





'''simple docstring'''






from __future__ import annotations

from queue import Queue
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase						):





      """simple docstring"""


      a ^= b
      b ^= a
      a ^= b
      return a, b
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase						):





      """simple docstring"""


      _lowerCAmelCase							  =							1
      while (1 << j) < max_node:
            for i in range(1							,			max_node + 1						):
                  _lowerCAmelCase							  =							parent[j - 1][parent[j - 1][i]]
            j += 1
      return parent
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase						):





      """simple docstring"""


      if level[u] < level[v]:
            _lowerCAmelCase  ,							_lowerCAmelCase							  =							swap(lowerCAmelCase							,			lowerCAmelCase						)
      # making depth of u same as depth of v
      for i in range(18							,			-1							,			-1						):
            if level[u] - (1 << i) >= level[v]:
                  _lowerCAmelCase							  =							parent[i][u]
    # at the same depth if u==v that mean lca is found
      if u == v:
            return u
      # moving both nodes upwards till lca in found
      for i in range(18							,			-1							,			-1						):
            if parent[i][u] not in [0, parent[i][v]]:
                  _lowerCAmelCase  ,							_lowerCAmelCase							  =							parent[i][u], parent[i][v]
    # returning longest common ancestor of u,v
      return parent[0][u]
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase = 1							,			):





      """simple docstring"""


      _lowerCAmelCase							  =							0
      _lowerCAmelCase							  =							Queue(maxsize=lowerCAmelCase						)
      q.put(lowerCAmelCase						)
      while q.qsize() != 0:
            _lowerCAmelCase							  =							q.get()
            for v in graph[u]:
                  if level[v] == -1:
                        _lowerCAmelCase							  =							level[u] + 1
                        q.put(lowerCAmelCase						)
                        _lowerCAmelCase							  =							u
      return level, parent
def      UpperCamelCase__   (	):





      """simple docstring"""


      _lowerCAmelCase							  =							13
      # initializing with 0
      _lowerCAmelCase							  =							[[0 for _ in range(max_node + 10						)] for _ in range(20						)]
      # initializing with -1 which means every node is unvisited
      _lowerCAmelCase							  =							[-1 for _ in range(max_node + 10						)]
      _lowerCAmelCase							  =							{
          1: [2, 3, 4],
          2: [5],
          3: [6, 7],
          4: [8],
          5: [9, 10],
          6: [11],
          7: [],
          8: [12, 13],
          9: [],
          10: [],
          11: [],
          12: [],
          13: [],
      }
      _lowerCAmelCase  ,							_lowerCAmelCase							  =							breadth_first_search(lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			1						)
      _lowerCAmelCase							  =							create_sparse(lowerCAmelCase							,			lowerCAmelCase						)
      print("""LCA of node 1 and 3 is: """							,			lowest_common_ancestor(1							,			3							,			lowerCAmelCase							,			lowerCAmelCase						)						)
      print("""LCA of node 5 and 6 is: """							,			lowest_common_ancestor(5							,			6							,			lowerCAmelCase							,			lowerCAmelCase						)						)
      print("""LCA of node 7 and 11 is: """							,			lowest_common_ancestor(7							,			11							,			lowerCAmelCase							,			lowerCAmelCase						)						)
      print("""LCA of node 6 and 7 is: """							,			lowest_common_ancestor(6							,			7							,			lowerCAmelCase							,			lowerCAmelCase						)						)
      print("""LCA of node 4 and 12 is: """							,			lowest_common_ancestor(4							,			12							,			lowerCAmelCase							,			lowerCAmelCase						)						)
      print("""LCA of node 8 and 8 is: """							,			lowest_common_ancestor(8							,			8							,			lowerCAmelCase							,			lowerCAmelCase						)						)


if __name__ == "__main__":
      main()








'''simple docstring'''






import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer




class 				UpperCAmelCase  (    nn.Linear     ):




      def __init__(    self    :				str    ,    __snake_case    :				int    ,    __snake_case    :				int    ,    __snake_case    :				bool = True    ,    __snake_case    :				str = "constant"    ,    __snake_case    :				float = 0.0    ,    __snake_case    :				str = "topK"    ,    )       ->   int:
            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"]
            _lowerCAmelCase							  =							pruning_method

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




      def        lowercase__					(    self    :				Any						)       ->   str:
            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        lowercase__					(    self    :				Dict    ,    __snake_case    :				torch.tensor    ,    __snake_case    :				float						)       ->   Union[str, Any]:
            # Get the mask
            if self.pruning_method == "topK":
                  _lowerCAmelCase							  =							TopKBinarizer.apply(self.mask_scores    ,    __snake_case						)
            elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
                  _lowerCAmelCase							  =							"""sigmoied""" in self.pruning_method
                  _lowerCAmelCase							  =							ThresholdBinarizer.apply(self.mask_scores    ,    __snake_case    ,    __snake_case						)
            elif self.pruning_method == "magnitude":
                  _lowerCAmelCase							  =							MagnitudeBinarizer.apply(self.weight    ,    __snake_case						)
            elif self.pruning_method == "l0":
                  _lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase							  =							-0.1, 1.1, 2 / 3
                  if self.training:
                        _lowerCAmelCase							  =							torch.zeros_like(self.mask_scores						).uniform_().clamp(0.00_01    ,    0.99_99						)
                        _lowerCAmelCase							  =							torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b						)
                  else:
                        _lowerCAmelCase							  =							torch.sigmoid(self.mask_scores						)
                  _lowerCAmelCase							  =							s * (r - l) + l
                  _lowerCAmelCase							  =							s_bar.clamp(min=0.0    ,    max=1.0						)
            # Mask weights with computed mask
            _lowerCAmelCase							  =							mask * self.weight
            # Compute output (linear layer) with masked weights
            return nn.functional.linear(__snake_case    ,    __snake_case    ,    self.bias						)






