




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						:		Optional[Any]   ,		_snake_case						:		int   ,		_snake_case						:		int   ,		_snake_case						:		bool = True   ,		_snake_case						:		str = "constant"   ,		_snake_case						:		float = 0.0   ,		_snake_case						:		str = "topK"   ,		):

        """simple docstring"""


        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_		=				pruning_method

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







   def lowerCamelCase		( self						:		List[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 lowerCamelCase		( self						:		List[Any]   ,		_snake_case						:		torch.tensor   ,		_snake_case						:		float):

        """simple docstring"""


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












def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 9 / 5) + 32  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round(float(__A			) + 273.15  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 9 / 5) + 491.67  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) - 32) * 5 / 9  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round(((float(__A			) - 32) * 5 / 9) + 273.15  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round(float(__A			) + 459.67  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round(float(__A			) - 273.15  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round(((float(__A			) - 273.15) * 9 / 5) + 32  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 9 / 5)  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) - 491.67) * 5 / 9  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round(float(__A			) - 459.67  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 5 / 9)  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 1.25 + 273.15)  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 2.25 + 32)  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 1.25)  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 2.25 + 32 + 459.67)  ,							__A			)


if __name__ == "__main__":
    import doctest

    doctest.testmod()






