import random


class 						a__							:



			def __init__(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase = 0.01      ,						UpperCAmelCase = 1_0_0_0      ,						UpperCAmelCase = -1      ,						) ->    None:
								__a							=   sample
								if len(self.sample    ) == 0:
													raise ValueError('Sample data can not be empty'    )
								__a							=   target
								if len(self.target    ) == 0:
													raise ValueError('Target data can not be empty'    )
								if len(self.sample    ) != len(self.target    ):
													raise ValueError('Sample data and Target data do not have matching lengths'    )
								__a							=   learning_rate
								__a							=   epoch_number
								__a							=   bias
								__a							=   len(UpperCAmelCase    )
								__a							=   len(sample[0]    )  # number of columns in dataset
								__a							=   []



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    None:
								for sample in self.sample:
													sample.insert(0      ,						self.bias    )

								for _ in range(self.col_sample    ):
													self.weight.append(random.random()    )

								self.weight.insert(0      ,						self.bias    )

								__a							=   0

								while True:
													__a							=   False
													for i in range(self.number_sample    ):
																		__a							=   0
																		for j in range(self.col_sample + 1    ):
																							__a							=   u + self.weight[j] * self.sample[i][j]
																		__a							=   self.sign(UpperCAmelCase    )
																		if y != self.target[i]:
																							for j in range(self.col_sample + 1    ):
																												__a							=   (
																												    self.weight[j]
																												    + self.learning_rate
																												    * (self.target[i] - y)
																												    * self.sample[i][j]
																												)
																							__a							=   True
            # print('Epoch: \n',epoch_count)
													__a							=   epoch_count + 1
													# if you want control the epoch or just by error
													if not has_misclassified:
																		print(('\nEpoch:\n', epoch_count)    )
																		print('------------------------\n'    )
																		# if epoch_count > self.epoch_number or not error:
																		break



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase    ) ->    None:
								if len(self.sample    ) == 0:
													raise ValueError('Sample data can not be empty'    )
								sample.insert(0      ,						self.bias    )
								__a							=   0
								for i in range(self.col_sample + 1    ):
													__a							=   u + self.weight[i] * sample[i]

								__a							=   self.sign(UpperCAmelCase    )

								if y == -1:
													print(('Sample: ', sample)    )
													print('classification: P1'    )
								else:
													print(('Sample: ', sample)    )
													print('classification: P2'    )







			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase    ) ->    int:
								return 1 if u >= 0 else -1






lowerCamelCase_				:		Tuple       						=       [
    [-0.6_5_0_8, 0.1_0_9_7, 4.0_0_0_9],
    [-1.4_4_9_2, 0.8_8_9_6, 4.4_0_0_5],
    [2.0_8_5_0, 0.6_8_7_6, 1_2.0_7_1_0],
    [0.2_6_2_6, 1.1_4_7_6, 7.7_9_8_5],
    [0.6_4_1_8, 1.0_2_3_4, 7.0_4_2_7],
    [0.2_5_6_9, 0.6_7_3_0, 8.3_2_6_5],
    [1.1_1_5_5, 0.6_0_4_3, 7.4_4_4_6],
    [0.0_9_1_4, 0.3_3_9_9, 7.0_6_7_7],
    [0.0_1_2_1, 0.5_2_5_6, 4.6_3_1_6],
    [-0.0_4_2_9, 0.4_6_6_0, 5.4_3_2_3],
    [0.4_3_4_0, 0.6_8_7_0, 8.2_2_8_7],
    [0.2_7_3_5, 1.0_2_8_7, 7.1_9_3_4],
    [0.4_8_3_9, 0.4_8_5_1, 7.4_8_5_0],
    [0.4_0_8_9, -0.1_2_6_7, 5.5_0_1_9],
    [1.4_3_9_1, 0.1_6_1_4, 8.5_8_4_3],
    [-0.9_1_1_5, -0.1_9_7_3, 2.1_9_6_2],
    [0.3_6_5_4, 1.0_4_7_5, 7.4_8_5_8],
    [0.2_1_4_4, 0.7_5_1_5, 7.1_6_9_9],
    [0.2_0_1_3, 1.0_0_1_4, 6.5_4_8_9],
    [0.6_4_8_3, 0.2_1_8_3, 5.8_9_9_1],
    [-0.1_1_4_7, 0.2_2_4_2, 7.2_4_3_5],
    [-0.7_9_7_0, 0.8_7_9_5, 3.8_7_6_2],
    [-1.0_6_2_5, 0.6_3_6_6, 2.4_7_0_7],
    [0.5_3_0_7, 0.1_2_8_5, 5.6_8_8_3],
    [-1.2_2_0_0, 0.7_7_7_7, 1.7_2_5_2],
    [0.3_9_5_7, 0.1_0_7_6, 5.6_6_2_3],
    [-0.1_0_1_3, 0.5_9_8_9, 7.1_8_1_2],
    [2.4_4_8_2, 0.9_4_5_5, 1_1.2_0_9_5],
    [2.0_1_4_9, 0.6_1_9_2, 1_0.9_2_6_3],
    [0.2_0_1_2, 0.2_6_1_1, 5.4_6_3_1],
]

lowerCamelCase_				:		int       						=       [
    -1,
    -1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    -1,
    -1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    -1,
    1,
    -1,
    1,
]


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	lowerCamelCase_				:		List[str]       						=       Perceptron(
	    sample=samples, target=target, learning_rate=0.0_1, epoch_number=1_000, bias=-1
	)
	network.training()
	print("""Finished training perceptron""")
	print("""Enter values to predict or q to exit""")
	while True:
		lowerCamelCase_				:		list       						=       []
		for i in range(len(samples[0])):
			lowerCamelCase_				:		Any       						=       input("""value: """).strip()
			if user_input == "q":
				break
			lowerCamelCase_				:		Any       						=       float(user_input)
			sample.insert(i, observation)
		network.sort(sample)












