

"""simple docstring"""




import unittest

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


if is_torch_available():
	import torch

	from transformers import XLMProphetNetForConditionalGeneration, XLMProphetNetTokenizer





@require_torch
class 			__UpperCamelCase (   unittest.TestCase						):


		@slow
		def 					SCREAMING_SNAKE_CASE__   (self :		List[str]):
							A			=					XLMProphetNetForConditionalGeneration.from_pretrained("microsoft/xprophetnet-large-wiki100-cased")
							model.to(__SCREAMING_SNAKE_CASE)

							# encoder-decoder outputs
							A			=					torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]).to(__SCREAMING_SNAKE_CASE)
							A			=					torch.tensor(
							    [[2, 2_5_0, 9_9_5_3, 3_4, 6_9_4_8_9, 1_6_2_0, 3_2, 1_1_8_4_2_4, 6_2_4, 2_1_0, 1_0_5, 2_9_1_3, 1_0_3_2, 3_5_1]]).to(__SCREAMING_SNAKE_CASE)
							A			=					model(
							    input_ids=__SCREAMING_SNAKE_CASE     ,     attention_mask=__SCREAMING_SNAKE_CASE     ,     encoder_outputs=__SCREAMING_SNAKE_CASE     ,     decoder_input_ids=__SCREAMING_SNAKE_CASE)
							A			=					output[0]
							A			=					torch.Size((1, 1_4, 2_5_0_0_1_2))
							self.assertEqual(output_predited_logis.shape     ,     __SCREAMING_SNAKE_CASE)
							A			=					torch.tensor(
							    [[[-6.3_9_8_6, -8.2_3_9_1, 1_2.5_1_8_9], [-6.3_2_8_9, -8.0_8_6_4, 1_2.6_2_1_1], [-6.2_4_1_8, -8.0_4_4_5, 1_2.7_9_6_8]]]).to(__SCREAMING_SNAKE_CASE)
							self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]     ,     __SCREAMING_SNAKE_CASE     ,     atol=1E-4))

							# encoder outputs
							A			=					model.prophetnet.encoder(__SCREAMING_SNAKE_CASE)[0]
							A			=					torch.tensor(
							    [[[-1.4_2_6_0, -0.7_6_2_8, 0.8_4_5_3], [-1.4_7_1_9, -0.1_3_9_1, 0.7_8_0_7], [-1.7_6_7_8, 0.0_1_1_4, 0.4_6_4_6]]]).to(__SCREAMING_SNAKE_CASE)
							A			=					torch.Size((1, 4, 1_0_2_4))
							self.assertEqual(encoder_outputs.shape     ,     __SCREAMING_SNAKE_CASE)
							self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]     ,     __SCREAMING_SNAKE_CASE     ,     atol=1E-4))

							# decoder outputs
							A			=					model.prophetnet.decoder(
							    __SCREAMING_SNAKE_CASE     ,     encoder_hidden_states=__SCREAMING_SNAKE_CASE     ,     )
							A			=					decoder_outputs[1].view(1     ,     model.config.ngram     ,     1_4     ,     -1)
							A			=					model.lm_head(__SCREAMING_SNAKE_CASE)
							A			=					predicting_streams_logits[:, 0]
							self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]     ,     __SCREAMING_SNAKE_CASE     ,     atol=1E-4))


		@slow
		def 					SCREAMING_SNAKE_CASE__   (self :		Union[str, Any]):
							A			=					XLMProphetNetForConditionalGeneration.from_pretrained(
							    "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg")
							model.to(__SCREAMING_SNAKE_CASE)

							A			=					torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]).to(__SCREAMING_SNAKE_CASE)
							A			=					torch.tensor(
							    [[2, 2_5_0, 9_9_5_3, 3_4, 6_9_4_8_9, 1_6_2_0, 3_2, 1_1_8_4_2_4, 6_2_4, 2_1_0, 1_0_5, 2_9_1_3, 1_0_3_2, 3_5_1]]).to(__SCREAMING_SNAKE_CASE)
							A			=					model(
							    input_ids=__SCREAMING_SNAKE_CASE     ,     attention_mask=__SCREAMING_SNAKE_CASE     ,     encoder_outputs=__SCREAMING_SNAKE_CASE     ,     decoder_input_ids=__SCREAMING_SNAKE_CASE)
							A			=					output[0]
							A			=					torch.Size((1, 1_4, 2_5_0_0_1_2))
							self.assertEqual(output_predited_logis.shape     ,     __SCREAMING_SNAKE_CASE)
							# compare the actual values for a slice.
							A			=					torch.tensor(
							    [[[-9.2_2_5_3, -9.7_1_7_3, -6.3_5_2_9], [-7.6_7_0_1, -9.0_1_4_5, -1.9_3_8_2], [-8.0_1_9_5, -7.0_0_0_4, -0.1_5_2_3]]]).to(__SCREAMING_SNAKE_CASE)

							self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]     ,     __SCREAMING_SNAKE_CASE     ,     atol=1E-4))


		@slow
		def 					SCREAMING_SNAKE_CASE__   (self :		Any):
							A			=					XLMProphetNetForConditionalGeneration.from_pretrained(
							    "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg")
							model.to(__SCREAMING_SNAKE_CASE)
							A			=					5_1_2

							A			=					XLMProphetNetTokenizer.from_pretrained("microsoft/xprophetnet-large-wiki100-cased-xglue-ntg")

