code: 
  
  
  
  """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 		snake_case_      (  self  :			Optional[Any]		):
  											_UpperCAmelCase    :			Any              =					XLMProphetNetForConditionalGeneration.from_pretrained("microsoft/xprophetnet-large-wiki100-cased"		)
  											model.to(A		)
  
  											# encoder-decoder outputs
  											_UpperCAmelCase    :			Dict              =					torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]		).to(A		)
  											_UpperCAmelCase    :			Any              =					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(A		)
  											_UpperCAmelCase    :			Any              =					model(
  											    input_ids=A  ,			attention_mask=A  ,			encoder_outputs=A  ,			decoder_input_ids=A		)
  											_UpperCAmelCase    :			Any              =					output[0]
  											_UpperCAmelCase    :			Optional[Any]              =					torch.Size((1, 1_4, 2_5_0_0_1_2)		)
  											self.assertEqual(output_predited_logis.shape  ,			A		)
  											_UpperCAmelCase    :			int              =					torch.tensor(
  											    [[[-6.3_986, -8.2_391, 12.5_189], [-6.3_289, -8.0_864, 12.6_211], [-6.2_418, -8.0_445, 12.7_968]]]		).to(A		)
  											self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]  ,			A  ,			atol=1e-4		)		)
  
  											# encoder outputs
  											_UpperCAmelCase    :			int              =					model.prophetnet.encoder(A		)[0]
  											_UpperCAmelCase    :			Optional[Any]              =					torch.tensor(
  											    [[[-1.4_260, -0.7_628, 0.8_453], [-1.4_719, -0.1_391, 0.7_807], [-1.7_678, 0.0_114, 0.4_646]]]		).to(A		)
  											_UpperCAmelCase    :			Optional[Any]              =					torch.Size((1, 4, 1_0_2_4)		)
  											self.assertEqual(encoder_outputs.shape  ,			A		)
  											self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]  ,			A  ,			atol=1e-4		)		)
  
  											# decoder outputs
  											_UpperCAmelCase    :			List[Any]              =					model.prophetnet.decoder(
  											    A  ,			encoder_hidden_states=A  ,			)
  											_UpperCAmelCase    :			str              =					decoder_outputs[1].view(1  ,			model.config.ngram  ,			1_4  ,			-1		)
  											_UpperCAmelCase    :			Optional[int]              =					model.lm_head(A		)
  											_UpperCAmelCase    :			Union[str, Any]              =					predicting_streams_logits[:, 0]
  											self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]  ,			A  ,			atol=1e-4		)		)
  
  
  
  						@slow
  						def 		snake_case_      (  self  :			Any		):
  											_UpperCAmelCase    :			Tuple              =					XLMProphetNetForConditionalGeneration.from_pretrained(
  											    "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"		)
  											model.to(A		)
  
  											_UpperCAmelCase    :			int              =					torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]		).to(A		)
  											_UpperCAmelCase    :			Any              =					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(A		)
  											_UpperCAmelCase    :			int              =					model(
  											    input_ids=A  ,			attention_mask=A  ,			encoder_outputs=A  ,			decoder_input_ids=A		)
  											_UpperCAmelCase    :			Dict              =					output[0]
  											_UpperCAmelCase    :			Optional[Any]              =					torch.Size((1, 1_4, 2_5_0_0_1_2)		)
  											self.assertEqual(output_predited_logis.shape  ,			A		)
  											# compare the actual values for a slice.
  											_UpperCAmelCase    :			Tuple              =					torch.tensor(
  											    [[[-9.2_253, -9.7_173, -6.3_529], [-7.6_701, -9.0_145, -1.9_382], [-8.0_195, -7.0_004, -0.1_523]]]		).to(A		)
  
  											self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]  ,			A  ,			atol=1e-4		)		)
  
  
  
  						@slow
  						def 		snake_case_      (  self  :			str		):
  											_UpperCAmelCase    :			Optional[Any]              =					XLMProphetNetForConditionalGeneration.from_pretrained(
  											    "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"		)
  											model.to(A		)
  											_UpperCAmelCase    :			int              =					5_1_2
  
  											_UpperCAmelCase    :			str              =					XLMProphetNetTokenizer.from_pretrained("microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"		)
  
