import argparse
import gc
import os
import sys
from pathlib import Path
from typing import List  # noqa: F401

import pytorch_lightning as pl
import torch
from finetune import SummarizationModule, TranslationModule
from finetune import main as ft_main
from make_student import create_student_by_copying_alternating_layers, get_layers_to_supervise
from torch import nn

from transformers import AutoModelForSeqaSeqLM, MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import calculate_bleu, check_output_dir, freeze_params, label_smoothed_nll_loss, use_task_specific_params


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import generic_train  # noqa


class   lowercase			(   SCREAMING_SNAKE_CASE__   ):
							lowercase__  :				Optional[int]									=							["""loss""", """ce_loss""", """mlm_loss""", """hid_loss_enc""", """hid_loss_dec"""]




							def __init__(  self      :    Optional[Any]		,							_UpperCamelCase      :    int					)    -> str:



												'''simple docstring'''

												assert Path(hparams.data_dir					).exists()
												SCREAMING_SNAKE_CASE				=				Path(hparams.output_dir					)
												self.output_dir.mkdir(exist_ok=snake_case__					)

												SCREAMING_SNAKE_CASE				=				self.output_dir.joinpath("student"					)

												SCREAMING_SNAKE_CASE				=				str(snake_case__					)  # Tell lightning we are training the student
												SCREAMING_SNAKE_CASE				=				AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher					).eval()
												use_task_specific_params(snake_case__		,							hparams.task					)  # We copy good generation parameters to student by default
												if hparams.student is not None:
																	SCREAMING_SNAKE_CASE				=				AutoModelForSeqaSeqLM.from_pretrained(hparams.student					)
																	use_task_specific_params(snake_case__		,							hparams.task					)
																	SCREAMING_SNAKE_CASE				=				None, None
												else:
																	SCREAMING_SNAKE_CASE				=				create_student_by_copying_alternating_layers(
																	    snake_case__		,							e=hparams.student_encoder_layers		,							d=hparams.student_decoder_layers		,							save_path=snake_case__					)

												if hparams.length_penalty != -1:
																	SCREAMING_SNAKE_CASE				=				hparams.length_penalty
												SCREAMING_SNAKE_CASE				=				hparams.teacher  # Use teacher's tokenizer
												super().__init__(snake_case__		,							model=snake_case__		,							config=student.config					)
												assert student.config.model_type == teacher.config.model_type, (
												    F"teacher, student model types should be the same, got {student.config.model_type} !="
												    F" {teacher.config.model_type}"
												)

												if student.config.model_type == "t5":
																	SCREAMING_SNAKE_CASE				=				len(student.get_encoder().block					)
																	SCREAMING_SNAKE_CASE				=				len(student.get_decoder().block					)
																	SCREAMING_SNAKE_CASE				=				len(teacher.get_encoder().block					)
																	SCREAMING_SNAKE_CASE				=				len(teacher.get_decoder().block					)
												else:
																	SCREAMING_SNAKE_CASE				=				student.config.encoder_layers
																	SCREAMING_SNAKE_CASE				=				student.config.decoder_layers
																	SCREAMING_SNAKE_CASE				=				teacher.config.encoder_layers
																	SCREAMING_SNAKE_CASE				=				teacher.config.decoder_layers

												SCREAMING_SNAKE_CASE				=				not (hparams.student is None or hparams.teacher == hparams.student)
												SCREAMING_SNAKE_CASE				=				(not self.different_base_models) and hparams.alpha_hid > 0
												SCREAMING_SNAKE_CASE				=				self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
												# self.different_encoder determines whether we need to run the teacher encoder
												SCREAMING_SNAKE_CASE				=				teacher
												freeze_params(self.teacher					)

												if not self.different_encoder:  # To save RAM, delete teacher encoder and freeze student encoder.
																	try:
																						del self.teacher.model.encoder
																	except AttributeError:  # T5
																						del self.teacher.encoder

												if e_layer_ids is None:
																	SCREAMING_SNAKE_CASE				=				list(range(snake_case__					)					)
												if d_layer_ids is None:
																	SCREAMING_SNAKE_CASE				=				list(range(snake_case__					)					)

