






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					(       UpperCamelCase_   :			float      ,				UpperCamelCase_   :			float      ,				UpperCamelCase_   :			int				)	->				list:
						"""simple docstring"""







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


def     _a					(       UpperCamelCase_   :			int      ,				UpperCamelCase_   :			list				)	->				list:
						"""simple docstring"""







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


def     _a					(       UpperCamelCase_   :			int      ,				UpperCamelCase_   :			list				)	->				float:
						"""simple docstring"""







						return sum(UpperCamelCase_				) / instance_count


def     _a					(       UpperCamelCase_   :			int      ,				UpperCamelCase_   :			int				)	->				float:
						"""simple docstring"""







						return instance_count / total_count


def     _a					(       UpperCamelCase_   :			list      ,				UpperCamelCase_   :			list      ,				UpperCamelCase_   :			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(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
						lowerCAmelCase__	 =  len(UpperCamelCase_				)  # Number of classes in dataset
						return 1 / (total_count - n_classes) * sum(UpperCamelCase_				)


def     _a					(       UpperCamelCase_   :			list      ,				UpperCamelCase_   :			list      ,				UpperCamelCase_   :			float      ,				UpperCamelCase_   :			list				)	->				list:
						"""simple docstring"""







						lowerCAmelCase__	 =  []
						# 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]				)				):
																		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(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     _a					(       UpperCamelCase_   :			list      ,				UpperCamelCase_   :			list				)	->				float:
						"""simple docstring"""







						lowerCAmelCase__	 =  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_				)) * 100


a_							=      TypeVar('''num''')


def     _a					(       UpperCamelCase_   :			Callable[[object], num]      ,				UpperCamelCase_   :			str      ,				UpperCamelCase_   :			str      ,				UpperCamelCase_   :			Callable[[num], bool] = lambda UpperCamelCase_				: True      ,				UpperCamelCase_   :			str | None = None      ,				)	->				num:
						"""simple docstring"""







						while True:
												try:
																		lowerCAmelCase__	 =  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     _a					(       )	->				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=UpperCamelCase_      ,				condition=lambda UpperCamelCase_				: 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
												lowerCAmelCase__	 =  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("-" * 100				)

												# 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(UpperCamelCase_				):
																		lowerCAmelCase__	 =  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("-" * 100				)

												# An empty list to store values of user-entered means of classes
												lowerCAmelCase__	 =  []
												for a in range(UpperCamelCase_				):
																		lowerCAmelCase__	 =  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("-" * 100				)

												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("-" * 100				)

												# 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("-" * 100				)

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

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

												# Calculating the value of actual mean for each class
												lowerCAmelCase__	 =  [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("-" * 100				)

												# Calculating the value of probabilities for each class
												lowerCAmelCase__	 =  [
												    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("-" * 100				)

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

												# Predicting Y values
												# storing predicted Y values in 'pre_indexes' variable
												lowerCAmelCase__	 =  predict_y_values(UpperCamelCase_      ,				UpperCamelCase_      ,				UpperCamelCase_      ,				UpperCamelCase_				)
												print("-" * 100				)

												# Calculating Accuracy of the model
												print(F"Accuracy: {accuracy(UpperCamelCase_      ,				UpperCamelCase_				)}"				)
												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

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor


class        lowercase__       (     unittest.TestCase		):

	@property
	def 						UpperCAmelCase					(							self    )->   List[str]:







							'''simple docstring'''





							lowerCAmelCase__	 =  1
							lowerCAmelCase__	 =  3
							lowerCAmelCase__	 =  8
							lowerCAmelCase__	 =  8

							lowerCAmelCase__	 =  torch.rand((batch_size, num_channels, height, width)    )

							return sample

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   Dict:







							'''simple docstring'''





							if isinstance(image[0]						,      PIL.Image.Image    ):
													return np.stack([np.array(__UpperCAmelCase    ) for i in image]						,      axis=0    )
							elif isinstance(__UpperCAmelCase						,      torch.Tensor    ):
													return image.cpu().numpy().transpose(0						,      2						,      3						,      1    )
							return image

