


'''simple docstring'''



import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer



class       a							(						nn.Linear			):


     """simple docstring"""






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

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

     def 					lowerCamelCase__					(							self	:						str     )		->				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 					lowerCamelCase__					(							self	:						Tuple				,					snake_case	:						torch.tensor				,					snake_case	:						float     )		->				Optional[int]:
          # Get the mask
          if self.pruning_method == "topK":
               __UpperCAmelCase      :					Union[str, Any]    				=     TopKBinarizer.apply(self.mask_scores				,					snake_case     )
          elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
               __UpperCAmelCase      :					Union[str, Any]    				=     '''sigmoied''' in self.pruning_method
               __UpperCAmelCase      :					List[Any]    				=     ThresholdBinarizer.apply(self.mask_scores				,					snake_case				,					snake_case     )
          elif self.pruning_method == "magnitude":
               __UpperCAmelCase      :					List[str]    				=     MagnitudeBinarizer.apply(self.weight				,					snake_case     )
          elif self.pruning_method == "l0":
               __UpperCAmelCase				,  __UpperCAmelCase				,  __UpperCAmelCase      :					Union[str, Any]    				=     -0.1, 1.1, 2 / 3
               if self.training:
                    __UpperCAmelCase      :					Optional[Any]    				=     torch.zeros_like(self.mask_scores     ).uniform_().clamp(0.0_001				,					0.9_999     )
                    __UpperCAmelCase      :					Tuple    				=     torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b     )
               else:
                    __UpperCAmelCase      :					Union[str, Any]    				=     torch.sigmoid(self.mask_scores     )
               __UpperCAmelCase      :					Union[str, Any]    				=     s * (r - l) + l
               __UpperCAmelCase      :					Union[str, Any]    				=     s_bar.clamp(min=0.0				,					max=1.0     )
          # Mask weights with computed mask
          __UpperCAmelCase      :					List[Any]    				=     mask * self.weight
          # Compute output (linear layer) with masked weights
          return nn.functional.linear(snake_case				,					snake_case				,					self.bias     )



'''simple docstring'''



import re




def       _a      (		_lowercase   :      List[Any]    ):





     '''simple docstring'''






     def docstring_decorator(_lowercase   :      str    ):
          __UpperCAmelCase      :					Tuple    				=     fn.__doc__
          __UpperCAmelCase      :					Tuple    				=     func_doc.split('''\n'''    )
          __UpperCAmelCase      :					Optional[Any]    				=     0
          while i < len(_lowercase    ) and re.search(R'''^\s*Examples?:\s*$'''     ,   lines[i]    ) is None:
               i += 1
          if i < len(_lowercase    ):
               __UpperCAmelCase      :					Optional[int]    				=     example_docstring
               __UpperCAmelCase      :					Tuple    				=     '''\n'''.join(_lowercase    )
          else:
               raise ValueError(
                   F'The function {fn} should have an empty \'Examples:\' in its docstring as placeholder, '
                   F'current docstring is:\n{func_doc}'    )
          __UpperCAmelCase      :					Tuple    				=     func_doc
          return fn

     return docstring_decorator