  											_UpperCAmelCase    :			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."
  											)
  											_UpperCAmelCase    :			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."
  											)
  											_UpperCAmelCase    :			Optional[Any]              =					(
  											    "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    :			Any              =					tokenizer(
  											    [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]  ,			padding=A  ,			max_length=2_5_5  ,			return_tensors="pt"		).input_ids
  											_UpperCAmelCase    :			Union[str, Any]              =					input_ids.to(A		)
  
  											_UpperCAmelCase    :			Optional[Any]              =					model.generate(
  											    A  ,			num_beams=1_0  ,			length_penalty=1.0  ,			no_repeat_ngram_size=3  ,			early_stopping=A		)
  											_UpperCAmelCase    :			Optional[int]              =					[tokenizer.decode(A  ,			skip_special_tokens=A		) for g in summary_ids]
  											_UpperCAmelCase    :			Dict              =					"Microsoft to end Windows 7 free support after January 14, 2020"
  											_UpperCAmelCase    :			Tuple              =					"Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"
  											_UpperCAmelCase    :			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]  ,			A  ,			)
  
  											_UpperCAmelCase    :			Union[str, Any]              =					model.generate(
  											    A  ,			num_beams=1  ,			length_penalty=1.0  ,			no_repeat_ngram_size=3  ,			early_stopping=A		)
  											_UpperCAmelCase    :			Optional[Any]              =					[
  											    tokenizer.convert_ids_to_tokens(A  ,			skip_special_tokens=A		) for g in summary_ids_beama
  											]
  											_UpperCAmelCase    :			int              =					"▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7".split(" "		)
  											_UpperCAmelCase    :			Dict              =					"▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года".split(
  											    " "		)
  											_UpperCAmelCase    :			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]  ,			A  ,			)
  
style_context: 
  
  
  
  """simple docstring"""
  
  
  
  
  import os
  import sys
  from pathlib import Path
  
  import torch
  
  from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
  from utils import load_json
  
  
  _lowerCAmelCase       :      Optional[Any]							=							torch.cuda.is_available()
  _lowerCAmelCase       :      str							=							[" Sam ate lunch today.", "Sams lunch ingredients."]
  _lowerCAmelCase       :      Dict							=							["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
  _lowerCAmelCase       :      Union[str, Any]							=							{
      "max_tokens_per_batch": None,
      "supervise_forward": True,
      "normalize_hidden": True,
      "label_smoothing": 0.2,
      "eval_max_gen_length": None,
      "eval_beams": 1,
      "val_metric": "loss",
      "save_top_k": 1,
      "adafactor": True,
      "early_stopping_patience": 2,
      "logger_name": "default",
      "length_penalty": 0.5,
      "cache_dir": "",
      "task": "summarization",
      "num_workers": 2,
      "alpha_hid": 0,
      "freeze_embeds": True,
      "enc_only": False,
      "tgt_suffix": "",
      "resume_from_checkpoint": None,
      "sortish_sampler": True,
      "student_decoder_layers": 1,
      "val_check_interval": 1.0,
      "output_dir": "",
      "fp16": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
      "no_teacher": False,
      "fp16_opt_level": "O1",
      "gpus": 1 if CUDA_AVAILABLE else 0,
      "n_tpu_cores": 0,
      "max_grad_norm": 1.0,
      "do_train": True,
      "do_predict": True,
      "accumulate_grad_batches": 1,
      "server_ip": "",
      "server_port": "",
      "seed": 42,
      "model_name_or_path": "sshleifer/bart-tiny-random",
      "config_name": "",
      "tokenizer_name": "facebook/bart-large",
      "do_lower_case": False,
      "learning_rate": 0.3,
      "lr_scheduler": "linear",
      "weight_decay": 0.0,
      "adam_epsilon": 1E-08,
      "warmup_steps": 0,
      "max_epochs": 1,
      "train_batch_size": 2,
      "eval_batch_size": 2,
      "max_source_length": 12,
      "max_target_length": 12,
      "val_max_target_length": 12,
      "test_max_target_length": 12,
      "fast_dev_run": False,
      "no_cache": False,
      "n_train": -1,
      "n_val": -1,
      "n_test": -1,
      "student_encoder_layers": 1,
      "freeze_encoder": False,
      "auto_scale_batch_size": False,
      "overwrite_output_dir": False,
      "student": None,
  }
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Path		,						SCREAMING_SNAKE_CASE__			:		list       )						->     Dict:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Tuple              =					"\n".join(SCREAMING_SNAKE_CASE__       )
       Path(SCREAMING_SNAKE_CASE__       ).open("w"       ).writelines(SCREAMING_SNAKE_CASE__       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		List[Any]       )						->     Union[str, Any]:
  
