





'''simple docstring'''

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





def lowerCamelCase						(					__lowerCamelCase			:   int ,	__lowerCamelCase			:   list   )						->list:
    return [k for k in range(__lowerCamelCase   ) for _ in range(instance_count[k]   )]





def lowerCamelCase						(					__lowerCamelCase			:   int ,	__lowerCamelCase			:   list   )						->float:
    return sum(__lowerCamelCase   ) / instance_count





def lowerCamelCase						(					__lowerCamelCase			:   int ,	__lowerCamelCase			:   int   )						->float:
    return instance_count / total_count





def lowerCamelCase						(					__lowerCamelCase			:   list ,	__lowerCamelCase			:   list ,	__lowerCamelCase			:   int   )						->float:
    _SCREAMING_SNAKE_CASE     =      []  # 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
    _SCREAMING_SNAKE_CASE     =      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:
    _SCREAMING_SNAKE_CASE     =      []
    # 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]   )   ):
            _SCREAMING_SNAKE_CASE     =      []  # 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:
    _SCREAMING_SNAKE_CASE     =      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   )) * 100


lowercase_         =			TypeVar("""num""")





def lowerCamelCase						(					__lowerCamelCase			:   Callable[[object], num] ,	__lowerCamelCase			:   str ,	__lowerCamelCase			:   str ,	__lowerCamelCase			:   Callable[[num], bool] = lambda __lowerCamelCase   : True ,	__lowerCamelCase			:   str | None = None ,	)						->num:
    while True:
        try:
            _SCREAMING_SNAKE_CASE     =      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						(					)						->Tuple:
    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
        _SCREAMING_SNAKE_CASE     =      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("""-""" * 100   )

        # Trying to get the value of standard deviation
        _SCREAMING_SNAKE_CASE     =      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("""-""" * 100   )

        # Trying to get number of instances in classes and theirs means to generate
        # dataset
        _SCREAMING_SNAKE_CASE     =      []  # An empty list to store instance counts of classes in dataset
        for i in range(__lowerCamelCase   ):
            _SCREAMING_SNAKE_CASE     =      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("""-""" * 100   )

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

        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("""-""" * 100   )

        # 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("""-""" * 100   )

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

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

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

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

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

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

        # Calculating Accuracy of the model
        print(F'Accuracy: {accuracy(__lowerCamelCase ,	__lowerCamelCase   )}'   )
        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'''

import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class  a_					(    snake_case_	):

    '''simple docstring'''
    UpperCamelCase			   =			(LMSDiscreteScheduler,)
    UpperCamelCase			   =			10


    def       snake_case_(		self			,		**A	)		->       str:
        _SCREAMING_SNAKE_CASE     =      {
            """num_train_timesteps""": 1100,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
        }

        config.update(**A	)
        return config


    def       snake_case_(		self	)		->       str:
        for timesteps in [10, 50, 100, 1000]:
            self.check_over_configs(num_train_timesteps=A	)


    def       snake_case_(		self	)		->       Optional[Any]:
        for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]			,		[0.0002, 0.002, 0.02]	):
            self.check_over_configs(beta_start=A			,		beta_end=A	)


    def       snake_case_(		self	)		->       Tuple:
        for schedule in ["linear", "scaled_linear"]:
            self.check_over_configs(beta_schedule=A	)


    def       snake_case_(		self	)		->       List[str]:
        for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=A	)


    def       snake_case_(		self	)		->       Optional[int]:
        for t in [0, 500, 800]:
            self.check_over_forward(time_step=A	)


    def       snake_case_(		self	)		->       str:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A	)

        scheduler.set_timesteps(self.num_inference_steps	)

        _SCREAMING_SNAKE_CASE     =      self.dummy_model()
        _SCREAMING_SNAKE_CASE     =      self.dummy_sample_deter * scheduler.init_noise_sigma

        for i, t in enumerate(scheduler.timesteps	):
            _SCREAMING_SNAKE_CASE     =      scheduler.scale_model_input(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

        _SCREAMING_SNAKE_CASE     =      torch.sum(torch.abs(A	)	)
        _SCREAMING_SNAKE_CASE     =      torch.mean(torch.abs(A	)	)

        assert abs(result_sum.item() - 1006.388	) < 1e-2
        assert abs(result_mean.item() - 1.31	) < 1e-3


    def       snake_case_(		self	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config(prediction_type="""v_prediction"""	)
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A	)

        scheduler.set_timesteps(self.num_inference_steps	)

        _SCREAMING_SNAKE_CASE     =      self.dummy_model()
        _SCREAMING_SNAKE_CASE     =      self.dummy_sample_deter * scheduler.init_noise_sigma

        for i, t in enumerate(scheduler.timesteps	):
            _SCREAMING_SNAKE_CASE     =      scheduler.scale_model_input(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

        _SCREAMING_SNAKE_CASE     =      torch.sum(torch.abs(A	)	)
        _SCREAMING_SNAKE_CASE     =      torch.mean(torch.abs(A	)	)

        assert abs(result_sum.item() - 0.0017	) < 1e-2
        assert abs(result_mean.item() - 2.26_76e-06	) < 1e-3


    def       snake_case_(		self	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A	)

        scheduler.set_timesteps(self.num_inference_steps			,		device=A	)

        _SCREAMING_SNAKE_CASE     =      self.dummy_model()
        _SCREAMING_SNAKE_CASE     =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        _SCREAMING_SNAKE_CASE     =      sample.to(A	)

        for i, t in enumerate(scheduler.timesteps	):
            _SCREAMING_SNAKE_CASE     =      scheduler.scale_model_input(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

        _SCREAMING_SNAKE_CASE     =      torch.sum(torch.abs(A	)	)
        _SCREAMING_SNAKE_CASE     =      torch.mean(torch.abs(A	)	)

        assert abs(result_sum.item() - 1006.388	) < 1e-2
        assert abs(result_mean.item() - 1.31	) < 1e-3





    def       snake_case_(		self	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A			,		use_karras_sigmas=A	)

        scheduler.set_timesteps(self.num_inference_steps			,		device=A	)

        _SCREAMING_SNAKE_CASE     =      self.dummy_model()
        _SCREAMING_SNAKE_CASE     =      self.dummy_sample_deter.to(A	) * scheduler.init_noise_sigma
        _SCREAMING_SNAKE_CASE     =      sample.to(A	)

        for t in scheduler.timesteps:
            _SCREAMING_SNAKE_CASE     =      scheduler.scale_model_input(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

        _SCREAMING_SNAKE_CASE     =      torch.sum(torch.abs(A	)	)
        _SCREAMING_SNAKE_CASE     =      torch.mean(torch.abs(A	)	)

        assert abs(result_sum.item() - 3812.9927	) < 2e-2
        assert abs(result_mean.item() - 4.9648	) < 1e-3