												SCREAMING_SNAKE_CASE				=				e_layer_ids, d_layer_ids  # type: List[int], List[int]

												if self.do_calc_hidden_loss:  # Intermediate supervision: Decide which layers to supervise
																	if hparams.supervise_forward:
																						SCREAMING_SNAKE_CASE				=				get_layers_to_supervise(
																						    n_student=len(self.e_layer_ids					)		,							n_teacher=snake_case__					)
																						SCREAMING_SNAKE_CASE				=				get_layers_to_supervise(
																						    n_student=len(self.d_layer_ids					)		,							n_teacher=snake_case__					)
																	else:  # student layer should emulate hidden states of the teacher layer it was copied from
																						SCREAMING_SNAKE_CASE				=				self.e_layer_ids
																						SCREAMING_SNAKE_CASE				=				self.d_layer_ids
												else:
																	SCREAMING_SNAKE_CASE				=				None
																	SCREAMING_SNAKE_CASE				=				None

												SCREAMING_SNAKE_CASE				=				nn.KLDivLoss(reduction="batchmean"					)
												SCREAMING_SNAKE_CASE				=				2.0
												SCREAMING_SNAKE_CASE				=				hparams.alpha_mlm
												SCREAMING_SNAKE_CASE				=				hparams.alpha_ce
												SCREAMING_SNAKE_CASE				=				hparams.alpha_hid
												gc.collect()
												torch.cuda.empty_cache()




							def 		__snake_case(  self      :    Any		,							_UpperCamelCase      :    int		,							_UpperCamelCase      :    Union[str, Any]		,							_UpperCamelCase      :    Tuple					)    -> Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				mask[:, :, None].expand_as(snake_case__					)
												SCREAMING_SNAKE_CASE				=				s_logits.size(-1					)
												SCREAMING_SNAKE_CASE				=				torch.masked_select(snake_case__		,							snake_case__					)  # (bs * seq_length * voc_size) modulo the 1s in mask
												SCREAMING_SNAKE_CASE				=				torch.masked_select(snake_case__		,							snake_case__					)  # (bs * seq_length * voc_size) modulo the 1s in mask
												SCREAMING_SNAKE_CASE				=				s_logits_slct.view(-1		,							snake_case__					)  # (bs * seq_length, voc_size) modulo the 1s in mask
												SCREAMING_SNAKE_CASE				=				t_logits_slct.view(-1		,							snake_case__					)  # (bs * seq_length, voc_size) modulo the 1s in mask
												assert t_logits_slct.size() == s_logits_slct.size()
												SCREAMING_SNAKE_CASE				=				(
												    self.ce_loss_fct(
												        nn.functional.log_softmax(s_logits_slct / self.temperature		,							dim=-1					)		,							nn.functional.softmax(t_logits_slct / self.temperature		,							dim=-1					)		,							)
												    * (self.temperature) ** 2
												)
												return loss_ce




							@staticmethod
							def 		__snake_case(  _UpperCamelCase      :    Optional[Any]		,							_UpperCamelCase      :    str					)    -> Dict:



												'''simple docstring'''

												SummarizationModule.add_model_specific_args(snake_case__		,							snake_case__					)
												add_distill_args(snake_case__					)
												return parser




							def 		__snake_case(  self      :    Dict		,							_UpperCamelCase      :    Dict					)    -> Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				self.tokenizer.pad_token_id
												SCREAMING_SNAKE_CASE				=				batch["input_ids"], batch["attention_mask"], batch["labels"]
												if isinstance(self.model		,							snake_case__					):
																	SCREAMING_SNAKE_CASE				=				self.model._shift_right(snake_case__					)
												else:
																	SCREAMING_SNAKE_CASE				=				shift_tokens_right(snake_case__		,							snake_case__					)

												# noinspection PyCallingNonCallable
												SCREAMING_SNAKE_CASE				=				self(
												    snake_case__		,							attention_mask=snake_case__		,							decoder_input_ids=snake_case__		,							output_hidden_states=self.do_calc_hidden_loss		,							output_attentions=snake_case__		,							use_cache=snake_case__		,							)
												SCREAMING_SNAKE_CASE				=				student_outputs["logits"]

