


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






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )->    list:




      """simple docstring"""

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






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )->    list:




      """simple docstring"""

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






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )->    float:




      """simple docstring"""

      return sum(SCREAMING_SNAKE_CASE   ) / instance_count






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )->    float:




      """simple docstring"""

      return instance_count / total_count






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )->    float:




      """simple docstring"""

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






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )->    list:




      """simple docstring"""

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

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






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )->    float:




      """simple docstring"""

      snake_case_			= sum(1 for i, j in zip(SCREAMING_SNAKE_CASE   ,   SCREAMING_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(SCREAMING_SNAKE_CASE   )) * 100


UpperCAmelCase     =       TypeVar("""num""")






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE = lambda SCREAMING_SNAKE_CASE   : True   ,   SCREAMING_SNAKE_CASE = None   ,   )->    num:




      """simple docstring"""

      while True:
            try:
                  snake_case_			= input_type(input(SCREAMING_SNAKE_CASE   ).strip() or default   )
                  if condition(SCREAMING_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 			__lowerCAmelCase       ()->    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
            snake_case_			= valid_input(
                input_type=SCREAMING_SNAKE_CASE   ,   condition=lambda SCREAMING_SNAKE_CASE   : 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
            snake_case_			= valid_input(
                input_type=SCREAMING_SNAKE_CASE   ,   condition=lambda SCREAMING_SNAKE_CASE   : 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
            snake_case_			= []  # An empty list to store instance counts of classes in dataset
            for i in range(SCREAMING_SNAKE_CASE   ):
                  snake_case_			= valid_input(
                      input_type=SCREAMING_SNAKE_CASE   ,   condition=lambda SCREAMING_SNAKE_CASE   : x > 0   ,   input_msg=(f'''Enter The number of instances for class_{i+1}: ''')   ,   err_msg='''Number of instances should be positive!'''   ,   )
                  counts.append(SCREAMING_SNAKE_CASE   )
            print('''-''' * 100   )

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

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

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

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

            # Calculating the value of actual mean for each class
            snake_case_			= [calculate_mean(counts[k]   ,   x[k]   ) for k in range(SCREAMING_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(SCREAMING_SNAKE_CASE   ,   1   ):
                  print(f'''Actual(Real) mean of class_{i} is: {actual_mean}'''   )
            print('''-''' * 100   )

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

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

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

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

            # Calculating Accuracy of the model
            print(f'''Accuracy: {accuracy(SCREAMING_SNAKE_CASE   ,   SCREAMING_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()



import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
   from PIL import Image







class lowerCAmelCase_		(					unittest.TestCase   ,		lowerCamelCase__			):

 '''simple docstring'''

 def 		UpperCamelCase__	(      self   ):
       snake_case_			= load_tool('''image-question-answering'''   )
       self.tool.setup()
       snake_case_			= load_tool('''image-question-answering'''						, remote=_UpperCAmelCase   )



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''   )   ) / '''000000039769.png'''   )
       snake_case_			= self.tool(_UpperCAmelCase						, '''How many cats are sleeping on the couch?'''   )
       self.assertEqual(_UpperCAmelCase						, '''2'''   )



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''   )   ) / '''000000039769.png'''   )
       snake_case_			= self.remote_tool(_UpperCAmelCase						, '''How many cats are sleeping on the couch?'''   )
       self.assertEqual(_UpperCAmelCase						, '''2'''   )



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''   )   ) / '''000000039769.png'''   )
       snake_case_			= self.tool(image=_UpperCAmelCase						, question='''How many cats are sleeping on the couch?'''   )
       self.assertEqual(_UpperCAmelCase						, '''2'''   )





 def 		UpperCamelCase__	(      self   ):
       snake_case_			= Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''   )   ) / '''000000039769.png'''   )
       snake_case_			= self.remote_tool(image=_UpperCAmelCase						, question='''How many cats are sleeping on the couch?'''   )
       self.assertEqual(_UpperCAmelCase						, '''2'''   )