
"""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_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			list:


					'''simple docstring'''
					seed(1 )
					return [gauss(lowerCamelCase_	,				lowerCamelCase_ ) for _ in range(lowerCamelCase_ )]


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			list:


					'''simple docstring'''
					return [k for k in range(lowerCamelCase_ ) for _ in range(instance_count[k] )]


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			float:


					'''simple docstring'''
					return sum(lowerCamelCase_ ) / instance_count


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			float:


					'''simple docstring'''
					return instance_count / total_count


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			float:


					'''simple docstring'''
					__a:						Dict		    =					[]  # 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
					__a:						Tuple		    =					len(lowerCamelCase_ )  # Number of classes in dataset
					return 1 / (total_count - n_classes) * sum(lowerCamelCase_ )


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			list:


					'''simple docstring'''
					__a:						List[str]		    =					[]
					# 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] ) ):
															__a:						Optional[Any]		    =					[]  # 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_	,				lowerCamelCase_ )     ->			float:


					'''simple docstring'''
					__a:						Optional[Any]		    =					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_ )) * 1_00


lowercase		:						str =					TypeVar('num')


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ = lambda lowerCamelCase_ : True	,				lowerCamelCase_ = None	,				)     ->			num:


					'''simple docstring'''
					while True:
										try:
															__a:						List[Any]		    =					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_    (	)     ->			List[Any]:


					'''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:						Optional[int]		    =					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("""-""" * 1_00 )

										# Trying to get the value of standard deviation
										__a:						int		    =					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("""-""" * 1_00 )

										# Trying to get number of instances in classes and theirs means to generate
										# dataset
										__a:						int		    =					[]  # An empty list to store instance counts of classes in dataset
										for i in range(lowerCamelCase_ ):
															__a:						List[str]		    =					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("""-""" * 1_00 )

										# An empty list to store values of user-entered means of classes
										__a:						List[Any]		    =					[]
										for a in range(lowerCamelCase_ ):
															__a:						List[Any]		    =					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("""-""" * 1_00 )

										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("""-""" * 1_00 )

										# 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("""-""" * 1_00 )

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

										# Generating Ys to detecting corresponding classes
										__a:						List[Any]		    =					y_generator(lowerCamelCase_	,				lowerCamelCase_ )
										print("""Generated Corresponding Ys: \n"""	,				lowerCamelCase_ )
										print("""-""" * 1_00 )

										# Calculating the value of actual mean for each class
										__a:						Union[str, Any]		    =					[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("""-""" * 1_00 )

										# Calculating the value of probabilities for each class
										__a:						Optional[Any]		    =					[
										    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("""-""" * 1_00 )

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

										# Predicting Y values
										# storing predicted Y values in 'pre_indexes' variable
										__a:						Optional[Any]		    =					predict_y_values(lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )
										print("""-""" * 1_00 )

										# Calculating Accuracy of the model
										print(f"""Accuracy: {accuracy(lowerCamelCase_	,				lowerCamelCase_ )}""" )
										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()


"""simple docstring"""
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
			import torch

			from transformers import CamembertModel




@require_torch
@require_sentencepiece
@require_tokenizers
class    _snake_case  (						unittest.TestCase       ):






							@slow
							def 							__lowercase  (					self						:			Optional[int]    ):






												"""simple docstring"""



												__a:						Any		    =					CamembertModel.from_pretrained("""camembert-base"""    )
												model.to(snake_case__    )

												__a:						str		    =					torch.tensor(
												    [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]						, device=snake_case__						, dtype=torch.long						, )  # J'aime le camembert !
												with torch.no_grad():
																	__a:						str		    =					model(snake_case__    )["""last_hidden_state"""]
												__a:						Optional[Any]		    =					torch.Size((1, 1_0, 7_6_8)    )
												self.assertEqual(output.shape						, snake_case__    )
												# compare the actual values for a slice.
												__a:						Union[str, Any]		    =					torch.tensor(
												    [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]]						, device=snake_case__						, dtype=torch.float						, )
												# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
												# camembert.eval()
												# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

												self.assertTrue(torch.allclose(output[:, :3, :3]						, snake_case__						, atol=1e-4    )    )
