
    קg[                     r    d dl Z d dlmc 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e          ZdS )    N)constraints)Distribution)broadcast_alllazy_propertylogits_to_probsprobs_to_logitsNegativeBinomialc                   X    e Zd ZdZ ej        d           ej        dd          ej        dZej	        Z
d fd	Zd fd	Zd	 Zed
             Zed             Zed             Zed             Zed             Zed             Zed             Z ej                    fdZd Z x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                    |d u |d u k    rt          d          |Bt          ||          \  | _        | _        | j                            | j                  | _        nAt          ||          \  | _        | _        | j                            | j                  | _        || j        n| j        | _        | j                                        }t                      	                    ||           d S )Nz;Either `probs` or `logits` must be specified, but not both.validate_args)

ValueErrorr   r   r   type_asr   _paramsizesuper__init__)selfr   r   r   r   batch_shape	__class__s         a/var/www/html/ai-engine/env/lib/python3.11/site-packages/torch/distributions/negative_binomial.pyr   zNegativeBinomial.__init__&   s    TMv~..M    k511 
#/77
CCD
 k622 #/77DDD$)$5djj4;k&&((MBBBBB    c                    |                      t          |          }t          j        |          }| j                            |          |_        d| j        v r+| j                            |          |_        |j        |_        d| j        v r+| j	                            |          |_	        |j	        |_        t          t          |                              |d           | j        |_        |S )Nr   r   Fr   )_get_checked_instancer	   torchSizer   expand__dict__r   r   r   r   r   _validate_args)r   r   	_instancenewr   s       r   r"   zNegativeBinomial.expand<   s    (()99EEj--*11+>>dm##
))+66CICJt}$$++K88CJCJ$$--k-OOO!0
r   c                 &     | j         j        |i |S N)r   r&   )r   argskwargss      r   _newzNegativeBinomial._newJ   s    t{////r   c                 D    | j         t          j        | j                  z  S r(   )r   r    expr   r   s    r   meanzNegativeBinomial.meanM   s    %)DK"8"888r   c                     | j         dz
  | j                                        z                                                      d          S )N   r   )min)r   r   r-   floorclampr.   s    r   modezNegativeBinomial.modeQ   s>    !A%):)::AACCIIcIRRRr   c                 F    | j         t          j        | j                   z  S r(   )r/   r    sigmoidr   r.   s    r   variancezNegativeBinomial.varianceU   s    y5=$+6666r   c                 .    t          | j        d          S NT)	is_binary)r   r   r.   s    r   r   zNegativeBinomial.logitsY   s    tzT::::r   c                 .    t          | j        d          S r:   )r   r   r.   s    r   r   zNegativeBinomial.probs]   s    t{d;;;;r   c                 4    | j                                         S r(   )r   r   r.   s    r   param_shapezNegativeBinomial.param_shapea   s    {!!!r   c                     t           j                            | j        t          j        | j                   d          S )NF)concentrationrater   )r    distributionsGammar   r-   r   r.   s    r   _gammazNegativeBinomial._gammae   s>     "((*DK<(( ) 
 
 	
r   c                     t          j                    5  | j                            |          }t          j        |          cd d d            S # 1 swxY w Y   d S )N)sample_shape)r    no_gradrD   samplepoisson)r   rF   rA   s      r   rH   zNegativeBinomial.samplen   s    ]__ 	' 	';%%<%@@D=&&	' 	' 	' 	' 	' 	' 	' 	' 	' 	' 	' 	' 	' 	' 	' 	' 	' 	's   /AAAc                    | j         r|                     |           | j        t          j        | j                   z  |t          j        | j                  z  z   }t          j        | j        |z              t          j        d|z             z   t          j        | j                  z   }|                    | j        |z   dk    d          }||z
  S )Nr   r   )	r$   _validate_sampler   F
logsigmoidr   r    lgammamasked_fill)r   valuelog_unnormalized_problog_normalizations       r   log_probzNegativeBinomial.log_probs   s     	)!!%((( $ 01<[L4
 4
 !
AL---!.
 \$*U2333l3;''(l4+,,- 	 .99u$+S
 
 %'888r   )NNNr(   )__name__
__module____qualname____doc__r   greater_than_eqhalf_open_intervalrealarg_constraintsnonnegative_integersupportr   r"   r+   propertyr/   r5   r8   r   r   r   r>   rD   r    r!   rH   rS   __classcell__)r   s   @r   r	   r	      s         3{2155//S99" O
 -GC C C C C C,     0 0 0 9 9 X9 S S XS 7 7 X7 ; ; ]; < < ]< " " X" 
 
 ]
 #-%*,, ' ' ' '
9 9 9 9 9 9 9r   )r    torch.nn.functionalnn
functionalrL   torch.distributionsr    torch.distributions.distributionr   torch.distributions.utilsr   r   r   r   __all__r	    r   r   <module>rh      s              + + + + + + 9 9 9 9 9 9            
v9 v9 v9 v9 v9| v9 v9 v9 v9 v9r   