


import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer







class      __lowercase  (nn.Linear   ):





							"""simple docstring"""







							def __init__(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__ = True       ,		lowerCAmelCase__ = "constant"       ,		lowerCAmelCase__ = 0.0       ,		lowerCAmelCase__ = "topK"       ,		):





										"""simple docstring"""




										super(lowerCAmelCase_       ,		self    ).__init__(in_features=lowerCAmelCase_       ,		out_features=lowerCAmelCase_       ,		bias=lowerCAmelCase_    )
										assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= pruning_method

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






							def  UpperCamelCase__			(	self    ):





										"""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  UpperCamelCase__			(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__    ):





										"""simple docstring"""




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











'''simple docstring'''


from __future__ import annotations

from enum import Enum

class 			A    (		_a    ):
							lowercase_							=				24
							lowercase_							=				21
							lowercase_							=				18
							lowercase_							=				15
							lowercase_							=				12
							lowercase_							=				9
							lowercase_							=				6
							lowercase_							=				3
							lowercase_							=				2
							lowercase_							=				1
							lowercase_							=				-1
							lowercase_							=				-2
							lowercase_							=				-3
							lowercase_							=				-6
							lowercase_							=				-9
							lowercase_							=				-12
							lowercase_							=				-15
							lowercase_							=				-18
							lowercase_							=				-21
							lowercase_							=				-24



class 			A    (		_a    ):
							lowercase_							=				8
							lowercase_							=				7
							lowercase_							=				6
							lowercase_							=				5
							lowercase_							=				4
							lowercase_							=				3
							lowercase_							=				2
							lowercase_							=				1



def 		snake_case_    (UpperCamelCase       :  float ,						UpperCamelCase       :  str | SIUnit ,						UpperCamelCase       :  str | SIUnit ,						):





							'''simple docstring'''


							if isinstance(UpperCamelCase ,						UpperCamelCase      ):
														_a	     =				SIUnit[known_prefix.lower()]
							if isinstance(UpperCamelCase ,						UpperCamelCase      ):
														_a	     =				SIUnit[unknown_prefix.lower()]
							_a	     =				known_amount * (
							    10 ** (known_prefix.value - unknown_prefix.value)
							)
							return unknown_amount
def 		snake_case_    (UpperCamelCase       :  float ,						UpperCamelCase       :  str | BinaryUnit ,						UpperCamelCase       :  str | BinaryUnit ,						):





							'''simple docstring'''


							if isinstance(UpperCamelCase ,						UpperCamelCase      ):
														_a	     =				BinaryUnit[known_prefix.lower()]
							if isinstance(UpperCamelCase ,						UpperCamelCase      ):
														_a	     =				BinaryUnit[unknown_prefix.lower()]
							_a	     =				known_amount * (
							    2 ** ((known_prefix.value - unknown_prefix.value) * 10)
							)
							return unknown_amount


if __name__ == "__main__":
				import doctest

				doctest.testmod()

