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_					(    SCREAMING_SNAKE_CASE__    ):
  	A						       =   ['''loss''', '''ce_loss''', '''mlm_loss''', '''hid_loss_enc''', '''hid_loss_dec''']
  
  
  
  
  
  	def __init__(       self			,    SCREAMING_SNAKE_CASE			)     ->				Optional[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				assert Path(hparams.data_dir			).exists()
  				SCREAMING_SNAKE_CASE_			=	Path(hparams.output_dir			)
  				self.output_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	self.output_dir.joinpath('student'			)
  
  				SCREAMING_SNAKE_CASE_			=	str(SCREAMING_SNAKE_CASE			)  # Tell lightning we are training the student
  				SCREAMING_SNAKE_CASE_			=	AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher			).eval()
  				use_task_specific_params(SCREAMING_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(SCREAMING_SNAKE_CASE			,    hparams.task			)
  							SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	None, None
  				else:
  							SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	create_student_by_copying_alternating_layers(
  							    SCREAMING_SNAKE_CASE			,    e=hparams.student_encoder_layers			,    d=hparams.student_decoder_layers			,    save_path=SCREAMING_SNAKE_CASE			)
  
  				if hparams.length_penalty != -1:
  							SCREAMING_SNAKE_CASE_			=	hparams.length_penalty
  				SCREAMING_SNAKE_CASE_			=	hparams.teacher  # Use teacher's tokenizer
  				super().__init__(SCREAMING_SNAKE_CASE			,    model=SCREAMING_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(SCREAMING_SNAKE_CASE			)			)
  				if d_layer_ids is None:
  							SCREAMING_SNAKE_CASE_			=	list(range(SCREAMING_SNAKE_CASE			)			)
  
  				SCREAMING_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=SCREAMING_SNAKE_CASE			)
  										SCREAMING_SNAKE_CASE_			=	get_layers_to_supervise(
  										    n_student=len(self.d_layer_ids			)			,    n_teacher=SCREAMING_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 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)     ->				Optional[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	mask[:, :, None].expand_as(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	s_logits.size(-1			)
  				SCREAMING_SNAKE_CASE_			=	torch.masked_select(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)  # (bs * seq_length * voc_size) modulo the 1s in mask
  				SCREAMING_SNAKE_CASE_			=	torch.masked_select(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)  # (bs * seq_length * voc_size) modulo the 1s in mask
  				SCREAMING_SNAKE_CASE_			=	s_logits_slct.view(-1			,    SCREAMING_SNAKE_CASE			)  # (bs * seq_length, voc_size) modulo the 1s in mask
  				SCREAMING_SNAKE_CASE_			=	t_logits_slct.view(-1			,    SCREAMING_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 	A_(       SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)     ->				Union[str, Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SummarizationModule.add_model_specific_args(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  				add_distill_args(SCREAMING_SNAKE_CASE			)
  				return parser
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			)     ->				tuple:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	self.tokenizer.pad_token_id
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	batch['input_ids'], batch['attention_mask'], batch['labels']
  				if isinstance(self.model			,    SCREAMING_SNAKE_CASE			):
  							SCREAMING_SNAKE_CASE_			=	self.model._shift_right(SCREAMING_SNAKE_CASE			)
  				else:
  							SCREAMING_SNAKE_CASE_			=	shift_tokens_right(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  
  				# noinspection PyCallingNonCallable
  				SCREAMING_SNAKE_CASE_			=	self(
  				    SCREAMING_SNAKE_CASE			,    attention_mask=SCREAMING_SNAKE_CASE			,    decoder_input_ids=SCREAMING_SNAKE_CASE			,    output_hidden_states=self.do_calc_hidden_loss			,    output_attentions=SCREAMING_SNAKE_CASE			,    use_cache=SCREAMING_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=SCREAMING_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(SCREAMING_SNAKE_CASE			,    dim=-1			)
  							SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	label_smoothed_nll_loss(
  							    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    self.hparams.label_smoothing			,    ignore_index=SCREAMING_SNAKE_CASE			)
  
  				def zero_tensor():
  							return torch.tensor(0.0			).type_as(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	student_outputs[
  				    'encoder_last_hidden_state'
  				]  # use this unless self.different_base_models
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	zero_tensor(), zero_tensor()
  				if self.different_encoder:  # compute encoder hidden state loss
  							SCREAMING_SNAKE_CASE_			=	self.teacher.get_encoder()(
  							    SCREAMING_SNAKE_CASE			,    attention_mask=SCREAMING_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(
  										    SCREAMING_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(
  				    SCREAMING_SNAKE_CASE			,    attention_mask=SCREAMING_SNAKE_CASE			,    encoder_outputs=(teacher_enc_outputs,)			,    decoder_input_ids=SCREAMING_SNAKE_CASE			,    output_hidden_states=self.do_calc_hidden_loss			,    use_cache=SCREAMING_SNAKE_CASE			,    )
  				SCREAMING_SNAKE_CASE_			=	decoder_input_ids.ne(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	self.calc_ce_loss(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    teacher_outputs['logits']			)
  				if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
  							SCREAMING_SNAKE_CASE_			=	self.calc_hidden_loss(
  							    SCREAMING_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 	A_(       SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	'expected list or tuple for hidden_states, got tensor of shape: '
  				assert not isinstance(SCREAMING_SNAKE_CASE			,    torch.Tensor			), f'{msg}{hidden_states.shape}'
  				assert not isinstance(SCREAMING_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(SCREAMING_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(SCREAMING_SNAKE_CASE			,    student_states.shape[1:]			)
  							SCREAMING_SNAKE_CASE_			=	nn.functional.layer_norm(SCREAMING_SNAKE_CASE			,    teacher_states.shape[1:]			)
  				SCREAMING_SNAKE_CASE_			=	nn.functional.mse_loss(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    reduction='none'			)
  				SCREAMING_SNAKE_CASE_			=	(mse * mask.unsqueeze(0			).unsqueeze(-1			)).sum() / valid_count
  				return masked_mse
  
