code: 
  
  
  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    A					(     A_				):
  							UpperCamelCase_     :							Any =['''loss''', '''ce_loss''', '''mlm_loss''', '''hid_loss_enc''', '''hid_loss_dec''']
  
  
  							def __init__(self   ,       lowerCAmelCase      ):
  									assert Path(hparams.data_dir      ).exists()
  									__lowercase=    Path(hparams.output_dir      )
  									self.output_dir.mkdir(exist_ok=lowerCAmelCase      )
  
  									__lowercase=    self.output_dir.joinpath('student'      )
  
  									__lowercase=    str(lowerCAmelCase      )  # Tell lightning we are training the student
  									__lowercase=    AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher      ).eval()
  									use_task_specific_params(lowerCAmelCase   ,       hparams.task      )  # We copy good generation parameters to student by default
  									if hparams.student is not None:
  											__lowercase=    AutoModelForSeqaSeqLM.from_pretrained(hparams.student      )
  											use_task_specific_params(lowerCAmelCase   ,       hparams.task      )
  											__lowercase,							__lowercase=    None, None
  									else:
  											__lowercase,							__lowercase,							__lowercase=    create_student_by_copying_alternating_layers(
  											    lowerCAmelCase   ,       e=hparams.student_encoder_layers   ,       d=hparams.student_decoder_layers   ,       save_path=lowerCAmelCase      )
  
  									if hparams.length_penalty != -1:
  											__lowercase=    hparams.length_penalty
  									__lowercase=    hparams.teacher  # Use teacher's tokenizer
  									super().__init__(lowerCAmelCase   ,       model=lowerCAmelCase   ,       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":
  											__lowercase=    len(student.get_encoder().block      )
  											__lowercase=    len(student.get_decoder().block      )
  											__lowercase=    len(teacher.get_encoder().block      )
  											__lowercase=    len(teacher.get_decoder().block      )
  									else:
  											__lowercase=    student.config.encoder_layers
  											__lowercase=    student.config.decoder_layers
  											__lowercase=    teacher.config.encoder_layers
  											__lowercase=    teacher.config.decoder_layers
  
  									__lowercase=    not (hparams.student is None or hparams.teacher == hparams.student)
  									__lowercase=    (not self.different_base_models) and hparams.alpha_hid > 0
  									__lowercase=    self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
  									# self.different_encoder determines whether we need to run the teacher encoder
  									__lowercase=    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:
  											__lowercase=    list(range(lowerCAmelCase      )      )
  									if d_layer_ids is None:
  											__lowercase=    list(range(lowerCAmelCase      )      )
  
  									__lowercase,							__lowercase=    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:
  													__lowercase=    get_layers_to_supervise(
  													    n_student=len(self.e_layer_ids      )   ,       n_teacher=lowerCAmelCase      )
  													__lowercase=    get_layers_to_supervise(
  													    n_student=len(self.d_layer_ids      )   ,       n_teacher=lowerCAmelCase      )
  											else:  # student layer should emulate hidden states of the teacher layer it was copied from
  													__lowercase=    self.e_layer_ids
  													__lowercase=    self.d_layer_ids
  									else:
  											__lowercase=    None
  											__lowercase=    None
  
  									__lowercase=    nn.KLDivLoss(reduction='batchmean'      )
  									__lowercase=    2.0
  									__lowercase=    hparams.alpha_mlm
  									__lowercase=    hparams.alpha_ce
  									__lowercase=    hparams.alpha_hid
  									gc.collect()
  									torch.cuda.empty_cache()
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      ):
  									__lowercase=    mask[:, :, None].expand_as(lowerCAmelCase      )
  									__lowercase=    s_logits.size(-1      )
  									__lowercase=    torch.masked_select(lowerCAmelCase   ,       lowerCAmelCase      )  # (bs * seq_length * voc_size) modulo the 1s in mask
  									__lowercase=    torch.masked_select(lowerCAmelCase   ,       lowerCAmelCase      )  # (bs * seq_length * voc_size) modulo the 1s in mask
  									__lowercase=    s_logits_slct.view(-1   ,       lowerCAmelCase      )  # (bs * seq_length, voc_size) modulo the 1s in mask
  									__lowercase=    t_logits_slct.view(-1   ,       lowerCAmelCase      )  # (bs * seq_length, voc_size) modulo the 1s in mask
  									assert t_logits_slct.size() == s_logits_slct.size()
  									__lowercase=    (
  									    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     _A (lowerCAmelCase   ,       lowerCAmelCase      ):
  									SummarizationModule.add_model_specific_args(lowerCAmelCase   ,       lowerCAmelCase      )
  									add_distill_args(lowerCAmelCase      )
  									return parser
  
