
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_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )   ->      list:

							"""simple docstring"""






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




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )   ->      list:

							"""simple docstring"""






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




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )   ->      float:

							"""simple docstring"""






							return sum(_SCREAMING_SNAKE_CASE  ) / instance_count




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )   ->      float:

							"""simple docstring"""






							return instance_count / total_count




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )   ->      float:

							"""simple docstring"""






							_UpperCAmelCase  :			int	       =		[]  # An empty list to store all squared differences
							# iterate over number of elements in items
							for i in range(len(_SCREAMING_SNAKE_CASE  )  ):
														# 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  :			Any	       =		len(_SCREAMING_SNAKE_CASE  )  # Number of classes in dataset
							return 1 / (total_count - n_classes) * sum(_SCREAMING_SNAKE_CASE  )




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )   ->      list:

							"""simple docstring"""






							_UpperCAmelCase  :			Any	       =		[]
							# for loop iterates over number of elements in list
							for i in range(len(_SCREAMING_SNAKE_CASE  )  ):
														# for loop iterates over number of inner items of each element
														for j in range(len(x_items[i]  )  ):
																					_UpperCAmelCase  :			Dict	       =		[]  # 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(_SCREAMING_SNAKE_CASE  )  ):
																												# 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(_SCREAMING_SNAKE_CASE  )

							return [result.index(max(_SCREAMING_SNAKE_CASE  )  ) for result in results]




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )   ->      float:

							"""simple docstring"""






							_UpperCAmelCase  :			List[Any]	       =		sum(1 for i, j in zip(_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  ) 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(_SCREAMING_SNAKE_CASE  )) * 1_0_0


__lowerCamelCase								=					TypeVar('num')




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

							"""simple docstring"""






							while True:
														try:
																					_UpperCAmelCase  :			Dict	       =		input_type(input(_SCREAMING_SNAKE_CASE  ).strip() or default  )
																					if condition(_SCREAMING_SNAKE_CASE  ):
																												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_     (     )   ->      Any:

							"""simple docstring"""






							while True:
														print(" Linear Discriminant Analysis ".center(5_0      ,	"*"  )  )
														print("*" * 5_0      ,	"\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  :			Any	       =		valid_input(
														    input_type=_SCREAMING_SNAKE_CASE      ,	condition=lambda _SCREAMING_SNAKE_CASE  : x > 0      ,	input_msg="Enter the number of classes (Data Groupings): "      ,	err_msg="Number of classes should be positive!"      ,	)

														print("-" * 1_0_0  )

														# Trying to get the value of standard deviation
														_UpperCAmelCase  :			str	       =		valid_input(
														    input_type=_SCREAMING_SNAKE_CASE      ,	condition=lambda _SCREAMING_SNAKE_CASE  : 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_0_0  )

														# Trying to get number of instances in classes and theirs means to generate
														# dataset
														_UpperCAmelCase  :			Dict	       =		[]  # An empty list to store instance counts of classes in dataset
														for i in range(_SCREAMING_SNAKE_CASE  ):
																					_UpperCAmelCase  :			List[str]	       =		valid_input(
																					    input_type=_SCREAMING_SNAKE_CASE      ,	condition=lambda _SCREAMING_SNAKE_CASE  : x > 0      ,	input_msg=(F"""Enter The number of instances for class_{i+1}: """)      ,	err_msg="Number of instances should be positive!"      ,	)
																					counts.append(_SCREAMING_SNAKE_CASE  )
														print("-" * 1_0_0  )

														# An empty list to store values of user-entered means of classes
														_UpperCAmelCase  :			int	       =		[]
														for a in range(_SCREAMING_SNAKE_CASE  ):
																					_UpperCAmelCase  :			Tuple	       =		valid_input(
																					    input_type=_SCREAMING_SNAKE_CASE      ,	input_msg=(F"""Enter the value of mean for class_{a+1}: """)      ,	err_msg="This is an invalid value."      ,	)
																					user_means.append(_SCREAMING_SNAKE_CASE  )
														print("-" * 1_0_0  )

														print("Standard deviation: "      ,	_SCREAMING_SNAKE_CASE  )
														# print out the number of instances in classes in separated line
														for i, count in enumerate(_SCREAMING_SNAKE_CASE      ,	1  ):
																					print(F"""Number of instances in class_{i} is: {count}"""  )
														print("-" * 1_0_0  )