  
  
  
  def lowercase						(   SCREAMING_SNAKE_CASE   ) ->       Optional[Any]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			parser.add_argument('--teacher'					,						type=SCREAMING_SNAKE_CASE   )
  			parser.add_argument('--alpha_ce'					,						default=0.8					,						type=SCREAMING_SNAKE_CASE   )
  			parser.add_argument('--alpha_mlm'					,						default=0.2					,						type=SCREAMING_SNAKE_CASE   )
  			parser.add_argument('--alpha_hid'					,						default=0.0					,						type=SCREAMING_SNAKE_CASE					,						required=SCREAMING_SNAKE_CASE   )
  			parser.add_argument('--student'					,						type=SCREAMING_SNAKE_CASE					,						required=SCREAMING_SNAKE_CASE   )
  			parser.add_argument('--student_decoder_layers'					,						default=12					,						type=SCREAMING_SNAKE_CASE					,						required=SCREAMING_SNAKE_CASE   )
  			parser.add_argument('--student_encoder_layers'					,						default=12					,						type=SCREAMING_SNAKE_CASE					,						required=SCREAMING_SNAKE_CASE   )
  			parser.add_argument('--no_teacher'					,						action='store_true'					,						default=SCREAMING_SNAKE_CASE   )
  			parser.add_argument('--length_penalty'					,						type=SCREAMING_SNAKE_CASE					,						default=-1   )
  			parser.add_argument('--supervise_forward'					,						action='store_true'					,						default=SCREAMING_SNAKE_CASE   )
  			parser.add_argument('--normalize_hidden'					,						action='store_true'					,						default=SCREAMING_SNAKE_CASE   )
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__    ):
  	A						       =   '''translation'''
  	A						       =   ['''bleu''']
  	A						       =   '''bleu'''
  
  
  
  
  
  	def __init__(       self			,    SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)     ->				str:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				super().__init__(SCREAMING_SNAKE_CASE			,    **SCREAMING_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			,    SCREAMING_SNAKE_CASE			):
  							SCREAMING_SNAKE_CASE_			=	self.tokenizer.lang_code_to_id[hparams.tgt_lang]
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)     ->				dict:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return calculate_bleu(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	@staticmethod
  	def 	A_(       SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)     ->				List[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				TranslationModule.add_model_specific_args(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  				add_distill_args(SCREAMING_SNAKE_CASE			)
  				return parser
  
  
  
  
  def lowercase						(   SCREAMING_SNAKE_CASE   ) ->       List[Any]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			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(SCREAMING_SNAKE_CASE   )
  			return model
  
  def lowercase						(   SCREAMING_SNAKE_CASE   ) ->       Tuple:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			Path(args.output_dir   ).mkdir(exist_ok=SCREAMING_SNAKE_CASE   )
  			check_output_dir(SCREAMING_SNAKE_CASE					,						expected_items=3   )
  
  			SCREAMING_SNAKE_CASE_			=	create_module(SCREAMING_SNAKE_CASE   )
  			return ft_main(SCREAMING_SNAKE_CASE					,						model=SCREAMING_SNAKE_CASE   )
  
  
  if __name__ == "__main__":
  				SCREAMING_SNAKE_CASE__    :    Optional[Any]								=					argparse.ArgumentParser()
  				SCREAMING_SNAKE_CASE__    :    Optional[int]								=					pl.Trainer.add_argparse_args(parser)
  				SCREAMING_SNAKE_CASE__    :    int								=					SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
  				SCREAMING_SNAKE_CASE__    :    Dict								=					parser.parse_args()
  
  				distill_main(args)
  
style_context: 
  
  
  
  
  SCREAMING_SNAKE_CASE__    :    List[str]								=					"\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
  
  SCREAMING_SNAKE_CASE__    :    List[str]								=					[{"type": "code", "content": INSTALL_CONTENT}]
  SCREAMING_SNAKE_CASE__    :    List[Any]								=					{
      "{processor_class}": "FakeProcessorClass",
      "{model_class}": "FakeModelClass",
      "{object_class}": "FakeObjectClass",
  }
  