






"""simple docstring"""



import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

_UpperCamelCase						:    List[str]		=						logging.getLogger()
_UpperCamelCase						:    Tuple		=						torch.cuda.is_available()
_UpperCamelCase						:    Tuple		=						{
    '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': 4_2,
    '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-0_8,
    'warmup_steps': 0,
    'max_epochs': 1,
    'train_batch_size': 2,
    'eval_batch_size': 2,
    'max_source_length': 1_2,
    'max_target_length': 1_2,
    'val_max_target_length': 1_2,
    'test_max_target_length': 1_2,
    '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 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Path						,   __snake_case     :    list       ):





  '''simple docstring'''





  lowercase				       =			'\n'.join(__snake_case       )
  Path(__snake_case       ).open('w'       ).writelines(__snake_case       )


_UpperCamelCase						:    int		=						[' Sam ate lunch today.', 'Sams lunch ingredients.']
_UpperCamelCase						:    Optional[Any]		=						['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
_UpperCamelCase						:    Any		=						'patrickvonplaten/t5-tiny-random'
_UpperCamelCase						:    Union[str, Any]		=						'sshleifer/t5-tinier-random'
_UpperCamelCase						:    str		=						'sshleifer/bart-tiny-random'
_UpperCamelCase						:    int		=						'sshleifer/tiny-mbart'
_UpperCamelCase						:    List[str]		=						'sshleifer/tiny-marian-en-de'
_UpperCamelCase						:    Any		=						'stas/tiny-wmt19-en-de'


_UpperCamelCase						:    Union[str, Any]		=						logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    List[Any]       ):





  '''simple docstring'''





  for split in ["train", "val", "test"]:
    _dump_articles(os.path.join(__snake_case						,   f'{split}.source'       )						,   __snake_case       )
    _dump_articles(os.path.join(__snake_case						,   f'{split}.target'       )						,   __snake_case       )
  return tmp_dir

class 			a	(  a_      ):







    @classmethod
    def      UpperCamelCase_ (     cls			):
      logging.disable(logging.CRITICAL			)  # remove noisy download output from tracebacks
      return cls







    @slow
    @require_torch_gpu
    def      UpperCamelCase_ (     self			):
      lowercase				       =			list_models()
      lowercase				       =			'sshleifer'
      lowercase				       =			[x.modelId for x in model_list if x.modelId.startswith(_lowerCamelCase			)]
      lowercase				       =			['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
      lowercase				       =			[]
      for m in model_ids:
        if m in allowed_to_be_broken:
          continue
        try:
          AutoConfig.from_pretrained(_lowerCamelCase			)
        except Exception:
          failures.append(_lowerCamelCase			)
      assert not failures, F'The following models could not be loaded through AutoConfig: {failures}'







    def      UpperCamelCase_ (     self			):
      lowercase				       =			{'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
      self._test_distiller_cli(_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			{
          'student_encoder_layers': 2,
          'student_decoder_layers': 1,
          'max_epochs': 4,
          'val_check_interval': 0.2_5,
          'alpha_hid': 2.0,
          'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
      }
      lowercase				       =			self._test_distiller_cli(_lowerCamelCase				,						check_contents=_lowerCamelCase			)

      lowercase				       =			list(Path(model.output_dir			).glob('*.ckpt'			)			)
      self.assertEqual(1				,						len(_lowerCamelCase			)			)
      lowercase				       =			list(Path(model.output_dir			).glob('**/*.bin'			)			)
      self.assertEqual(len(_lowerCamelCase			)				,						2			)
      lowercase				       =			lmap(str.strip				,						Path(model.hparams.data_dir			).joinpath('test.source'			).open().readlines()			)
      lowercase				       =			tempfile.mktemp()  # XXX: not being cleaned up
      generate_summaries_or_translations(_lowerCamelCase				,						_lowerCamelCase				,						str(model.output_dir / 'best_tfmr'			)			)
      self.assertTrue(Path(_lowerCamelCase			).exists()			)

