code: 
  
  
  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):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							@slow
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		XLMProphetNetForConditionalGeneration.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased"""		)
  													model.to(_UpperCAmelCase		)
  
  													# encoder-decoder outputs
  													a_					=		torch.tensor([[17, 96_208, 103_471, 2]]		).to(_UpperCAmelCase		)
  													a_					=		torch.tensor(
  													    [[2, 250, 9_953, 34, 69_489, 1_620, 32, 118_424, 624, 210, 105, 2_913, 1_032, 351]]		).to(_UpperCAmelCase		)
  													a_					=		model(
  													    input_ids=_UpperCAmelCase			,    attention_mask=_UpperCAmelCase			,    encoder_outputs=_UpperCAmelCase			,    decoder_input_ids=_UpperCAmelCase		)
  													a_					=		output[0]
  													a_					=		torch.Size((1, 14, 250_012)		)
  													self.assertEqual(output_predited_logis.shape			,    _UpperCAmelCase		)
  													a_					=		torch.tensor(
  													    [[[-6.3_9_8_6, -8.2_3_9_1, 12.5_189], [-6.3_2_8_9, -8.0_8_6_4, 12.6_211], [-6.2_4_1_8, -8.0_4_4_5, 12.7_968]]]		).to(_UpperCAmelCase		)
  													self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]			,    _UpperCAmelCase			,    atol=1e-4		)		)
  
  													# encoder outputs
  													a_					=		model.prophetnet.encoder(_UpperCAmelCase		)[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(_UpperCAmelCase		)
  													a_					=		torch.Size((1, 4, 1_024)		)
  													self.assertEqual(encoder_outputs.shape			,    _UpperCAmelCase		)
  													self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]			,    _UpperCAmelCase			,    atol=1e-4		)		)
  
  													# decoder outputs
  													a_					=		model.prophetnet.decoder(
  													    _UpperCAmelCase			,    encoder_hidden_states=_UpperCAmelCase			,    )
  													a_					=		decoder_outputs[1].view(1			,    model.config.ngram			,    14			,    -1		)
  													a_					=		model.lm_head(_UpperCAmelCase		)
  													a_					=		predicting_streams_logits[:, 0]
  													self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]			,    _UpperCAmelCase			,    atol=1e-4		)		)
  
  
  							@slow
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		XLMProphetNetForConditionalGeneration.from_pretrained(
  													    """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""		)
  													model.to(_UpperCAmelCase		)
  
  													a_					=		torch.tensor([[17, 96_208, 103_471, 2]]		).to(_UpperCAmelCase		)
  													a_					=		torch.tensor(
  													    [[2, 250, 9_953, 34, 69_489, 1_620, 32, 118_424, 624, 210, 105, 2_913, 1_032, 351]]		).to(_UpperCAmelCase		)
  													a_					=		model(
  													    input_ids=_UpperCAmelCase			,    attention_mask=_UpperCAmelCase			,    encoder_outputs=_UpperCAmelCase			,    decoder_input_ids=_UpperCAmelCase		)
  													a_					=		output[0]
  													a_					=		torch.Size((1, 14, 250_012)		)
  													self.assertEqual(output_predited_logis.shape			,    _UpperCAmelCase		)
  													# 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(_UpperCAmelCase		)
  
  													self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]			,    _UpperCAmelCase			,    atol=1e-4		)		)
  
  
  
  
  
  
  							@slow
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		XLMProphetNetForConditionalGeneration.from_pretrained(
  													    """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""		)
  													model.to(_UpperCAmelCase		)
  													a_					=		512
  
  													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=_UpperCAmelCase			,    max_length=255			,    return_tensors="""pt"""		).input_ids
  													a_					=		input_ids.to(_UpperCAmelCase		)
  
  													a_					=		model.generate(
  													    _UpperCAmelCase			,    num_beams=10			,    length_penalty=1.0			,    no_repeat_ngram_size=3			,    early_stopping=_UpperCAmelCase		)
  													a_					=		[tokenizer.decode(_UpperCAmelCase			,    skip_special_tokens=_UpperCAmelCase		) 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]			,    _UpperCAmelCase			,    )
  
  													a_					=		model.generate(
  													    _UpperCAmelCase			,    num_beams=1			,    length_penalty=1.0			,    no_repeat_ngram_size=3			,    early_stopping=_UpperCAmelCase		)
  													a_					=		[
  													    tokenizer.convert_ids_to_tokens(_UpperCAmelCase			,    skip_special_tokens=_UpperCAmelCase		) 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]			,    _UpperCAmelCase			,    )
style_context: 
  
  
  import importlib
  import sys
  from argparse import REMAINDER, ArgumentParser
  from pathlib import Path
  
  import torch_xla.distributed.xla_multiprocessing as xmp
  
  
  
  
  def   lowerCamelCase_       (					):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		ArgumentParser(
  						    description=(
  						        """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
  						    )      )
  
  						# Optional arguments for the launch helper
  						parser.add_argument("""--num_cores"""       ,    type=UpperCAmelCase__       ,    default=1       ,    help="""Number of TPU cores to use (1 or 8)."""      )
  
  						# positional
  						parser.add_argument(
  						    """training_script"""       ,    type=UpperCAmelCase__       ,    help=(
  						        """The full path to the single TPU training """
  						        """program/script to be launched in parallel, """
  						        """followed by all the arguments for the """
  						        """training script"""
  						    )       ,    )
  
  						# rest from the training program
  						parser.add_argument("""training_script_args"""       ,    nargs=UpperCAmelCase__      )
  
  						return parser.parse_args()
  
  
  
  
  def   lowerCamelCase_       (					):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		parse_args()
  
  						# Import training_script as a module.
  						a_					=		Path(args.training_script      )
  						sys.path.append(str(script_fpath.parent.resolve()      )      )
  						a_					=		script_fpath.stem
  						a_					=		importlib.import_module(UpperCAmelCase__      )
  
  						# Patch sys.argv
  						a_					=		[args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores      )]
  
  						xmp.spawn(mod._mp_fn       ,    args=()       ,    nprocs=args.num_cores      )
  
  
  if __name__ == "__main__":
  	main()