o
    hX                     @   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   R/var/www/html/ai/venv/lib/python3.10/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 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   type_asr   _paramsizesuper__init__)selfr   r   r   r   batch_shape	__class__r   r   r   1   s$   
zBinomial.__init__c                    s   |  t|}t|}| j||_d| jv r"| j||_|j|_d| jv r2| j	||_	|j	|_t
t|j|dd | j|_|S )Nr   r   Fr   )_get_checked_instancer   torchSizer   expand__dict__r   r   r   r   r   _validate_args)r   r   	_instancenewr    r   r   r%   G   s   


zBinomial.expandc                 O   s   | j j|i |S N)r   r)   )r   argskwargsr   r   r   _newU   s   zBinomial._newr   )is_discrete	event_dimc                 C   s   t d| jS )Nr   )r   integer_intervalr   r   r   r   r   supportX      zBinomial.supportc                 C   s   | j | j S r*   r   r   r1   r   r   r   mean\   s   zBinomial.meanc                 C   s   | j d | j  j| j dS )Nr   r
   )r   r   floorr   r1   r   r   r   mode`   s   zBinomial.modec                 C   s   | j | j d| j  S Nr   r4   r1   r   r   r   varianced   s   zBinomial.variancec                 C      t | jddS NT)	is_binary)r   r   r1   r   r   r   r   h   r3   zBinomial.logitsc                 C   r:   r;   )r   r   r1   r   r   r   r   l   r3   zBinomial.probsc                 C   s
   | j  S r*   )r   r   r1   r   r   r   param_shapep   s   
zBinomial.param_shapec                 C   sR   |  |}t  t| j|| j|W  d    S 1 s"w   Y  d S r*   )_extended_shaper#   no_gradbinomialr   r%   r   )r   sample_shapeshaper   r   r   samplet   s   

$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 r8   )
r'   _validate_sampler#   lgammar   r   r   log1pexpabs)r   valuelog_factorial_nlog_factorial_klog_factorial_nmk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	   NotImplementedErrorrN   enumerate_supportr#   rG   sum)r   r   rN   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  }|r6|d| j }|S )Nz?Inhomogeneous total count not supported by `enumerate_support`.r   )dtypedevice))r   )rO   r   r   r	   rP   r#   aranger   rT   rU   viewlen_batch_shaper%   )r   r%   r   valuesr   r   r   rQ      s   zBinomial.enumerate_support)r   NNNr*   )T)__name__
__module____qualname____doc__r   nonnegative_integerunit_intervalrealarg_constraintshas_enumerate_supportr   r%   r-   dependent_propertyr2   propertyr5   r7   r9   r   r   r   r=   r#   r$   rC   rN   rS   rQ   __classcell__r   r   r    r   r      s8    







)r#   torch.distributionsr    torch.distributions.distributionr   torch.distributions.utilsr   r   r   r   __all__r   r   r   r   r   r   <module>   s    