      lowercase				       =			self.get_auto_remove_tmp_dir()
      convert_pl_to_hf(ckpts[0]				,						transformer_ckpts[0].parent				,						_lowerCamelCase			)
      assert os.path.exists(os.path.join(_lowerCamelCase				,						'pytorch_model.bin'			)			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			AutoModelForSeqaSeqLM.from_pretrained(_lowerCamelCase			)
      lowercase       ,	lowercase				       =			model.dummy_inputs['input_ids'], model.dummy_inputs['attention_mask']
      lowercase				       =			torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]				,						dtype=torch.long				,						device=model.device			)
      lowercase				       =			target_ids[:, :-1].contiguous()  # Why this line?
      lowercase				       =			target_ids[:, 1:].clone()  # why clone?
      lowercase				       =			model(
          _lowerCamelCase				,						attention_mask=_lowerCamelCase				,						decoder_input_ids=_lowerCamelCase				,						labels=_lowerCamelCase				,						use_cache=_lowerCamelCase			).loss

      lowercase				       =			model(_lowerCamelCase				,						attention_mask=_lowerCamelCase				,						decoder_input_ids=_lowerCamelCase				,						use_cache=_lowerCamelCase			).logits

      lowercase				       =			nn.functional.log_softmax(_lowerCamelCase				,						dim=-1			)
      lowercase       ,	lowercase				       =			label_smoothed_nll_loss(
          _lowerCamelCase				,						_lowerCamelCase				,						0.1				,						ignore_index=model.config.pad_token_id			)
      with self.assertRaises(_lowerCamelCase			):
        # TODO: understand why this breaks
        self.assertEqual(_lowerCamelCase				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			{
          'student_encoder_layers': 2,
          'student_decoder_layers': 1,
          'num_train_epochs': 4,
          'val_check_interval': 0.2_5,
          'alpha_hid': 2.0,
          'task': 'translation',
          'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
          'tokenizer_name': MBART_TINY,
          'teacher': MBART_TINY,
          'src_lang': 'en_XX',
          'tgt_lang': 'ro_RO',
      }
      lowercase				       =			self._test_distiller_cli(_lowerCamelCase				,						check_contents=_lowerCamelCase			)
      assert model.model.config.model_type == "mbart"

      lowercase				       =			list(Path(model.output_dir			).glob('*.ckpt'			)			)
      self.assertEqual(1				,						len(_lowerCamelCase			)			)
      lowercase				       =			list(Path(model.output_dir			).glob('**/*.bin'			)			)
      lowercase				       =			list(Path(model.output_dir			).glob('best_tfmr/*'			)			)
      assert len(_lowerCamelCase			) > 2
      self.assertEqual(len(_lowerCamelCase			)				,						2			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			{
          'student_encoder_layers': 1,
          'student_decoder_layers': 1,
          'alpha_hid': 2.0,
          'teacher': T5_TINY,
          'model_name_or_path': T5_TINY,
          'tokenizer_name': T5_TINY,
      }
      self._test_distiller_cli(_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			{
          'teacher': T5_TINY,
          'student': T5_TINIER,
          'model_name_or_path': T5_TINIER,
          'tokenizer_name': T5_TINIER,
      }
      self._test_distiller_cli(_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase=True			):
      lowercase				       =			{
          '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(_lowerCamelCase			)
      lowercase				       =			CHEAP_ARGS.copy()
      lowercase				       =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
      lowercase				       =			self.get_auto_remove_tmp_dir()

      args_d.update(data_dir=_lowerCamelCase				,						output_dir=_lowerCamelCase				,						**_lowerCamelCase			)
      lowercase				       =			distill_main(argparse.Namespace(**_lowerCamelCase			)			)
      if not check_contents:
        return model
      lowercase				       =			os.listdir(_lowerCamelCase			)
      lowercase				       =			{os.path.basename(_lowerCamelCase			) for p in contents}
      lowercase				       =			[p for p in contents if p.endswith('ckpt'			)]
      assert len(_lowerCamelCase			) > 0

