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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#dededee ddf fd	d
Zd# fdd	Zej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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std| j j}| jjd d }tt|t|D ]\}}|dkrJ|dkrJ||krJtd| d| dq/| j j	j
d }| jjd }	|d uro|	d uro||	krot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$`mixture_distribution.batch_shape` (z>) is not compatible with `component_distribution.batch_shape`()z"`mixture_distribution component` (z;) does not equal `component_distribution.batch_shape[-1]` (batch_shapeevent_shaper   )_mixture_distribution_component_distribution
isinstancer   
ValueErrorr   r   zipreversedlogitsshape_num_componentr   len_event_ndimssuper__init__)selfr
   r   r   ZmdbsZcdbsZsize1Zsize2kmkcr   	__class__ m/home/www/facesmatcher.com/frenv_anti/lib/python3.10/site-packages/torch/distributions/mixture_same_family.pyr    <   sD   

zMixtureSameFamily.__init__c                    sx   t |}|| jf }| t|}| j||_| j||_| j|_| j|_|jj	}t
t|j||dd | j|_|S )NFr   )torchSizer   Z_get_checked_instancer   r   expandr   r   r   r   r    _validate_args)r!   r   Z	_instanceZbatch_shape_compnewr   r$   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.supportc                 C      | j S r-   )r   r/   r&   r&   r'   r
         z&MixtureSameFamily.mixture_distributionc                 C   r0   r-   )r   r/   r&   r&   r'   r      r1   z(MixtureSameFamily.component_distributionc                 C   s*   |  | jj}tj|| jj d| j dS Nr   dim)_pad_mixture_dimensionsr
   probsr(   sumr   meanr   )r!   r6   r&   r&   r'   r8      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   r3   g       @)r5   r
   r6   r(   r7   r   variancer   r8   _padpow)r!   r6   Zmean_cond_varZvar_cond_meanr&   r&   r'   r9      s   zMixtureSameFamily.variancec                 C   s0   |  |}| j|}| jj}tj|| ddS r2   )r:   r   cdfr
   r6   r(   r7   )r!   xZcdf_xZmix_probr&   r&   r'   r<      s   
zMixtureSameFamily.cdfc                 C   sJ   | j r| | | |}| j|}tj| jjdd}tj	|| ddS r2   )
r+   Z_validate_sampler:   r   log_probr(   Zlog_softmaxr
   r   Z	logsumexp)r!   r=   Z
log_prob_xZlog_mix_probr&   r&   r'   r>      s   

zMixtureSameFamily.log_probc              	   C   s   t  Y t|}t| j}|| }| j}| j|}|j}| j|}|	|t 
dgt|d   }	|	t 
dgt| t 
dg | }	t |||	}
|
|W  d    S 1 s`w   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"   

"$zMixtureSameFamily.samplec                 C   s   | d| j S )Nr   )Z	unsqueezer   )r!   r=   r&   r&   r'   r:      s   zMixtureSameFamily._padc                 C   st   t | j}t | jj}|dkrdn|| }|j}||d d t|dg  |dd   t| jdg  }|S )Nr   r   r   )r   r   r
   r   r@   r(   r)   r   )r!   r=   Zdist_batch_ndimsZcat_batch_ndimsZ	pad_ndimsZxsr&   r&   r'   r5      s   


z)MixtureSameFamily._pad_mixture_dimensionsc                 C   s    d| j  d| j }d| d S )Nz
  z,
  zMixtureSameFamily(r   )r
   r   )r!   args_stringr&   r&   r'   __repr__   s   zMixtureSameFamily.__repr__r-   )"__name__
__module____qualname____doc__r	   dictstrr   
Constraint__annotations__Zhas_rsampler   r   r   boolr    r*   Zdependent_propertyr.   propertyr
   r   r   r8   r9   r<   r>   r(   r)   r?   r:   r5   rD   __classcell__r&   r&   r$   r'   r      s>   
 *1

)typingr   r(   r   Ztorch.distributionsr   r   Ztorch.distributions.constraintsr   Z torch.distributions.distributionr   __all__r   r&   r&   r&   r'   <module>   s   