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







							'''simple docstring'''





							lowerCAmelCase__	 =  VaeImageProcessor(do_resize=__UpperCAmelCase						,      do_normalize=__UpperCAmelCase    )

							lowerCAmelCase__	 =  self.dummy_sample
							lowerCAmelCase__	 =  self.to_np(__UpperCAmelCase    )

							for output_type in ["pt", "np", "pil"]:
													lowerCAmelCase__	 =  image_processor.postprocess(
													    image_processor.preprocess(__UpperCAmelCase    )						,      output_type=__UpperCAmelCase						,      )
													lowerCAmelCase__	 =  self.to_np(__UpperCAmelCase    )
													lowerCAmelCase__	 =  (input_np * 255).round() if output_type == "pil" else input_np
													assert (
													    np.abs(in_np - out_np    ).max() < 1E-6
													), F"decoded output does not match input for output_type {output_type}"

	def 						UpperCAmelCase					(							self    )->   int:







							'''simple docstring'''





							lowerCAmelCase__	 =  VaeImageProcessor(do_resize=__UpperCAmelCase						,      do_normalize=__UpperCAmelCase    )
							lowerCAmelCase__	 =  self.dummy_sample.cpu().numpy().transpose(0						,      2						,      3						,      1    )

							for output_type in ["pt", "np", "pil"]:
													lowerCAmelCase__	 =  image_processor.postprocess(image_processor.preprocess(__UpperCAmelCase    )						,      output_type=__UpperCAmelCase    )

													lowerCAmelCase__	 =  self.to_np(__UpperCAmelCase    )
													lowerCAmelCase__	 =  (input_np * 255).round() if output_type == "pil" else input_np
													assert (
													    np.abs(in_np - out_np    ).max() < 1E-6
													), F"decoded output does not match input for output_type {output_type}"

	def 						UpperCAmelCase					(							self    )->   Any:







							'''simple docstring'''





							lowerCAmelCase__	 =  VaeImageProcessor(do_resize=__UpperCAmelCase						,      do_normalize=__UpperCAmelCase    )

							lowerCAmelCase__	 =  self.dummy_sample.cpu().numpy().transpose(0						,      2						,      3						,      1    )
							lowerCAmelCase__	 =  image_processor.numpy_to_pil(__UpperCAmelCase    )

							for output_type in ["pt", "np", "pil"]:
													lowerCAmelCase__	 =  image_processor.postprocess(image_processor.preprocess(__UpperCAmelCase    )						,      output_type=__UpperCAmelCase    )
													for i, o in zip(__UpperCAmelCase						,      __UpperCAmelCase    ):
																			lowerCAmelCase__	 =  np.array(__UpperCAmelCase    )
																			lowerCAmelCase__	 =  self.to_np(__UpperCAmelCase    ) if output_type == "pil" else (self.to_np(__UpperCAmelCase    ) * 255).round()
																			assert (
																			    np.abs(in_np - out_np    ).max() < 1E-6
																			), F"decoded output does not match input for output_type {output_type}"

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







							'''simple docstring'''





							lowerCAmelCase__	 =  VaeImageProcessor(do_resize=__UpperCAmelCase						,      do_normalize=__UpperCAmelCase    )

							lowerCAmelCase__	 =  self.dummy_sample
							lowerCAmelCase__	 =  input_pt_ad.squeeze(0    )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )
							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							lowerCAmelCase__	 =  self.to_np(self.dummy_sample    )
							lowerCAmelCase__	 =  input_np_ad.squeeze(0    )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )
							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							assert np.abs(out_pt_ad - out_pt_ad    ).max() < 1E-6
							assert np.abs(out_np_ad - out_np_ad    ).max() < 1E-6


	def 						UpperCAmelCase					(							self    )->   List[Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  VaeImageProcessor(do_resize=__UpperCAmelCase						,      do_normalize=__UpperCAmelCase    )

							lowerCAmelCase__	 =  self.dummy_sample
							lowerCAmelCase__	 =  list(__UpperCAmelCase    )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							lowerCAmelCase__	 =  self.to_np(self.dummy_sample    )
							list(__UpperCAmelCase    )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							assert np.abs(out_pt_ad - out_pt_list    ).max() < 1E-6
							assert np.abs(out_np_ad - out_np_list    ).max() < 1E-6






