o
    h                     @   sb   d dl Z d dl mZ d dlmZmZ d dlmZ d dlmZ d dl	m
Z
 dgZG dd deZdS )	    N)inf)Categoricalconstraints)Binomial)Distribution)broadcast_allMultinomialc                       s   e Zd ZU dZejejdZee	d< e
dd Ze
d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 fddZdd Zdd Z  ZS )"r   a`  
    Creates a Multinomial distribution parameterized by :attr:`total_count` and
    either :attr:`probs` or :attr:`logits` (but not both). The innermost dimension of
    :attr:`probs` indexes over categories. All other dimensions index over batches.

    Note that :attr:`total_count` need not be specified if only :meth:`log_prob` is
    called (see example below)

    .. note:: The `probs` argument must be non-negative, finite and have a non-zero sum,
              and it will be normalized to sum to 1 along the last dimension. :attr:`probs`
              will return this normalized value.
              The `logits` argument will be interpreted as unnormalized log probabilities
              and can therefore be any real number. It will likewise be normalized so that
              the resulting probabilities sum to 1 along the last dimension. :attr:`logits`
              will return this normalized value.

    -   :meth:`sample` requires a single shared `total_count` for all
        parameters and samples.
    -   :meth:`log_prob` allows different `total_count` for each parameter and
        sample.

    Example::

        >>> # xdoctest: +SKIP("FIXME: found invalid values")
        >>> m = Multinomial(100, torch.tensor([ 1., 1., 1., 1.]))
        >>> x = m.sample()  # equal probability of 0, 1, 2, 3
        tensor([ 21.,  24.,  30.,  25.])

        >>> Multinomial(probs=torch.tensor([1., 1., 1., 1.])).log_prob(x)
        tensor([-4.1338])

    Args:
        total_count (int): number of trials
        probs (Tensor): event probabilities
        logits (Tensor): event log probabilities (unnormalized)
    probslogitstotal_countc                 C   s   | j | j S N)r
   r   self r   U/var/www/html/ai/venv/lib/python3.10/site-packages/torch/distributions/multinomial.pymean3      zMultinomial.meanc                 C   s   | j | j d| j  S )N   r   r
   r   r   r   r   variance7   s   zMultinomial.variancer   Nc                    sd   t |ts	td|| _t||d| _t|| jd| _| jj	}| jj
dd  }t j|||d d S )Nz*inhomogeneous total_count is not supportedr	   r   validate_args)
isinstanceintNotImplementedErrorr   r   _categoricalr   r
   	_binomialbatch_shapeparam_shapesuper__init__)r   r   r
   r   r   r   event_shape	__class__r   r   r"   ;   s   
zMultinomial.__init__c                    sP   |  t|}t|}| j|_| j||_tt|j|| j	dd | j
|_
|S )NFr   )_get_checked_instancer   torchSizer   r   expandr!   r"   r#   _validate_args)r   r   	_instancenewr$   r   r   r)   E   s   

zMultinomial.expandc                 O   s   | j j|i |S r   )r   _new)r   argskwargsr   r   r   r-   P   s   zMultinomial._newT)is_discrete	event_dimc                 C   s   t | jS r   )r   multinomialr   r   r   r   r   supportS   r   zMultinomial.supportc                 C      | j jS r   )r   r   r   r   r   r   r   W      zMultinomial.logitsc                 C   r4   r   )r   r
   r   r   r   r   r
   [   r5   zMultinomial.probsc                 C   r4   r   )r   r    r   r   r   r   r    _   r5   zMultinomial.param_shapec                 C   s   t |}| jt | jf| }tt| }||	d |j
| }|| | }|d|t | || jS )Nr   r   )r'   r(   r   sampler   listrangedimappendpoppermuter,   _extended_shapezero_scatter_add_	ones_liketype_asr
   )r   sample_shapesamplesshifted_idxcountsr   r   r   r6   c   s   

zMultinomial.samplec                 C   s|   t | j}| j }|| t |d  }| jjdddd  }t | j	|}t |d }|| 
ddg}|| S )Nr   F)r)   r   r   )r'   tensorr   r   entropylgammar   enumerate_supportexplog_probsum)r   ncat_entropyterm1r3   binomial_probsweightsterm2r   r   r   rG   q   s   
zMultinomial.entropyc                 C   s   | j r| | t| j|\}}|jtjd}t|dd }t|d d}d||dk|t	 k@ < || d}|| | S )N)memory_formatr   r   r   )
r*   _validate_sampler   r   cloner'   contiguous_formatrH   rL   r   )r   valuer   log_factorial_nlog_factorial_xs
log_powersr   r   r   rK   ~   s   
zMultinomial.log_prob)r   NNNr   )__name__
__module____qualname____doc__r   simplexreal_vectorarg_constraintsr   __annotations__propertyr   r   r"   r)   r-   dependent_propertyr3   r   r
   r    r'   r(   r6   rG   rK   __classcell__r   r   r$   r   r      s,   
 $






)r'   r   torch.distributionsr   r   torch.distributions.binomialr    torch.distributions.distributionr   torch.distributions.utilsr   __all__r   r   r   r   r   <module>   s    