+
    &j                         R t ^ RIt^ RIHt ^RIHt ^RIHtHt ^RIHt ^RIHt ^RI	H	t	 ^RI
H
t
 ^R	IHt ^R
IHt ^RIHt ^RIHt ^RIHt R R ltR# )zFunctional interface.N)Tensor)adadelta)_make_sparseadagrad)adam)adamax)adamw)asgd)nadam)radam)rmsprop)rprop)sgdc                   V ^8  d   QhR\         \        ,          R\         \        ,          R\         \        ,          R\         \        ,          R\         \        ,          R\        R\        R\        R	\        R
\        RR/# )   paramsgradsexp_avgsexp_avg_sqsstate_stepsepsbeta1beta2lrmaximizereturnN)listr   intfloatbool)formats   "o/Users/jameslopez/projects/CWCArchive/cwc-podcast/.venv/lib/python3.14/site-packages/torch/optim/_functional.py__annotate__r"      s     <@ <@L<@<<@ 6l<@ f	<@
 c<@ 
<@ <@ <@ 	<@ <@ 
<@    c          	     H  aaa \        V 4       EF  w  rW,          oV	'       g   SMS) oSP                  4       oSP                  4       oSP                  4       pVP	                  4       ^ 8X  d   Kb  SP                  4       oW*,          pW:,          pWJ,          pVVV3R lpVP                  S4      P                  4       pVP                  V4      P                  ^V,
          4      pVP                  V! V4      4       VP                  S4      P                  4       pVP                  ^4      P                  V4      P                  ^V,
          4      pVP                  V! V4      4       VP                  V4      pVP                  V4       VP                  4       P                  V4      p??^Wo,          ,
          p^W,          ,
          pV\        P                  ! V4      ,          V,          pVP                  V! V) VP                  V4      ,          4      4       EK  	  R# )zsFunctional API that performs Sparse Adam algorithm computation.

See :class:`~torch.optim.SparseAdam` for details.
c                    < SP                   pSP                  4       ^ 8X  g   V P                  4       ^ 8X  d   V! 4       P                  S4      # V! SV S4      # )    )newdim
resize_as_)valuesconstructorgradgrad_indicessizes   & r!   make_sparse sparse_adam.<locals>.make_sparse8   sL    ((K!Q&&**,!*;"}//55|VT::r#   N)	enumeratecoalesce_indices_valuesnumelr.   sparse_masksubmul_add_powsub_sqrt_mathsqrtdiv_)r   r   r   r   r   r   r   r   r   r   iparamgrad_valuesexp_avg
exp_avg_sqstepr/   old_exp_avg_valuesexp_avg_update_valuesold_exp_avg_sq_valuesexp_avg_sq_update_valuesnumerdenombias_correction1bias_correction2	step_sizer,   r-   r.   s   &&&&&$$$$$                @@@r!   sparse_adamrO      s   " f%x#t$}}}}lln!#yy{+ ^
~	; %006>>@ +0B C H HU S[!678 * 6 6t < D D FOOA##$9:??E	J 	! 	$<=> &**+=> %%&;<(..055c:!#;u{?u{?#3447GG	

;	zEJJu,==>?W &r#   )__doc__r=   torchr   r   r   r   r   r   r   r	   r
   r   r   r   r   rO    r#   r!   <module>rS      s5        *         <@r#   