												# Same cross entropy vs. label smoothing logic as finetune.py
												assert lm_logits.shape[-1] == self.model.config.vocab_size
												if self.hparams.label_smoothing == 0:
																	# Same behavior as modeling_bart.py, besides ignoring pad_token_id
																	SCREAMING_SNAKE_CASE				=				nn.CrossEntropyLoss(ignore_index=snake_case__					)
																	SCREAMING_SNAKE_CASE				=				loss_fct(lm_logits.view(-1		,							lm_logits.shape[-1]					)		,							labels.view(-1					)					)
												else:
																	SCREAMING_SNAKE_CASE				=				nn.functional.log_softmax(snake_case__		,							dim=-1					)
																	SCREAMING_SNAKE_CASE				=				label_smoothed_nll_loss(
																	    snake_case__		,							snake_case__		,							self.hparams.label_smoothing		,							ignore_index=snake_case__					)

												def zero_tensor():
																	return torch.tensor(0.0					).type_as(snake_case__					)

												SCREAMING_SNAKE_CASE				=				student_outputs[
												    "encoder_last_hidden_state"
												]  # use this unless self.different_base_models
												SCREAMING_SNAKE_CASE				=				zero_tensor(), zero_tensor()
												if self.different_encoder:  # compute encoder hidden state loss
																	SCREAMING_SNAKE_CASE				=				self.teacher.get_encoder()(
																	    snake_case__		,							attention_mask=snake_case__		,							output_hidden_states=self.do_calc_hidden_loss		,							)
																	if self.different_base_models:
																						SCREAMING_SNAKE_CASE				=				all_teacher_encoder_outputs["last_hidden_state"]
																	elif self.do_calc_hidden_loss:
																						SCREAMING_SNAKE_CASE				=				self.calc_hidden_loss(
																						    snake_case__		,							student_outputs["encoder_hidden_states"]		,							all_teacher_encoder_outputs["hidden_states"]		,							self.e_matches		,							normalize_hidden=self.hparams.normalize_hidden		,							)

												SCREAMING_SNAKE_CASE				=				self.teacher(
												    snake_case__		,							attention_mask=snake_case__		,							encoder_outputs=(teacher_enc_outputs,)		,							decoder_input_ids=snake_case__		,							output_hidden_states=self.do_calc_hidden_loss		,							use_cache=snake_case__		,							)
												SCREAMING_SNAKE_CASE				=				decoder_input_ids.ne(snake_case__					)
												SCREAMING_SNAKE_CASE				=				self.calc_ce_loss(snake_case__		,							snake_case__		,							teacher_outputs["logits"]					)
												if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
																	SCREAMING_SNAKE_CASE				=				self.calc_hidden_loss(
																	    snake_case__		,							student_outputs["decoder_hidden_states"]		,							teacher_outputs["decoder_hidden_states"]		,							self.d_matches		,							normalize_hidden=self.hparams.normalize_hidden		,							)

												SCREAMING_SNAKE_CASE				=				(
												    self.alpha_ce * loss_ce
												    + self.alpha_mlm * student_lm_loss
												    + self.hparams.alpha_hid * (hid_loss_enc + hid_loss_dec)
												)
												return blended_loss, loss_ce, student_lm_loss, hid_loss_enc, hid_loss_dec





							@staticmethod
							def 		__snake_case(  _UpperCamelCase      :    int		,							_UpperCamelCase      :    List[Any]		,							_UpperCamelCase      :    List[str]		,							_UpperCamelCase      :    Dict		,							_UpperCamelCase      :    List[Any]					)    -> Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				"expected list or tuple for hidden_states, got tensor of shape: "
												assert not isinstance(snake_case__		,							torch.Tensor					), F"{msg}{hidden_states.shape}"
												assert not isinstance(snake_case__		,							torch.Tensor					), F"{msg}{hidden_states_T.shape}"
												SCREAMING_SNAKE_CASE				=				attention_mask.to(hidden_states[0]					)
												SCREAMING_SNAKE_CASE				=				mask.sum() * hidden_states[0].size(-1					)
												SCREAMING_SNAKE_CASE				=				torch.stack([hidden_states[i] for i in range(len(snake_case__					)					)]					)
												SCREAMING_SNAKE_CASE				=				torch.stack([hidden_states_T[j] for j in matches]					)
												assert student_states.shape == teacher_states.shape, F"{student_states.shape} != {teacher_states.shape}"
												if normalize_hidden:
																	SCREAMING_SNAKE_CASE				=				nn.functional.layer_norm(snake_case__		,							student_states.shape[1:]					)
																	SCREAMING_SNAKE_CASE				=				nn.functional.layer_norm(snake_case__		,							teacher_states.shape[1:]					)
												SCREAMING_SNAKE_CASE				=				nn.functional.mse_loss(snake_case__		,							snake_case__		,							reduction="none"					)
												SCREAMING_SNAKE_CASE				=				(mse * mask.unsqueeze(0					).unsqueeze(-1					)).sum() / valid_count
												return masked_mse





