


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

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Tuple     ,					UpperCamelCase		:      int     ,					UpperCamelCase		:      Dict	)     ->	Optional[Any]:



	"""simple docstring"""






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

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Optional[int]     ,					UpperCamelCase		:      str	)     ->	List[str]:



	"""simple docstring"""






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

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Tuple     ,					UpperCamelCase		:      Optional[int]	)     ->	str:



	"""simple docstring"""






	return sum(SCREAMING_SNAKE_CASE_	) / instance_count

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Optional[Any]     ,					UpperCamelCase		:      Optional[Any]	)     ->	Optional[int]:



	"""simple docstring"""






	return instance_count / total_count

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      List[Any]     ,					UpperCamelCase		:      Optional[int]     ,					UpperCamelCase		:      Optional[Any]	)     ->	Tuple:



	"""simple docstring"""






	a_     			=   []  # 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
	a_     			=   len(SCREAMING_SNAKE_CASE_	)  # Number of classes in dataset
	return 1 / (total_count - n_classes) * sum(SCREAMING_SNAKE_CASE_	)

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      str     ,					UpperCamelCase		:      Optional[Any]     ,					UpperCamelCase		:      Tuple     ,					UpperCamelCase		:      Optional[int]	)     ->	List[Any]:



	"""simple docstring"""






	a_     			=   []
	# 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]	)	):
			a_     			=   []  # 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 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      str     ,					UpperCamelCase		:      Any	)     ->	List[Any]:



	"""simple docstring"""






	a_     			=   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


_A							     =       TypeVar('num')

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      List[Any]     ,					UpperCamelCase		:      Dict     ,					UpperCamelCase		:      List[Any]     ,					UpperCamelCase		:      List[str] = lambda UpperCamelCase	: True     ,					UpperCamelCase		:      int = None     ,					)     ->	Optional[int]:



	"""simple docstring"""






	while True:
		try:
			a_     			=   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 			__SCREAMING_SNAKE_CASE		(		)     ->	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
		a_     			=   valid_input(
		    input_type=SCREAMING_SNAKE_CASE_     ,					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
		a_     			=   valid_input(
		    input_type=SCREAMING_SNAKE_CASE_     ,					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
		a_     			=   []  # An empty list to store instance counts of classes in dataset
		for i in range(SCREAMING_SNAKE_CASE_	):
			a_     			=   valid_input(
			    input_type=SCREAMING_SNAKE_CASE_     ,					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(SCREAMING_SNAKE_CASE_	)
		print("""-""" * 100	)

		# An empty list to store values of user-entered means of classes
		a_     			=   []
		for a in range(SCREAMING_SNAKE_CASE_	):
			a_     			=   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
		a_     			=   [
		    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
		a_     			=   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
		a_     			=   [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
		a_     			=   [
		    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
		a_     			=   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
		a_     			=   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()
'''simple docstring'''




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

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class    UpperCAmelCase__	(							_snake_case      ):






   """simple docstring"""
   A				:							Union[List[PIL.Image.Image], np.ndarray]
   A				:							Optional[List[bool]]


if is_transformers_available() and is_torch_available():
 from .modeling_roberta_series import RobertaSeriesModelWithTransformation
 from .pipeline_alt_diffusion import AltDiffusionPipeline
 from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline




