r� rs r� r� �pr< r< r= r � sL
� ��zRandom.gammavariatec C s( | � |d�}|r||| � |d� S dS )z�Beta distribution.
Conditions on the parameters are alpha > 0 and beta > 0.
Returned values range between 0 and 1.
r3 r� )r )r: r� r� �yr< r< r= r � s zRandom.betavariatec C s d| � � }|d| S )z3Pareto distribution. alpha is the shape parameter.r3 g �r� )r: r� r� r< r< r= r# s zRandom.paretovariatec C s" d| � � }|t|� d| S )zfWeibull distribution.
alpha is the scale parameter and beta is the shape parameter.
r3 )r& r� )r: r� r� r� r< r< r= r/ s zRandom.weibullvariaterd )Nr6 )r� r3 N)%�__name__�
__module__� __qualname__�__doc__rZ r> r) r r* re rf rg ro rk �BPFrl rh r$ r~ r' r% r r+ r( r r- r, r" r r! r r. r r r# r/ �
__classcell__r< r< rX r= r g s@
* !
J
d(
&
*Ar c @ s@ e Zd ZdZdd� Zdd� Zdd� Zdd � Zd
d � Ze Z Z
d S )
r z�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).
c C s t �td�d�d? t S )z3Get the next random number in the range [0.0, 1.0).r? rB r8 )rL rM �_urandom� RECIP_BPFr[ r< r< r= r& 6 s zSystemRandom.randomc C s< |dk rt d��|d d }t�t|�d�}||d | ? S )z:getrandbits(k) -> x. Generates an int with k random bits.r z#number of bits must be non-negativer? ry rB )r` rL rM r� )r: rr �numbytesr; r< r< r= r : s
zSystemRandom.getrandbitsc C s t |�S )rx )r� r| r< r< r= r$ B s zSystemRandom.randbytesc O s dS )z<Stub method. Not used for a system random number generator.Nr< �r: �args�kwdsr< r< r= r) H s zSystemRandom.seedc O s t d��)zAMethod should not be called for a system random number generator.z*System entropy source does not have state.)�NotImplementedErrorr� r< r< r= �_notimplementedL s z SystemRandom._notimplementedN) r� r� r� r� r& r r$ r) r� r r* r< r< r< r= r - s r c
s� ddl m}m} ddlm} |� }� �fdd�td | �D �}|� }||�} ||| �}
t|�} t|�} t|| d�d| � d�j � �� td | |
| | f � d S )
Nr )�stdev�fmean)� perf_counterc s g | ]}�� � �qS r< r< r� �r� �funcr< r= r� { s z#_test_generator.<locals>.<listcomp>z.3fz sec, z times z"avg %g, stddev %g, min %g, max %g
)
�
statisticsr� r� �timer� r� �min�max�printr� )
rq r� r� r� �meanr� �t0�data�t1�xbarr� r� r� r< r� r= �_test_generatorv s
r� �� c C s� t | td� t | td� t | td� t | td� t | td� t | td� t | td� t | td� t | td� t | td� t | td � t | td
� t | td � t | td� t | td � t | td
� d S )Nr< )r� r3 )g{�G�z�?r3 )皙�����?r3 )r� r1 )r� r3 )g�������?r3 )r3 r3 )r1 r3 )g 4@r3 )g i@r3 )� @r� )r� r3 gUUUUUU�?) r� r& r" r! r. r r r r, )�Nr< r< r= �_test� s r� �fork)�after_in_child�__main__)r� )[r� �warningsr rR �mathr r� r r� r r� r r� r r� r r� r r� r
r� r r� r r� r
rt r r� �osr r� �_collections_abcr r� r r� �operatorr r} � itertoolsr r� r r� r r� �_os�_randomrN r � ImportError�hashlib�__all__r� r� r� r� r� r~ r r �_instr) r& r- r, r% r r' r( r+ r r" r! r r. r r r r# r/ r r* r r$ r� r� �hasattr�register_at_forkr� r< r< r< r= �<module> s| / �
K,
