




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

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase)    ->  list:
     seed(1)
     return [gauss(__UpperCamelCase	,      __UpperCamelCase) for _ in range(__UpperCamelCase)]

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase)    ->  list:
     return [k for k in range(__UpperCamelCase) for _ in range(instance_count[k])]

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase)    ->  float:
     return sum(__UpperCamelCase) / instance_count

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase)    ->  float:
     return instance_count / total_count

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase)    ->  float:
     a											=   []  # An empty list to store all squared differences
     # iterate over number of elements in items
     for i in range(len(__UpperCamelCase)):
          # 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(__UpperCamelCase)  # Number of classes in dataset
     return 1 / (total_count - n_classes) * sum(__UpperCamelCase)

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase)    ->  list:
     a											=   []
     # for loop iterates over number of elements in list
     for i in range(len(__UpperCamelCase)):
          # 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(__UpperCamelCase)):
                    # 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(__UpperCamelCase)

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

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase)    ->  float:
     a											=   sum(1 for i, j in zip(__UpperCamelCase	,      __UpperCamelCase) 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(__UpperCamelCase)) * 1_00


lowercase__   :    List[str] 					=      TypeVar("num")

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase = lambda __UpperCamelCase: True	,      __UpperCamelCase = None	,      )    ->  num:
     while True:
          try:
               a											=   input_type(input(__UpperCamelCase).strip() or default)
               if condition(__UpperCamelCase):
                    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       SCREAMING_SNAKE_CASE							(     )    ->  List[Any]:
     while True:
          print(" Linear Discriminant Analysis ".center(50	,      "*"))
          print("*" * 50	,      "\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=__UpperCamelCase	,      condition=lambda __UpperCamelCase: x > 0	,      input_msg="Enter the number of classes (Data Groupings): "	,      err_msg="Number of classes should be positive!"	,      )

          print("-" * 1_00)

          # Trying to get the value of standard deviation
          a											=   valid_input(
              input_type=__UpperCamelCase	,      condition=lambda __UpperCamelCase: 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_00)

          # 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(__UpperCamelCase):
               a											=   valid_input(
                   input_type=__UpperCamelCase	,      condition=lambda __UpperCamelCase: x > 0	,      input_msg=(f'''Enter The number of instances for class_{i+1}: ''')	,      err_msg="Number of instances should be positive!"	,      )
               counts.append(__UpperCamelCase)
          print("-" * 1_00)

          # An empty list to store values of user-entered means of classes
          a											=   []
          for a in range(__UpperCamelCase):
               a											=   valid_input(
                   input_type=__UpperCamelCase	,      input_msg=(f'''Enter the value of mean for class_{a+1}: ''')	,      err_msg="This is an invalid value."	,      )
               user_means.append(__UpperCamelCase)
          print("-" * 1_00)

          print("Standard deviation: "	,      __UpperCamelCase)
          # print out the number of instances in classes in separated line
          for i, count in enumerate(__UpperCamelCase	,      1):
               print(f'''Number of instances in class_{i} is: {count}''')
          print("-" * 1_00)

          # print out mean values of classes separated line
          for i, user_mean in enumerate(__UpperCamelCase	,      1):
               print(f'''Mean of class_{i} is: {user_mean}''')
          print("-" * 1_00)

          # Generating training dataset drawn from gaussian distribution
          a											=   [
              gaussian_distribution(user_means[j]	,      __UpperCamelCase	,      counts[j])
              for j in range(__UpperCamelCase)
          ]
          print("Generated Normal Distribution: \n"	,      __UpperCamelCase)
          print("-" * 1_00)

          # Generating Ys to detecting corresponding classes
          a											=   y_generator(__UpperCamelCase	,      __UpperCamelCase)
          print("Generated Corresponding Ys: \n"	,      __UpperCamelCase)
          print("-" * 1_00)

          # Calculating the value of actual mean for each class
          a											=   [calculate_mean(counts[k]	,      x[k]) for k in range(__UpperCamelCase)]
          # for loop iterates over number of elements in 'actual_means' list and print
          # out them in separated line
          for i, actual_mean in enumerate(__UpperCamelCase	,      1):
               print(f'''Actual(Real) mean of class_{i} is: {actual_mean}''')
          print("-" * 1_00)

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

          # for loop iterates over number of elements in 'probabilities' list and print
          # out them in separated line
          for i, probability in enumerate(__UpperCamelCase	,      1):
               print(f'''Probability of class_{i} is: {probability}''')
          print("-" * 1_00)

          # Calculating the values of variance for each class
          a											=   calculate_variance(__UpperCamelCase	,      __UpperCamelCase	,      sum(__UpperCamelCase))
          print("Variance: "	,      __UpperCamelCase)
          print("-" * 1_00)

          # Predicting Y values
          # storing predicted Y values in 'pre_indexes' variable
          a											=   predict_y_values(__UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase)
          print("-" * 1_00)

          # Calculating Accuracy of the model
          print(f'''Accuracy: {accuracy(__UpperCamelCase	,      __UpperCamelCase)}''')
          print("-" * 1_00)
          print(" DONE ".center(1_00	,      "+"))

          if input("Press any key to restart or 'q' for quit: ").strip().lower() == "q":
               print("\n" + "GoodBye!".center(1_00	,      "-") + "\n")
               break
          system("cls" if name == "nt" else "clear")  # noqa: S605


if __name__ == "__main__":
   main()







import unittest

from transformers.image_processing_utils import get_size_dict




class     a__ (      unittest.TestCase   ):







     def     lowerCAmelCase_       (			self				)						->					Optional[int]:



          '''simple docstring'''



          a											=   {"wrong_key": 224}
          with self.assertRaises(A				):
               get_size_dict(A				)

          a											=   {"height": 224}
          with self.assertRaises(A				):
               get_size_dict(A				)

          a											=   {"width": 224, "shortest_edge": 224}
          with self.assertRaises(A				):
               get_size_dict(A				)

          # Test a dict with the correct keys is returned as is
          a											=   {"height": 224, "width": 224}
          a											=   get_size_dict(A				)
          self.assertEqual(A						,	A				)

          a											=   {"shortest_edge": 224}
          a											=   get_size_dict(A				)
          self.assertEqual(A						,	{"shortest_edge": 224}				)

          a											=   {"longest_edge": 224, "shortest_edge": 224}
          a											=   get_size_dict(A				)
          self.assertEqual(A						,	{"longest_edge": 224, "shortest_edge": 224}				)

          # Test a single int value which  represents (size, size)
          a											=   get_size_dict(224				)
          self.assertEqual(A						,	{"height": 224, "width": 224}				)

          # Test a single int value which represents the shortest edge
          a											=   get_size_dict(224						,	default_to_square=A				)
          self.assertEqual(A						,	{"shortest_edge": 224}				)

          # Test a tuple of ints which represents (height, width)
          a											=   get_size_dict((150, 200)				)
          self.assertEqual(A						,	{"height": 150, "width": 200}				)

          # Test a tuple of ints which represents (width, height)
          a											=   get_size_dict((150, 200)						,	height_width_order=A				)
          self.assertEqual(A						,	{"height": 200, "width": 150}				)

          # Test an int representing the shortest edge and max_size which represents the longest edge
          a											=   get_size_dict(224						,	max_size=256						,	default_to_square=A				)
          self.assertEqual(A						,	{"shortest_edge": 224, "longest_edge": 256}				)

          # Test int with default_to_square=True and max_size fails
          with self.assertRaises(A				):
               get_size_dict(224						,	max_size=256						,	default_to_square=A				)