      self.assertIn('test_generations.txt'				,						_lowerCamelCase			)
      self.assertIn('test_results.txt'				,						_lowerCamelCase			)

      lowercase				       =			load_json(model.metrics_save_path			)
      lowercase				       =			metrics['val'][-1]
      self.assertGreaterEqual(last_step_stats['val_avg_gen_time']				,						0.0_1			)
      self.assertGreaterEqual(1.0				,						last_step_stats['val_avg_gen_time']			)
      self.assertIsInstance(last_step_stats[F'val_avg_{model.val_metric}']				,						_lowerCamelCase			)
      lowercase				       =			int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) + 1			)
      self.assertEqual(len(metrics['val']			)				,						_lowerCamelCase			)
      self.assertEqual(len(metrics['test']			)				,						1			)
      return model


class 			a	(  a_      ):







    @parameterized.expand(
        [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]				,						)
    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase				       =			CHEAP_ARGS.copy()
      lowercase				       =			'translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
      lowercase				       =			0.1 if task == 'translation' else 0

      lowercase				       =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
      lowercase				       =			self.get_auto_remove_tmp_dir()
      args_d.update(
          data_dir=_lowerCamelCase				,						model_name_or_path=_lowerCamelCase				,						tokenizer_name=_lowerCamelCase				,						train_batch_size=2				,						eval_batch_size=2				,						output_dir=_lowerCamelCase				,						do_predict=_lowerCamelCase				,						task=_lowerCamelCase				,						src_lang='en_XX'				,						tgt_lang='ro_RO'				,						freeze_encoder=_lowerCamelCase				,						freeze_embeds=_lowerCamelCase				,						)
      assert "n_train" in args_d
      lowercase				       =			argparse.Namespace(**_lowerCamelCase			)
      lowercase				       =			main(_lowerCamelCase			)

      lowercase				       =			module.model.get_input_embeddings()
      assert not input_embeds.weight.requires_grad
      if model == T5_TINY:
        lowercase				       =			module.model.lm_head
        assert not lm_head.weight.requires_grad
        assert (lm_head.weight == input_embeds.weight).all().item()
      elif model == FSMT_TINY:
        lowercase				       =			module.model.model
        lowercase				       =			fsmt.decoder.embed_positions
        assert not embed_pos.weight.requires_grad
        assert not fsmt.decoder.embed_tokens.weight.requires_grad
        # check that embeds are not the same
        assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
      else:
        lowercase				       =			module.model.model
        lowercase				       =			bart.decoder.embed_positions
        assert not embed_pos.weight.requires_grad
        assert not bart.shared.weight.requires_grad
        # check that embeds are the same
        assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
        assert bart.decoder.embed_tokens == bart.shared

      lowercase				       =			load_json(module.output_dir / 'text_batch.json'			)
      assert isinstance(_lowerCamelCase				,						_lowerCamelCase			)
      assert len(_lowerCamelCase			) >= 4







    def      UpperCamelCase_ (     self			):
      lowercase				       =			CHEAP_ARGS.copy()

      lowercase				       =			'summarization'
      lowercase				       =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)

      args_d.update(
          data_dir=_lowerCamelCase				,						tokenizer_name=_lowerCamelCase				,						train_batch_size=2				,						eval_batch_size=2				,						do_predict=_lowerCamelCase				,						task=_lowerCamelCase				,						src_lang='en_XX'				,						tgt_lang='ro_RO'				,						freeze_encoder=_lowerCamelCase				,						freeze_embeds=_lowerCamelCase				,						)

      # test models whose config includes the extra_model_args
      lowercase				       =			BART_TINY
      lowercase				       =			self.get_auto_remove_tmp_dir()
      lowercase				       =			args_d.copy()
      args_da.update(
          model_name_or_path=_lowerCamelCase				,						output_dir=_lowerCamelCase				,						)
      lowercase				       =			('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
      for p in extra_model_params:
        lowercase				       =			0.5
      lowercase				       =			argparse.Namespace(**_lowerCamelCase			)
      lowercase				       =			main(_lowerCamelCase			)
      for p in extra_model_params:
        assert getattr(model.config				,						_lowerCamelCase			) == 0.5, F'failed to override the model config for param {p}'

