U
    9%eaC                     @   sN   d dl Z d dlmZ ddlmZ dgZdddZdddZG dd deZdS )    N)reduce   )	OptimizerLBFGSc                 C   s   |d k	r|\}}n| |kr"| |fn|| f\}}|| d||  | |   }	|	d ||  }
|
dkr|
  }| |kr|||  || |	 || d|     }n(| | | || |	 || d|     }tt|||S || d S d S )N      r   g       @)sqrtminmax)x1f1g1Zx2f2g2boundsZ
xmin_boundZ
xmax_boundZd1Z	d2_squareZd2Zmin_pos r   P/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/torch/optim/lbfgs.py_cubic_interpolate   s    
	*(r   -C6??&.>   c           !   	   C   s   |   }|jtjd}| |||\}}d}||}d|||f\}}}}d}d}||
k r|||| |  ks|dkr||kr||g}||g}||jtjdg}||g}qt || | kr|g}|g}|g}d}q|dkr||g}||g}||jtjdg}||g}q|d||   }|d }|}t||||||||fd}|}|}|jtjd}|}| |||\}}|d7 }||}|d7 }qT||
krd|g}||g}||g}d}|d |d	 krd
nd\}}|s||
k rt |d |d  | |	k rqt|d |d |d |d |d |d }dt|t|  } tt|| |t| | k r|s|t|ks|t|krt |t| t |t| k rt||  }nt||  }d}nd}nd}| |||\}}|d7 }||}|d7 }|||| |  ks ||| krj|||< |||< |jtjd||< |||< |d |d kr`d
nd\}}nt || | krd}nJ||| ||   dkr|| ||< || ||< || ||< || ||< |||< |||< |jtjd||< |||< q|| }|| }|| }||||fS )NZmemory_formatr   r   FTg{Gz?
   )r   )r   r   )r   r   g?)absr
   clonetorchcontiguous_formatdotr   r	   )!obj_funcxtdfggtdc1c2tolerance_changeZmax_lsZd_normZf_newZg_newls_func_evalsZgtd_newZt_prevZf_prevZg_prevZgtd_prevdoneZls_iterZbracketZ	bracket_fZ	bracket_gZbracket_gtdZmin_stepZmax_steptmpZinsuf_progressZlow_posZhigh_posZepsr   r   r   _strong_wolfe#   s    

$




  
 ""
$ r-   c                       sb   e Zd ZdZd fdd		Zd
d Zdd Zdd Zdd Zdd Z	dd Z
e dd Z  ZS )r   a  Implements L-BFGS algorithm, heavily inspired by `minFunc
    <https://www.cs.ubc.ca/~schmidtm/Software/minFunc.html>`_.

    .. warning::
        This optimizer doesn't support per-parameter options and parameter
        groups (there can be only one).

    .. warning::
        Right now all parameters have to be on a single device. This will be
        improved in the future.

    .. note::
        This is a very memory intensive optimizer (it requires additional
        ``param_bytes * (history_size + 1)`` bytes). If it doesn't fit in memory
        try reducing the history size, or use a different algorithm.

    Args:
        lr (float): learning rate (default: 1)
        max_iter (int): maximal number of iterations per optimization step
            (default: 20)
        max_eval (int): maximal number of function evaluations per optimization
            step (default: max_iter * 1.25).
        tolerance_grad (float): termination tolerance on first order optimality
            (default: 1e-7).
        tolerance_change (float): termination tolerance on function
            value/parameter changes (default: 1e-9).
        history_size (int): update history size (default: 100).
        line_search_fn (str): either 'strong_wolfe' or None (default: None).
    r      NHz>r   d   c	           
   	      sh   |d kr|d d }t |||||||d}	t ||	 t| jdkrNtd| jd d | _d | _d S )N      )lrmax_itermax_evaltolerance_gradr)   history_sizeline_search_fnr   z>LBFGS doesn't support per-parameter options (parameter groups)r   params)dictsuper__init__lenparam_groups
ValueError_params_numel_cache)
selfr9   r3   r4   r5   r6   r)   r7   r8   defaults	__class__r   r   r<      s     	zLBFGS.__init__c                 C   s$   | j d krtdd | jd| _ | j S )Nc                 S   s   | |   S N)numel)totalpr   r   r   <lambda>       zLBFGS._numel.<locals>.<lambda>r   )rA   r   r@   rB   r   r   r   _numel   s    
zLBFGS._numelc                 C   sj   g }| j D ]R}|jd kr,||  }n&|jjrF|j d}n|jd}|| q
t	
|dS )Nr   r   )r@   ZgradnewrG   Zzero_Z	is_sparseZto_denseviewappendr   cat)rB   ZviewsrI   rO   r   r   r   _gather_flat_grad   s    

