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



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



def    lowerCamelCase_   (  _lowercase ,     _lowercase    )							->	float:
		return sum(_lowercase    ) / instance_count



def    lowerCamelCase_   (  _lowercase ,     _lowercase    )							->	float:
		return instance_count / total_count



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



def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase ,     _lowercase    )							->	list:
		__A							:   List[Any]	  =				[]
		# for loop iterates over number of elements in list
		for i in range(len(_lowercase    )    ):
				# for loop iterates over number of inner items of each element
				for j in range(len(x_items[i]    )    ):
						__A							:   Optional[Any]	  =				[]  # 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(_lowercase    )    ):
								# 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(_lowercase    )

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



def    lowerCamelCase_   (  _lowercase ,     _lowercase    )							->	float:
		__A							:   List[str]	  =				sum(1 for i, j in zip(_lowercase ,     _lowercase    ) 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(_lowercase    )) * 100


UpperCamelCase      			=     TypeVar('num')



def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase ,     _lowercase = lambda _lowercase    : True ,     _lowercase = None ,     )							->	num:
		while True:
				try:
						__A							:   Any	  =				input_type(input(_lowercase    ).strip() or default    )
						if condition(_lowercase    ):
								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_   (  )							->	Dict:
		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							:   List[Any]	  =				valid_input(
				    input_type=_lowercase ,     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
				__A							:   str	  =				valid_input(
				    input_type=_lowercase ,     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
				__A							:   Optional[Any]	  =				[]  # An empty list to store instance counts of classes in dataset
				for i in range(_lowercase    ):
						__A							:   int	  =				valid_input(
						    input_type=_lowercase ,     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(_lowercase    )
				print("-" * 100    )

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

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

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

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

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

				# Calculating the value of probabilities for each class
				__A							:   List[Any]	  =				[
				    calculate_probabilities(counts[i] ,     sum(_lowercase    )    ) for i in range(_lowercase    )
				]

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

				# Calculating the values of variance for each class
				__A							:   List[str]	  =				calculate_variance(_lowercase ,     _lowercase ,     sum(_lowercase    )    )
				print("Variance: " ,     _lowercase    )
				print("-" * 100    )

				# Predicting Y values
				# storing predicted Y values in 'pre_indexes' variable
				__A							:   Dict	  =				predict_y_values(_lowercase ,     _lowercase ,     _lowercase ,     _lowercase    )
				print("-" * 100    )

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






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
					from .image_encoder import PaintByExampleImageEncoder
					from .pipeline_paint_by_example import PaintByExamplePipeline