      # test models whose config doesn't include the extra_model_args
      lowercase				       =			T5_TINY
      lowercase				       =			self.get_auto_remove_tmp_dir()
      lowercase				       =			args_d.copy()
      args_da.update(
          model_name_or_path=_lowerCamelCase				,						output_dir=_lowerCamelCase				,						)
      lowercase				       =			'encoder_layerdrop'
      lowercase				       =			0.5
      lowercase				       =			argparse.Namespace(**_lowerCamelCase			)
      with pytest.raises(_lowerCamelCase			) as excinfo:
        lowercase				       =			main(_lowerCamelCase			)
      assert str(excinfo.value			) == F'model config doesn\'t have a `{unsupported_param}` attribute'







    def      UpperCamelCase_ (     self			):
      lowercase				       =			CHEAP_ARGS.copy()

      lowercase				       =			'summarization'
      lowercase				       =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)

      lowercase				       =			BART_TINY
      lowercase				       =			self.get_auto_remove_tmp_dir()

      args_d.update(
          data_dir=_lowerCamelCase				,						model_name_or_path=_lowerCamelCase				,						output_dir=_lowerCamelCase				,						tokenizer_name=_lowerCamelCase				,						train_batch_size=2				,						eval_batch_size=2				,						do_predict=_lowerCamelCase				,						task=_lowerCamelCase				,						src_lang='en_XX'				,						tgt_lang='ro_RO'				,						freeze_encoder=_lowerCamelCase				,						freeze_embeds=_lowerCamelCase				,						)

      # emulate finetune.py
      lowercase				       =			argparse.ArgumentParser()
      lowercase				       =			pl.Trainer.add_argparse_args(_lowerCamelCase			)
      lowercase				       =			SummarizationModule.add_model_specific_args(_lowerCamelCase				,						os.getcwd()			)
      lowercase				       =			{'--help': True}

      # --help test
      with pytest.raises(_lowerCamelCase			) as excinfo:
        with CaptureStdout() as cs:
          lowercase				       =			parser.parse_args(_lowerCamelCase			)
        assert False, "--help is expected to sys.exit"
      assert excinfo.type == SystemExit
      lowercase				       =			lightning_base.arg_to_scheduler_metavar
      assert expected in cs.out, "--help is expected to list the supported schedulers"

      # --lr_scheduler=non_existing_scheduler test
      lowercase				       =			'non_existing_scheduler'
      lowercase				       =			{F'--lr_scheduler={unsupported_param}'}
      with pytest.raises(_lowerCamelCase			) as excinfo:
        with CaptureStderr() as cs:
          lowercase				       =			parser.parse_args(_lowerCamelCase			)
        assert False, "invalid argument is expected to sys.exit"
      assert excinfo.type == SystemExit
      lowercase				       =			F'invalid choice: \'{unsupported_param}\''
      assert expected in cs.err, F'should have bailed on invalid choice of scheduler {unsupported_param}'

      # --lr_scheduler=existing_scheduler test
      lowercase				       =			'cosine'
      lowercase				       =			args_d.copy()
      lowercase				       =			supported_param
      lowercase				       =			argparse.Namespace(**_lowerCamelCase			)
      lowercase				       =			main(_lowerCamelCase			)
      assert (
          getattr(model.hparams				,						'lr_scheduler'			) == supported_param
      ), F'lr_scheduler={supported_param} shouldn\'t fail'










"""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 			a	(  unittest.TestCase      ):







    @slow
    def      UpperCamelCase_ (     self			):
      lowercase				       =			XLMProphetNetForConditionalGeneration.from_pretrained('microsoft/xprophetnet-large-wiki100-cased'			)
      model.to(_lowerCamelCase			)

