o
    Zh                     @   s|   d dl Z d dlm  mZ d dl m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G dd de	ZdS )	    N)Tensor)constraints)Distribution)Gamma)broadcast_alllazy_propertylogits_to_probsprobs_to_logitsNegativeBinomialc                       s   e Zd ZdZededdejdZej	Z
d  fdd	Zd! fd	d
	Z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defddZe fddZdd Z  ZS )"r
   ao  
    Creates a Negative Binomial distribution, i.e. distribution
    of the number of successful independent and identical Bernoulli trials
    before :attr:`total_count` failures are achieved. The probability
    of success of each Bernoulli trial is :attr:`probs`.

    Args:
        total_count (float or Tensor): non-negative number of negative Bernoulli
            trials to stop, although the distribution is still valid for real
            valued count
        probs (Tensor): Event probabilities of success in the half open interval [0, 1)
        logits (Tensor): Event log-odds for probabilities of success
    r                 ?)total_countprobslogitsNc                    s   |d u |d u krt d|d ur"t||\| _| _| j| j| _n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.validate_args)

ValueErrorr   r   r   Ztype_asr   _paramsizesuper__init__)selfr   r   r   r   batch_shape	__class__ T/var/www/auris/lib/python3.10/site-packages/torch/distributions/negative_binomial.pyr   )   s$   
zNegativeBinomial.__init__c                    s   |  t|}t|}| j||_d| jv r"| j||_|j|_d| jv r2| j	||_	|j	|_t
t|j|dd | j|_|S )Nr   r   Fr   )Z_get_checked_instancer
   torchSizer   expand__dict__r   r   r   r   r   _validate_args)r   r   Z	_instancenewr   r   r   r   ?   s   


zNegativeBinomial.expandc                 O   s   | j j|i |S N)r   r"   )r   argskwargsr   r   r   _newM   s   zNegativeBinomial._newreturnc                 C   s   | j t| j S r#   )r   r   expr   r   r   r   r   meanP   s   zNegativeBinomial.meanc                 C   s    | j d | j   jddS )N   r   )min)r   r   r(   floorclampr)   r   r   r   modeT   s    zNegativeBinomial.modec                 C   s   | j t| j  S r#   )r*   r   Zsigmoidr   r)   r   r   r   varianceX   s   zNegativeBinomial.variancec                 C      t | jddS NT)Z	is_binary)r	   r   r)   r   r   r   r   \      zNegativeBinomial.logitsc                 C   r1   r2   )r   r   r)   r   r   r   r   `   r3   zNegativeBinomial.probsc                 C   s
   | j  S r#   )r   r   r)   r   r   r   param_shaped   s   
zNegativeBinomial.param_shapec                 C   s   t | jt| j ddS )NF)Zconcentrationrater   )r   r   r   r(   r   r)   r   r   r   _gammah   s
   zNegativeBinomial._gammac                 C   sD   t   | jj|d}t |W  d    S 1 sw   Y  d S )N)sample_shape)r   Zno_gradr6   sampleZpoisson)r   r7   r5   r   r   r   r8   q   s   
$zNegativeBinomial.samplec                 C   s~   | j r| | | jt| j  |t| j  }t| j|  td|  t| j }|| j| dkd}|| S )Nr   r   )	r!   Z_validate_sampler   FZ
logsigmoidr   r   lgammaZmasked_fill)r   valueZlog_unnormalized_probZlog_normalizationr   r   r   log_probv   s"   

zNegativeBinomial.log_prob)NNNr#   )__name__
__module____qualname____doc__r   Zgreater_than_eqZhalf_open_intervalrealZarg_constraintsZnonnegative_integerZsupportr   r   r&   propertyr   r*   r/   r0   r   r   r   r   r   r4   r   r6   r8   r<   __classcell__r   r   r   r   r
      s4    
)r   Ztorch.nn.functionalnnZ
functionalr9   r   Ztorch.distributionsr   Z torch.distributions.distributionr   Ztorch.distributions.gammar   Ztorch.distributions.utilsr   r   r   r	   __all__r
   r   r   r   r   <module>   s   