
"""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 			A_			(					lowercase						, lowercase						, lowercase      )     ->							list:


					"""simple docstring"""







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




def 			A_			(					lowercase						, lowercase      )     ->							list:


					"""simple docstring"""







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




def 			A_			(					lowercase						, lowercase      )     ->							float:


					"""simple docstring"""







					return sum(snake_case_      ) / instance_count




def 			A_			(					lowercase						, lowercase      )     ->							float:


					"""simple docstring"""







					return instance_count / total_count




def 			A_			(					lowercase						, lowercase						, lowercase      )     ->							float:


					"""simple docstring"""







					UpperCAmelCase_						:    List[Any]    					=  []  # An empty list to store all squared differences
					# iterate over number of elements in items
					for i in range(len(snake_case_      )      ):
										# 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
					UpperCAmelCase_						:    Tuple    					=  len(snake_case_      )  # Number of classes in dataset
					return 1 / (total_count - n_classes) * sum(snake_case_      )




def 			A_			(					lowercase						, lowercase						, lowercase						, lowercase      )     ->							list:


					"""simple docstring"""







					UpperCAmelCase_						:    List[Any]    					=  []
					# for loop iterates over number of elements in list
					for i in range(len(snake_case_      )      ):
										# for loop iterates over number of inner items of each element
										for j in range(len(x_items[i]      )      ):
															UpperCAmelCase_						:    List[str]    					=  []  # 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(snake_case_      )      ):
																				# 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(snake_case_      )

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




def 			A_			(					lowercase						, lowercase      )     ->							float:


					"""simple docstring"""







					UpperCAmelCase_						:    Dict    					=  sum(1 for i, j in zip(snake_case_						, snake_case_      ) 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(snake_case_      )) * 100


lowercase_						    =					TypeVar("num")




def 			A_			(					lowercase						, lowercase						, lowercase						, lowercase = lambda lowercase      : True						, lowercase = None						, )     ->							num:


					"""simple docstring"""







					while True:
										try:
															UpperCAmelCase_						:    Union[str, Any]    					=  input_type(input(snake_case_      ).strip() or default      )
															if condition(snake_case_      ):
																				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 			A_			(					)     ->							Optional[int]:


					"""simple docstring"""







					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
										UpperCAmelCase_						:    Union[str, Any]    					=  valid_input(
										    input_type=snake_case_						, condition=lambda lowercase      : x > 0						, input_msg="""Enter the number of classes (Data Groupings): """						, err_msg="""Number of classes should be positive!"""						, )

										print("""-""" * 100      )

										# Trying to get the value of standard deviation
										UpperCAmelCase_						:    Any    					=  valid_input(
										    input_type=snake_case_						, 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("""-""" * 100      )

										# Trying to get number of instances in classes and theirs means to generate
										# dataset
										UpperCAmelCase_						:    Optional[int]    					=  []  # An empty list to store instance counts of classes in dataset
										for i in range(snake_case_      ):
															UpperCAmelCase_						:    int    					=  valid_input(
															    input_type=snake_case_						, 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(snake_case_      )
										print("""-""" * 100      )

										# An empty list to store values of user-entered means of classes
										UpperCAmelCase_						:    Tuple    					=  []
										for a in range(snake_case_      ):
															UpperCAmelCase_						:    Any    					=  valid_input(
															    input_type=snake_case_						, input_msg=(f'''Enter the value of mean for class_{a+1}: ''')						, err_msg="""This is an invalid value."""						, )
															user_means.append(snake_case_      )
										print("""-""" * 100      )

										print("""Standard deviation: """						, snake_case_      )
										# print out the number of instances in classes in separated line
										for i, count in enumerate(snake_case_						, 1      ):
															print(f'''Number of instances in class_{i} is: {count}'''      )
										print("""-""" * 100      )

										# print out mean values of classes separated line
										for i, user_mean in enumerate(snake_case_						, 1      ):
															print(f'''Mean of class_{i} is: {user_mean}'''      )
										print("""-""" * 100      )

										# Generating training dataset drawn from gaussian distribution
										UpperCAmelCase_						:    Optional[int]    					=  [
										    gaussian_distribution(user_means[j]						, snake_case_						, counts[j]      )
										    for j in range(snake_case_      )
										]
										print("""Generated Normal Distribution: \n"""						, snake_case_      )
										print("""-""" * 100      )

										# Generating Ys to detecting corresponding classes
										UpperCAmelCase_						:    Any    					=  y_generator(snake_case_						, snake_case_      )
										print("""Generated Corresponding Ys: \n"""						, snake_case_      )
										print("""-""" * 100      )

										# Calculating the value of actual mean for each class
										UpperCAmelCase_						:    int    					=  [calculate_mean(counts[k]						, x[k]      ) for k in range(snake_case_      )]
										# for loop iterates over number of elements in 'actual_means' list and print
										# out them in separated line
										for i, actual_mean in enumerate(snake_case_						, 1      ):
															print(f'''Actual(Real) mean of class_{i} is: {actual_mean}'''      )
										print("""-""" * 100      )