def 		__lowerCamelCase							(UpperCAmelCase__   :							Tuple     ):
					parser.add_argument("--teacher"					,       type=UpperCAmelCase__     )
					parser.add_argument("--alpha_ce"					,       default=0.8					,       type=UpperCAmelCase__     )
					parser.add_argument("--alpha_mlm"					,       default=0.2					,       type=UpperCAmelCase__     )
					parser.add_argument("--alpha_hid"					,       default=0.0					,       type=UpperCAmelCase__					,       required=UpperCAmelCase__     )
					parser.add_argument("--student"					,       type=UpperCAmelCase__					,       required=UpperCAmelCase__     )
					parser.add_argument("--student_decoder_layers"					,       default=1_2					,       type=UpperCAmelCase__					,       required=UpperCAmelCase__     )
					parser.add_argument("--student_encoder_layers"					,       default=1_2					,       type=UpperCAmelCase__					,       required=UpperCAmelCase__     )
					parser.add_argument("--no_teacher"					,       action="store_true"					,       default=UpperCAmelCase__     )
					parser.add_argument("--length_penalty"					,       type=UpperCAmelCase__					,       default=-1     )
					parser.add_argument("--supervise_forward"					,       action="store_true"					,       default=UpperCAmelCase__     )
					parser.add_argument("--normalize_hidden"					,       action="store_true"					,       default=UpperCAmelCase__     )


class   lowercase			(   SCREAMING_SNAKE_CASE__   ):
							lowercase__  :				Tuple									=							"""translation"""
							lowercase__  :				int									=							["""bleu"""]
							lowercase__  :				Dict									=							"""bleu"""




							def __init__(  self      :    Optional[Any]		,							_UpperCamelCase      :    Union[str, Any]		,							**_UpperCamelCase      :    Any					)    -> Dict:



												'''simple docstring'''

												super().__init__(snake_case__		,							**snake_case__					)
												assert hparams.src_lang is not None
												assert hparams.tgt_lang is not None
												SCREAMING_SNAKE_CASE				=				hparams.src_lang
												SCREAMING_SNAKE_CASE				=				hparams.tgt_lang
												if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer		,							snake_case__					):
																	SCREAMING_SNAKE_CASE				=				self.tokenizer.lang_code_to_id[hparams.tgt_lang]




							def 		__snake_case(  self      :    List[str]		,							_UpperCamelCase      :    Any		,							_UpperCamelCase      :    Dict					)    -> int:



												'''simple docstring'''

												return calculate_bleu(snake_case__		,							snake_case__					)





							@staticmethod
							def 		__snake_case(  _UpperCamelCase      :    Dict		,							_UpperCamelCase      :    Tuple					)    -> List[Any]:



												'''simple docstring'''

												TranslationModule.add_model_specific_args(snake_case__		,							snake_case__					)
												add_distill_args(snake_case__					)
												return parser





def 		__lowerCamelCase							(UpperCAmelCase__   :							List[Any]     ):



					if args.no_teacher:
										SCREAMING_SNAKE_CASE				=				TranslationModule if "translation" in args.task else SummarizationModule
					else:  # DISTILL WITH TEACHER
										SCREAMING_SNAKE_CASE				=				TranslationDistiller if "translation" in args.task else SummarizationDistiller
					SCREAMING_SNAKE_CASE				=				module_cls.__name__
					print(F"using module {args.setup_cls}"     )
					SCREAMING_SNAKE_CASE				=				module_cls(UpperCAmelCase__     )
					return model





def 		__lowerCamelCase							(UpperCAmelCase__   :							List[str]     ):
					Path(args.output_dir     ).mkdir(exist_ok=UpperCAmelCase__     )
					check_output_dir(UpperCAmelCase__					,       expected_items=3     )

