U
    9%e?                     @   sd   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
mZmZmZ dgZG dd deZdS )    N)constraints)Distribution)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d Zedd Zedd Zedd Zedd Zedd Ze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 k|d kkrt d|d k	rDt||\| _| _| j| j| _n"t||\| _| _| j| j| _|d k	rt| 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__ d/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/torch/distributions/negative_binomial.pyr   $   s$    
zNegativeBinomial.__init__c                    s   |  t|}t|}| j||_d| jkrD| j||_|j|_d| jkrd| 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||S N)r   r    )r   argskwargsr   r   r   _newH   s    zNegativeBinomial._newc                 C   s   | j t| j S r!   )r   r   expr   r   r   r   r   meanK   s    zNegativeBinomial.meanc                 C   s    | j d | j   jddS )N   r	   )min)r   r   r%   floorclampr&   r   r   r   modeO   s    zNegativeBinomial.modec                 C   s   | j t| j  S r!   )r'   r   Zsigmoidr   r&   r   r   r   varianceS   s    zNegativeBinomial.variancec                 C   s   t | jddS NT)Z	is_binary)r   r   r&   r   r   r   r   W   s    zNegativeBinomial.logitsc                 C   s   t | jddS r.   )r   r   r&   r   r   r   r   [   s    zNegativeBinomial.probsc                 C   s
   | j  S r!   )r   r   r&   r   r   r   param_shape_   s    zNegativeBinomial.param_shapec                 C   s   t jj| jt | j ddS )NF)Zconcentrationrater   )r   distributionsGammar   r%   r   r&   r   r   r   _gammac   s
    zNegativeBinomial._gammac              
   C   s8   t  & | jj|d}t |W  5 Q R  S Q R X d S )N)sample_shape)r   Zno_gradr3   sampleZpoisson)r   r4   r0   r   r   r   r5   l   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_probq   s$    

 zNegativeBinomial.log_prob)NNN)N)__name__
__module____qualname____doc__r   Zgreater_than_eqZhalf_open_intervalrealZarg_constraintsZnonnegative_integerZsupportr   r   r$   propertyr'   r,   r-   r   r   r   r/   r3   r   r   r5   r9   __classcell__r   r   r   r   r      s2   







)r   Ztorch.nn.functionalnnZ
functionalr6   Ztorch.distributionsr   Z torch.distributions.distributionr   Ztorch.distributions.utilsr   r   r   r   __all__r   r   r   r   r   <module>   s   