o
    ,h#                     @   s   d dl Z 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mZ d dlmZ d dlmZmZmZ d	gZG d
d	 d	e	ZdS )    N)OptionalUnion)Tensor)constraints)ExponentialFamily)broadcast_allclamp_probslazy_propertylogits_to_probsprobs_to_logits) binary_cross_entropy_with_logits)_Number_sizeNumberContinuousBernoullic                       s  e Zd ZdZejejdZejZdZ	dZ
				d5deeeef  deeeef  d	eeef d
ee ddf
 fddZd6 fdd	Zdd Zdd Z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  fd$d%Z"e  fd&e#defd'd(Z$d)d* Z%d+d, Z&d-d. Z'd/d0 Z(edee fd1d2Z)d3d4 Z*  Z+S )7r   a  
    Creates a continuous Bernoulli distribution parameterized by :attr:`probs`
    or :attr:`logits` (but not both).

    The distribution is supported in [0, 1] and parameterized by 'probs' (in
    (0,1)) or 'logits' (real-valued). Note that, unlike the Bernoulli, 'probs'
    does not correspond to a probability and 'logits' does not correspond to
    log-odds, but the same names are used due to the similarity with the
    Bernoulli. See [1] for more details.

    Example::

        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
        >>> m = ContinuousBernoulli(torch.tensor([0.3]))
        >>> m.sample()
        tensor([ 0.2538])

    Args:
        probs (Number, Tensor): (0,1) valued parameters
        logits (Number, Tensor): real valued parameters whose sigmoid matches 'probs'

    [1] The continuous Bernoulli: fixing a pervasive error in variational
    autoencoders, Loaiza-Ganem G and Cunningham JP, NeurIPS 2019.
    https://arxiv.org/abs/1907.06845
    )probslogitsr   TNgV-?gx&1?r   r   limsvalidate_argsreturnc                    s   |d u |d u krt d|d ur5t|t}t|\| _|d ur.| jd | j s.t dt| j| _n|d us;J t|t}t|\| _	|d urM| jn| j	| _
|rXt }n| j
 }|| _t j||d d S )Nz;Either `probs` or `logits` must be specified, but not both.r   z&The parameter probs has invalid valuesr   )
ValueError
isinstancer   r   r   arg_constraintscheckallr   r   _paramtorchSizesize_limssuper__init__)selfr   r   r   r   	is_scalarbatch_shape	__class__ c/var/www/html/scripts/venv/lib/python3.10/site-packages/torch/distributions/continuous_bernoulli.pyr#   7   s(   



zContinuousBernoulli.__init__c                    s~   |  t|}| j|_t|}d| jv r| j||_|j|_d| jv r/| j	||_	|j	|_t
t|j|dd | j|_|S )Nr   r   Fr   )_get_checked_instancer   r!   r   r   __dict__r   expandr   r   r"   r#   _validate_args)r$   r&   	_instancenewr'   r)   r*   r-   W   s   