"""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							):




						"""simple docstring"""




						@slow
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Optional[int]=       XLMProphetNetForConditionalGeneration.from_pretrained("microsoft/xprophetnet-large-wiki100-cased"			)
												model.to(snake_case__			)

												# encoder-decoder outputs
												lowercase__				:      Dict=       torch.tensor([[17, 96208, 103471, 2]]			).to(snake_case__			)
												lowercase__				:      Optional[Any]=       torch.tensor(
												    [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]			).to(snake_case__			)
												lowercase__				:      Dict=       model(
												    input_ids=snake_case__       ,  attention_mask=snake_case__       ,  encoder_outputs=snake_case__       ,  decoder_input_ids=snake_case__			)
												lowercase__				:      int=       output[0]
												lowercase__				:      str=       torch.Size((1, 14, 250012)			)
												self.assertEqual(output_predited_logis.shape       ,  snake_case__			)
												lowercase__				:      Union[str, Any]=       torch.tensor(
												    [[[-6.39_86, -8.23_91, 12.51_89], [-6.32_89, -8.08_64, 12.62_11], [-6.24_18, -8.04_45, 12.79_68]]]			).to(snake_case__			)
												self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]       ,  snake_case__       ,  atol=1e-4			)			)

												# encoder outputs
												lowercase__				:      Tuple=       model.prophetnet.encoder(snake_case__			)[0]
												lowercase__				:      Union[str, Any]=       torch.tensor(
												    [[[-1.42_60, -0.76_28, 0.84_53], [-1.47_19, -0.13_91, 0.78_07], [-1.76_78, 0.01_14, 0.46_46]]]			).to(snake_case__			)
												lowercase__				:      List[Any]=       torch.Size((1, 4, 1024)			)
												self.assertEqual(encoder_outputs.shape       ,  snake_case__			)
												self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]       ,  snake_case__       ,  atol=1e-4			)			)

												# decoder outputs
												lowercase__				:      int=       model.prophetnet.decoder(
												    snake_case__       ,  encoder_hidden_states=snake_case__       ,  )
												lowercase__				:      Union[str, Any]=       decoder_outputs[1].view(1       ,  model.config.ngram       ,  14       ,  -1			)
												lowercase__				:      List[Any]=       model.lm_head(snake_case__			)
												lowercase__				:      str=       predicting_streams_logits[:, 0]
												self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]       ,  snake_case__       ,  atol=1e-4			)			)




						@slow
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Union[str, Any]=       XLMProphetNetForConditionalGeneration.from_pretrained(
												    "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"			)
												model.to(snake_case__			)

												lowercase__				:      Dict=       torch.tensor([[17, 96208, 103471, 2]]			).to(snake_case__			)
												lowercase__				:      List[str]=       torch.tensor(
												    [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]			).to(snake_case__			)
												lowercase__				:      List[Any]=       model(
												    input_ids=snake_case__       ,  attention_mask=snake_case__       ,  encoder_outputs=snake_case__       ,  decoder_input_ids=snake_case__			)
												lowercase__				:      str=       output[0]
												lowercase__				:      Optional[Any]=       torch.Size((1, 14, 250012)			)
												self.assertEqual(output_predited_logis.shape       ,  snake_case__			)
												# compare the actual values for a slice.
												lowercase__				:      str=       torch.tensor(
												    [[[-9.22_53, -9.71_73, -6.35_29], [-7.67_01, -9.01_45, -1.93_82], [-8.01_95, -7.00_04, -0.15_23]]]			).to(snake_case__			)

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




						@slow
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Tuple=       XLMProphetNetForConditionalGeneration.from_pretrained(
												    "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"			)
												model.to(snake_case__			)
												lowercase__				:      Optional[int]=       512

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

												lowercase__				:      str=       (
												    "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."
												)
												lowercase__				:      Tuple=       (
												    "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."
												)
												lowercase__				:      Optional[int]=       (
												    "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。"
												)

												lowercase__				:      Dict=       tokenizer(
												    [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]       ,  padding=snake_case__       ,  max_length=255       ,  return_tensors="pt"			).input_ids
												lowercase__				:      Any=       input_ids.to(snake_case__			)

												lowercase__				:      List[Any]=       model.generate(
												    snake_case__       ,  num_beams=10       ,  length_penalty=1.0       ,  no_repeat_ngram_size=3       ,  early_stopping=snake_case__			)
												lowercase__				:      str=       [tokenizer.decode(snake_case__       ,  skip_special_tokens=snake_case__			) for g in summary_ids]
												lowercase__				:      Dict=       "Microsoft to end Windows 7 free support after January 14, 2020"
												lowercase__				:      int=       "Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"
												lowercase__				:      List[str]=       "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]       ,  snake_case__       ,  )

												lowercase__				:      List[str]=       model.generate(
												    snake_case__       ,  num_beams=1       ,  length_penalty=1.0       ,  no_repeat_ngram_size=3       ,  early_stopping=snake_case__			)
												lowercase__				:      Optional[Any]=       [
												    tokenizer.convert_ids_to_tokens(snake_case__       ,  skip_special_tokens=snake_case__			) for g in summary_ids_beama
												]
												lowercase__				:      List[str]=       "▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7".split(" "			)
												lowercase__				:      List[Any]=       "▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года".split(
												    " "			)
												lowercase__				:      int=       "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]       ,  snake_case__       ,  )






