




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_	(     lowerCamelCase:				float   , lowerCamelCase:				float   , lowerCamelCase:				int  )    ->	list:


      '''simple docstring'''




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

def 	lowerCAmelCase_	(     lowerCamelCase:				int   , lowerCamelCase:				list  )    ->	list:


      '''simple docstring'''




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

def 	lowerCAmelCase_	(     lowerCamelCase:				int   , lowerCamelCase:				list  )    ->	float:


      '''simple docstring'''




      return sum(lowerCamelCase  ) / instance_count

def 	lowerCAmelCase_	(     lowerCamelCase:				int   , lowerCamelCase:				int  )    ->	float:


      '''simple docstring'''




      return instance_count / total_count

def 	lowerCAmelCase_	(     lowerCamelCase:				list   , lowerCamelCase:				list   , lowerCamelCase:				int  )    ->	float:


      '''simple docstring'''




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

def 	lowerCAmelCase_	(     lowerCamelCase:				list   , lowerCamelCase:				list   , lowerCamelCase:				float   , lowerCamelCase:				list  )    ->	list:


      '''simple docstring'''




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

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

def 	lowerCAmelCase_	(     lowerCamelCase:				list   , lowerCamelCase:				list  )    ->	float:


      '''simple docstring'''




      lowerCamelCase__					     = sum(1 for i, j in zip(lowerCamelCase   , lowerCamelCase  ) 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(lowerCamelCase  )) * 1_00


__SCREAMING_SNAKE_CASE			:      str      			=TypeVar("""num""")

def 	lowerCAmelCase_	(     lowerCamelCase:				Callable[[object], num]   , lowerCamelCase:				str   , lowerCamelCase:				str   , lowerCamelCase:				Callable[[num], bool] = lambda lowerCamelCase  : True   , lowerCamelCase:				str | None = None   , )    ->	num:


      '''simple docstring'''




      while True:
            try:
                  lowerCamelCase__					     = input_type(input(lowerCamelCase  ).strip() or default  )
                  if condition(lowerCamelCase  ):
                        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_	(     )    ->	str:


      '''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
            lowerCamelCase__					     = valid_input(
                input_type=lowerCamelCase   , condition=lambda lowerCamelCase  : 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
            lowerCamelCase__					     = valid_input(
                input_type=lowerCamelCase   , condition=lambda lowerCamelCase  : 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
            lowerCamelCase__					     = []  # An empty list to store instance counts of classes in dataset
            for i in range(lowerCamelCase  ):
                  lowerCamelCase__					     = valid_input(
                      input_type=lowerCamelCase   , condition=lambda lowerCamelCase  : x > 0   , input_msg=(f"""Enter The number of instances for class_{i+1}: """)   , err_msg='Number of instances should be positive!'   , )
                  counts.append(lowerCamelCase  )
            print('-' * 1_00  )

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

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

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

            # Generating Ys to detecting corresponding classes
            lowerCamelCase__					     = y_generator(lowerCamelCase   , lowerCamelCase  )
            print('Generated Corresponding Ys: \n'   , lowerCamelCase  )
            print('-' * 1_00  )

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

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

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

            # Calculating the values of variance for each class
            lowerCamelCase__					     = calculate_variance(lowerCamelCase   , lowerCamelCase   , sum(lowerCamelCase  )  )
            print('Variance: '   , lowerCamelCase  )
            print('-' * 1_00  )

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

            # Calculating Accuracy of the model
            print(f"""Accuracy: {accuracy(lowerCamelCase   , lowerCamelCase  )}"""  )
            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()





from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 	A	(						_lowerCAmelCase						):
     """simple docstring"""

     def __init__(			self     :      Union[str, Any]		,     A__     :      Dict		,     A__     :      int	) ->				Union[str, Any]:
           super().__init__()
           self.register_modules(unet=A__		,     scheduler=A__	)




     @torch.no_grad()
     def __call__(			self     :      int		,     A__     :      int = 1		,     A__     :      Optional[Union[torch.Generator, List[torch.Generator]]] = None		,     A__     :      int = 1_0_0_0		,     A__     :      Optional[str] = "pil"		,     A__     :      bool = True		,     ) ->				Union[ImagePipelineOutput, Tuple]:
           # Sample gaussian noise to begin loop
           if isinstance(self.unet.config.sample_size		,     A__	):
                 lowerCamelCase__					     = (
                     batch_size,
                     self.unet.config.in_channels,
                     self.unet.config.sample_size,
                     self.unet.config.sample_size,
                 )
           else:
                 lowerCamelCase__					     = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

           if self.device.type == "mps":
                 # randn does not work reproducibly on mps
                 lowerCamelCase__					     = randn_tensor(A__		,     generator=A__	)
                 lowerCamelCase__					     = image.to(self.device	)
           else:
                 lowerCamelCase__					     = randn_tensor(A__		,     generator=A__		,     device=self.device	)

           # set step values
           self.scheduler.set_timesteps(A__	)

           for t in self.progress_bar(self.scheduler.timesteps	):
                 # 1. predict noise model_output
                 lowerCamelCase__					     = self.unet(A__		,     A__	).sample

                 # 2. compute previous image: x_t -> x_t-1
                 lowerCamelCase__					     = self.scheduler.step(A__		,     A__		,     A__		,     generator=A__	).prev_sample

           lowerCamelCase__					     = (image / 2 + 0.5).clamp(0		,     1	)
           lowerCamelCase__					     = image.cpu().permute(0		,     2		,     3		,     1	).numpy()
           if output_type == "pil":
                 lowerCamelCase__					     = self.numpy_to_pil(A__	)

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=A__	)