
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 							lowercase   (						unittest.TestCase  ):



		@slow
		def __UpperCamelCase					(					self			)    ->   Optional[Any]:
				"""simple docstring"""
				UpperCamelCase            =	XLMProphetNetForConditionalGeneration.from_pretrained('microsoft/xprophetnet-large-wiki100-cased'			)
				model.to(A_			)

				# encoder-decoder outputs
				UpperCamelCase            =	torch.tensor([[17, 96_208, 103_471, 2]]			).to(A_			)
				UpperCamelCase            =	torch.tensor(
				    [[2, 250, 9_953, 34, 69_489, 1_620, 32, 118_424, 624, 210, 105, 2_913, 1_032, 351]]			).to(A_			)
				UpperCamelCase            =	model(
				    input_ids=A_							, attention_mask=A_							, encoder_outputs=A_							, decoder_input_ids=A_			)
				UpperCamelCase            =	output[0]
				UpperCamelCase            =	torch.Size((1, 14, 250_012)			)
				self.assertEqual(output_predited_logis.shape							, A_			)
				UpperCamelCase            =	torch.tensor(
				    [[[-6.3986, -8.2391, 12.5189], [-6.3289, -8.0864, 12.6211], [-6.2418, -8.0445, 12.7968]]]			).to(A_			)
				self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]							, A_							, atol=1e-4			)			)

				# encoder outputs
				UpperCamelCase            =	model.prophetnet.encoder(A_			)[0]
				UpperCamelCase            =	torch.tensor(
				    [[[-1.4260, -0.7628, 0.8453], [-1.4719, -0.1391, 0.7807], [-1.7678, 0.0114, 0.4646]]]			).to(A_			)
				UpperCamelCase            =	torch.Size((1, 4, 1_024)			)
				self.assertEqual(encoder_outputs.shape							, A_			)
				self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]							, A_							, atol=1e-4			)			)

				# decoder outputs
				UpperCamelCase            =	model.prophetnet.decoder(
				    A_							, encoder_hidden_states=A_							, )
				UpperCamelCase            =	decoder_outputs[1].view(1							, model.config.ngram							, 14							, -1			)
				UpperCamelCase            =	model.lm_head(A_			)
				UpperCamelCase            =	predicting_streams_logits[:, 0]
				self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]							, A_							, atol=1e-4			)			)



		@slow
		def __UpperCamelCase					(					self			)    ->   Optional[int]:
				"""simple docstring"""
				UpperCamelCase            =	XLMProphetNetForConditionalGeneration.from_pretrained(
				    'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'			)
				model.to(A_			)

				UpperCamelCase            =	torch.tensor([[17, 96_208, 103_471, 2]]			).to(A_			)
				UpperCamelCase            =	torch.tensor(
				    [[2, 250, 9_953, 34, 69_489, 1_620, 32, 118_424, 624, 210, 105, 2_913, 1_032, 351]]			).to(A_			)
				UpperCamelCase            =	model(
				    input_ids=A_							, attention_mask=A_							, encoder_outputs=A_							, decoder_input_ids=A_			)
				UpperCamelCase            =	output[0]
				UpperCamelCase            =	torch.Size((1, 14, 250_012)			)
				self.assertEqual(output_predited_logis.shape							, A_			)
				# compare the actual values for a slice.
				UpperCamelCase            =	torch.tensor(
				    [[[-9.2253, -9.7173, -6.3529], [-7.6701, -9.0145, -1.9382], [-8.0195, -7.0004, -0.1523]]]			).to(A_			)

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



		@slow
		def __UpperCamelCase					(					self			)    ->   Tuple:
				"""simple docstring"""
				UpperCamelCase            =	XLMProphetNetForConditionalGeneration.from_pretrained(
				    'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'			)
				model.to(A_			)
				UpperCamelCase            =	512

				UpperCamelCase            =	XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'			)

				UpperCamelCase            =	(
				    '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.'
				)
				UpperCamelCase            =	(
				    '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.'
				)
				UpperCamelCase            =	(
				    '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。'
				)

				UpperCamelCase            =	tokenizer(
				    [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]							, padding=A_							, max_length=255							, return_tensors='pt'			).input_ids
				UpperCamelCase            =	input_ids.to(A_			)

				UpperCamelCase            =	model.generate(
				    A_							, num_beams=10							, length_penalty=1.0							, no_repeat_ngram_size=3							, early_stopping=A_			)
				UpperCamelCase            =	[tokenizer.decode(A_							, skip_special_tokens=A_			) for g in summary_ids]
				UpperCamelCase            =	'Microsoft to end Windows 7 free support after January 14, 2020'
				UpperCamelCase            =	'Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года'
				UpperCamelCase            =	'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]							, A_							, )

				UpperCamelCase            =	model.generate(
				    A_							, num_beams=1							, length_penalty=1.0							, no_repeat_ngram_size=3							, early_stopping=A_			)
				UpperCamelCase            =	[
				    tokenizer.convert_ids_to_tokens(A_							, skip_special_tokens=A_			) for g in summary_ids_beama
				]
				UpperCamelCase            =	'▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7'.split(' '			)
				UpperCamelCase            =	'▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года'.split(
				    ' '			)
				UpperCamelCase            =	'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]							, A_							, )


def  A			(   lowercase					)  ->    str:
		'''simple docstring'''

		if not isinstance(lowercase       ,			lowercase					):
				UpperCamelCase            =	(
				    'operation can not be conducted on a object of type '
				    f'''{type(lowercase					).__name__}'''
				)
				raise TypeError(lowercase					)
		UpperCamelCase            =	''
		for _ in range(0       ,			32					):
				bit_string += str(number % 2					)
				UpperCamelCase            =	number >> 1
		return bit_string


def  A			(   lowercase					)  ->    str:
		'''simple docstring'''

		if number < 0:
				raise ValueError('the value of input must be positive'					)
		elif isinstance(lowercase       ,			lowercase					):
				raise TypeError('Input value must be a \'int\' type'					)
		elif isinstance(lowercase       ,			lowercase					):
				raise TypeError('\'<\' not supported between instances of \'str\' and \'int\''					)
		UpperCamelCase            =	0
		# iterator over [1 to 32],since we are dealing with 32 bit integer
		for _ in range(1       ,			33					):
				# left shift the bits by unity
				UpperCamelCase            =	result << 1
				# get the end bit
				UpperCamelCase            =	number % 2
				# right shift the bits by unity
				UpperCamelCase            =	number >> 1
				# add that bit to our ans
				UpperCamelCase            =	result | end_bit
		return get_reverse_bit_string(lowercase					)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