  
  							def     _A (self   ,       lowerCAmelCase      ):
  									__lowercase=    self.tokenizer.pad_token_id
  									__lowercase,							__lowercase,							__lowercase=    batch['input_ids'], batch['attention_mask'], batch['labels']
  									if isinstance(self.model   ,       lowerCAmelCase      ):
  											__lowercase=    self.model._shift_right(lowerCAmelCase      )
  									else:
  											__lowercase=    shift_tokens_right(lowerCAmelCase   ,       lowerCAmelCase      )
  
  									# noinspection PyCallingNonCallable
  									__lowercase=    self(
  									    lowerCAmelCase   ,       attention_mask=lowerCAmelCase   ,       decoder_input_ids=lowerCAmelCase   ,       output_hidden_states=self.do_calc_hidden_loss   ,       output_attentions=lowerCAmelCase   ,       use_cache=lowerCAmelCase   ,       )
  									__lowercase=    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
  											__lowercase=    nn.CrossEntropyLoss(ignore_index=lowerCAmelCase      )
  											__lowercase=    loss_fct(lm_logits.view(-1   ,       lm_logits.shape[-1]      )   ,       labels.view(-1      )      )
  									else:
  											__lowercase=    nn.functional.log_softmax(lowerCAmelCase   ,       dim=-1      )
  											__lowercase,							__lowercase=    label_smoothed_nll_loss(
  											    lowerCAmelCase   ,       lowerCAmelCase   ,       self.hparams.label_smoothing   ,       ignore_index=lowerCAmelCase      )
  
  									def zero_tensor():
  											return torch.tensor(0.0      ).type_as(lowerCAmelCase      )
  
  									__lowercase=    student_outputs[
  									    'encoder_last_hidden_state'
  									]  # use this unless self.different_base_models
  									__lowercase,							__lowercase=    zero_tensor(), zero_tensor()
  									if self.different_encoder:  # compute encoder hidden state loss
  											__lowercase=    self.teacher.get_encoder()(
  											    lowerCAmelCase   ,       attention_mask=lowerCAmelCase   ,       output_hidden_states=self.do_calc_hidden_loss   ,       )
  											if self.different_base_models:
  													__lowercase=    all_teacher_encoder_outputs['last_hidden_state']
  											elif self.do_calc_hidden_loss:
  													__lowercase=    self.calc_hidden_loss(
  													    lowerCAmelCase   ,       student_outputs['encoder_hidden_states']   ,       all_teacher_encoder_outputs['hidden_states']   ,       self.e_matches   ,       normalize_hidden=self.hparams.normalize_hidden   ,       )
  
  									__lowercase=    self.teacher(
  									    lowerCAmelCase   ,       attention_mask=lowerCAmelCase   ,       encoder_outputs=(teacher_enc_outputs,)   ,       decoder_input_ids=lowerCAmelCase   ,       output_hidden_states=self.do_calc_hidden_loss   ,       use_cache=lowerCAmelCase   ,       )
  									__lowercase=    decoder_input_ids.ne(lowerCAmelCase      )
  									__lowercase=    self.calc_ce_loss(lowerCAmelCase   ,       lowerCAmelCase   ,       teacher_outputs['logits']      )
  									if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
  											__lowercase=    self.calc_hidden_loss(
  											    lowerCAmelCase   ,       student_outputs['decoder_hidden_states']   ,       teacher_outputs['decoder_hidden_states']   ,       self.d_matches   ,       normalize_hidden=self.hparams.normalize_hidden   ,       )
  
