

'''simple docstring'''
import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
class  _snake_case						(nn.Linear):






			def __init__(   self       ,_snake_case       ,_snake_case       ,_snake_case = True       ,_snake_case = "constant"       ,_snake_case = 0.0       ,_snake_case = "topK"       ,):
				super(lowerCamelCase_       ,self			).__init__(in_features=lowerCamelCase_       ,out_features=lowerCamelCase_       ,bias=lowerCamelCase_			)
				assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
				UpperCAmelCase_			:	Union[str, Any]      =   pruning_method

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






			def 						UpperCamelCase__				(   self			):



				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 						UpperCamelCase__				(   self       ,_snake_case       ,_snake_case			):



				if self.pruning_method == "topK":
					UpperCAmelCase_			:	int      =   TopKBinarizer.apply(self.mask_scores       ,lowerCamelCase_			)
				elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
					UpperCAmelCase_			:	str      =   'sigmoied' in self.pruning_method
					UpperCAmelCase_			:	int      =   ThresholdBinarizer.apply(self.mask_scores       ,lowerCamelCase_       ,lowerCamelCase_			)
				elif self.pruning_method == "magnitude":
					UpperCAmelCase_			:	int      =   MagnitudeBinarizer.apply(self.weight       ,lowerCamelCase_			)
				elif self.pruning_method == "l0":
					UpperCAmelCase_			:	str      =   -0.1, 1.1, 2 / 3
					if self.training:
						UpperCAmelCase_			:	int      =   torch.zeros_like(self.mask_scores			).uniform_().clamp(0.0001       ,0.9999			)
						UpperCAmelCase_			:	Any      =   torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b			)
					else:
						UpperCAmelCase_			:	List[Any]      =   torch.sigmoid(self.mask_scores			)
					UpperCAmelCase_			:	Tuple      =   s * (r - l) + l
					UpperCAmelCase_			:	List[str]      =   s_bar.clamp(min=0.0       ,max=1.0			)
				# Mask weights with computed mask
				UpperCAmelCase_			:	List[str]      =   mask * self.weight
				# Compute output (linear layer) with masked weights
				return nn.functional.linear(lowerCamelCase_       ,lowerCamelCase_       ,self.bias			)












"""simple docstring"""
def        lowerCamelCase_  (						_lowerCamelCase				):
 lowerCamelCase__     :			int     				=						5381
 for x in s:
  lowerCamelCase__     :			Union[str, Any]     				=						((hash_value << 5) + hash_value) + ord(_lowerCamelCase				)
 return hash_value & 0Xffffffff

