U
    9%eX                     @   sZ   d dl 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 )	    N)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   [/var/www/html/Darija-Ai-API/env/lib/python3.8/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$ fdd	Z
d% fdd		Zd
d Zejddd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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-deterinistic")
        >>> 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   Nc                    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__r   r   r   1   s$    
zBinomial.__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#   G   s    


zBinomial.expandc                 O   s   | j j||S N)r   r&   )r   argskwargsr   r   r   _newU   s    zBinomial._newr   )Zis_discreteZ	event_dimc                 C   s   t d| jS )Nr   )r   Zinteger_intervalr   r   r   r   r   supportX   s    zBinomial.supportc                 C   s   | j | j S r'   r   r   r+   r   r   r   mean\   s    zBinomial.meanc                 C   s   | j d | j  j| j dS )Nr   r
   )r   r   floorr   r+   r   r   r   mode`   s    zBinomial.modec                 C   s   | j | j d| j  S Nr   r-   r+   r   r   r   varianced   s    zBinomial.variancec                 C   s   t | jddS NT)Z	is_binary)r   r   r+   r   r   r   r   h   s    zBinomial.logitsc                 C   s   t | jddS r3   )r   r   r+   r   r   r   r   l   s    zBinomial.probsc                 C   s
   | j  S r'   )r   r   r+   r   r   r   param_shapep   s    zBinomial.param_shapec              
   C   sF   |  |}t * t| j|| j|W  5 Q R  S Q R X d S r'   )Z_extended_shaper!   Zno_gradZbinomialr   r#   r   )r   Zsample_shapeshaper   r   r   samplet   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 r1   )
r%   Z_validate_sampler!   lgammar   r   r   log1pexpabs)r   valueZlog_factorial_nZlog_factorial_kZlog_factorial_nmkZ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	   NotImplementedErrorr<   enumerate_supportr!   r9   sum)r   r   r<   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  }|rl|d| j }|S )Nz?Inhomogeneous total count not supported by `enumerate_support`.r   )dtypedevice))r   )r=   r   r   r	   r>   r!   Zaranger   rB   rC   viewlenZ_batch_shaper#   )r   r#   r   valuesr   r   r   r?      s      zBinomial.enumerate_support)r   NNN)N)T)__name__
__module____qualname____doc__r   Znonnegative_integerZunit_intervalrealZarg_constraintsZhas_enumerate_supportr   r#   r*   Zdependent_propertyr,   propertyr.   r0   r2   r   r   r   r4   r!   r"   r6   r<   rA   r?   __classcell__r   r   r   r   r      s6   







)r!   Ztorch.distributionsr   Z torch.distributions.distributionr   Ztorch.distributions.utilsr   r   r   r   __all__r   r   r   r   r   r   <module>   s   