  									__lowercase=    (
  									    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     _A (lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      ):
  									__lowercase=    'expected list or tuple for hidden_states, got tensor of shape: '
  									assert not isinstance(lowerCAmelCase   ,       torch.Tensor      ), f'{msg}{hidden_states.shape}'
  									assert not isinstance(lowerCAmelCase   ,       torch.Tensor      ), f'{msg}{hidden_states_T.shape}'
  									__lowercase=    attention_mask.to(hidden_states[0]      )
  									__lowercase=    mask.sum() * hidden_states[0].size(-1      )
  									__lowercase=    torch.stack([hidden_states[i] for i in range(len(lowerCAmelCase      )      )]      )
  									__lowercase=    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:
  											__lowercase=    nn.functional.layer_norm(lowerCAmelCase   ,       student_states.shape[1:]      )
  											__lowercase=    nn.functional.layer_norm(lowerCAmelCase   ,       teacher_states.shape[1:]      )
  									__lowercase=    nn.functional.mse_loss(lowerCAmelCase   ,       lowerCAmelCase   ,       reduction='none'      )
  									__lowercase=    (mse * mask.unsqueeze(0      ).unsqueeze(-1      )).sum() / valid_count
  									return masked_mse
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__			)		->			str:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		parser.add_argument('--teacher'     ,				type=lowercase__			)
  		parser.add_argument('--alpha_ce'     ,				default=0.8     ,				type=lowercase__			)
  		parser.add_argument('--alpha_mlm'     ,				default=0.2     ,				type=lowercase__			)
  		parser.add_argument('--alpha_hid'     ,				default=0.0     ,				type=lowercase__     ,				required=lowercase__			)
  		parser.add_argument('--student'     ,				type=lowercase__     ,				required=lowercase__			)
  		parser.add_argument('--student_decoder_layers'     ,				default=1_2     ,				type=lowercase__     ,				required=lowercase__			)
  		parser.add_argument('--student_encoder_layers'     ,				default=1_2     ,				type=lowercase__     ,				required=lowercase__			)
  		parser.add_argument('--no_teacher'     ,				action='store_true'     ,				default=lowercase__			)
  		parser.add_argument('--length_penalty'     ,				type=lowercase__     ,				default=-1			)
  		parser.add_argument('--supervise_forward'     ,				action='store_true'     ,				default=lowercase__			)
  		parser.add_argument('--normalize_hidden'     ,				action='store_true'     ,				default=lowercase__			)
  
  
  
  
  
  class    A					(     A_				):
  							UpperCamelCase_     :							str ='''translation'''
  							UpperCamelCase_     :							Union[str, Any] =['''bleu''']
  							UpperCamelCase_     :							Optional[int] ='''bleu'''
  
  
  							def __init__(self   ,       lowerCAmelCase   ,       **lowerCAmelCase      ):
  									super().__init__(lowerCAmelCase   ,       **lowerCAmelCase      )
  									assert hparams.src_lang is not None
  									assert hparams.tgt_lang is not None
  									__lowercase=    hparams.src_lang
  									__lowercase=    hparams.tgt_lang
  									if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer   ,       lowerCAmelCase      ):
  											__lowercase=    self.tokenizer.lang_code_to_id[hparams.tgt_lang]
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase      ):
  									return calculate_bleu(lowerCAmelCase   ,       lowerCAmelCase      )
  
  
  
  
  
  
  
  							@staticmethod
  							def     _A (lowerCAmelCase   ,       lowerCAmelCase      ):
  									TranslationModule.add_model_specific_args(lowerCAmelCase   ,       lowerCAmelCase      )
  									add_distill_args(lowerCAmelCase      )
  									return parser
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__			)		->			Optional[int]:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		if args.no_teacher:
  				__lowercase=    TranslationModule if 'translation' in args.task else SummarizationModule
  		else:  # DISTILL WITH TEACHER
  				__lowercase=    TranslationDistiller if 'translation' in args.task else SummarizationDistiller
  		__lowercase=    module_cls.__name__
  		print(F'using module {args.setup_cls}'			)
  		__lowercase=    module_cls(lowercase__			)
  		return model
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__			)		->			Any:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		Path(args.output_dir			).mkdir(exist_ok=lowercase__			)
  		check_output_dir(lowercase__     ,				expected_items=3			)
  
  		__lowercase=    create_module(lowercase__			)
  		return ft_main(lowercase__     ,				model=lowercase__			)
  
  
  if __name__ == "__main__":
  	lowerCAmelCase					 =	argparse.ArgumentParser()
  	lowerCAmelCase					 =	pl.Trainer.add_argparse_args(parser)
  	lowerCAmelCase					 =	SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
  	lowerCAmelCase					 =	parser.parse_args()
  
  	distill_main(args)
  
  
  
  
  
  
  
style_context: 
  
  
  def  _lowerCamelCase(			lowercase__			)		->			str:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return " ".join([word[::-1] for word in input_str.split()]			)
  
  
  if __name__ == "__main__":
  	import doctest
  
  	doctest.testmod()
  
  
  
  
  
  
  