
    i                     0   d dl Z d dlmZ 	 d dlZd dlZd dlZdZej                  Z eej                        Z		 ej                  j                  j                          dZe	j                  dk\  rej                   Znej$                  Z	 d dlZd dlZdZej                  j1                         d k7  Z eej6                  d      xr$ ej6                  j8                  j;                         Zexr$ ej6                  j8                  j?                         Z eZ! e e"ej                              Z#e# ed      k\  xr/ ej                  jH                  jJ                  jM                          Z'd	 Z(da)da*da+d
 Z,da-da.	 d dl/Z/	 d dl0Z0dZ1exs e Z2g dZ3y# ej                  j                  j                  $ r dZY Vw xY w# eef$ r dZdZdZ ed      Z	dZdZdZY Pw xY w# e$ r dZdZdZdZ!dZdZ dZ' ed      Z#Y w xY w# e$ r dZ/Y w xY w# e$ r dZ0dZ1Y w xY w)    N)VersionTF
   z0.0.0mpsz1.9.0c                      d} t        j                  | t               dd ladd ladat        t        j                  j                  d            dkD  a	y )NzBuilt-in TensorFlow support will be removed in Thinc v9. If you need TensorFlow support in the future, you can transition to using a custom copy of the current TensorFlowWrapper in your package or project.r   TGPU)
warningswarnDeprecationWarning
tensorflowtensorflow.experimental.dlpackhas_tensorflowlenconfigget_visible_deviceshas_tensorflow_gpuwarn_msgs    ]/var/www/vps2.regionflexible.com/Desarrollo/venv/lib/python3.12/site-packages/thinc/compat.pyenable_tensorflowr   ;   sI    	  MM(./)NZ..BB5IJQN    c                  H    d} t        j                  | t               dd laday )NzBuilt-in MXNet support will be removed in Thinc v9. If you need MXNet support in the future, you can transition to using a custom copy of the current MXNetWrapper in your package or project.r   T)r   r	   r
   mxnet	has_mxnetr   s    r   enable_mxnetr   P   s%    	  MM(./Ir   )cupycupyxtorchr   r   h5pyos_signpost)4r   packaging.versionr   r   cupy.cublasr   has_cupycublas__version__cupy_versioncudaruntimegetDeviceCounthas_cupy_gpuCUDARuntimeErrormajorfrom_dlpackcupy_from_dlpack
fromDlpackImportErrorAttributeErrorr   torch.utils.dlpack	has_torchdevice_counthas_torch_cuda_gpuhasattrbackendsr   is_builthas_torch_mpsis_availablehas_torch_mps_gpuhas_torch_gpustrtorch_versionampcommonamp_definitely_not_availablehas_torch_ampr   r   r   r   r   r   r   r   r   has_os_signposthas_gpu__all__ r   r   <module>rF      s`    %H[[F4++,L		((* R++??%I002a7ENNE2Tu~~7I7I7R7R7TM%K%..*<*<*I*I*K&MC 1 123M)) 	E

%%BBDD O  
  		
O 
++G 99--  	^$ FDE7#LHL0  %EIMMMG$M%h  D  KOsk   ,F9 &F (F9 CG 7G< <H	 %F62F9 5F66F9 9GGG98G9<HH		HH