
"""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						):
   def __init__(      self     ,   _a     ,   _a     ,   _a = True     ,   _a = "constant"     ,   _a = 0.0     ,   _a = "topK"     ,   ):
          super(_a     ,   self    ).__init__(in_features=_a     ,   out_features=_a     ,   bias=_a    )
          assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
          lowercase   :    List[Any]			=							pruning_method

          if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
                 lowercase   :    Union[str, Any]			=							mask_scale
                 lowercase   :    Optional[Any]			=							mask_init
                 lowercase   :    List[Any]			=							nn.Parameter(torch.empty(self.weight.size()    )    )
                 self.init_mask()
   def       __magic_name__      (      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       __magic_name__      (      self     ,   _a     ,   _a    ):
          # Get the mask
          if self.pruning_method == "topK":
                 lowercase   :    List[str]			=							TopKBinarizer.apply(self.mask_scores     ,   _a    )
          elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
                 lowercase   :    Any			=							"sigmoied" in self.pruning_method
                 lowercase   :    str			=							ThresholdBinarizer.apply(self.mask_scores     ,   _a     ,   _a    )
          elif self.pruning_method == "magnitude":
                 lowercase   :    Tuple			=							MagnitudeBinarizer.apply(self.weight     ,   _a    )
          elif self.pruning_method == "l0":
                 lowercase		,   lowercase		,   lowercase   :    List[str]			=							-0.1, 1.1, 2 / 3
                 if self.training:
                        lowercase   :    List[Any]			=							torch.zeros_like(self.mask_scores    ).uniform_().clamp(0.0_0_0_1     ,   0.9_9_9_9    )
                        lowercase   :    Tuple			=							torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b    )
                 else:
                        lowercase   :    List[str]			=							torch.sigmoid(self.mask_scores    )
                 lowercase   :    List[Any]			=							s * (r - l) + l
                 lowercase   :    Dict			=							s_bar.clamp(min=0.0     ,   max=1.0    )
          # Mask weights with computed mask
          lowercase   :    Dict			=							mask * self.weight
          # Compute output (linear layer) with masked weights
          return nn.functional.linear(_a     ,   _a     ,   self.bias    )




"""simple docstring"""




from __future__ import annotations

_A      :	List[str]       		=	"""Alexander Joslin"""


def   __magic_name__     (				__snake_case    :	int  )			->    int:

       if number <= 0:
              lowercase   :    Optional[Any]			=							f"""n must be greater than 0. Got n = {number}"""
              raise ValueError(__snake_case  )

       lowercase   :    List[str]			=							[number + 1] * (number + 1)

       # starting position
       lowercase   :    Optional[Any]			=							0
       for i in range(1					,			__snake_case  ):
              lowercase   :    str			=							min(table[i + 1]					,			table[i] + 1  )
              # check if out of bounds
              if i * 2 <= number:
                     lowercase   :    int			=							min(table[i * 2]					,			table[i] + 1  )
              # check if out of bounds
              if i * 3 <= number:
                     lowercase   :    Optional[int]			=							min(table[i * 3]					,			table[i] + 1  )
       return table[number]


if __name__ == "__main__":
   import doctest

   doctest.testmod()