										# Calculating the value of probabilities for each class
										UpperCAmelCase_						:    str    					=  [
										    calculate_probabilities(counts[i]						, sum(snake_case_      )      ) for i in range(snake_case_      )
										]

										# for loop iterates over number of elements in 'probabilities' list and print
										# out them in separated line
										for i, probability in enumerate(snake_case_						, 1      ):
															print(f'''Probability of class_{i} is: {probability}'''      )
										print("""-""" * 100      )

										# Calculating the values of variance for each class
										UpperCAmelCase_						:    Any    					=  calculate_variance(snake_case_						, snake_case_						, sum(snake_case_      )      )
										print("""Variance: """						, snake_case_      )
										print("""-""" * 100      )

										# Predicting Y values
										# storing predicted Y values in 'pre_indexes' variable
										UpperCAmelCase_						:    Tuple    					=  predict_y_values(snake_case_						, snake_case_						, snake_case_						, snake_case_      )
										print("""-""" * 100      )

										# Calculating Accuracy of the model
										print(f'''Accuracy: {accuracy(snake_case_						, snake_case_      )}'''      )
										print("""-""" * 100      )
										print(""" DONE """.center(100						, """+"""      )      )

										if input("""Press any key to restart or \'q\' for quit: """      ).strip().lower() == "q":
															print("""\n""" + """GoodBye!""".center(100						, """-"""      ) + """\n"""      )
															break
										system("""cls""" if name == """nt""" else """clear"""      )  # noqa: S605


if __name__ == "__main__":
							main()



"""simple docstring"""







import unittest

from transformers.image_processing_utils import get_size_dict



class 		UpperCAmelCase_	(unittest.TestCase						):
 """simple docstring"""




 def   a      ( self       : Union[str, Any] )->					Optional[Any]:



      """simple docstring"""

      UpperCAmelCase_						:    Any    					=  {"""wrong_key""": 2_24}
      with self.assertRaises(a_ ):
           get_size_dict(a_ )

      UpperCAmelCase_						:    Union[str, Any]    					=  {"""height""": 2_24}
      with self.assertRaises(a_ ):
           get_size_dict(a_ )

      UpperCAmelCase_						:    Union[str, Any]    					=  {"""width""": 2_24, """shortest_edge""": 2_24}
      with self.assertRaises(a_ ):
           get_size_dict(a_ )

      # Test a dict with the correct keys is returned as is
      UpperCAmelCase_						:    int    					=  {"""height""": 2_24, """width""": 2_24}
      UpperCAmelCase_						:    Dict    					=  get_size_dict(a_ )
      self.assertEqual(a_     ,   a_ )

      UpperCAmelCase_						:    Optional[int]    					=  {"""shortest_edge""": 2_24}
      UpperCAmelCase_						:    Tuple    					=  get_size_dict(a_ )
      self.assertEqual(a_     ,   {"""shortest_edge""": 2_24} )

      UpperCAmelCase_						:    str    					=  {"""longest_edge""": 2_24, """shortest_edge""": 2_24}
      UpperCAmelCase_						:    Any    					=  get_size_dict(a_ )
      self.assertEqual(a_     ,   {"""longest_edge""": 2_24, """shortest_edge""": 2_24} )

      # Test a single int value which  represents (size, size)
      UpperCAmelCase_						:    Optional[Any]    					=  get_size_dict(2_24 )
      self.assertEqual(a_     ,   {"""height""": 2_24, """width""": 2_24} )

      # Test a single int value which represents the shortest edge
      UpperCAmelCase_						:    List[str]    					=  get_size_dict(2_24     ,   default_to_square=a_ )
      self.assertEqual(a_     ,   {"""shortest_edge""": 2_24} )

      # Test a tuple of ints which represents (height, width)
      UpperCAmelCase_						:    List[Any]    					=  get_size_dict((1_50, 2_00) )
      self.assertEqual(a_     ,   {"""height""": 1_50, """width""": 2_00} )

      # Test a tuple of ints which represents (width, height)
      UpperCAmelCase_						:    List[str]    					=  get_size_dict((1_50, 2_00)     ,   height_width_order=a_ )
      self.assertEqual(a_     ,   {"""height""": 2_00, """width""": 1_50} )

      # Test an int representing the shortest edge and max_size which represents the longest edge
      UpperCAmelCase_						:    List[Any]    					=  get_size_dict(2_24     ,   max_size=2_56     ,   default_to_square=a_ )
      self.assertEqual(a_     ,   {"""shortest_edge""": 2_24, """longest_edge""": 2_56} )

      # Test int with default_to_square=True and max_size fails
      with self.assertRaises(a_ ):
           get_size_dict(2_24     ,   max_size=2_56     ,   default_to_square=a_ )

