



'''simple docstring'''


import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer







class 						UpperCAmelCase						(		nn.Linear):


 '''simple docstring'''




 def __init__( self				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_ = True				,	lowerCAmelCase_ = "constant"				,	lowerCAmelCase_ = 0.0				,	lowerCAmelCase_ = "topK"				,	)			->   List[Any]:



      """simple docstring"""







      super(__SCREAMING_SNAKE_CASE				,	self).__init__(in_features=__SCREAMING_SNAKE_CASE				,	out_features=__SCREAMING_SNAKE_CASE				,	bias=__SCREAMING_SNAKE_CASE)
      assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
      a_									=pruning_method

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




 def    lowercase_	( self)			->   Optional[Any]:



      """simple docstring"""







      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    lowercase_	( self				,	lowerCAmelCase_				,	lowerCAmelCase_)			->   int:



      """simple docstring"""







      if self.pruning_method == "topK":
           a_									=TopKBinarizer.apply(self.mask_scores				,	__SCREAMING_SNAKE_CASE)
      elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
           a_									="sigmoied" in self.pruning_method
           a_									=ThresholdBinarizer.apply(self.mask_scores				,	__SCREAMING_SNAKE_CASE				,	__SCREAMING_SNAKE_CASE)
      elif self.pruning_method == "magnitude":
           a_									=MagnitudeBinarizer.apply(self.weight				,	__SCREAMING_SNAKE_CASE)
      elif self.pruning_method == "l0":
           a_									=-0.1, 1.1, 2 / 3
           if self.training:
                a_									=torch.zeros_like(self.mask_scores).uniform_().clamp(0.0_0_0_1				,	0.9_9_9_9)
                a_									=torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b)
           else:
                a_									=torch.sigmoid(self.mask_scores)
           a_									=s * (r - l) + l
           a_									=s_bar.clamp(min=0.0				,	max=1.0)
      # Mask weights with computed mask
      a_									=mask * self.weight
      # Compute output (linear layer) with masked weights
      return nn.functional.linear(__SCREAMING_SNAKE_CASE				,	__SCREAMING_SNAKE_CASE				,	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      )

