o
    }oi1                     @  s   d dl mZ d dlmZ d dlZd dlmZ d dlm	Z	 e Z
d dlmZ W d   n1 s0w   Y  erJd dlmZ d dlmZ d dlmZ n
d d	lmZ ed
Ze
 sae	eZed d)ddZ	 		!	"	#	$	$	%d*d+d'd(ZdS ),    )annotations)TYPE_CHECKINGN)
try_import)
get_loggergreenlet)Callable)Any)_LazyImportzscipy.optimizez}The 'greenlet' package is unavailable, falling back to sequential L-BFGS-B optimization. This may lead to slower suggestions.func_and_grad,Callable[..., tuple[np.ndarray, np.ndarray]]
x0_batched
np.ndarraybatched_argstuple[Any, ...]bounds list[tuple[float, float]] | Nonemintfactrfloatpgtol	max_evals	max_itersmax_line_searchreturn)tuple[np.ndarray, np.ndarray, np.ndarray]c
                   s2  j dksJ t}
ttj|
tdtj|
td
d 	
fddfd	d
t|
D }dd
 t|D }t	dd
 |D }t
tdd
 |D   |jr| |g R  \fdd
t|D }t	dd
 |D }t
tdd
 |D   dd
 t||D }|js\
fS )N   dtypeir   r   Nonec                   sv   ddd}t j|  t fd	d
D 
d
\}}}| < | < |d 	 < t jd d  d S )Nxr   argsr	   r   tuple[float, np.ndarray]c                 W  s&   t  j| |\}}t|| fS N)r   
getcurrentparentswitchr   copy)r"   r#   fvalgrad r,   M/home/ubuntu/.local/lib/python3.10/site-packages/optuna/_gp/batched_lbfgsb.py_func_and_grad5   s   z4_batched_lbfgsb.<locals>.run.<locals>._func_and_gradc                 3      | ]}|  V  qd S r%   r,   .0argr    r,   r-   	<genexpr>=       z/_batched_lbfgsb.<locals>.run.<locals>.<genexpr>
funcx0r#   r   r   r   r   maxfunmaxitermaxlsnit)r"   r   r#   r	   r   r$   )sofmin_l_bfgs_btupler   r&   r'   r(   )r    r.   x_optfval_optinfo)r   r   r   	fvals_optr   r   r   r   n_iterationsr   r   xs_optr3   r-   run4   s"   
z_batched_lbfgsb.<locals>.runc                   s   g | ]}t  qS r,   r   )r1   _)rF   r,   r-   
<listcomp>K   s    z#_batched_lbfgsb.<locals>.<listcomp>c                 S  s   g | ]	\}}| |qS r,   r(   r1   r    glr,   r,   r-   rH   L   s    c                 S     g | ]
\}}|d ur|qS r%   r,   r1   r"   rG   r,   r,   r-   rH   M       c                 S     g | ]
\}}|d ur|qS r%   r,   r1   rG   r#   r,   r,   r-   rH   N   rN   c                   s$   g | ]\}}|  | | qS r,   rI   rJ   )fvalsgradsr,   r-   rH   Q   s   $ c                 S  rL   r%   r,   rM   r,   r,   r-   rH   R   rN   c                 S  rO   r%   r,   rP   r,   r,   r-   rH   S   rN   c                 S  s    g | ]\\}}}|d ur|qS r%   r,   )r1   r"   rG   rK   r,   r,   r-   rH   T   s     )r    r   r   r!   )ndimlennp
empty_likeemptyr   r   range	enumeratearrayr?   zipsize)r   r   r   r   r   r   r   r   r   r   
batch_size	greenletsx_and_args_list	x_batchedr,   )r   r   r   rQ   rC   rR   r   r   r   r   rD   r   rF   r   rE   r-   _batched_lbfgsb"   s$   
$
ra   r,   
       cAh㈵>:     tuple[list[Any], ...]c
                   s\  |j \}
}t|D ]\}}t||
ks#J d| d|
 dt| dq	|du s?t ||dfks?J d|dt | dt r[t|d	kr[t |||||||||	d

\}}}nNd fdd}t|}tj|j d t	d}tj|j d t
d}t|D ])\}tj||tfdd|D |||||||	d
\|< |< }|d |< q|||fS )a  
    Batched L-BFGS-B optimization with/without greenlet.
    - `func_and_grad` is expected to take a 2D array as the first argument and return a tuple of
      a 1D array of function values and a 2D array of gradients.
    - `x0_batched` is a 2D array where each row is an initial point for optimization.
    - `batched_args` is a tuple of additional arguments to pass to `func_and_grad`. e.g., if
      `batched_args` is `([alpha1, ..., alphaB], [beta1, ..., betaB])`, then
      `func_and_grad` is called by
      `func_and_grad(x0_batched, [alpha1, ..., alphaB], [beta1, ..., betaB])`. Note that each
      argument in `batched_args` is expected to be a list of length `B` (batch size).
    zbatched_args[z] must have length z
, but got .Nr   z!The shape of bounds must be (dim=z, 2), but got    )
r   r   r   r   r   r   r   r   r   r   x_1dr   args_1dr	   r   r$   c                   sT   | j dksJ dd |D }| d d d f } |g|R  \}}| |d  fS )Nri   c                 s  s    | ]}|gV  qd S r%   r,   r0   r,   r,   r-   r4      s    zAbatched_lbfgsb.<locals>._func_and_grad_wrapper.<locals>.<genexpr>r   )rS   itemr)   )rj   rk   args_2dx_2dr*   r+   )r   r,   r-   _func_and_grad_wrapper   s
   z.batched_lbfgsb.<locals>._func_and_grad_wrapperr   r   c                 3  r/   r%   r,   r0   r3   r,   r-   r4      r5   z!batched_lbfgsb.<locals>.<genexpr>r6   r<   )rj   r   rk   r	   r   r$   )shaperY   rT   rU   _greenlet_importsis_successfulra   rV   rW   r   r   r=   r>   r?   )r   r   r   r   r   r   r   r   r   r   r]   dimjr2   rE   rC   rD   ro   r8   rB   r,   )r   r    r-   batched_lbfgsbY   sV   


ru   )r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   )r,   Nrb   rc   rd   re   re   rf   )r   r   r   r   r   rg   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   )
__future__r   typingr   numpyrU   optuna._importsr   optuna.loggingr   rq   r   collections.abcr   r	   scipy.optimizeoptimizer=   optunar
   rr   __name___loggerwarningra   ru   r,   r,   r,   r-   <module>   s:    
: