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 					__lowercase					(       a_     ):
  
  
  
  		"""simple docstring"""
  
  		UpperCamelCase    :				str 					=		["loss", "ce_loss", "mlm_loss", "hid_loss_enc", "hid_loss_dec"]
  
  
  
  
  
  
  
  		def __init__(   self		,			A  )     ->   Dict:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					assert Path(hparams.data_dir  ).exists()
  					lowerCamelCase								=     Path(hparams.output_dir  )
  					self.output_dir.mkdir(exist_ok=A  )
  
  					lowerCamelCase								=     self.output_dir.joinpath("""student"""  )
  
  					lowerCamelCase								=     str(A  )  # Tell lightning we are training the student
  					lowerCamelCase								=     AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher  ).eval()
  					use_task_specific_params(A		,			hparams.task  )  # We copy good generation parameters to student by default
  					if hparams.student is not None:
  								lowerCamelCase								=     AutoModelForSeqaSeqLM.from_pretrained(hparams.student  )
  								use_task_specific_params(A		,			hparams.task  )
  								lowerCamelCase		,		lowerCamelCase								=     None, None
  					else:
  								lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase								=     create_student_by_copying_alternating_layers(
  								    A		,			e=hparams.student_encoder_layers		,			d=hparams.student_decoder_layers		,			save_path=A  )
  
  					if hparams.length_penalty != -1:
  								lowerCamelCase								=     hparams.length_penalty
  					lowerCamelCase								=     hparams.teacher  # Use teacher's tokenizer
  					super().__init__(A		,			model=A		,			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":
  								lowerCamelCase								=     len(student.get_encoder().block  )
  								lowerCamelCase								=     len(student.get_decoder().block  )
  								lowerCamelCase								=     len(teacher.get_encoder().block  )
  								lowerCamelCase								=     len(teacher.get_decoder().block  )
  					else:
  								lowerCamelCase								=     student.config.encoder_layers
  								lowerCamelCase								=     student.config.decoder_layers
  								lowerCamelCase								=     teacher.config.encoder_layers
  								lowerCamelCase								=     teacher.config.decoder_layers
  
  					lowerCamelCase								=     not (hparams.student is None or hparams.teacher == hparams.student)
  					lowerCamelCase								=     (not self.different_base_models) and hparams.alpha_hid > 0
  					lowerCamelCase								=     self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
  					# self.different_encoder determines whether we need to run the teacher encoder
  					lowerCamelCase								=     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:
  								lowerCamelCase								=     list(range(A  )  )
  					if d_layer_ids is None:
  								lowerCamelCase								=     list(range(A  )  )
  
  					lowerCamelCase		,		lowerCamelCase								=     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:
  											lowerCamelCase								=     get_layers_to_supervise(
  											    n_student=len(self.e_layer_ids  )		,			n_teacher=A  )
  											lowerCamelCase								=     get_layers_to_supervise(
  											    n_student=len(self.d_layer_ids  )		,			n_teacher=A  )
  								else:  # student layer should emulate hidden states of the teacher layer it was copied from
  											lowerCamelCase								=     self.e_layer_ids
  											lowerCamelCase								=     self.d_layer_ids
  					else:
  								lowerCamelCase								=     None
  								lowerCamelCase								=     None
  
  					lowerCamelCase								=     nn.KLDivLoss(reduction="""batchmean"""  )
  					lowerCamelCase								=     2.0
  					lowerCamelCase								=     hparams.alpha_mlm
  					lowerCamelCase								=     hparams.alpha_ce
  					lowerCamelCase								=     hparams.alpha_hid
  					gc.collect()
  					torch.cuda.empty_cache()
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A		,			A		,			A  )     ->   Any:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     mask[:, :, None].expand_as(A  )
  					lowerCamelCase								=     s_logits.size(-1  )
  					lowerCamelCase								=     torch.masked_select(A		,			A  )  # (bs * seq_length * voc_size) modulo the 1s in mask
  					lowerCamelCase								=     torch.masked_select(A		,			A  )  # (bs * seq_length * voc_size) modulo the 1s in mask
  					lowerCamelCase								=     s_logits_slct.view(-1		,			A  )  # (bs * seq_length, voc_size) modulo the 1s in mask
  					lowerCamelCase								=     t_logits_slct.view(-1		,			A  )  # (bs * seq_length, voc_size) modulo the 1s in mask
  					assert t_logits_slct.size() == s_logits_slct.size()
  					lowerCamelCase								=     (
  					    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      (   A		,			A  )     ->   int:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					SummarizationModule.add_model_specific_args(A		,			A  )
  					add_distill_args(A  )
  					return parser
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A  )     ->   tuple:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     self.tokenizer.pad_token_id
  					lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase								=     batch["""input_ids"""], batch["""attention_mask"""], batch["""labels"""]
  					if isinstance(self.model		,			A  ):
  								lowerCamelCase								=     self.model._shift_right(A  )
  					else:
  								lowerCamelCase								=     shift_tokens_right(A		,			A  )
  
