
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			:    float				,    __UpperCamelCase			:    float				,    __UpperCamelCase			:    int							)			->							list:

				"""simple docstring"""







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

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    int				,    __UpperCamelCase			:    list							)			->							list:

				"""simple docstring"""







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

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    int				,    __UpperCamelCase			:    list							)			->							float:

				"""simple docstring"""







				return sum(__UpperCamelCase							) / instance_count

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    int				,    __UpperCamelCase			:    int							)			->							float:

				"""simple docstring"""







				return instance_count / total_count

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    list				,    __UpperCamelCase			:    list				,    __UpperCamelCase			:    int							)			->							float:

				"""simple docstring"""







				A__	:						int								=				[]  # 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__	:						Any								=				len(__UpperCamelCase							)  # Number of classes in dataset
				return 1 / (total_count - n_classes) * sum(__UpperCamelCase							)

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    list				,    __UpperCamelCase			:    list				,    __UpperCamelCase			:    float				,    __UpperCamelCase			:    list							)			->							list:

				"""simple docstring"""







				A__	:						Union[str, Any]								=				[]
				# 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__	:						List[str]								=				[]  # 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 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    list				,    __UpperCamelCase			:    list							)			->							float:

				"""simple docstring"""







				A__	:						Tuple								=				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							)) * 1_00


_SCREAMING_SNAKE_CASE     :   Dict							 =      TypeVar('num')

def 					SCREAMING_SNAKE_CASE  (   __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:
												A__	:						Optional[int]								=				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 					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__	:						Union[str, Any]								=				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('''-''' * 1_00							)

								# Trying to get the value of standard deviation
								A__	:						Optional[Any]								=				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('''-''' * 1_00							)

								# Trying to get number of instances in classes and theirs means to generate
								# dataset
								A__	:						Dict								=				[]  # An empty list to store instance counts of classes in dataset
								for i in range(__UpperCamelCase							):
												A__	:						List[Any]								=				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('''-''' * 1_00							)

								# An empty list to store values of user-entered means of classes
								A__	:						int								=				[]
								for a in range(__UpperCamelCase							):
												A__	:						Union[str, Any]								=				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('''-''' * 1_00							)

								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('''-''' * 1_00							)

								# 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('''-''' * 1_00							)

								# Generating training dataset drawn from gaussian distribution
								A__	:						Tuple								=				[
								    gaussian_distribution(user_means[j]				,    __UpperCamelCase				,    counts[j]							)
								    for j in range(__UpperCamelCase							)
								]
								print('''Generated Normal Distribution: \n'''				,    __UpperCamelCase							)
								print('''-''' * 1_00							)

								# Generating Ys to detecting corresponding classes
								A__	:						int								=				y_generator(__UpperCamelCase				,    __UpperCamelCase							)
								print('''Generated Corresponding Ys: \n'''				,    __UpperCamelCase							)
								print('''-''' * 1_00							)

								# Calculating the value of actual mean for each class
								A__	:						Dict								=				[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('''-''' * 1_00							)

								# Calculating the value of probabilities for each class
								A__	:						Dict								=				[
								    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('''-''' * 1_00							)

								# Calculating the values of variance for each class
								A__	:						Dict								=				calculate_variance(__UpperCamelCase				,    __UpperCamelCase				,    sum(__UpperCamelCase							)							)
								print('''Variance: '''				,    __UpperCamelCase							)
								print('''-''' * 1_00							)

								# Predicting Y values
								# storing predicted Y values in 'pre_indexes' variable
								A__	:						Any								=				predict_y_values(__UpperCamelCase				,    __UpperCamelCase				,    __UpperCamelCase				,    __UpperCamelCase							)
								print('''-''' * 1_00							)

								# Calculating Accuracy of the model
								print(F"Accuracy: {accuracy(__UpperCamelCase				,    __UpperCamelCase							)}"							)
								print('''-''' * 1_00							)
								print(''' DONE '''.center(1_00				,    '''+'''							)							)

								if input('''Press any key to restart or \'q\' for quit: '''							).strip().lower() == "q":
												print('''\n''' + '''GoodBye!'''.center(1_00				,    '''-'''							) + '''\n'''							)
												break
								system('''cls''' if name == '''nt''' else '''clear'''							)  # noqa: S605


if __name__ == "__main__":
					main()

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_SCREAMING_SNAKE_CASE     :   Optional[Any]							 =      {
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE     :   Tuple							 =      [
					    'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'FlaubertForMultipleChoice',
					    'FlaubertForQuestionAnswering',
					    'FlaubertForQuestionAnsweringSimple',
					    'FlaubertForSequenceClassification',
					    'FlaubertForTokenClassification',
					    'FlaubertModel',
					    'FlaubertWithLMHeadModel',
					    'FlaubertPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE     :   Dict							 =      [
					    'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFFlaubertForMultipleChoice',
					    'TFFlaubertForQuestionAnsweringSimple',
					    'TFFlaubertForSequenceClassification',
					    'TFFlaubertForTokenClassification',
					    'TFFlaubertModel',
					    'TFFlaubertPreTrainedModel',
					    'TFFlaubertWithLMHeadModel',
					]


if TYPE_CHECKING:
					from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
					from .tokenization_flaubert import FlaubertTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flaubert import (
										    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaubertForMultipleChoice,
										    FlaubertForQuestionAnswering,
										    FlaubertForQuestionAnsweringSimple,
										    FlaubertForSequenceClassification,
										    FlaubertForTokenClassification,
										    FlaubertModel,
										    FlaubertPreTrainedModel,
										    FlaubertWithLMHeadModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_flaubert import (
										    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFFlaubertForMultipleChoice,
										    TFFlaubertForQuestionAnsweringSimple,
										    TFFlaubertForSequenceClassification,
										    TFFlaubertForTokenClassification,
										    TFFlaubertModel,
										    TFFlaubertPreTrainedModel,
										    TFFlaubertWithLMHeadModel,
										)

else:
					import sys

					_SCREAMING_SNAKE_CASE     :   List[Any]							 =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)