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    h!                     @   sb   d dl mZ 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gZG dd de
ZdS )	    )OptionalN)Tensor)Categoricalconstraints)MixtureSameFamilyConstraint)DistributionMixtureSameFamilyc                       s   e Zd ZU dZi Zeeejf e	d< dZ
d!eeee dd fddZd" fdd		Zejd
d ZeedddZeedddZeedddZeedddZdd Zdd Ze fddZdd Zdd Zdd  Z   Z!S )#r   a  
    The `MixtureSameFamily` distribution implements a (batch of) mixture
    distribution where all component are from different parameterizations of
    the same distribution type. It is parameterized by a `Categorical`
    "selecting distribution" (over `k` component) and a component
    distribution, i.e., a `Distribution` with a rightmost batch shape
    (equal to `[k]`) which indexes each (batch of) component.

    Examples::

        >>> # xdoctest: +SKIP("undefined vars")
        >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally
        >>> # weighted normal distributions
        >>> mix = D.Categorical(torch.ones(5,))
        >>> comp = D.Normal(torch.randn(5,), torch.rand(5,))
        >>> gmm = MixtureSameFamily(mix, comp)

        >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally
        >>> # weighted bivariate normal distributions
        >>> mix = D.Categorical(torch.ones(5,))
        >>> comp = D.Independent(D.Normal(
        ...          torch.randn(5,2), torch.rand(5,2)), 1)
        >>> gmm = MixtureSameFamily(mix, comp)

        >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each
        >>> # consisting of 5 random weighted bivariate normal distributions
        >>> mix = D.Categorical(torch.rand(3,5))
        >>> comp = D.Independent(D.Normal(
        ...         torch.randn(3,5,2), torch.rand(3,5,2)), 1)
        >>> gmm = MixtureSameFamily(mix, comp)

    Args:
        mixture_distribution: `torch.distributions.Categorical`-like
            instance. Manages the probability of selecting component.
            The number of categories must match the rightmost batch
            dimension of the `component_distribution`. Must have either
            scalar `batch_shape` or `batch_shape` matching
            `component_distribution.batch_shape[:-1]`
        component_distribution: `torch.distributions.Distribution`-like
            instance. Right-most batch dimension indexes component.
    arg_constraintsFN)mixture_distributioncomponent_distributionvalidate_argsreturnc                    s  || _ || _t| j ts tdt| jts4td| j j}| jjd d }tt|t|D ]6\}}|dkr^|dkr^||kr^td| d| dq^| j j	j
d }| jjd }	|d ur|	d ur||	krtd| d	|	 d|| _| jj}
t|
| _t j||
|d
 d S )NzU The Mixture distribution needs to be an  instance of torch.distributions.CategoricalzUThe Component distribution need to be an instance of torch.distributions.Distribution   z$`mixture_distribution.batch_shape` (z>) is not compatible with `component_distribution.batch_shape`()z"`mixture_distribution component` (z;) does not equal `component_distribution.batch_shape[-1]` (batch_shapeevent_shaper   )_mixture_distribution_component_distribution
isinstancer   
ValueErrorr   r   zipreversedlogitsshape_num_componentr   len_event_ndimssuper__init__)selfr
   r   r   ZmdbsZcdbsZsize1Zsize2kmZkcr   	__class__ U/var/www/auris/lib/python3.9/site-packages/torch/distributions/mixture_same_family.pyr    <   sB    
zMixtureSameFamily.__init__c                    sx   t |}|| jf }| t|}| j||_| j||_| j|_| j|_|jj	}t
t|j||dd | j|_|S )NFr   )torchSizer   Z_get_checked_instancer   r   expandr   r   r   r   r    _validate_args)r!   r   Z	_instanceZbatch_shape_compnewr   r#   r%   r&   r)   m   s    

zMixtureSameFamily.expandc                 C   s   t | jjS N)r   r   supportr!   r%   r%   r&   r-   ~   s    zMixtureSameFamily.support)r   c                 C   s   | j S r,   )r   r.   r%   r%   r&   r
      s    z&MixtureSameFamily.mixture_distributionc                 C   s   | j S r,   )r   r.   r%   r%   r&   r      s    z(MixtureSameFamily.component_distributionc                 C   s*   |  | jj}tj|| jj d| j dS Nr   Zdim)_pad_mixture_dimensionsr
   probsr'   sumr   meanr   )r!   r2   r%   r%   r&   r4      s    zMixtureSameFamily.meanc                 C   s`   |  | jj}tj|| jj d| j d}tj|| jj| 	| j 
d d| j d}|| S )Nr   r0   g       @)r1   r
   r2   r'   r3   r   variancer   r4   _padpow)r!   r2   Zmean_cond_varZvar_cond_meanr%   r%   r&   r5      s    zMixtureSameFamily.variancec                 C   s0   |  |}| j|}| jj}tj|| ddS r/   )r6   r   cdfr
   r2   r'   r3   )r!   xZcdf_xZmix_probr%   r%   r&   r8      s    
zMixtureSameFamily.cdfc                 C   sJ   | j r| | | |}| j|}tj| jjdd}tj	|| ddS r/   )
r*   Z_validate_sampler6   r   log_probr'   Zlog_softmaxr
   r   Z	logsumexp)r!   r9   Z
log_prob_xZlog_mix_probr%   r%   r&   r:      s    

zMixtureSameFamily.log_probc              	   C   s   t   t|}t| j}|| }| j}| j|}|j}| j|}|	|t 
dgt|d   }	|	t 
dgt| t 
dg | }	t |||	}
|
|W  d    S 1 s0    Y  d S )Nr   )r'   Zno_gradr   r   r   r
   sampler   r   reshaper(   repeatZgatherZsqueeze)r!   Zsample_shapeZ
sample_lenZ	batch_lenZ
gather_dimesZ
mix_sampleZ	mix_shapeZcomp_samplesZmix_sample_rZsamplesr%   r%   r&   r;      s     

"zMixtureSameFamily.samplec                 C   s   | d| j S )Nr   )Z	unsqueezer   )r!   r9   r%   r%   r&   r6      s    zMixtureSameFamily._padc                 C   st   t | j}t | jj}|dkr"dn|| }|j}||d d t|dg  |dd   t| jdg  }|S )Nr   r   r   )r   r   r
   r   r<   r'   r(   r   )r!   r9   Zdist_batch_ndimsZcat_batch_ndimsZ	pad_ndimsxsr%   r%   r&   r1      s    


z)MixtureSameFamily._pad_mixture_dimensionsc                 C   s    d| j  d| j }d| d S )Nz
  z,
  zMixtureSameFamily(r   )r
   r   )r!   args_stringr%   r%   r&   __repr__   s    zMixtureSameFamily.__repr__)N)N)"__name__
__module____qualname____doc__r	   dictstrr   
Constraint__annotations__Zhas_rsampler   r   r   boolr    r)   Zdependent_propertyr-   propertyr
   r   r   r4   r5   r8   r:   r'   r(   r;   r6   r1   rA   __classcell__r%   r%   r#   r&   r      s6   
* 1

)typingr   r'   r   Ztorch.distributionsr   r   Ztorch.distributions.constraintsr   Z torch.distributions.distributionr   __all__r   r%   r%   r%   r&   <module>   s   