zLBFGS._gather_flat_gradc                 C   sT   d}| j D ]4}| }|j||||  ||d ||7 }q
||  ksPtd S )Nr   alpha)r@   rG   add_Zview_asrM   AssertionError)rB   Z	step_sizeupdateoffsetrI   rG   r   r   r   	_add_grad  s    
 
zLBFGS._add_gradc                 C   s   dd | j D S )Nc                 S   s   g | ]}|j tjd qS )r   )r   r   r   ).0rI   r   r   r   
<listcomp>  s     z&LBFGS._clone_param.<locals>.<listcomp>)r@   rL   r   r   r   _clone_param  s    zLBFGS._clone_paramc                 C   s$   t | j|D ]\}}|| qd S rF   )zipr@   copy_)rB   Zparams_datarI   Zpdatar   r   r   
_set_param  s    zLBFGS._set_paramc                 C   s0   |  || t| }|  }| | ||fS rF   )rY   floatrR   r_   )rB   closurer!   r"   r#   loss	flat_gradr   r   r   _directional_evaluate  s
    

zLBFGS._directional_evaluatec           &   	      s  t jdkstt   jd }|d }|d }|d }|d }|d }|d }|d	 }	jjd  }
|
d
d |
dd   }t|}d}|
d
  d7  < 	 }|
  |k}|r|S |
d}|
d}|
d}|
d}|
d}|
d}|
d}|
d}d}||k r^|d7 }|
d  d7  < |
d dkrj| }g }g }g }d}nN||}||}||}|dkrt ||	kr|d |d |d || || |d|  ||| }t |}d|
krdg|	 |
d< |
d }| }t|d ddD ]8}|| |||  ||< |j|| ||  d q.t|| }} t|D ]6}|| | ||  }!| j|| || |! d q|dkr|jtjd}n
|| |}|
d dkr
tdd|
   | }n|}||}"|"| kr(q^d}#|dk	r|dkrJtdn2 }$ fdd}%t|%|$|||||"\}}}}#|| |
  |k}nP|| ||krt  t  }W 5 Q R X 	 }|
  |k}d}#||#7 }|
d
  |#7  < ||krq^||krq^|r(q^||
  |krDq^t
|| |k rq^q||
d< ||
d< ||
d< ||
d< ||
d< ||
d< ||
d< ||
d< |S )zPerforms a single optimization step.

        Args:
            closure (Callable): A closure that reevaluates the model
                and returns the loss.
        r   r   r3   r4   r5   r6   r)   r8   r7   Z
func_evalsn_iterr#   r"   old_dirsold_stpsroH_diagprev_flat_grad	prev_lossg|=g      ?alNr   rS   r   Zstrong_wolfez only 'strong_wolfe' is supportedc                    s     | ||S rF   )rd   )r!   r"   r#   ra   rB   r   r   r      s    zLBFGS.step.<locals>.obj_func)r=   r>   rV   r   Zenable_gradstater@   
setdefaultr`   rR   r   r
   getnegsubmulr   poprP   rangerU   r   r   r^   r	   sumRuntimeErrorr\   r-   rY   )&rB   ra   groupr3   r4   r5   r6   r)   r8   r7   rn   Z	orig_lossrb   Zcurrent_evalsrc   Zopt_condr#   r"   rf   rg   rh   ri   rj   rk   re   ysZysZnum_oldrl   qirZbe_ir&   r*   Zx_initr    r   rm   r   step  s    

























      



z
LBFGS.step)r   r.   Nr/   r   r0   N)__name__
__module____qualname____doc__r<   rM   rR   rY   r\   r_   rd   r   Zno_gradr~   __classcell__r   r   rD   r   r      s"           	)N)r   r   r   r   )	r   	functoolsr   Z	optimizerr   __all__r   r-   r   r   r   r   r   <module>   s   
#    
 