



'''simple docstring'''


from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar






def 			UpperCAmelCase_							(		lowercase__							,						lowercase__							,						lowercase__							):


     '''simple docstring'''







     seed(1							)
     return [gauss(lowercase__							,						lowercase__							) for _ in range(lowercase__							)]






def 			UpperCAmelCase_							(		lowercase__							,						lowercase__							):


     '''simple docstring'''







     return [k for k in range(lowercase__							) for _ in range(instance_count[k]							)]






def 			UpperCAmelCase_							(		lowercase__							,						lowercase__							):


     '''simple docstring'''







     return sum(lowercase__							) / instance_count






def 			UpperCAmelCase_							(		lowercase__							,						lowercase__							):


     '''simple docstring'''







     return instance_count / total_count






def 			UpperCAmelCase_							(		lowercase__							,						lowercase__							,						lowercase__							):


     '''simple docstring'''







     a_									=[]  # An empty list to store all squared differences
     # iterate over number of elements in items
     for i in range(len(lowercase__							)							):
          # for loop iterates over number of elements in inner layer of items
          for j in range(len(items[i]							)							):
               # appending squared differences to 'squared_diff' list
               squared_diff.append((items[i][j] - means[i]) ** 2							)

    # one divided by (the number of all instances - number of classes) multiplied by
    # sum of all squared differences
     a_									=len(lowercase__							)  # Number of classes in dataset
     return 1 / (total_count - n_classes) * sum(lowercase__							)






def 			UpperCAmelCase_							(		lowercase__							,						lowercase__							,						lowercase__							,						lowercase__							):


     '''simple docstring'''







     a_									=[]
     # for loop iterates over number of elements in list
     for i in range(len(lowercase__							)							):
          # for loop iterates over number of inner items of each element
          for j in range(len(x_items[i]							)							):
               a_									=[]  # to store all discriminant values of each item as a list
               # for loop iterates over number of classes we have in our dataset
               for k in range(len(lowercase__							)							):
                    # appending values of discriminants for each class to 'temp' list
                    temp.append(
                        x_items[i][j] * (means[k] / variance)
                        - (means[k] ** 2 / (2 * variance))
                        + log(probabilities[k]							)							)
               # appending discriminant values of each item to 'results' list
               results.append(lowercase__							)

     return [result.index(max(lowercase__							)							) for result in results]






def 			UpperCAmelCase_							(		lowercase__							,						lowercase__							):


     '''simple docstring'''







     a_									=sum(1 for i, j in zip(lowercase__							,						lowercase__							) if i == j							)
     # percentage of accuracy equals to number of correct predictions divided by number
     # of all data and multiplied by 100
     return (correct / len(lowercase__							)) * 1_0_0


lowercase											=      TypeVar('''num''')






def 			UpperCAmelCase_							(		lowercase__							,						lowercase__							,						lowercase__							,						lowercase__ = lambda lowercase__							: True							,						lowercase__ = None							,						):


     '''simple docstring'''







     while True:
          try:
               a_									=input_type(input(lowercase__							).strip() or default							)
               if condition(lowercase__							):
                    return user_input
               else:
                    print(F"""{user_input}: {err_msg}"""							)
                    continue
          except ValueError:
               print(
                   F"""{user_input}: Incorrect input type, expected {input_type.__name__!r}"""							)






def 			UpperCAmelCase_							(		):


     '''simple docstring'''







     while True:
          print(" Linear Discriminant Analysis ".center(5_0							,						"*"							)							)
          print("*" * 5_0							,						"\n"							)
          print("First of all we should specify the number of classes that"							)
          print("we want to generate as training dataset"							)
          # Trying to get number of classes
          a_									=valid_input(
              input_type=lowercase__							,						condition=lambda lowercase__							: x > 0							,						input_msg="Enter the number of classes (Data Groupings): "							,						err_msg="Number of classes should be positive!"							,						)

          print("-" * 1_0_0							)

          # Trying to get the value of standard deviation
          a_									=valid_input(
              input_type=lowercase__							,						condition=lambda lowercase__							: x >= 0							,						input_msg=(
                  "Enter the value of standard deviation"
                  "(Default value is 1.0 for all classes): "
              )							,						err_msg="Standard deviation should not be negative!"							,						default="1.0"							,						)

          print("-" * 1_0_0							)

          # Trying to get number of instances in classes and theirs means to generate
          # dataset
          a_									=[]  # An empty list to store instance counts of classes in dataset
          for i in range(lowercase__							):
               a_									=valid_input(
                   input_type=lowercase__							,						condition=lambda lowercase__							: x > 0							,						input_msg=(F"""Enter The number of instances for class_{i+1}: """)							,						err_msg="Number of instances should be positive!"							,						)
               counts.append(lowercase__							)
          print("-" * 1_0_0							)

          # An empty list to store values of user-entered means of classes
          a_									=[]
          for a in range(lowercase__							):
               a_									=valid_input(
                   input_type=lowercase__							,						input_msg=(F"""Enter the value of mean for class_{a+1}: """)							,						err_msg="This is an invalid value."							,						)
               user_means.append(lowercase__							)
          print("-" * 1_0_0							)

          print("Standard deviation: "							,						lowercase__							)
          # print out the number of instances in classes in separated line
          for i, count in enumerate(lowercase__							,						1							):
               print(F"""Number of instances in class_{i} is: {count}"""							)
          print("-" * 1_0_0							)