  					# noinspection PyCallingNonCallable
  					lowerCamelCase								=     self(
  					    A		,			attention_mask=A		,			decoder_input_ids=A		,			output_hidden_states=self.do_calc_hidden_loss		,			output_attentions=A		,			use_cache=A		,			)
  					lowerCamelCase								=     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
  								lowerCamelCase								=     nn.CrossEntropyLoss(ignore_index=A  )
  								lowerCamelCase								=     loss_fct(lm_logits.view(-1		,			lm_logits.shape[-1]  )		,			labels.view(-1  )  )
  					else:
  								lowerCamelCase								=     nn.functional.log_softmax(A		,			dim=-1  )
  								lowerCamelCase		,		lowerCamelCase								=     label_smoothed_nll_loss(
  								    A		,			A		,			self.hparams.label_smoothing		,			ignore_index=A  )
  
  					def zero_tensor():
  								return torch.tensor(0.0  ).type_as(A  )
  
  					lowerCamelCase								=     student_outputs[
  					    """encoder_last_hidden_state"""
  					]  # use this unless self.different_base_models
  					lowerCamelCase		,		lowerCamelCase								=     zero_tensor(), zero_tensor()
  					if self.different_encoder:  # compute encoder hidden state loss
  								lowerCamelCase								=     self.teacher.get_encoder()(
  								    A		,			attention_mask=A		,			output_hidden_states=self.do_calc_hidden_loss		,			)
  								if self.different_base_models:
  											lowerCamelCase								=     all_teacher_encoder_outputs["""last_hidden_state"""]
  								elif self.do_calc_hidden_loss:
  											lowerCamelCase								=     self.calc_hidden_loss(
  											    A		,			student_outputs["""encoder_hidden_states"""]		,			all_teacher_encoder_outputs["""hidden_states"""]		,			self.e_matches		,			normalize_hidden=self.hparams.normalize_hidden		,			)
  
  					lowerCamelCase								=     self.teacher(
  					    A		,			attention_mask=A		,			encoder_outputs=(teacher_enc_outputs,)		,			decoder_input_ids=A		,			output_hidden_states=self.do_calc_hidden_loss		,			use_cache=A		,			)
  					lowerCamelCase								=     decoder_input_ids.ne(A  )
  					lowerCamelCase								=     self.calc_ce_loss(A		,			A		,			teacher_outputs["""logits"""]  )
  					if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
  								lowerCamelCase								=     self.calc_hidden_loss(
  								    A		,			student_outputs["""decoder_hidden_states"""]		,			teacher_outputs["""decoder_hidden_states"""]		,			self.d_matches		,			normalize_hidden=self.hparams.normalize_hidden		,			)
  
  					lowerCamelCase								=     (
  					    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      (   A		,			A		,			A		,			A		,			A  )     ->   int:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     """expected list or tuple for hidden_states, got tensor of shape: """
  					assert not isinstance(A		,			torch.Tensor  ), F'{msg}{hidden_states.shape}'
  					assert not isinstance(A		,			torch.Tensor  ), F'{msg}{hidden_states_T.shape}'
  					lowerCamelCase								=     attention_mask.to(hidden_states[0]  )
  					lowerCamelCase								=     mask.sum() * hidden_states[0].size(-1  )
  					lowerCamelCase								=     torch.stack([hidden_states[i] for i in range(len(A  )  )]  )
  					lowerCamelCase								=     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:
  								lowerCamelCase								=     nn.functional.layer_norm(A		,			student_states.shape[1:]  )
  								lowerCamelCase								=     nn.functional.layer_norm(A		,			teacher_states.shape[1:]  )
  					lowerCamelCase								=     nn.functional.mse_loss(A		,			A		,			reduction="""none"""  )
  					lowerCamelCase								=     (mse * mask.unsqueeze(0  ).unsqueeze(-1  )).sum() / valid_count
  					return masked_mse
  
  
  
