o
    #i                     @   sj   U d dl mZmZmZmZ d dlZd dlm  mZ	 d dlm
Z
 g Zee ed< ejjG dd dZdS )    )DictListOptionalTupleN)Tensor__all__c                   @   sh   e Zd Z							ddee dedeeef d	ed
edededefddZdee	e  fddZ
dS )_FunctionalAdamaxMbP?g?g+?:0yE>        Fparamslrbetasepsweight_decayforeachmaximize_allow_empty_param_listc	           	      C   s
  d|kst d| d|kst d| d|d   kr"dk s,n t d|d  d|d   kr8dk sBn t d|d  d|ksMt d	| |||d |d |d
| _|| _|| _tjttjtt	tjf f i | _
t|dkr~|s~t dd|i| _d S )Nr   zInvalid learning rate: zInvalid epsilon value: r   g      ?z#Invalid beta parameter at index 0:    z#Invalid beta parameter at index 1: zInvalid weight_decay value: )r   r   beta1beta2r   z%optimizer got an empty parameter listr   )
ValueErrordefaultsr   r   torchjitannotater   r   strstatelenparam_group)	selfr   r   r   r   r   r   r   r    r"   g/home/ubuntu/SoloSpeech/.venv/lib/python3.10/site-packages/torch/distributed/optim/functional_adamax.py__init__   s,   $z_FunctionalAdamax.__init__	gradientsc                 C   s  | j d }g }g }g }g }g }t|t|kr*tddt| d dt|  d}t| j d |D ]]\}	}
|
d ur|t|	O }||	 ||
 |	| jvrwi | j|	< | j|	 }td|d< tj	|	tj
d	|d
< tj	|	tj
d	|d< | j|	 }||d
  ||d  ||d  q4t , tj|||||| jd | jd | jd | jd | jd | j| j|d W d    d S 1 sw   Y  d S )Nr   zEthe gradients passed in does not equal to the size of the parameters!zParams length: z. zGradients length: Fr   step)memory_formatexp_avgexp_infr   r   r   r   r   )r   r   r   r   r   r   r   has_complex)r    r   r   zipr   
is_complexappendr   tensor
zeros_likepreserve_formatno_gradFadamaxr   r   r   )r!   r%   r   params_with_gradgradsexp_avgsexp_infsstate_stepsr*   paramgradientr   r"   r"   r#   r&   >   sh   









"z_FunctionalAdamax.stepN)r	   r
   r   r   FFF)__name__
__module____qualname__r   r   floatr   boolr$   r   r&   r"   r"   r"   r#   r      s4    
	
(r   )typingr   r   r   r   r   torch.optim._functionaloptim_functionalr2   r   r   r   __annotations__r   scriptr   r"   r"   r"   r#   <module>   s   