          # print out mean values of classes separated line
          for i, user_mean in enumerate(lowercase__							,						1							):
               print(F"""Mean of class_{i} is: {user_mean}"""							)
          print("-" * 1_0_0							)

          # Generating training dataset drawn from gaussian distribution
          a_									=[
              gaussian_distribution(user_means[j]							,						lowercase__							,						counts[j]							)
              for j in range(lowercase__							)
          ]
          print("Generated Normal Distribution: \n"							,						lowercase__							)
          print("-" * 1_0_0							)

          # Generating Ys to detecting corresponding classes
          a_									=y_generator(lowercase__							,						lowercase__							)
          print("Generated Corresponding Ys: \n"							,						lowercase__							)
          print("-" * 1_0_0							)

          # Calculating the value of actual mean for each class
          a_									=[calculate_mean(counts[k]							,						x[k]							) for k in range(lowercase__							)]
          # for loop iterates over number of elements in 'actual_means' list and print
          # out them in separated line
          for i, actual_mean in enumerate(lowercase__							,						1							):
               print(F"""Actual(Real) mean of class_{i} is: {actual_mean}"""							)
          print("-" * 1_0_0							)

          # Calculating the value of probabilities for each class
          a_									=[
              calculate_probabilities(counts[i]							,						sum(lowercase__							)							) for i in range(lowercase__							)
          ]

          # for loop iterates over number of elements in 'probabilities' list and print
          # out them in separated line
          for i, probability in enumerate(lowercase__							,						1							):
               print(F"""Probability of class_{i} is: {probability}"""							)
          print("-" * 1_0_0							)

          # Calculating the values of variance for each class
          a_									=calculate_variance(lowercase__							,						lowercase__							,						sum(lowercase__							)							)
          print("Variance: "							,						lowercase__							)
          print("-" * 1_0_0							)

          # Predicting Y values
          # storing predicted Y values in 'pre_indexes' variable
          a_									=predict_y_values(lowercase__							,						lowercase__							,						lowercase__							,						lowercase__							)
          print("-" * 1_0_0							)

          # Calculating Accuracy of the model
          print(F"""Accuracy: {accuracy(lowercase__							,						lowercase__							)}"""							)
          print("-" * 1_0_0							)
          print(" DONE ".center(1_0_0							,						"+"							)							)

          if input("Press any key to restart or 'q' for quit: "							).strip().lower() == "q":
               print("\n" + "GoodBye!".center(1_0_0							,						"-"							) + "\n"							)
               break
          system("cls" if name == "nt" else "clear"							)  # noqa: S605


if __name__ == "__main__":
  main()







'''simple docstring'''


import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     a_									=args.serialization_dir
     a_									=args.pruning_method
     a_									=args.threshold

     a_									=torch.load(os.path.join(lowercase__							,						"pytorch_model.bin"							)							,						map_location="cpu"							)

     a_									=0  # Number of remaining (not pruned) params in the encoder
     a_									=0  # Number of params in the encoder

     print("name".ljust(6_0							,						" "							)							,						"Remaining Weights %"							,						"Remaining Weight"							)
     for name, param in st.items():
          if "encoder" not in name:
               continue

          if "mask_scores" in name:
               if pruning_method == "topK":
                    a_									=TopKBinarizer.apply(lowercase__							,						lowercase__							).sum().item()
               elif pruning_method == "sigmoied_threshold":
                    a_									=ThresholdBinarizer.apply(lowercase__							,						lowercase__							,						lowercase__							).sum().item()
               elif pruning_method == "l0":
                    a_		,    a_									=-0.1, 1.1
                    a_									=torch.sigmoid(lowercase__							)
                    a_									=s * (r - l) + l
                    a_									=s_bar.clamp(min=0.0							,						max=1.0							)
                    a_									=(mask > 0.0).sum().item()
               else:
                    raise ValueError("Unknown pruning method"							)
               remaining_count += mask_ones
               print(name.ljust(6_0							,						" "							)							,						str(round(1_0_0 * mask_ones / param.numel()							,						3							)							).ljust(2_0							,						" "							)							,						str(lowercase__							)							)
          else:
               encoder_count += param.numel()
               if "bias" in name or "LayerNorm" in name:
                    remaining_count += param.numel()

     print(""							)
     print("Remaining Weights (global) %: "							,						1_0_0 * remaining_count / encoder_count							)


if __name__ == "__main__":
  lowercase											=      argparse.ArgumentParser()

  parser.add_argument(
      '''--pruning_method''',
      choices=['''l0''', '''topK''', '''sigmoied_threshold'''],
      type=str,
      required=True,
      help=(
          '''Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement'''
          ''' pruning)'''
      ),
  )
  parser.add_argument(
      '''--threshold''',
      type=float,
      required=False,
      help=(
          '''For `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'''
          '''For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'''
          '''Not needed for `l0`'''
      ),
  )
  parser.add_argument(
      '''--serialization_dir''',
      type=str,
      required=True,
      help='''Folder containing the model that was previously fine-pruned''',
  )

  lowercase											=      parser.parse_args()

  main(args)