														# print out mean values of classes separated line
														for i, user_mean in enumerate(_SCREAMING_SNAKE_CASE      ,	1  ):
																					print(F"""Mean of class_{i} is: {user_mean}"""  )
														print("-" * 1_0_0  )

														# Generating training dataset drawn from gaussian distribution
														_UpperCAmelCase  :			int	       =		[
														    gaussian_distribution(user_means[j]      ,	_SCREAMING_SNAKE_CASE      ,	counts[j]  )
														    for j in range(_SCREAMING_SNAKE_CASE  )
														]
														print("Generated Normal Distribution: \n"      ,	_SCREAMING_SNAKE_CASE  )
														print("-" * 1_0_0  )

														# Generating Ys to detecting corresponding classes
														_UpperCAmelCase  :			str	       =		y_generator(_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )
														print("Generated Corresponding Ys: \n"      ,	_SCREAMING_SNAKE_CASE  )
														print("-" * 1_0_0  )

														# Calculating the value of actual mean for each class
														_UpperCAmelCase  :			List[Any]	       =		[calculate_mean(counts[k]      ,	x[k]  ) for k in range(_SCREAMING_SNAKE_CASE  )]
														# for loop iterates over number of elements in 'actual_means' list and print
														# out them in separated line
														for i, actual_mean in enumerate(_SCREAMING_SNAKE_CASE      ,	1  ):
																					print(F"""Actual(Real) mean of class_{i} is: {actual_mean}"""  )
														print("-" * 1_0_0  )

														# Calculating the value of probabilities for each class
														_UpperCAmelCase  :			List[str]	       =		[
														    calculate_probabilities(counts[i]      ,	sum(_SCREAMING_SNAKE_CASE  )  ) for i in range(_SCREAMING_SNAKE_CASE  )
														]

														# for loop iterates over number of elements in 'probabilities' list and print
														# out them in separated line
														for i, probability in enumerate(_SCREAMING_SNAKE_CASE      ,	1  ):
																					print(F"""Probability of class_{i} is: {probability}"""  )
														print("-" * 1_0_0  )

														# Calculating the values of variance for each class
														_UpperCAmelCase  :			Optional[Any]	       =		calculate_variance(_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	sum(_SCREAMING_SNAKE_CASE  )  )
														print("Variance: "      ,	_SCREAMING_SNAKE_CASE  )
														print("-" * 1_0_0  )

														# Predicting Y values
														# storing predicted Y values in 'pre_indexes' variable
														_UpperCAmelCase  :			Any	       =		predict_y_values(_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )
														print("-" * 1_0_0  )

														# Calculating Accuracy of the model
														print(F"""Accuracy: {accuracy(_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )}"""  )
														print("-" * 1_0_0  )
														print(" DONE ".center(1_0_0      ,	"+"  )  )

														if input("Press any key to restart or 'q' for quit: "  ).strip().lower() == "q":
																					print("\n" + "GoodBye!".center(1_0_0      ,	"-"  ) + "\n"  )
																					break
														system("cls" if name == "nt" else "clear"  )  # noqa: S605


if __name__ == "__main__":
				main()






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    _UpperCamelCase(   unittest.TestCase				):

						@slow
						def 						a__ (					self		:			Optional[Any]			):
													_UpperCAmelCase  :			Tuple	       =		CamembertModel.from_pretrained("camembert-base"			)
													model.to(_lowerCamelCase			)

													_UpperCAmelCase  :			List[Any]	       =		torch.tensor(
													    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]	,						device=_lowerCamelCase	,						dtype=torch.long	,						)  # J'aime le camembert !
													with torch.no_grad():
																				_UpperCAmelCase  :			Dict	       =		model(_lowerCamelCase			)["last_hidden_state"]
													_UpperCAmelCase  :			Optional[int]	       =		torch.Size((1, 10, 7_68)			)
													self.assertEqual(output.shape	,						_lowerCamelCase			)
													# compare the actual values for a slice.
													_UpperCAmelCase  :			List[str]	       =		torch.tensor(
													    [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]	,						device=_lowerCamelCase	,						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]	,						_lowerCamelCase	,						atol=1E-4			)			)




