
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 					lowerCAmelCase__		(				self       :				str					):
							UpperCamelCase_:							Any		=  XLMProphetNetForConditionalGeneration.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased"""					)
							model.to(snake_case_					)

							# encoder-decoder outputs
							UpperCamelCase_:							Optional[int]		=  torch.tensor([[17, 9_6208, 10_3471, 2]]					).to(snake_case_					)
							UpperCamelCase_:							Union[str, Any]		=  torch.tensor(
							    [[2, 250, 9953, 34, 6_9489, 1620, 32, 11_8424, 624, 210, 105, 2913, 1032, 351]]					).to(snake_case_					)
							UpperCamelCase_:							Any		=  model(
							    input_ids=snake_case_  ,		attention_mask=snake_case_  ,		encoder_outputs=snake_case_  ,		decoder_input_ids=snake_case_					)
							UpperCamelCase_:							int		=  output[0]
							UpperCamelCase_:							Optional[int]		=  torch.Size((1, 14, 25_0012)					)
							self.assertEqual(output_predited_logis.shape  ,		snake_case_					)
							UpperCamelCase_:							Dict		=  torch.tensor(
							    [[[-6.3986, -8.2391, 12.5189], [-6.3289, -8.0864, 12.6211], [-6.2418, -8.0445, 12.7968]]]					).to(snake_case_					)
							self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]  ,		snake_case_  ,		atol=1e-4					)					)

							# encoder outputs
							UpperCamelCase_:							Optional[int]		=  model.prophetnet.encoder(snake_case_					)[0]
							UpperCamelCase_:							Dict		=  torch.tensor(
							    [[[-1.4260, -0.7628, 0.8453], [-1.4719, -0.1391, 0.7807], [-1.7678, 0.0114, 0.4646]]]					).to(snake_case_					)
							UpperCamelCase_:							Tuple		=  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
							UpperCamelCase_:							List[str]		=  model.prophetnet.decoder(
							    snake_case_  ,		encoder_hidden_states=snake_case_  ,		)
							UpperCamelCase_:							Optional[Any]		=  decoder_outputs[1].view(1  ,		model.config.ngram  ,		14  ,		-1					)
							UpperCamelCase_:							Union[str, Any]		=  model.lm_head(snake_case_					)
							UpperCamelCase_:							Union[str, Any]		=  predicting_streams_logits[:, 0]
							self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]  ,		snake_case_  ,		atol=1e-4					)					)

			@slow
			def 					lowerCAmelCase__		(				self       :				int					):
							UpperCamelCase_:							int		=  XLMProphetNetForConditionalGeneration.from_pretrained(
							    """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""					)
							model.to(snake_case_					)

							UpperCamelCase_:							int		=  torch.tensor([[17, 9_6208, 10_3471, 2]]					).to(snake_case_					)
							UpperCamelCase_:							Tuple		=  torch.tensor(
							    [[2, 250, 9953, 34, 6_9489, 1620, 32, 11_8424, 624, 210, 105, 2913, 1032, 351]]					).to(snake_case_					)
							UpperCamelCase_:							int		=  model(
							    input_ids=snake_case_  ,		attention_mask=snake_case_  ,		encoder_outputs=snake_case_  ,		decoder_input_ids=snake_case_					)
							UpperCamelCase_:							List[Any]		=  output[0]
							UpperCamelCase_:							str		=  torch.Size((1, 14, 25_0012)					)
							self.assertEqual(output_predited_logis.shape  ,		snake_case_					)
							# compare the actual values for a slice.
							UpperCamelCase_:							int		=  torch.tensor(
							    [[[-9.2253, -9.7173, -6.3529], [-7.6701, -9.0145, -1.9382], [-8.0195, -7.0004, -0.1523]]]					).to(snake_case_					)

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




			@slow
			def 					lowerCAmelCase__		(				self       :				Optional[Any]					):
							UpperCamelCase_:							Tuple		=  XLMProphetNetForConditionalGeneration.from_pretrained(
							    """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""					)
							model.to(snake_case_					)
							UpperCamelCase_:							Optional[int]		=  512

							UpperCamelCase_:							Union[str, Any]		=  XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""					)

							UpperCamelCase_:							Any		=  (
							    """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_:							List[str]		=  (
							    """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_:							List[str]		=  (
							    """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_:							str		=  tokenizer(
							    [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]  ,		padding=snake_case_  ,		max_length=255  ,		return_tensors="""pt"""					).input_ids
							UpperCamelCase_:							Dict		=  input_ids.to(snake_case_					)

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

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








"""simple docstring"""




def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):
					if not isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
										SCREAMING_SNAKE_CASE				=	(
										    'operation can not be conducted on a object of type '
										    f'''{type(SCREAMING_SNAKE_CASE_			).__name__}'''
										)
										raise TypeError(SCREAMING_SNAKE_CASE_			)
					SCREAMING_SNAKE_CASE				=	''
					for _ in range(0,     3_2			):
										bit_string += str(number % 2			)
										SCREAMING_SNAKE_CASE				=	number >> 1
					return bit_string

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):
					if number < 0:
										raise ValueError('the value of input must be positive'			)
					elif isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
										raise TypeError('Input value must be a \'int\' type'			)
					elif isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
										raise TypeError('\'<\' not supported between instances of \'str\' and \'int\''			)
					SCREAMING_SNAKE_CASE				=	0
					# iterator over [1 to 32],since we are dealing with 32 bit integer
					for _ in range(1,     3_3			):
										# left shift the bits by unity
										SCREAMING_SNAKE_CASE				=	result << 1
										# get the end bit
										SCREAMING_SNAKE_CASE				=	number % 2
										# right shift the bits by unity
										SCREAMING_SNAKE_CASE				=	number >> 1
										# add that bit to our ans
										SCREAMING_SNAKE_CASE				=	result | end_bit
					return get_reverse_bit_string(SCREAMING_SNAKE_CASE_			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