      # encoder-decoder outputs
      lowercase				       =			torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]			).to(_lowerCamelCase			)
      lowercase				       =			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(_lowerCamelCase			)
      lowercase				       =			model(
          input_ids=_lowerCamelCase				,						attention_mask=_lowerCamelCase				,						encoder_outputs=_lowerCamelCase				,						decoder_input_ids=_lowerCamelCase			)
      lowercase				       =			output[0]
      lowercase				       =			torch.Size((1, 1_4, 2_5_0_0_1_2)			)
      self.assertEqual(output_predited_logis.shape				,						_lowerCamelCase			)
      lowercase				       =			torch.tensor(
          [[[-6.3_9_8_6, -8.2_3_9_1, 1_2.5_1_8_9], [-6.3_2_8_9, -8.0_8_6_4, 1_2.6_2_1_1], [-6.2_4_1_8, -8.0_4_4_5, 1_2.7_9_6_8]]]			).to(_lowerCamelCase			)
      self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]				,						_lowerCamelCase				,						atol=1e-4			)			)

      # encoder outputs
      lowercase				       =			model.prophetnet.encoder(_lowerCamelCase			)[0]
      lowercase				       =			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(_lowerCamelCase			)
      lowercase				       =			torch.Size((1, 4, 1_0_2_4)			)
      self.assertEqual(encoder_outputs.shape				,						_lowerCamelCase			)
      self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]				,						_lowerCamelCase				,						atol=1e-4			)			)

      # decoder outputs
      lowercase				       =			model.prophetnet.decoder(
          _lowerCamelCase				,						encoder_hidden_states=_lowerCamelCase				,						)
      lowercase				       =			decoder_outputs[1].view(1				,						model.config.ngram				,						1_4				,						-1			)
      lowercase				       =			model.lm_head(_lowerCamelCase			)
      lowercase				       =			predicting_streams_logits[:, 0]
      self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]				,						_lowerCamelCase				,						atol=1e-4			)			)







    @slow
    def      UpperCamelCase_ (     self			):
      lowercase				       =			XLMProphetNetForConditionalGeneration.from_pretrained(
          'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'			)
      model.to(_lowerCamelCase			)

      lowercase				       =			torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]			).to(_lowerCamelCase			)
      lowercase				       =			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(_lowerCamelCase			)
      lowercase				       =			model(
          input_ids=_lowerCamelCase				,						attention_mask=_lowerCamelCase				,						encoder_outputs=_lowerCamelCase				,						decoder_input_ids=_lowerCamelCase			)
      lowercase				       =			output[0]
      lowercase				       =			torch.Size((1, 1_4, 2_5_0_0_1_2)			)
      self.assertEqual(output_predited_logis.shape				,						_lowerCamelCase			)
      # compare the actual values for a slice.
      lowercase				       =			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(_lowerCamelCase			)

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







    @slow
    def      UpperCamelCase_ (     self			):
      lowercase				       =			XLMProphetNetForConditionalGeneration.from_pretrained(
          'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'			)
      model.to(_lowerCamelCase			)
      lowercase				       =			5_1_2

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

      lowercase				       =			(
          '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				       =			(
          '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				       =			(
          '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				       =			tokenizer(
          [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]				,						padding=_lowerCamelCase				,						max_length=2_5_5				,						return_tensors='pt'			).input_ids
      lowercase				       =			input_ids.to(_lowerCamelCase			)

      lowercase				       =			model.generate(
          _lowerCamelCase				,						num_beams=1_0				,						length_penalty=1.0				,						no_repeat_ngram_size=3				,						early_stopping=_lowerCamelCase			)
      lowercase				       =			[tokenizer.decode(_lowerCamelCase				,						skip_special_tokens=_lowerCamelCase			) for g in summary_ids]
      lowercase				       =			'Microsoft to end Windows 7 free support after January 14, 2020'
      lowercase				       =			'Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года'
      lowercase				       =			'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]				,						_lowerCamelCase				,						)

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


