



import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer

class        _A			(      nn.Linear       ):



       def __init__(						self				:	Optional[Any]	,						__SCREAMING_SNAKE_CASE				:	int	,						__SCREAMING_SNAKE_CASE				:	int	,						__SCREAMING_SNAKE_CASE				:	bool = True	,						__SCREAMING_SNAKE_CASE				:	str = "constant"	,						__SCREAMING_SNAKE_CASE				:	float = 0.0	,						__SCREAMING_SNAKE_CASE				:	str = "topK"	,						):


           '''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 						_lowerCamelCase     (						self				:	Union[str, 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				:	int	,						__SCREAMING_SNAKE_CASE				:	torch.tensor	,						__SCREAMING_SNAKE_CASE				:	float):


           '''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			,  __a			,  __a					=							-0.1, 1.1, 2 / 3
               if self.training:
                   __a					=							torch.zeros_like(self.mask_scores).uniform_().clamp(0.00_01	,						0.99_99)
                   __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)










def 	__snake_case      (     _UpperCAmelCase							):
    __a					=							1
    __a					=							0
    __a					=							1
    __a					=							1
    __a					=							3
    __a					=							3
    __a					=							limit
    __a					=							0

    __a					=							''''''
    while counter != decimal + 1:
        if 4 * q + r - t < n * t:
            result += str(_UpperCAmelCase							)
            if counter == 0:
                result += "."

            if decimal == counter:
                break

            counter += 1
            __a					=							10 * (r - n * t)
            __a					=							((10 * (3 * q + r)) // t) - 10 * n
            q *= 10
            __a					=							nr
        else:
            __a					=							(2 * q + r) * l
            __a					=							(q * (7 * k) + 2 + (r * l)) // (t * l)
            q *= k
            t *= l
            l += 2
            k += 1
            __a					=							nn
            __a					=							nr
    return result





def 	__snake_case      (     ):
    print(f'{calculate_pi(50							) = }'							)
    import doctest

    doctest.testmod()


if __name__ == "__main__":
 main()





