
    קgu                     f    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 Z G d de          ZdS )    N)constraints)Distribution)broadcast_alllazy_propertylogits_to_probsprobs_to_logitsBinomialc                 h    |                      d          | z   |                      d          z
  dz  S )Nr   )minmax   )clamp)xs    X/var/www/html/ai-engine/env/lib/python3.11/site-packages/torch/distributions/binomial.py_clamp_by_zeror      s/    GGGNNQQ/144    c                   V    e Zd ZdZej        ej        ej        dZdZ	d fd	Z
d fd	Zd Z ej        dd	
          d             Zed             Zed             Zed             Zed             Zed             Zed             Z ej                    fdZd Zd ZddZ x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-deterministic")
        >>> 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                    |d u |d u k    rt          d          |Bt          ||          \  | _        | _        | j                            | j                  | _        nAt          ||          \  | _        | _        | j                            | j                  | _        || j        n| j        | _        | j                                        }t                      	                    ||           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__s         r   r!   zBinomial.__init__3   s    TMv~..M    k511 
#/77
CCD
 k622 #/77DDD$)$5djj4;k&&((MBBBBBr   c                    |                      t          |          }t          j        |          }| j                            |          |_        d| j        v r+| j                            |          |_        |j        |_        d| j        v r+| j	                            |          |_	        |j	        |_        t          t          |                              |d           | j        |_        |S )Nr   r   Fr   )_get_checked_instancer	   torchSizer   expand__dict__r   r   r   r    r!   _validate_args)r"   r#   	_instancenewr$   s       r   r)   zBinomial.expandI   s    ((9==j--*11+>>dm##
))+66CICJt}$$++K88CJCJh%%k%GGG!0
r   c                 &     | j         j        |i |S N)r   r-   )r"   argskwargss      r   _newzBinomial._newW   s    t{////r   r   )is_discrete	event_dimc                 6    t          j        d| j                  S )Nr   )r   integer_intervalr   r"   s    r   supportzBinomial.supportZ   s    +At/?@@@r   c                      | j         | j        z  S r/   r   r   r7   s    r   meanzBinomial.mean^   s    $*,,r   c                 |    | j         dz   | j        z                                                      | j                   S )Nr   r   )r   r   floorr   r7   s    r   modezBinomial.modeb   s7    !A%3::<<BBtGWBXXXr   c                 6    | j         | j        z  d| j        z
  z  S Nr   r:   r7   s    r   variancezBinomial.variancef   s    $*,DJ??r   c                 .    t          | j        d          S NT)	is_binary)r   r   r7   s    r   r   zBinomial.logitsj   s    tzT::::r   c                 .    t          | j        d          S rC   )r   r   r7   s    r   r   zBinomial.probsn   s    t{d;;;;r   c                 4    | j                                         S r/   )r   r   r7   s    r   param_shapezBinomial.param_shaper   s    {!!!r   c                    |                      |          }t          j                    5  t          j        | j                            |          | j                            |                    cd d d            S # 1 swxY w Y   d S r/   )_extended_shaper'   no_gradbinomialr   r)   r   )r"   sample_shapeshapes      r   samplezBinomial.samplev   s    $$\22]__ 	 	> ''..
0A0A%0H0H 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	s   AA;;A?A?c           	         | j         r|                     |           t          j        | j        dz             }t          j        |dz             }t          j        | j        |z
  dz             }| j        t          | j                  z  | j        t          j        t          j        t          j	        | j                                       z  z   |z
  }|| j        z  |z
  |z
  |z
  S r@   )
r+   _validate_sampler'   lgammar   r   r   log1pexpabs)r"   valuelog_factorial_nlog_factorial_klog_factorial_nmknormalize_terms         r   log_probzBinomial.log_prob}   s     	)!!%(((,t'7!';<<,uqy11!L)9E)AA)EFF ~dk:::UY	$+8N8N7N-O-O!P!PPQ 	 DK/14EEV	
r   c                 L   t          | j                                                  }| j                                        |k    st	          d          |                     |                     d                    }t          j        |          |z  	                    d           S )Nz5Inhomogeneous total count not supported by `entropy`.Fr   )
intr   r   r   NotImplementedErrorrZ   enumerate_supportr'   rS   sum)r"   r   rZ   s      r   entropyzBinomial.entropy   s    $*..0011##%%44%G   ==!7!7!>!>??8$$x/44Q7777r   c                    t          | j                                                  }| j                                        |k    st	          d          t          j        d|z   | j        j        | j        j	                  }|
                    ddt          | j                  z  z             }|r|                    d| j        z             }|S )Nz?Inhomogeneous total count not supported by `enumerate_support`.r   )dtypedevice))r   )r\   r   r   r   r]   r'   aranger   rb   rc   viewlen_batch_shaper)   )r"   r)   r   valuess       r   r^   zBinomial.enumerate_support   s    $*..0011##%%44%Q   O4;#4T[=O
 
 
 UTC0A,B,B%BBCC 	>]]54+<#<==Fr   )r   NNNr/   )T)__name__
__module____qualname____doc__r   nonnegative_integerunit_intervalrealarg_constraintshas_enumerate_supportr!   r)   r2   dependent_propertyr8   propertyr;   r>   rA   r   r   r   rG   r'   r(   rN   rZ   r`   r^   __classcell__)r$   s   @r   r	   r	      s        . #6*" O
 !C C C C C C,     0 0 0 $[#BBBA A CBA - - X- Y Y XY @ @ X@ ; ; ]; < < ]< " " X" #-%*,,    
 
 
(8 8 8       r   )r'   torch.distributionsr    torch.distributions.distributionr   torch.distributions.utilsr   r   r   r   __all__r   r	    r   r   <module>r{      s     + + + + + + 9 9 9 9 9 9            ,5 5 5
R R R R R| R R R R Rr   