


# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class        lowercase_				( snake_case__):






							"""simple docstring"""







							def __init__(   self			,    _UpperCAmelCase			,    _UpperCAmelCase		):







													"""simple docstring"""


													super().__init__()
													self.register_modules(unet=__snake_case			,    scheduler=__snake_case		)






							@torch.no_grad()
							def __call__(   self			,    _UpperCAmelCase = 1			,    _UpperCAmelCase = None			,    _UpperCAmelCase = 50			,    _UpperCAmelCase = "pil"			,    _UpperCAmelCase = True			,    **_UpperCAmelCase			,    ):







													"""simple docstring"""


													a_					=		torch.randn(
													    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)			,    generator=__snake_case			,    )
													a_					=		image.to(self.device		)

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

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

																			# 2. predict previous mean of image x_t-1 and add variance depending on eta
																			# eta corresponds to η in paper and should be between [0, 1]
																			# do x_t -> x_t-1
																			a_					=		self.scheduler.step(__snake_case			,    __snake_case			,    __snake_case		).prev_sample

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

													if not return_dict:
																			return (image,), "This is a local test"

													return ImagePipelineOutput(images=__snake_case		), "This is a local test"



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_       (					UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__      ):



						"""simple docstring"""




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




def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):



						"""simple docstring"""




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




def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):



						"""simple docstring"""




						return sum(UpperCAmelCase__      ) / instance_count




def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):



						"""simple docstring"""




						return instance_count / total_count




def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__      ):



						"""simple docstring"""




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




def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__      ):



						"""simple docstring"""




						a_					=		[]
						# 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]      )      ):
																		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(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   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):



						"""simple docstring"""




						a_					=		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_    :					Dict             =TypeVar("""num""")




def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__ = lambda UpperCAmelCase__      : True       ,    UpperCAmelCase__ = None       ,    ):



						"""simple docstring"""




						while True:
												try:
																		a_					=		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   lowerCamelCase_       (					):



						"""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=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
												a_					=		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
												a_					=		[]  # An empty list to store instance counts of classes in dataset
												for i in range(UpperCAmelCase__      ):
																		a_					=		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
												a_					=		[]
												for a in range(UpperCAmelCase__      ):
																		a_					=		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
												a_					=		[
												    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
												a_					=		y_generator(UpperCAmelCase__       ,    UpperCAmelCase__      )
												print("""Generated Corresponding Ys: \n"""       ,    UpperCAmelCase__      )
												print("""-""" * 100      )

												# Calculating the value of actual mean for each class
												a_					=		[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
												a_					=		[
												    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
												a_					=		calculate_variance(UpperCAmelCase__       ,    UpperCAmelCase__       ,    sum(UpperCAmelCase__      )      )
												print("""Variance: """       ,    UpperCAmelCase__      )
												print("""-""" * 100      )

												# Predicting Y values
												# storing predicted Y values in 'pre_indexes' variable
												a_					=		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()