							A			=					(
							    "Microsoft Corporation intends to officially end free support for the Windows 7 operating system after"
							    " January 14, 2020, according to the official portal of the organization. From that day, users of this"
							    " system will not be able to receive security updates, which could make their computers vulnerable to"
							    " cyber attacks."
							)
							A			=					(
							    "орпорация Microsoft намерена официально прекратить бесплатную поддержку операционной системы Windows 7"
							    " после 14 января 2020 года, сообщается на официальном портале организации . С указанного дня пользователи"
							    " этой системы не смогут получать обновления безопасности, из-за чего их компьютеры могут стать уязвимыми"
							    " к кибератакам."
							)
							A			=					(
							    "根据该组织的官方门户网站，微软公司打算在2020年1月14日之后正式终止对Windows 7操作系统的免费支持。从那时起，该系统的用户将无法接收安全更新，这可能会使他们的计算机容易受到网络攻击。"
							)

							A			=					tokenizer(
							    [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]     ,     padding=__SCREAMING_SNAKE_CASE     ,     max_length=2_5_5     ,     return_tensors="pt").input_ids
							A			=					input_ids.to(__SCREAMING_SNAKE_CASE)

							A			=					model.generate(
							    __SCREAMING_SNAKE_CASE     ,     num_beams=1_0     ,     length_penalty=1.0     ,     no_repeat_ngram_size=3     ,     early_stopping=__SCREAMING_SNAKE_CASE)
							A			=					[tokenizer.decode(__SCREAMING_SNAKE_CASE     ,     skip_special_tokens=__SCREAMING_SNAKE_CASE) for g in summary_ids]
							A			=					"Microsoft to end Windows 7 free support after January 14, 2020"
							A			=					"Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"
							A			=					"微软打算终止对Windows 7操作系统的免费支持"
							self.assertListEqual(
							    [EXPECTED_TITLE_EN, EXPECTED_TITLE_RU, EXPECTED_TITLE_ZH]     ,     __SCREAMING_SNAKE_CASE     ,     )

							A			=					model.generate(
							    __SCREAMING_SNAKE_CASE     ,     num_beams=1     ,     length_penalty=1.0     ,     no_repeat_ngram_size=3     ,     early_stopping=__SCREAMING_SNAKE_CASE)
							A			=					[
							    tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE     ,     skip_special_tokens=__SCREAMING_SNAKE_CASE) for g in summary_ids_beama
							]
							A			=					"▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7".split(" ")
							A			=					"▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года".split(
							    " ")
							A			=					"微软 公司 打算 终止 对 Windows ▁7 操作 系统的 免费 支持".split(" ")
							self.assertListEqual(
							    [EXPECTED_TITLE_EN_BEAM1_TOK, EXPECTED_TITLE_RU_BEAM1_TOK, EXPECTED_TITLE_ZH_BEAM1_TOK]     ,     __SCREAMING_SNAKE_CASE     ,     )











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  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->list:






					"""simple docstring"""






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







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->list:






					"""simple docstring"""






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







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->float:






					"""simple docstring"""






					return sum(UpperCAmelCase       ) / instance_count







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->float:






					"""simple docstring"""






					return instance_count / total_count







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->float:






					"""simple docstring"""






					__magic_name__					:							Any		   =							[]  # 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
					__magic_name__					:							Optional[int]		   =							len(UpperCAmelCase       )  # Number of classes in dataset
					return 1 / (total_count - n_classes) * sum(UpperCAmelCase       )







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->list:






					"""simple docstring"""






					__magic_name__					:							int		   =							[]
					# 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]       )       ):
															__magic_name__					:							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(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  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->float:






					"""simple docstring"""






					__magic_name__					:							Optional[Any]		   =							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       )) * 100


lowercase_		=							TypeVar('''num''')







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






					"""simple docstring"""






					while True:
										try:
															__magic_name__					:							List[Any]		   =							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  lowerCAmelCase  (	)  ->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
										__magic_name__					:							List[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('''-''' * 100       )

										# Trying to get the value of standard deviation
										__magic_name__					:							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('''-''' * 100       )

										# Trying to get number of instances in classes and theirs means to generate
										# dataset
										__magic_name__					:							Optional[Any]		   =							[]  # An empty list to store instance counts of classes in dataset
										for i in range(UpperCAmelCase       ):
															__magic_name__					:							Dict		   =							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('''-''' * 100       )

										# An empty list to store values of user-entered means of classes
										__magic_name__					:							Dict		   =							[]
										for a in range(UpperCAmelCase       ):
															__magic_name__					:							Dict		   =							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('''-''' * 100       )

										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('''-''' * 100       )

										# 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('''-''' * 100       )

										# Generating training dataset drawn from gaussian distribution
										__magic_name__					:							Optional[int]		   =							[
										    gaussian_distribution(user_means[j],	UpperCAmelCase,	counts[j]       )
										    for j in range(UpperCAmelCase       )
										]
										print('''Generated Normal Distribution: \n''',	UpperCAmelCase       )
										print('''-''' * 100       )

										# Generating Ys to detecting corresponding classes
										__magic_name__					:							List[str]		   =							y_generator(UpperCAmelCase,	UpperCAmelCase       )
										print('''Generated Corresponding Ys: \n''',	UpperCAmelCase       )
										print('''-''' * 100       )

										# Calculating the value of actual mean for each class
										__magic_name__					:							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('''-''' * 100       )

										# Calculating the value of probabilities for each class
										__magic_name__					:							Union[str, Any]		   =							[
										    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('''-''' * 100       )

										# Calculating the values of variance for each class
										__magic_name__					:							List[Any]		   =							calculate_variance(UpperCAmelCase,	UpperCAmelCase,	sum(UpperCAmelCase       )       )
										print('''Variance: ''',	UpperCAmelCase       )
										print('''-''' * 100       )

										# Predicting Y values
										# storing predicted Y values in 'pre_indexes' variable
										__magic_name__					:							List[Any]		   =							predict_y_values(UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )
										print('''-''' * 100       )

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



