o
    ,h                     @   sv   d dl mZmZ d dlZd dlmZ d dlmZ d dlmZ d dl	m
Z
mZmZmZ dgZdd	 ZG d
d deZdS )    )OptionalUnionN)Tensor)constraints)Distribution)broadcast_alllazy_propertylogits_to_probsprobs_to_logitsBinomialc                 C   s    | j dd|  | j dd d S )Nr   )minmax   )clamp)x r   W/var/www/html/scripts/venv/lib/python3.10/site-packages/torch/distributions/binomial.py_clamp_by_zero   s    r   c                       s.  e Zd ZdZejejejdZdZ					d)de
eef dee dee d	ee d
df
 fddZd* fdd	Zdd Zejddddd Zed
efddZed
efddZed
efddZed
efddZed
efddZed
ejfdd Ze fd!d"Zd#d$ Zd%d& Z d+d'd(Z!  Z"S ),r   a  
    Creates a Binomial distribution parameterized by :attr:`total_count` and
    either :attr:`probs` or :attr:`logits` (but not both). :attr:`total_count` must be
    broadcastable with :attr:`probs`/:attr:`logits`.

    Example::

        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
        >>> m = Binomial(100, torch.tensor([0 , .2, .8, 1]))
        >>> x = m.sample()
        tensor([   0.,   22.,   71.,  100.])

        >>> m = Binomial(torch.tensor([[5.], [10.]]), torch.tensor([0.5, 0.8]))
        >>> x = m.sample()
        tensor([[ 4.,  5.],
                [ 7.,  6.]])

    Args:
        total_count (int or Tensor): number of Bernoulli trials
        probs (Tensor): Event probabilities
        logits (Tensor): Event log-odds
    )total_countprobslogitsT   Nr   r   r   validate_argsreturnc                    s   |d u |d u krt d|d ur"t||\| _| _| j| j| _n|d us(J t||\| _| _| j| j| _|d ur@| jn| j| _| j }t j	||d d S )Nz;Either `probs` or `logits` must be specified, but not both.r   )

ValueErrorr   r   r   type_asr   _paramsizesuper__init__)selfr   r   r   r   batch_shape	__class__r   r   r!   7   s&   
zBinomial.__init__c                    s   |  t|}t|}| j||_d| jv r"| j||_|j|_d| jv r2| j	||_	|j	|_t
t|j|dd | j|_|S )Nr   r   Fr   )_get_checked_instancer   torchSizer   expand__dict__r   r   r   r    r!   _validate_args)r"   r#   	_instancenewr$   r   r   r)   T   s   


zBinomial.expandc                 O   s   | j j|i |S N)r   r-   )r"   argskwargsr   r   r   _newb   s   zBinomial._newr   )is_discrete	event_dimc                 C   s   t d| jS )Nr   )r   integer_intervalr   r"   r   r   r   supporte      zBinomial.supportc                 C   s   | j | j S r.   r   r   r5   r   r   r   meani   s   zBinomial.meanc                 C   s   | j d | j  j| j dS )Nr   r   )r   r   floorr   r5   r   r   r   modem   s   zBinomial.modec                 C   s   | j | j d| j  S Nr   r8   r5   r   r   r   varianceq   s   zBinomial.variancec                 C      t | jddS NT)	is_binary)r
   r   r5   r   r   r   r   u   r7   zBinomial.logitsc                 C   r>   r?   )r	   r   r5   r   r   r   r   y   r7   zBinomial.probsc                 C   s
   | j  S r.   )r   r   r5   r   r   r   param_shape}   s   
zBinomial.param_shapec                 C   sR   |  |}t  t| j|| j|W  d    S 1 s"w   Y  d S r.   )_extended_shaper'   no_gradbinomialr   r)   r   )r"   sample_shapeshaper   r   r   sample   s   

$zBinomial.samplec              	   C   s   | j r| | t| jd }t|d }t| j| d }| jt| j | jttt	| j   | }|| j | | | S r<   )
r+   _validate_sampler'   lgammar   r   r   log1pexpabs)r"   valuelog_factorial_nlog_factorial_klog_factorial_nmknormalize_termr   r   r   log_prob   s   
zBinomial.log_probc                 C   sJ   t | j }| j |kstd| | d}t|| 	d S )Nz5Inhomogeneous total count not supported by `entropy`.Fr   )
intr   r   r   NotImplementedErrorrR   enumerate_supportr'   rK   sum)r"   r   rR   r   r   r   entropy   s   zBinomial.entropyc                 C   sp   t | j }| j |kstdtjd| | jj| jj	d}|
ddt| j  }|r6|d| j }|S )Nz?Inhomogeneous total count not supported by `enumerate_support`.r   )dtypedevice))r   )rS   r   r   r   rT   r'   aranger   rX   rY   viewlen_batch_shaper)   )r"   r)   r   valuesr   r   r   rU      s   zBinomial.enumerate_support)r   NNNr.   )T)#__name__
__module____qualname____doc__r   nonnegative_integerunit_intervalrealarg_constraintshas_enumerate_supportr   r   rS   r   boolr!   r)   r1   dependent_propertyr6   propertyr9   r;   r=   r   r   r   r'   r(   rA   rG   rR   rW   rU   __classcell__r   r   r$   r   r      sT    


)typingr   r   r'   r   torch.distributionsr    torch.distributions.distributionr   torch.distributions.utilsr   r   r	   r
   __all__r   r   r   r   r   r   <module>   s   