


from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar
def     UpperCamelCase_(			_A				:float			,       _A				:float			,       _A				:int					)->						list:
			seed(1					)
			return [gauss(_A			,       _A					) for _ in range(_A					)]
def     UpperCamelCase_(			_A				:int			,       _A				:list					)->						list:
			return [k for k in range(_A					) for _ in range(instance_count[k]					)]
def     UpperCamelCase_(			_A				:int			,       _A				:list					)->						float:
			return sum(_A					) / instance_count
def     UpperCamelCase_(			_A				:int			,       _A				:int					)->						float:
			return instance_count / total_count
def     UpperCamelCase_(			_A				:list			,       _A				:list			,       _A				:int					)->						float:
			UpperCamelCase__   =    []  # An empty list to store all squared differences
			# iterate over number of elements in items
			for i in range(len(_A					)					):
						# 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
			UpperCamelCase__   =    len(_A					)  # Number of classes in dataset
			return 1 / (total_count - n_classes) * sum(_A					)
def     UpperCamelCase_(			_A				:list			,       _A				:list			,       _A				:float			,       _A				:list					)->						list:
			UpperCamelCase__   =    []
			# for loop iterates over number of elements in list
			for i in range(len(_A					)					):
						# for loop iterates over number of inner items of each element
						for j in range(len(x_items[i]					)					):
									UpperCamelCase__   =    []  # 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(_A					)					):
												# 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(_A					)

			return [result.index(max(_A					)					) for result in results]
def     UpperCamelCase_(			_A				:list			,       _A				:list					)->						float:
			UpperCamelCase__   =    sum(1 for i, j in zip(_A			,       _A					) 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(_A					)) * 1_00


__UpperCamelCase      						=    TypeVar('num')
def     UpperCamelCase_(			_A				:Callable[[object], num]			,       _A				:str			,       _A				:str			,       _A				:Callable[[num], bool] = lambda _A					: True			,       _A				:str | None = None			,       )->						num:
			while True:
						try:
									UpperCamelCase__   =    input_type(input(_A					).strip() or default					)
									if condition(_A					):
												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     UpperCamelCase_(			)->						Optional[int]:
			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
						UpperCamelCase__   =    valid_input(
						    input_type=_A			,       condition=lambda _A					: 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
						UpperCamelCase__   =    valid_input(
						    input_type=_A			,       condition=lambda _A					: 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
						UpperCamelCase__   =    []  # An empty list to store instance counts of classes in dataset
						for i in range(_A					):
									UpperCamelCase__   =    valid_input(
									    input_type=_A			,       condition=lambda _A					: x > 0			,       input_msg=(F'''Enter The number of instances for class_{i+1}: ''')			,       err_msg="Number of instances should be positive!"			,       )
									counts.append(_A					)
						print("-" * 1_00					)

						# An empty list to store values of user-entered means of classes
						UpperCamelCase__   =    []
						for a in range(_A					):
									UpperCamelCase__   =    valid_input(
									    input_type=_A			,       input_msg=(F'''Enter the value of mean for class_{a+1}: ''')			,       err_msg="This is an invalid value."			,       )
									user_means.append(_A					)
						print("-" * 1_00					)

						print("Standard deviation: "			,       _A					)
						# print out the number of instances in classes in separated line
						for i, count in enumerate(_A			,       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(_A			,       1					):
									print(F'''Mean of class_{i} is: {user_mean}'''					)
						print("-" * 1_00					)

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

						# Generating Ys to detecting corresponding classes
						UpperCamelCase__   =    y_generator(_A			,       _A					)
						print("Generated Corresponding Ys: \n"			,       _A					)
						print("-" * 1_00					)

						# Calculating the value of actual mean for each class
						UpperCamelCase__   =    [calculate_mean(counts[k]			,       x[k]					) for k in range(_A					)]
						# for loop iterates over number of elements in 'actual_means' list and print
						# out them in separated line
						for i, actual_mean in enumerate(_A			,       1					):
									print(F'''Actual(Real) mean of class_{i} is: {actual_mean}'''					)
						print("-" * 1_00					)

						# Calculating the value of probabilities for each class
						UpperCamelCase__   =    [
						    calculate_probabilities(counts[i]			,       sum(_A					)					) for i in range(_A					)
						]

						# for loop iterates over number of elements in 'probabilities' list and print
						# out them in separated line
						for i, probability in enumerate(_A			,       1					):
									print(F'''Probability of class_{i} is: {probability}'''					)
						print("-" * 1_00					)

						# Calculating the values of variance for each class
						UpperCamelCase__   =    calculate_variance(_A			,       _A			,       sum(_A					)					)
						print("Variance: "			,       _A					)
						print("-" * 1_00					)

						# Predicting Y values
						# storing predicted Y values in 'pre_indexes' variable
						UpperCamelCase__   =    predict_y_values(_A			,       _A			,       _A			,       _A					)
						print("-" * 1_00					)

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

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


__UpperCamelCase      						=    [
    'AutomaticSpeechRecognition',
    'AudioClassification',
    'ImageClassification',
    'LanguageModeling',
    'QuestionAnsweringExtractive',
    'Summarization',
    'TaskTemplate',
    'TextClassification',
]

__UpperCamelCase      						=    get_logger(__name__)


__UpperCamelCase      						=    {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}
def     UpperCamelCase_(			_A				:dict					)->						Optional[TaskTemplate]:
			UpperCamelCase__   =    task_template_dict.get("task"					)
			if task_name is None:
						logger.warning(F'''Couldn\'t find template for task \'{task_name}\'. Available templates: {list(_A					)}'''					)
						return None
			UpperCamelCase__   =    NAMEaTEMPLATE.get(_A					)
			return template.from_dict(_A					)