  
  
  
  def   __lowerCamelCase	(       lowerCamelCase__		:	Dict			):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			parser.add_argument("""--teacher"""     ,       type=lowerCamelCase__			)
  			parser.add_argument("""--alpha_ce"""     ,       default=0.8     ,       type=lowerCamelCase__			)
  			parser.add_argument("""--alpha_mlm"""     ,       default=0.2     ,       type=lowerCamelCase__			)
  			parser.add_argument("""--alpha_hid"""     ,       default=0.0     ,       type=lowerCamelCase__     ,       required=lowerCamelCase__			)
  			parser.add_argument("""--student"""     ,       type=lowerCamelCase__     ,       required=lowerCamelCase__			)
  			parser.add_argument("""--student_decoder_layers"""     ,       default=12     ,       type=lowerCamelCase__     ,       required=lowerCamelCase__			)
  			parser.add_argument("""--student_encoder_layers"""     ,       default=12     ,       type=lowerCamelCase__     ,       required=lowerCamelCase__			)
  			parser.add_argument("""--no_teacher"""     ,       action="""store_true"""     ,       default=lowerCamelCase__			)
  			parser.add_argument("""--length_penalty"""     ,       type=lowerCamelCase__     ,       default=-1			)
  			parser.add_argument("""--supervise_forward"""     ,       action="""store_true"""     ,       default=lowerCamelCase__			)
  			parser.add_argument("""--normalize_hidden"""     ,       action="""store_true"""     ,       default=lowerCamelCase__			)
  
  
  
  
  
  
  
  class 					__lowercase					(       a_     ):
  
  
  
  		"""simple docstring"""
  
  		UpperCamelCase    :				Dict 					=		"translation"
  		UpperCamelCase    :				Optional[int] 					=		["bleu"]
  		UpperCamelCase    :				Optional[int] 					=		"bleu"
  
  
  
  
  
  
  
  		def __init__(   self		,			A		,			**A  )     ->   Optional[int]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					super().__init__(A		,			**A  )
  					assert hparams.src_lang is not None
  					assert hparams.tgt_lang is not None
  					lowerCamelCase								=     hparams.src_lang
  					lowerCamelCase								=     hparams.tgt_lang
  					if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer		,			A  ):
  								lowerCamelCase								=     self.tokenizer.lang_code_to_id[hparams.tgt_lang]
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A		,			A  )     ->   dict:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					return calculate_bleu(A		,			A  )
  
  
  
  
  
  
  
  		@staticmethod
  		def 				__A      (   A		,			A  )     ->   Optional[int]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					TranslationModule.add_model_specific_args(A		,			A  )
  					add_distill_args(A  )
  					return parser
  
  
  
  
  
  
  def   __lowerCamelCase	(       lowerCamelCase__		:	Optional[int]			):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			if args.no_teacher:
  						lowerCamelCase								=     TranslationModule if """translation""" in args.task else SummarizationModule
  			else:  # DISTILL WITH TEACHER
  						lowerCamelCase								=     TranslationDistiller if """translation""" in args.task else SummarizationDistiller
  			lowerCamelCase								=     module_cls.__name__
  			print(f'using module {args.setup_cls}'			)
  			lowerCamelCase								=     module_cls(lowerCamelCase__			)
  			return model
  
  
  
  
  
  
  def   __lowerCamelCase	(       lowerCamelCase__		:	Optional[Any]			):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			Path(args.output_dir			).mkdir(exist_ok=lowerCamelCase__			)
  			check_output_dir(lowerCamelCase__     ,       expected_items=3			)
  
  			lowerCamelCase								=     create_module(lowerCamelCase__			)
  			return ft_main(lowerCamelCase__     ,       model=lowerCamelCase__			)
  
  
  if __name__ == "__main__":
  		UpperCAmelCase      :						List[str]    =		argparse.ArgumentParser()
  		UpperCAmelCase      :						str    =		pl.Trainer.add_argparse_args(parser)
  		UpperCAmelCase      :						Union[str, Any]    =		SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
  		UpperCAmelCase      :						List[str]    =		parser.parse_args()
  
  		distill_main(args)
  
  
style_context: 
  
  
  def   __lowerCamelCase	(       lowerCamelCase__		:	int = 100     ,       lowerCamelCase__		:	int = 100			):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			return max(
  			    sum(int(lowerCamelCase__			) for x in str(base**power			)			)
  			    for base in range(lowerCamelCase__			)
  			    for power in range(lowerCamelCase__			)			)
  
  
  # Tests
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  
  