  
  
  
  
       '''simple docstring'''
  
       for split in ["train", "val", "test"]:
            _dump_articles(os.path.join(SCREAMING_SNAKE_CASE__		,						f'{split}.source'       )		,						SCREAMING_SNAKE_CASE__       )
            _dump_articles(os.path.join(SCREAMING_SNAKE_CASE__		,						f'{split}.target'       )		,						SCREAMING_SNAKE_CASE__       )
       return tmp_dir
  
  
  
  
  
  
  class 			UpperCAmelCase_						(       _UpperCamelCase		):
  
  
  
        @classmethod
        def 		snake_case_      (  cls  :			Optional[int]		):
             return cls
  
  
  
        @require_torch_multi_gpu
        def 		snake_case_      (  self  :			Union[str, Any]		):
             _UpperCAmelCase    :			Dict              =					{
                 "no_teacher": True,
                 "freeze_encoder": True,
                 "gpus": 2,
                 "overwrite_output_dir": True,
                 "sortish_sampler": True,
             }
             self._test_distiller_cli_fork(A  ,			check_contents=A		)
  
  
  
        def 		snake_case_      (  self  :			List[str]  ,			A  :			List[Any]  ,			A  :			Optional[Any]=True		):
             _UpperCAmelCase    :			int              =					{
                 "label_smoothing": 0.0,
                 "early_stopping_patience": -1,
                 "train_batch_size": 1,
                 "eval_batch_size": 2,
                 "max_epochs": 2,
                 "alpha_mlm": 0.2,
                 "alpha_ce": 0.8,
                 "do_predict": True,
                 "model_name_or_path": "sshleifer/tinier_bart",
                 "teacher": CHEAP_ARGS["model_name_or_path"],
                 "val_check_interval": 0.5,
             }
             default_updates.update(A		)
             _UpperCAmelCase    :			dict              =					CHEAP_ARGS.copy()
             _UpperCAmelCase    :			Union[str, Any]              =					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
             _UpperCAmelCase    :			List[Any]              =					self.get_auto_remove_tmp_dir()
             args_d.update(data_dir=A  ,			output_dir=A  ,			**A		)
  
             def convert(A  :			List[Any]  ,			A  :			Optional[int]		):
                  if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
                       return ""
                  if v is False or v is None:
                       return ""
                  if v is True:  # or len(str(v))==0:
                       return f'--{k}'
                  return f'--{k}={v}'
  
             _UpperCAmelCase    :			Tuple              =					[x for x in (convert(A  ,			A		) for k, v in args_d.items()) if len(A		)]
             _UpperCAmelCase    :			Union[str, Any]              =					[sys.executable, f'{self.test_file_dir}/distillation.py'] + cli_args
             execute_subprocess_async(A  ,			env=self.get_env()		)
  
             _UpperCAmelCase    :			int              =					os.listdir(A		)
             _UpperCAmelCase    :			List[Any]              =					{os.path.basename(A		) for p in contents}
             _UpperCAmelCase    :			List[Any]              =					[p for p in contents if p.endswith("ckpt"		)]
             assert len(A		) > 0
  
             self.assertIn("test_generations.txt"  ,			A		)
             self.assertIn("test_results.txt"  ,			A		)
  
             # get the following from the module, (we don't have access to `model` here)
             _UpperCAmelCase    :			List[Any]              =					os.path.join(A  ,			"metrics.json"		)
             _UpperCAmelCase    :			str              =					"rouge2"
  
             _UpperCAmelCase    :			List[Any]              =					load_json(A		)
             # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
             print(A		)
             _UpperCAmelCase    :			List[str]              =					metrics["val"][-1]
             self.assertGreaterEqual(last_step_stats["val_avg_gen_time"]  ,			0.01		)
             self.assertIsInstance(last_step_stats[f'val_avg_{val_metric}']  ,			A		)
             self.assertEqual(len(metrics["test"]		)  ,			1		)
             _UpperCAmelCase    :			Dict              =					int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) / 2 + 1		)
             self.assertEqual(len(metrics["val"]		)  ,			A		)
  