import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer




class  __lowerCAmelCase	(							nn.Linear    ):



 '''simple docstring'''

 def __init__(      self							:		Optional[int]							,      lowerCamelCase							:		int							,      lowerCamelCase							:		int							,      lowerCamelCase							:		bool = True							,      lowerCamelCase							:		str = "constant"							,      lowerCamelCase							:		float = 0.0							,      lowerCamelCase							:		str = "topK"							,      )			->				Optional[int]:
        super(lowerCamelCase							,      self	).__init__(in_features=lowerCamelCase							,      out_features=lowerCamelCase							,      bias=lowerCamelCase	)
        assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
        _UpperCamelCase  :							Optional[Any]	    =	pruning_method

        if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
               _UpperCamelCase  :							Optional[Any]	    =	mask_scale
               _UpperCamelCase  :							Dict	    =	mask_init
               _UpperCamelCase  :							List[Any]	    =	nn.Parameter(torch.empty(self.weight.size()	)	)
               self.init_mask()

 def        __snake_case       (      self							:		Tuple	)			->				Optional[int]:
        if self.mask_init == "constant":
               init.constant_(self.mask_scores							,      val=self.mask_scale	)
        elif self.mask_init == "uniform":
               init.uniform_(self.mask_scores							,      a=-self.mask_scale							,      b=self.mask_scale	)
        elif self.mask_init == "kaiming":
               init.kaiming_uniform_(self.mask_scores							,      a=math.sqrt(5	)	)

 def        __snake_case       (      self							:		Union[str, Any]							,      lowerCamelCase							:		torch.tensor							,      lowerCamelCase							:		float	)			->				List[Any]:
        # Get the mask
        if self.pruning_method == "topK":
               _UpperCamelCase  :							List[str]	    =	TopKBinarizer.apply(self.mask_scores							,      lowerCamelCase	)
        elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
               _UpperCamelCase  :							Optional[Any]	    =	'sigmoied' in self.pruning_method
               _UpperCamelCase  :							Optional[Any]	    =	ThresholdBinarizer.apply(self.mask_scores							,      lowerCamelCase							,      lowerCamelCase	)
        elif self.pruning_method == "magnitude":
               _UpperCamelCase  :							Union[str, Any]	    =	MagnitudeBinarizer.apply(self.weight							,      lowerCamelCase	)
        elif self.pruning_method == "l0":
               _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							Union[str, Any]	    =	-0.1, 1.1, 2 / 3
               if self.training:
                      _UpperCamelCase  :							List[str]	    =	torch.zeros_like(self.mask_scores	).uniform_().clamp(0.0_0_0_1							,      0.9_9_9_9	)
                      _UpperCamelCase  :							Optional[Any]	    =	torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b	)
               else:
                      _UpperCamelCase  :							List[Any]	    =	torch.sigmoid(self.mask_scores	)
               _UpperCamelCase  :							List[Any]	    =	s * (r - l) + l
               _UpperCamelCase  :							Tuple	    =	s_bar.clamp(min=0.0							,      max=1.0	)
        # Mask weights with computed mask
        _UpperCamelCase  :							Optional[int]	    =	mask * self.weight
        # Compute output (linear layer) with masked weights
        return nn.functional.linear(lowerCamelCase							,      lowerCamelCase							,      self.bias	)







import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image
def 	_lowerCamelCase  (		A_  :	Optional[int]							,  A_  :	List[Any]=2_5_6      )			->			Tuple:

   '''simple docstring'''






   UpperCamelCase__ :			Optional[Any] 						=min(img.size      )

   if s < target_image_size:
      raise ValueError(f'''min dim for image {s} < {target_image_size}'''      )

   UpperCamelCase__ :			Optional[Any] 						=target_image_size / s
   UpperCamelCase__ :			Optional[Any] 						=(round(r * img.size[1]      ), round(r * img.size[0]      ))
   UpperCamelCase__ :			List[Any] 						=TF.resize(A_							,  A_							,  interpolation=PIL.Image.LANCZOS      )
   UpperCamelCase__ :			Optional[int] 						=TF.center_crop(A_							,  output_size=2 * [target_image_size]      )
   UpperCamelCase__ :			Union[str, Any] 						=torch.unsqueeze(T.ToTensor()(A_      )							,  0      )
   return img
def 	_lowerCamelCase  (		A_  :	Optional[int]      )			->			Optional[Any]:

   '''simple docstring'''






   UpperCamelCase__ :			List[Any] 						=2.0 * x - 1.0
   return x
def 	_lowerCamelCase  (		A_  :	Union[str, Any]							,  A_  :	Any=True							,  A_  :	int="RGB"      )			->			int:

   '''simple docstring'''






   UpperCamelCase__ :			Dict 						=x.detach().cpu()
   if process:
      UpperCamelCase__ :			Optional[int] 						=post_process_tensor(A_      )
   UpperCamelCase__ :			Optional[int] 						=x.numpy()
   if process:
      UpperCamelCase__ :			Dict 						=(2_5_5 * x).astype(np.uinta      )
   UpperCamelCase__ :			int 						=Image.fromarray(A_      )
   if not x.mode == mode:
      UpperCamelCase__ :			Tuple 						=x.convert(A_      )
   return x
def 	_lowerCamelCase  (		A_  :	Dict      )			->			List[str]:

   '''simple docstring'''






   UpperCamelCase__ :			List[str] 						=torch.clamp(A_							,  -1.0							,  1.0      )
   UpperCamelCase__ :			Union[str, Any] 						=(x + 1.0) / 2.0
   UpperCamelCase__ :			Union[str, Any] 						=x.permute(1							,  2							,  0      )
   return x
def 	_lowerCamelCase  (		A_  :	Tuple      )			->			List[str]:

   '''simple docstring'''






   UpperCamelCase__ :			List[str] 						=post_process_tensor(x.squeeze()      )
   return x.permute(2							,  0							,  1      ).unsqueeze(0      )