zContinuousBernoulli.expandc                 O   s   | j j|i |S N)r   r0   )r$   argskwargsr)   r)   r*   _newe   s   zContinuousBernoulli._newc                 C   s,   t t | j| jd t | j| jd S )Nr      )r   maxler   r!   gtr$   r)   r)   r*   _outside_unstable_regionh   s   $z,ContinuousBernoulli._outside_unstable_regionc                 C   s&   t |  | j| jd t | j S )Nr   )r   wherer:   r   r!   	ones_liker9   r)   r)   r*   
_cut_probsm   s
   zContinuousBernoulli._cut_probsc              	   C   s   |   }tt|d|t|}tt|d|t|}ttt	| t| tt|dt	d| td| d  }t
| jd d}tddd|  |  }t|  ||S )zLcomputes the log normalizing constant as a function of the 'probs' parameter      ?g              @      ?   gUUUUUU?g'}'}@)r=   r   r;   r7   
zeros_likeger<   logabslog1ppowr   mathr:   )r$   	cut_probscut_probs_below_halfcut_probs_above_halflog_normxtaylorr)   r)   r*   _cont_bern_log_normt   s&   
z'ContinuousBernoulli._cont_bern_log_normc                 C   sj   |   }|d| d  dt| t|   }| jd }dddt|d  |  }t|  ||S )Nr?   r@   r>   gUUUUUU?gll?rA   )r=   r   rF   rD   r   rG   r;   r:   )r$   rI   musrM   rN   r)   r)   r*   mean   s   
zContinuousBernoulli.meanc                 C   s   t | jS r1   )r   sqrtvariancer9   r)   r)   r*   stddev   s   zContinuousBernoulli.stddevc                 C   s   |   }||d  tdd|  d dtt| t| d  }t| jd d}ddd|  |  }t|  ||S )Nr@   r?   rA   r>   gUUUUUU?g?ggjV?)r=   r   rG   rF   rD   r   r;   r:   )r$   rI   varsrM   rN   r)   r)   r*   rS      s    zContinuousBernoulli.variancec                 C   s   t | jddS NT)	is_binary)r   r   r9   r)   r)   r*   r      s   zContinuousBernoulli.logitsc                 C   s   t t| jddS rV   )r   r
   r   r9   r)   r)   r*   r      s   zContinuousBernoulli.probsc                 C   s
   | j  S r1   )r   r    r9   r)   r)   r*   param_shape   s   
zContinuousBernoulli.param_shapec                 C   sX   |  |}tj|| jj| jjd}t  | |W  d    S 1 s%w   Y  d S N)dtypedevice)_extended_shaper   randr   rZ   r[   no_gradicdfr$   sample_shapeshapeur)   r)   r*   sample   s
   

$zContinuousBernoulli.samplera   c                 C   s,   |  |}tj|| jj| jjd}| |S rY   )r\   r   r]   r   rZ   r[   r_   r`   r)   r)   r*   rsample   s   

zContinuousBernoulli.rsamplec                 C   s8   | j r| | t| j|\}}t||dd |   S )Nnone)	reduction)r.   _validate_sampler   r   r   rO   )r$   valuer   r)   r)   r*   log_prob   s   
zContinuousBernoulli.log_probc              
   C   s   | j r| | |  }t||td| d|  | d d| d  }t|  ||}tt|dt|tt	|dt
||S )Nr@   r?   g        )r.   rh   r=   r   rG   r;   r:   r7   rB   rC   r<   )r$   ri   rI   cdfsunbounded_cdfsr)   r)   r*   cdf   s    


zContinuousBernoulli.cdfc              	   C   sT   |   }t|  t| |d| d   t|  t|t|   |S )Nr?   r@   )r=   r   r;   r:   rF   rD   )r$   ri   rI   r)   r)   r*   r_      s   
zContinuousBernoulli.icdfc                 C   s4   t | j }t | j}| j||  |   | S r1   )r   rF   r   rD   rQ   rO   )r$   
log_probs0
log_probs1r)   r)   r*   entropy   s   zContinuousBernoulli.entropyc                 C   s   | j fS r1   )r   r9   r)   r)   r*   _natural_params   s   z#ContinuousBernoulli._natural_paramsc                 C   s   t t || jd d t || jd d }t ||| jd d t | }t t t j	
|t t | }d| t |dd  t |dd  }t |||S )zLcomputes the log normalizing constant as a function of the natural parameterr   r>   r5   rA   g      8@   g     @)r   r6   r7   r!   r8   r;   r<   rD   rE   specialexpm1rG   )r$   rM   out_unst_regcut_nat_paramsrL   rN   r)   r)   r*   _log_normalizer   s   ((z#ContinuousBernoulli._log_normalizer)NNr   Nr1   ),__name__
__module____qualname____doc__r   unit_intervalrealr   support_mean_carrier_measurehas_rsampler   r   r   r   tuplefloatboolr#   r-   r4   r:   r=   rO   propertyrQ   rT   rS   r	   r   r   r   r   rX   rd   r   re   rj   rm   r_   rp   rq   rw   __classcell__r)   r)   r'   r*   r      s^    
 				)rH   typingr   r   r   r   torch.distributionsr   torch.distributions.exp_familyr   torch.distributions.utilsr   r   r	   r
   r   torch.nn.functionalr   torch.typesr   r   r   __all__r   r)   r)   r)   r*   <module>   s   