					SCREAMING_SNAKE_CASE				=				create_module(UpperCAmelCase__     )
					return ft_main(UpperCAmelCase__					,       model=UpperCAmelCase__     )


if __name__ == "__main__":
							_lowerCamelCase    :      int	 =						argparse.ArgumentParser()
							_lowerCamelCase    :      Dict	 =						pl.Trainer.add_argparse_args(parser)
							_lowerCamelCase    :      int	 =						SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
							_lowerCamelCase    :      List[str]	 =						parser.parse_args()

							distill_main(args)












"""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							):




						"""simple docstring"""




						@slow
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Optional[int]=       XLMProphetNetForConditionalGeneration.from_pretrained("microsoft/xprophetnet-large-wiki100-cased"			)
												model.to(snake_case__			)

												# encoder-decoder outputs
												lowercase__				:      Dict=       torch.tensor([[17, 96208, 103471, 2]]			).to(snake_case__			)
												lowercase__				:      Optional[Any]=       torch.tensor(
												    [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]			).to(snake_case__			)
												lowercase__				:      Dict=       model(
												    input_ids=snake_case__       ,  attention_mask=snake_case__       ,  encoder_outputs=snake_case__       ,  decoder_input_ids=snake_case__			)
												lowercase__				:      int=       output[0]
												lowercase__				:      str=       torch.Size((1, 14, 250012)			)
												self.assertEqual(output_predited_logis.shape       ,  snake_case__			)
												lowercase__				:      Union[str, Any]=       torch.tensor(
												    [[[-6.39_86, -8.23_91, 12.51_89], [-6.32_89, -8.08_64, 12.62_11], [-6.24_18, -8.04_45, 12.79_68]]]			).to(snake_case__			)
												self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]       ,  snake_case__       ,  atol=1e-4			)			)

												# encoder outputs
												lowercase__				:      Tuple=       model.prophetnet.encoder(snake_case__			)[0]
												lowercase__				:      Union[str, Any]=       torch.tensor(
												    [[[-1.42_60, -0.76_28, 0.84_53], [-1.47_19, -0.13_91, 0.78_07], [-1.76_78, 0.01_14, 0.46_46]]]			).to(snake_case__			)
												lowercase__				:      List[Any]=       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
												lowercase__				:      int=       model.prophetnet.decoder(
												    snake_case__       ,  encoder_hidden_states=snake_case__       ,  )
												lowercase__				:      Union[str, Any]=       decoder_outputs[1].view(1       ,  model.config.ngram       ,  14       ,  -1			)
												lowercase__				:      List[Any]=       model.lm_head(snake_case__			)
												lowercase__				:      str=       predicting_streams_logits[:, 0]
												self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]       ,  snake_case__       ,  atol=1e-4			)			)




						@slow
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Union[str, Any]=       XLMProphetNetForConditionalGeneration.from_pretrained(
												    "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"			)
												model.to(snake_case__			)

												lowercase__				:      Dict=       torch.tensor([[17, 96208, 103471, 2]]			).to(snake_case__			)
												lowercase__				:      List[str]=       torch.tensor(
												    [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]			).to(snake_case__			)
												lowercase__				:      List[Any]=       model(
												    input_ids=snake_case__       ,  attention_mask=snake_case__       ,  encoder_outputs=snake_case__       ,  decoder_input_ids=snake_case__			)
												lowercase__				:      str=       output[0]
												lowercase__				:      Optional[Any]=       torch.Size((1, 14, 250012)			)
												self.assertEqual(output_predited_logis.shape       ,  snake_case__			)
												# compare the actual values for a slice.
												lowercase__				:      str=       torch.tensor(
												    [[[-9.22_53, -9.71_73, -6.35_29], [-7.67_01, -9.01_45, -1.93_82], [-8.01_95, -7.00_04, -0.15_23]]]			).to(snake_case__			)

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




						@slow
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Tuple=       XLMProphetNetForConditionalGeneration.from_pretrained(
												    "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"			)
												model.to(snake_case__			)
												lowercase__				:      Optional[int]=       512

												lowercase__				:      int=       XLMProphetNetTokenizer.from_pretrained("microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"			)

												lowercase__				:      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."
												)
												lowercase__				:      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."
												)
												lowercase__				:      Optional[int]=       (
												    "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__				:      Dict=       tokenizer(
												    [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]       ,  padding=snake_case__       ,  max_length=255       ,  return_tensors="pt"			).input_ids
												lowercase__				:      Any=       input_ids.to(snake_case__			)

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

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






